Hello. Hello.

Hello, Dr. Glad you're back. So let me repeat my earlier question: what's one mathematical concept you genuinely enjoy introducing to students, and how do you typically set that up in your classroom?

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental.

Alright, you mentioned your background includes introductory research connecting mathematics, statistics, and public health. When you're teaching an entry-level mathematics course, how do you make abstract concepts accessible to students who might not have strong math backgrounds?

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID.

Alright, you mentioned giving examples to help students choose problems, especially with your experience in introductory research across mathematics, statistics, and public health. Can you give me a concrete example of a problem you present to students that ties those fields together?

Alright, so you try to connect math problems to real-world contexts, then go into the fundamentals. Can you walk me through a specific example where you did this—maybe a topic or lesson where that approach really clicked for your students?

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert. And and that in between. Uh, upper control limit and the threshold the points lie that one is. Early warning sign.

Okay, so you tie fundamental math concepts to real-world examples, like using early warning systems for SARS COVID to teach prediction and alerts. When you present a topic like this, how do you make sure students grasp the underlying mathematics and not just the application?

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert. And and that in between. Uh, upper control limit and the threshold the points lie that one is. Early warning sign. So main conclusion is that. Give real. Award example. Two students and. And introduce the basic fundamental. Then explain how this basic fundamentals help to. The public policy. And for the societal aspect. So main thing is that. Basic 2. In advance and helping to society.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert. And and that in between. Uh, upper control limit and the threshold the points lie that one is. Early warning sign. So main conclusion is that. Give real. Award example. Two students and. And introduce the basic fundamental. Then explain how this basic fundamentals help to. The public policy. And for the societal aspect. So main thing is that. Basic 2. In advance and helping to society. This is the mic. Final conclusion on that aspect.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert. And and that in between. Uh, upper control limit and the threshold the points lie that one is. Early warning sign. So main conclusion is that. Give real. Award example. Two students and. And introduce the basic fundamental. Then explain how this basic fundamentals help to. The public policy. And for the societal aspect. So main thing is that. Basic 2. In advance and helping to society. This is the mic. Final conclusion on that aspect. And in the sort. From a say that. I explained the concept. Using uh. Intuition. Real life example visualization.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert. And and that in between. Uh, upper control limit and the threshold the points lie that one is. Early warning sign. So main conclusion is that. Give real. Award example. Two students and. And introduce the basic fundamental. Then explain how this basic fundamentals help to. The public policy. And for the societal aspect. So main thing is that. Basic 2. In advance and helping to society. This is the mic. Final conclusion on that aspect. And in the sort. From a say that. I explained the concept. Using uh. Intuition. Real life example visualization. Then move for formal. Basic fundamental methods. With a strong focus. On application. Student interaction. Ask to every MO. Every group wise we divide. In class. Then ask two student. That you understand this problem. And that. Students individual interaction is also very protein in the class.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert. And and that in between. Uh, upper control limit and the threshold the points lie that one is. Early warning sign. So main conclusion is that. Give real. Award example. Two students and. And introduce the basic fundamental. Then explain how this basic fundamentals help to. The public policy. And for the societal aspect. So main thing is that. Basic 2. In advance and helping to society. This is the mic. Final conclusion on that aspect. And in the sort. From a say that. I explained the concept. Using uh. Intuition. Real life example visualization. Then move for formal. Basic fundamental methods. With a strong focus. On application. Student interaction. Ask to every MO. Every group wise we divide. In class. Then ask two student. That you understand this problem. And that. Students individual interaction is also very protein in the class. For understanding that. Real-world problem.

I prefer to answer this question. Uh, as my background is introductionary research, uh. Yeah, including mathematics, stats and public health. So I give a preferred 2 example, uh. To choose problem. Uh, explain in the real. The word manner. Then go for the fundamental, basic, fundamental. Approach. For for example I give one example. Well, uh, we recently developed early warning system for SARS COVID. And, uh, under this one, uh. We explained to. Students how? That basic fundamental. We use and that predict do. Early. And red alert for the task COVID. So basically we take. 3 replicate for that different sewage treatment plant and treated water. And through. That real time. You know the like. Sewage water. Bathroom, toiletry, water. That thing we take. And how to? That simple. I think it's, uh, converted before that, uh. Public health policy. So. We go through. This untreated water for the RTP. Then we go for. Uh, basic fundamental of mathematics. For example. Expo consulate is moving average. So give most. Resend points. Do a high weightage. And then estimate to earlier. So this is the one example. Example to provide. Early warning sign and. Red alert how? TWMA analysis helps you identify because that EWMA. Analysis. Contain one threshold line. And then up. Control and lower control. So upper. Call is upper control limit. That one is above that outlier points indicating red alert. And and that in between. Uh, upper control limit and the threshold the points lie that one is. Early warning sign. So main conclusion is that. Give real. Award example. Two students and. And introduce the basic fundamental. Then explain how this basic fundamentals help to. The public policy. And for the societal aspect. So main thing is that. Basic 2. In advance and helping to society. This is the mic. Final conclusion on that aspect. And in the sort. From a say that. I explained the concept. Using uh. Intuition. Real life example visualization. Then move for formal. Basic fundamental methods. With a strong focus. On application. Student interaction. Ask to every MO. Every group wise we divide. In class. Then ask two student. That you understand this problem. And that. Students individual interaction is also very protein in the class. For understanding that. Real-world problem. In that aspect of mathematical point of view. Thank you. You have another question.

Okay, so you use real-world public health problems like SARS COVID early warning systems to introduce fundamentals like moving averages and replicate sampling. When you're explaining something like moving average to students who aren't comfortable with statistics yet, what's your strategy for breaking down the math so it's accessible?

Alright, you described using real-world public health scenarios like early warning systems to introduce moving averages and weighting concepts in mathematics. When you see students struggling with the fundamentals, how do you adjust your approach to help them connect with the material?

Alright, so you use real-world public health scenarios like early warning systems to introduce moving averages and weighting concepts in mathematics. When you see students struggling with the abstract parts—like the math behind EWMA—how do you break it down so they really get it?

Alright, so you use real-world examples like the SARS COVID early warning system to help students connect mathematical fundamentals to practical problems. When you introduce something like moving averages in this context, how do you adjust your explanation for students who might be struggling with the abstract math versus those who grasp the real-world application right away?

Alright, so you use real-world public health scenarios like early warning systems to introduce moving averages and weighting concepts in mathematics. When you see students struggling with the abstract parts—like the math behind the model—how do you help them bridge that gap between the real-world problem and the mathematical concept?

Okay, so you use real-world examples like SARS COVID early warning systems to connect mathematical fundamentals to public health and policy. When you introduce the moving average method, what do you do if students seem confused by the weighting or the underlying math—how do you help them grasp it?

Alright, so you use real-world public health scenarios like early warning systems to introduce moving averages and weighting concepts in mathematics. When you see students struggling with the abstract fundamentals, how do you help them bridge that gap between theory and practical application?

Alright, so you use real-world examples like SARS COVID early warning systems to connect mathematical fundamentals to public health and policy. When you introduce the moving average method, what do you do if students are struggling to grasp the abstract side of it?

Alright, so you use real-world public health scenarios like early warning systems to introduce moving averages and weighting concepts in mathematics. When you see students struggling with the abstract part—say, with the intuition behind exponential weighting—what's your go-to method to help them connect with the idea?

Alright, so you use real-world examples like SARS COVID early warning systems and EWMA analysis to get students thinking about fundamentals and their impact

Yeah.

Yeah. Yeah, tell me please.

Okay. Since you’ve described your approach to introducing concepts and engaging students with real-world examples, let’s shift gears. When you’re designing a new course, what’s your process for deciding which topics and methods to include?

Alright.

You have another question.

You mentioned dividing the class into groups and using individual interaction to help students understand the real-world problem. How do you handle situations when a group or student just isn’t connecting with the material, no matter how many examples you provide?

How to handle? Can you repeat the question?

Of course. Here’s the question again: You talked about dividing the class into groups and using individual interaction to help students understand real-world math problems. What do you do when a group or student just isn’t connecting with the material, even after you’ve given multiple examples?

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not?

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not? So they argue. In that, in that, good man. Why this budget is good?

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not? So they argue. In that, in that, good man. Why this budget is good? And why is not good? And how to improve the?

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not? So they argue. In that, in that, good man. Why this budget is good? And why is not good? And how to improve the? Bajaj that in the. Upcoming time. So similarly manner in the mathematics problem. So we divide in. Group and. For example, any challenge so. In challenge aspect of India real life. Nitrogen paradox.

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not? So they argue. In that, in that, good man. Why this budget is good? And why is not good? And how to improve the? Bajaj that in the. Upcoming time. So similarly manner in the mathematics problem. So we divide in. Group and. For example, any challenge so. In challenge aspect of India real life. Nitrogen paradox. So I relate to real life problem. Nitrogen paradox is. In our. Tropical forest. Every time, green. With cut tropical forest. Then then rapidly grow. So normal scientists. Uh, this? Soil content. More rich nitrogen.

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not? So they argue. In that, in that, good man. Why this budget is good? And why is not good? And how to improve the? Bajaj that in the. Upcoming time. So similarly manner in the mathematics problem. So we divide in. Group and. For example, any challenge so. In challenge aspect of India real life. Nitrogen paradox. So I relate to real life problem. Nitrogen paradox is. In our. Tropical forest. Every time, green. With cut tropical forest. Then then rapidly grow. So normal scientists. Uh, this? Soil content. More rich nitrogen. But when they go for lab testing for soil, then they found very less nitrogen in compared to agriculture. Uh, soil. So how? What kind of? Fundamental. Biome method approach is. Ongoing. Biochemical Biogeo. Elections is ongoing. How the genes are regulated in? Through that that. Nitrogen cycle. So in the nitrogen paradox is mystery. So, uh. It's discuss. Picked when we go for the monotone dynamical system. Approach.

Dr., I noticed you were principal investigator on several projects, including the wastewater-based epidemiology early warning system for SARS-CoV-2 in Bengaluru. How has your research in modeling infectious disease dynamics influenced the way you teach undergraduate mathematics—especially when it comes to connecting abstract concepts to practical, real-world applications?

Alright, so you use debate and disagreements, like with the government budget, to get students involved even if they aren't connecting at first. When you set up that kind of debate, do you ever find that students just stick with their original opinion and don't budge, even after hearing the arguments? What do you do in those cases?

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not? So they argue. In that, in that, good man. Why this budget is good? And why is not good? And how to improve the? Bajaj that in the. Upcoming time. So similarly manner in the mathematics problem. So we divide in. Group and. For example, any challenge so. In challenge aspect of India real life. Nitrogen paradox. So I relate to real life problem. Nitrogen paradox is. In our. Tropical forest. Every time, green. With cut tropical forest. Then then rapidly grow. So normal scientists. Uh, this? Soil content. More rich nitrogen. But when they go for lab testing for soil, then they found very less nitrogen in compared to agriculture. Uh, soil. So how? What kind of? Fundamental. Biome method approach is. Ongoing. Biochemical Biogeo. Elections is ongoing. How the genes are regulated in? Through that that. Nitrogen cycle. So in the nitrogen paradox is mystery. So, uh. It's discuss. Picked when we go for the monotone dynamical system. Approach. Construct model. And dividing the part. And we identify that which. A gene is more. Important to regulatory for nitrogen cycle. So. In that aspect of class. We'd explain the problem. Divide and that. Two and three multiple groups. And as. Sign that discussion part is more important to. Refine that. Problem. For example, in that computer science divide and conclude. So one big. Problem dividing the multiple path. Part and sole individual. And then combine. So it's. Helping to large complex problem solve in that. Small manner.

Alright, so you're using debate and disagreement—like with the government budget—to get students involved even when they're not connecting at first. When you set up that kind of debate, do you ever find that some students still hold back or disengage? What do you do if they won't participate, even with the group dynamic?

So basically. In dividing the. Group students. You know the like. Group, uh, when we. That childhood in that school level, we. Assigned one topic. To debate aspect. So for example. Uh, I go in that another aspect. Financial Budget. The uh. Government announced the financial. Budget. So. So that like few students are agree, few are not agree. Right. So that budget is good or not? So they argue. In that, in that, good man. Why this budget is good? And why is not good? And how to improve the? Bajaj that in the. Upcoming time. So similarly manner in the mathematics problem. So we divide in. Group and. For example, any challenge so. In challenge aspect of India real life. Nitrogen paradox. So I relate to real life problem. Nitrogen paradox is. In our. Tropical forest. Every time, green. With cut tropical forest. Then then rapidly grow. So normal scientists. Uh, this? Soil content. More rich nitrogen. But when they go for lab testing for soil, then they found very less nitrogen in compared to agriculture. Uh, soil. So how? What kind of? Fundamental. Biome method approach is. Ongoing. Biochemical Biogeo. Elections is ongoing. How the genes are regulated in? Through that that. Nitrogen cycle. So in the nitrogen paradox is mystery. So, uh. It's discuss. Picked when we go for the monotone dynamical system. Approach. Construct model. And dividing the part. And we identify that which. A gene is more. Important to regulatory for nitrogen cycle. So. In that aspect of class. We'd explain the problem. Divide and that. Two and three multiple groups. And as. Sign that discussion part is more important to. Refine that. Problem. For example, in that computer science divide and conclude. So one big. Problem dividing the multiple path. Part and sole individual. And then combine. So it's. Helping to large complex problem solve in that. Small manner. Thank you. So yeah, it's my explain isn't clear or if you want more explanation. On that aspect. Hello.

Alright, so you set up debates around real-world issues like the government budget and the nitrogen paradox to get students thinking and engaging. When you run into a topic that’s especially complex—say, something like mathematical modeling in epidemiology—how do you make sure the debate stays productive and doesn’t just confuse them further?

So you use debates on budget issues or real-life phenomena like the nitrogen paradox to spark engagement and discussion. That’s a creative approach. Dr., I noticed you were principal investigator on several projects, including the wastewater-based epidemiology early warning system for SARS-CoV-2 in Bengaluru. How has your research in modeling infectious disease shaped the way you teach mathematical modeling to undergraduates?

Okay, so you use the nitrogen paradox and budget debates to spark discussion, even when students aren’t connecting right away. When you introduce something like monotone dynamical systems as part of that, do you have a strategy for helping students who struggle with the abstraction — maybe a way to tie it back to something concrete for them?

Alright, let’s shift toward your research. Given your background in modeling infectious diseases and applying advanced statistical methods to public health, where do you see the most fundable research directions over the next three years? Which grants or funding agencies would you target to build out your work?

Hello.

Alright,

Hello. Are you satisfied about my answer?

You mentioned dividing students into groups and using real-world problems like the nitrogen paradox to drive discussion and deeper understanding. When you’re designing these group activities, how do you decide which topics or problems are best suited for interdisciplinary collaboration, say between mathematics and public health?

Alright.

Yohai any another question?

You asked if I’m satisfied with your answer. What stood out was your use of group discussions

Yeah.

Yeah. Yeah, please tell me. Yeah.

Right, so when you introduce something like monotone dynamical systems as part of the nitrogen paradox discussion, how do you decide which mathematical tools or models are worth investing time in for student projects or research?

Alright.

Yeah, please tell me. What you want to?

Okay, so you’re eager for more. Given your publication record in mathematical modeling and public health, where do you see the most fundable research directions over the next three years, and which grants would you target first?

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war.

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges.

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now.

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics.

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics. And the pre COVID and post COVID. Analysis I already done and I now that like. How it's dangerous. In for us actually. So. Next five year my plan to develop. No, you know. Establish one interlusionary hub. Where where I drive advancement in UH in Texas disease.

So you're focusing on methods like EPLR and statistical modeling that have direct societal impact, especially for real-time outbreak detection. What’s your plan for building a research group around those topics, and how would you approach securing funding—are there particular agencies or grant programs you’d prioritize?

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics. And the pre COVID and post COVID. Analysis I already done and I now that like. How it's dangerous. In for us actually. So. Next five year my plan to develop. No, you know. Establish one interlusionary hub. Where where I drive advancement in UH in Texas disease. By fostering collaboration. Address to. Complex challenges. Mathematical Modeling. Learning. And develop new. Does indicate. Automatically data-driven. Tools apply. Data and. EP alert we recently. And we're filing to. Patent and Trademark for this one. And this perfectly works. Heavy hand over to this EP alert. Web tool to. ICMR. And funding. I have. Already 2. Funded project. One is ICMR.

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics. And the pre COVID and post COVID. Analysis I already done and I now that like. How it's dangerous. In for us actually. So. Next five year my plan to develop. No, you know. Establish one interlusionary hub. Where where I drive advancement in UH in Texas disease. By fostering collaboration. Address to. Complex challenges. Mathematical Modeling. Learning. And develop new. Does indicate. Automatically data-driven. Tools apply. Data and. EP alert we recently. And we're filing to. Patent and Trademark for this one. And this perfectly works. Heavy hand over to this EP alert. Web tool to. ICMR. And funding. I have. Already 2. Funded project. One is ICMR. Under the. Center of. Excellence. So it's 15,00,00,000 project grant. And the COPI it's multi use. Institutional, uh. Uh, project. And in this project my role is to develop. Quantitative clinical model. For Jane Mypathy. So GN mypathy is 1 type of rare genetic disorder. So during all the. Is decay that muscles is normal?

So you’re planning to build a research group that bridges mathematical modeling, data science, and public health, with a focus on real-time outbreak detection tools like your EPLR web application. What’s your approach for attracting external funding and ensuring sustainability—are there specific agencies or grant programs you’d prioritize, and how do you make your work stand out in a competitive funding landscape?

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics. And the pre COVID and post COVID. Analysis I already done and I now that like. How it's dangerous. In for us actually. So. Next five year my plan to develop. No, you know. Establish one interlusionary hub. Where where I drive advancement in UH in Texas disease. By fostering collaboration. Address to. Complex challenges. Mathematical Modeling. Learning. And develop new. Does indicate. Automatically data-driven. Tools apply. Data and. EP alert we recently. And we're filing to. Patent and Trademark for this one. And this perfectly works. Heavy hand over to this EP alert. Web tool to. ICMR. And funding. I have. Already 2. Funded project. One is ICMR. Under the. Center of. Excellence. So it's 15,00,00,000 project grant. And the COPI it's multi use. Institutional, uh. Uh, project. And in this project my role is to develop. Quantitative clinical model. For Jane Mypathy. So GN mypathy is 1 type of rare genetic disorder. So during all the. Is decay that muscles is normal? But that when muscles dig. Hey, during the age of UH-20. And 40, it's such kind of. Serious problem. Then initial symptom for that. Uh, lower limits, uh. Draw ankle drospection. And. Multiple upper limb is also impact. But. Its muscles is after a certain. It's decay. And it's not work. And. Major thing is that no vaccine is available worldwide.

You’re aiming to build a research group focused on modeling, data science, and public health, and you’ve already got collaborations with hospitals like CMC and Baptist. What’s your strategy for sustaining funding after the initial grant cycle—are you planning to pursue industry partnerships, national research programs, or something else?

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics. And the pre COVID and post COVID. Analysis I already done and I now that like. How it's dangerous. In for us actually. So. Next five year my plan to develop. No, you know. Establish one interlusionary hub. Where where I drive advancement in UH in Texas disease. By fostering collaboration. Address to. Complex challenges. Mathematical Modeling. Learning. And develop new. Does indicate. Automatically data-driven. Tools apply. Data and. EP alert we recently. And we're filing to. Patent and Trademark for this one. And this perfectly works. Heavy hand over to this EP alert. Web tool to. ICMR. And funding. I have. Already 2. Funded project. One is ICMR. Under the. Center of. Excellence. So it's 15,00,00,000 project grant. And the COPI it's multi use. Institutional, uh. Uh, project. And in this project my role is to develop. Quantitative clinical model. For Jane Mypathy. So GN mypathy is 1 type of rare genetic disorder. So during all the. Is decay that muscles is normal? But that when muscles dig. Hey, during the age of UH-20. And 40, it's such kind of. Serious problem. Then initial symptom for that. Uh, lower limits, uh. Draw ankle drospection. And. Multiple upper limb is also impact. But. Its muscles is after a certain. It's decay. And it's not work. And. Major thing is that no vaccine is available worldwide. So I'm contributing to develop. Based using baseline data. To develop uh. Clinical. Quantitative model. Using Bayesian modelling approach. And my goal is to have. Society in aspect of what kind of funding I apply.

You’re focusing on mathematical models and tools like EPLR for outbreak detection, and you mentioned collaborating with hospitals and aiming for societal impact. What’s your experience aligning your curriculum or research projects with accreditation standards, and how would you approach this at VIT to make sure your work fits institutional objectives?

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics. And the pre COVID and post COVID. Analysis I already done and I now that like. How it's dangerous. In for us actually. So. Next five year my plan to develop. No, you know. Establish one interlusionary hub. Where where I drive advancement in UH in Texas disease. By fostering collaboration. Address to. Complex challenges. Mathematical Modeling. Learning. And develop new. Does indicate. Automatically data-driven. Tools apply. Data and. EP alert we recently. And we're filing to. Patent and Trademark for this one. And this perfectly works. Heavy hand over to this EP alert. Web tool to. ICMR. And funding. I have. Already 2. Funded project. One is ICMR. Under the. Center of. Excellence. So it's 15,00,00,000 project grant. And the COPI it's multi use. Institutional, uh. Uh, project. And in this project my role is to develop. Quantitative clinical model. For Jane Mypathy. So GN mypathy is 1 type of rare genetic disorder. So during all the. Is decay that muscles is normal? But that when muscles dig. Hey, during the age of UH-20. And 40, it's such kind of. Serious problem. Then initial symptom for that. Uh, lower limits, uh. Draw ankle drospection. And. Multiple upper limb is also impact. But. Its muscles is after a certain. It's decay. And it's not work. And. Major thing is that no vaccine is available worldwide. So I'm contributing to develop. Based using baseline data. To develop uh. Clinical. Quantitative model. Using Bayesian modelling approach. And my goal is to have. Society in aspect of what kind of funding I apply. And I'm keen to interested to. Under that. Introduced Scenery Hub. To develop noble. Startup. Also. Extending in that uh. My vision. For, especially for. Example health index. So in this health index. In aspect of Indian. Like developing country. No Great Health Index is available. So. You know. Recently we discussed with the. PSO Principal. Scientific Advisor of PM office.

You mentioned earlier that you use real-world examples and group discussion to teach math concepts. Suppose you’re teaching a course on Advanced Statistical Methods at VIT University, and your session has two hundred students but you can’t use slides or a projector. How would you run that session to keep the students engaged and make sure they actually wrestle with the material—not just listen?

So it's a very interesting question for me actually. And. In my profile. File you see 3. Those publication. Directly relevant to societal. Uh, soluble things. So what? That I did during my past seven years, whatever the work. Directly implement on the societal. So my uh. Target always to develop. Uh, any methods or any statistical methods? Machine learning methods? And mathematical. Models so. With, uh, using the real world, uh. A problem and solve that, for example in infectious disease related. Emma, sorry. Recently I developed EPLR. On a key outcome for my research. EPL at a. Based web application. I'm real. Timely outbreak. D Dixon. Of infected disease. So basically that like it's helping to introduce scenery. Solving tourism interdictionary war. So aspect of funding and grants in next. Five years I'm looking that uh step list 1 research group. That called. Introducing Larry Hub. So where I bring unique individually expertise? Bridge mathematical modeling. Data science and public health. My research integrates advance uh. Mathematical. Modeling, statistical modeling and machine learning to address critical health. Global health challenges. Set as in AMR. And you? Interior microbial resistance. And it's very biggest problem right now. And I wish to work on this. Axon collaborate different, uh. Hospital. But CMC. Uh, in. I have already collaboration with the. Baptist Hospital in band. Or I walked on. Also but I. Ways to expand my network and work on this problem? Because that. If this is the big biggest problem right now. Over the counter we buy, uh, antibiotic. And we don't know. What is that draw? Drawback of this one. So after a certain time, the antibiotics. Work for us. And we are in that in very dangerous situation. Such as in that pandemic condition in the COVID time we consume so much antibiotics. And the pre COVID and post COVID. Analysis I already done and I now that like. How it's dangerous. In for us actually. So. Next five year my plan to develop. No, you know. Establish one interlusionary hub. Where where I drive advancement in UH in Texas disease. By fostering collaboration. Address to. Complex challenges. Mathematical Modeling. Learning. And develop new. Does indicate. Automatically data-driven. Tools apply. Data and. EP alert we recently. And we're filing to. Patent and Trademark for this one. And this perfectly works. Heavy hand over to this EP alert. Web tool to. ICMR. And funding. I have. Already 2. Funded project. One is ICMR. Under the. Center of. Excellence. So it's 15,00,00,000 project grant. And the COPI it's multi use. Institutional, uh. Uh, project. And in this project my role is to develop. Quantitative clinical model. For Jane Mypathy. So GN mypathy is 1 type of rare genetic disorder. So during all the. Is decay that muscles is normal? But that when muscles dig. Hey, during the age of UH-20. And 40, it's such kind of. Serious problem. Then initial symptom for that. Uh, lower limits, uh. Draw ankle drospection. And. Multiple upper limb is also impact. But. Its muscles is after a certain. It's decay. And it's not work. And. Major thing is that no vaccine is available worldwide. So I'm contributing to develop. Based using baseline data. To develop uh. Clinical. Quantitative model. Using Bayesian modelling approach. And my goal is to have. Society in aspect of what kind of funding I apply. And I'm keen to interested to. Under that. Introduced Scenery Hub. To develop noble. Startup. Also. Extending in that uh. My vision. For, especially for. Example health index. So in this health index. In aspect of Indian. Like developing country. No Great Health Index is available. So. You know. Recently we discussed with the. PSO Principal. Scientific Advisor of PM office. And he suggests to, you know, like. Every nodal center they develop data-driven Center for directly any infectious disease related reporting. So to deal with the pandemic condition. So I want to. Use such kind of data to driven to develop health index. And. Helping to. On to, uh, public policymakers. To take decision and implement to. Resources elevated advance. Uh, primary, uh. Arrangements for deal with that any type of outbreak conditions.

So you’ve already got ICMR funding and you’re working on tools like EP Alert for outbreak detection, plus aiming to build an interdisciplinary hub bridging math, data science, and public health. How do you make sure your curriculum development at VIT actually meets accreditation standards, especially when you’re introducing cutting-edge material like this?

You mentioned developing tools like EP Alert for outbreak detection and working on genetic disorders with multi-institutional funding — that’s impressive. Now, thinking about teaching, how would you design an active learning exercise for a course in Advanced Statistical Methods at VIT University, so students aren’t just listening but actually wrestling with the material?

So you’ve got that EP Alert tool for outbreak detection, funding from ICMR, and you’re developing quantitative models for rare disorders like GN myopathy. With your experience in aligning research to societal needs and securing multi-institutional grants, how would you translate that expertise into curriculum development at VIT—especially to meet accreditation standards and make sure the program stays relevant to industry and public health challenges?

Okay, so you’re developing quantitative models for rare genetic disorders, and you’ve managed multi-institutional grants like that ICMR Center of Excellence project. That’s quite a track record. Now, let’s shift back to teaching for a moment—how did you contribute to curriculum development in your previous roles, especially making sure courses met accreditation standards?

Alright, so you’ve got that interdisciplinary hub idea, collaboration with hospitals, and you’re looking to solve real health challenges using mathematical and statistical modeling. You even mentioned patenting your EP Alert tool, and working on grants like the big ICMR Center of Excellence project. That tracks well with what VIT’s aiming for — but let’s pivot for a moment. Professor, you talked earlier about tailoring your teaching to different learning styles and using real-world examples. Could you elaborate on your teaching philosophy and give a specific example of how you adapt your approach for students who struggle with abstract concepts in mathematics? You’ve got a strong record connecting mathematical modeling to real-world health issues and grant-funded projects. Now, Professor, could you elaborate on your teaching philosophy and provide examples of how you tailor your approach to accommodate diverse learning styles in Mathematics courses at VIT University?

Okay,

So basically. Most myfunded project. Goal to apply for another. International, uh. Funding also Bill Gates Foundation. I have certain, uh, international collaboration also. So I'm looking on that aspect. I have Indian grants right now to. Unfunded project 1 is. ICMR is near about 15,00,00,000 Another is. Uh, world without Gen. My path is 1 uh. Organization is NGO. And that that one funded for the especially for GMI 50 work actually. And we have certain collaboration with that Nimans.

So basically. Most myfunded project. Goal to apply for another. International, uh. Funding also Bill Gates Foundation. I have certain, uh, international collaboration also. So I'm looking on that aspect. I have Indian grants right now to. Unfunded project 1 is. ICMR is near about 15,00,00,000 Another is. Uh, world without Gen. My path is 1 uh. Organization is NGO. And that that one funded for the especially for GMI 50 work actually. And we have certain collaboration with that Nimans. So. So using that. A rare genetic disorders data and we. In the broader uh. Normal methods. And that that helping to developing the drug. Therapy for. Genetic disorders. So. My another funding up. Uh, I'm thinking to. One health approach.

Alright, so you’ve got Indian grants like ICMR and work with NGOs on rare disorders, plus you’re aiming for international funding from organizations like the Gates Foundation and building collaborations with Nimans. That’s a pretty wide-ranging funding strategy. Now let’s pivot a bit. Professor, could you elaborate on your teaching philosophy and give examples of how you tailor your approach for students with different learning styles in mathematics courses? So you’ve got ICMR funding, NGO partnerships, and international collaborations in mind — that’s ambitious. Professor, can you walk me through how you adapt your teaching approach to accommodate students with different learning styles in mathematics courses?

So basically. Most myfunded project. Goal to apply for another. International, uh. Funding also Bill Gates Foundation. I have certain, uh, international collaboration also. So I'm looking on that aspect. I have Indian grants right now to. Unfunded project 1 is. ICMR is near about 15,00,00,000 Another is. Uh, world without Gen. My path is 1 uh. Organization is NGO. And that that one funded for the especially for GMI 50 work actually. And we have certain collaboration with that Nimans. So. So using that. A rare genetic disorders data and we. In the broader uh. Normal methods. And that that helping to developing the drug. Therapy for. Genetic disorders. So. My another funding up. Uh, I'm thinking to. One health approach. So, uh. And the. That integrate to whole. One health approach. So one example in that AM. Mr. is the very serious problem for. Any type of. Species like. In the bloods that. Uh, for that four, uh. That veterinary and. Giving the. Loans related grow hormones. Related to antibiotics. So that. When is we consume that chicken and any non waste things that. Inside our body is also consumed at. Type of uh. Hormones, actually. So that one is not uh. Oh, thank you for.

So basically. Most myfunded project. Goal to apply for another. International, uh. Funding also Bill Gates Foundation. I have certain, uh, international collaboration also. So I'm looking on that aspect. I have Indian grants right now to. Unfunded project 1 is. ICMR is near about 15,00,00,000 Another is. Uh, world without Gen. My path is 1 uh. Organization is NGO. And that that one funded for the especially for GMI 50 work actually. And we have certain collaboration with that Nimans. So. So using that. A rare genetic disorders data and we. In the broader uh. Normal methods. And that that helping to developing the drug. Therapy for. Genetic disorders. So. My another funding up. Uh, I'm thinking to. One health approach. So, uh. And the. That integrate to whole. One health approach. So one example in that AM. Mr. is the very serious problem for. Any type of. Species like. In the bloods that. Uh, for that four, uh. That veterinary and. Giving the. Loans related grow hormones. Related to antibiotics. So that. When is we consume that chicken and any non waste things that. Inside our body is also consumed at. Type of uh. Hormones, actually. So that one is not uh. Oh, thank you for. So my. Conclusion is that. One health related. And. Health Index. Related and target to ICMR for applied. And DSD. And and my broad. It is that Bill Gates Foundation. I was the part of malaria elimination. Team at UC Berkeley. So I have certain collaboration. And we develop. New tools, not one. He's helping to society. Thank you. You have another question.

Alright, so you’re combining Indian grants like ICMR with international collaborations and even looking at the Gates Foundation. That’s a pretty ambitious funding strategy. Now, thinking about curriculum development—how have you contributed to aligning course content with accreditation standards, and what would you do to make sure VIT’s math programs stay ahead of regulatory requirements?

Alright, let’s shift gears a bit. Accreditation cycles can be demanding — if you joined our department and found that outcome assessment data was inconsistent across courses, how would you approach fixing that?

Alright, so you mentioned aiming for international grants like the Gates Foundation and building collaborations across Indian institutions, NGOs, and even a malaria elimination team at UC Berkeley. Now, thinking about department-level governance—beyond teaching and research, what role do you see yourself playing in curriculum committees or program reviews here at VIT?

So during my. Curriculum aspect during my PhD. And uh. I have different. Unique ground, you say? The. Success in computer science and mathematics. Integrated. This dual degree. And and during his. I my work is related to computational mathematics. With biological application. And aspect of the curriculum development. A guide, a few. Students are here in that project based.

So during my. Curriculum aspect during my PhD. And uh. I have different. Unique ground, you say? The. Success in computer science and mathematics. Integrated. This dual degree. And and during his. I my work is related to computational mathematics. With biological application. And aspect of the curriculum development. A guide, a few. Students are here in that project based. But I can't. Design, Curriculum design and pro. Review aspect of. With to keep courses aligned with the krund. Uh, current demand aspect forex. Based and. In industry-based research needs. And I would like to contribute by integrating data. In mathematical.

Alright, you’ve got that dual degree background—integrating computer science and mathematics—and you’ve worked on curriculum development and guided project-based learning. But accreditation cycles usually need clear outcome mapping and assessment standards. If you joined VIT and found outcome assessment data was inconsistent across courses, what steps would you take to fix that?

So during my. Curriculum aspect during my PhD. And uh. I have different. Unique ground, you say? The. Success in computer science and mathematics. Integrated. This dual degree. And and during his. I my work is related to computational mathematics. With biological application. And aspect of the curriculum development. A guide, a few. Students are here in that project based. But I can't. Design, Curriculum design and pro. Review aspect of. With to keep courses aligned with the krund. Uh, current demand aspect forex. Based and. In industry-based research needs. And I would like to contribute by integrating data. In mathematical. Statistical modeling and real world application. Into the curriculum. Specifically like. Uh, mathematics with the public health and AI. And for example, digital health. It's one new approach to. Understand that, uh. New approach for the under that. AI best. So based on. My experience. With collaboration and research projects, I. And you know. Helping to. Designing research oriented. Electives. Internship. Program and industry UH leg program. So overall. My aim to support Vit. And application data-driven. Mathematical. 11. That enhanced both academic and. And career ask. Aspect also. Thank you.

Alright, you mentioned integrating computer science and mathematics in curriculum design, plus aligning courses with industry needs—especially around data and mathematical methods. But accreditation requires rigorous outcome assessment. If you joined VIT and found that assessment data wasn’t consistent across math courses, what’s your practical approach to fixing that?

Alright, you’ve got experience designing research-oriented electives and integrating data-driven mathematical models into curricula. But accreditation cycles at VIT require outcome assessment data to be consistent across courses—if you found that data was messy or incomplete, how would you tackle fixing it?

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients. In in that first visit. So. This is the long term study for UH. Five years. Five years means time visits.

Okay, so you’ve been working with NCDC data for infectious diseases like dengue and malaria and have seen messy datasets over the years. What’s the first practical step you’d take to ensure that outcome assessment data for math courses at VIT stays complete and consistent semester after semester?

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients. In in that first visit. So. This is the long term study for UH. Five years. Five years means time visits. So out of 73. Patients, we don't know how many patients, uh. Till the end is. They survive or they visit. They prefer to visit only.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients. In in that first visit. So. This is the long term study for UH. Five years. Five years means time visits. So out of 73. Patients, we don't know how many patients, uh. Till the end is. They survive or they visit. They prefer to visit only. Because that rare genetic disorder for. For example, Jane Mathi is very difficult task to travel and. Satan properly. So. We are hoping to send. 20 out of 73. At least 30 patient baseline data is available, then we are. Successfully develop Bayesian model for them. And that one is helping to. Disease progression.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients. In in that first visit. So. This is the long term study for UH. Five years. Five years means time visits. So out of 73. Patients, we don't know how many patients, uh. Till the end is. They survive or they visit. They prefer to visit only. Because that rare genetic disorder for. For example, Jane Mathi is very difficult task to travel and. Satan properly. So. We are hoping to send. 20 out of 73. At least 30 patient baseline data is available, then we are. Successfully develop Bayesian model for them. And that one is helping to. Disease progression. So. Points we tackle in the different manner. It depends on the problem wise.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients. In in that first visit. So. This is the long term study for UH. Five years. Five years means time visits. So out of 73. Patients, we don't know how many patients, uh. Till the end is. They survive or they visit. They prefer to visit only. Because that rare genetic disorder for. For example, Jane Mathi is very difficult task to travel and. Satan properly. So. We are hoping to send. 20 out of 73. At least 30 patient baseline data is available, then we are. Successfully develop Bayesian model for them. And that one is helping to. Disease progression. So. Points we tackle in the different manner. It depends on the problem wise. And for a. Well, with. It's ATC rate is high. So it's import. To missing data points.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients. In in that first visit. So. This is the long term study for UH. Five years. Five years means time visits. So out of 73. Patients, we don't know how many patients, uh. Till the end is. They survive or they visit. They prefer to visit only. Because that rare genetic disorder for. For example, Jane Mathi is very difficult task to travel and. Satan properly. So. We are hoping to send. 20 out of 73. At least 30 patient baseline data is available, then we are. Successfully develop Bayesian model for them. And that one is helping to. Disease progression. So. Points we tackle in the different manner. It depends on the problem wise. And for a. Well, with. It's ATC rate is high. So it's import. To missing data points. Through that like. Canal method. Rather than two me. Accommodate.

It's very good in that I'm working last 7 to 8 years. Facing such kind of problem. For example, NCDC data last 15 year data I'm doing right now. From uh. For. Infectious disease. Related for dengue, malaria. Uh, chicken wing. Call in different. This is data so we we found multiple data missing. So how? Deal with the missing data. Points so it. Is very big. Question is come. So we develop such kind of. Tools that when I accommodate up. To our. Tolls work up to. 15% missing data points. And. It's accommodate different. Different method for example. Such kind of data for example. Mean imputation. More what mean and. And I prefer to using machine learning model. K9 Is that K nearest Neb? Are more model to deal with that accommodate. A missing data points. So earlier in my study for SARS COVID for Hyderabad city. And our publication in land set. And I'm the first, uh. Author on this publication. So under this one. Date missing data points by using K&N method. So, so Canon. It's for example 5. The one point is missing so. K Velu's deciding that like. How many nearest five data points? Then estimate one individual point. So it's very challenging to deal with the missing data points such kind of. For example, I'm working on the rare. Genetic disorders related. So. We have called, uh 73 patients. In in that first visit. So. This is the long term study for UH. Five years. Five years means time visits. So out of 73. Patients, we don't know how many patients, uh. Till the end is. They survive or they visit. They prefer to visit only. Because that rare genetic disorder for. For example, Jane Mathi is very difficult task to travel and. Satan properly. So. We are hoping to send. 20 out of 73. At least 30 patient baseline data is available, then we are. Successfully develop Bayesian model for them. And that one is helping to. Disease progression. So. Points we tackle in the different manner. It depends on the problem wise. And for a. Well, with. It's ATC rate is high. So it's import. To missing data points. Through that like. Canal method. Rather than two me. Accommodate. So it depends. Problem, Sir. Thank you, I hope you got my. Explanation.

Okay, so you actually worked with the NCDC’s infectious disease data and had to deal with up to fifteen percent missing points, and you mentioned using methods like mean imputation to address that. That’s a good example of handling raw data, but accreditation also involves documenting how you close those gaps and ensuring the process is auditable — how would you make sure your fixes and methods are transparent for an external reviewer?

Okay, so you’ve actually had to deal with messy datasets in your infectious disease research, and you mentioned using mean imputation and machine learning models like k-nearest neighbor to handle missing points. When it comes to aligning with accreditation requirements at VIT, how would you ensure that your outcome assessment methods are documented clearly and consistently for every course and instructor?

Alright, let's shift gears a bit. Imagine a student files a formal complaint alleging your grading is biased, but at the same time, your department head is pressuring you to improve pass rates. How would you handle that situation?

Right, so you’ve got hands-on experience dealing with missing data—like that NCDC infectious disease dataset—and you use models like k-nearest neighbor for imputation. Suppose your department head disagrees with your approach and insists on manual review for every missing value, even though you know that’s not scalable. How would you handle that situation?

Okay, so you’ve got active experience handling missing and messy data in projects with the NCDC, using approaches like mean imputation and k-nearest neighbor. Now, Professor, let’s imagine you’re asked to co-author a paper, but you notice some of the data your colleague brings in looks questionable—maybe even inconsistent with your own findings. What would be your next step?

Okay, so you’ve actually dealt with messy, incomplete datasets in your research and developed imputation tools that can handle up to fifteen percent missing data. That’s real hands-on experience. Now let’s shift a bit — imagine a student files a formal complaint alleging your grading is biased, but your department head is pressuring you to improve pass rates at the same time. How would you navigate that situation?

Alright, you’ve worked with messy datasets in public health and genetic disorders, and you’ve built tools to handle missing data points using methods like k-nearest neighbor — that’s hands-on experience. Now, Professor, let’s say a student files a formal complaint alleging your grading is biased, while at the same time your department head is pressuring you to improve pass rates. How would you navigate that situation?

You mentioned collaborating with NIMHANS and even working on rare disorders with only thirty baseline data points, so you’re used to navigating resource constraints. Now, Professor, let’s imagine a student files a formal complaint alleging your grading is biased, but at the same time, your department head is pressuring you to improve pass rates. How would you handle that situation?

Alright, you've described tackling missing data in everything from rare genetic disorders to infectious disease outbreaks, using methods like k-nearest neighbor and Bayesian modeling. Now, Professor, let's imagine a colleague asks you to co-author a paper, but you notice some data in their draft looks questionable. What would you actually do in that situation?

Alright, you’ve clearly tackled incomplete data in real research, whether it’s genetic disorders or infectious disease datasets, and you’ve built tools to handle those gaps. Now, let’s imagine a colleague asks you to co-author a paper, but you notice some of their data looks questionable—maybe inconsistent or not matching up with expected results. How would you handle that situation?

Okay, you’ve shown a lot of depth in research and curriculum design. Now, thinking specifically about industry connections—given your experience in public health modeling and collaborations, do you have any relationships with companies or organizations that could help students find internships or placements in related fields?

Alright, so you’ve actually dealt with messy, incomplete datasets in your research and developed imputation tools that can handle up to fifteen percent missing data. That’s real hands-on experience. Now thinking about industry alignment—given your experience in public health modeling and collaborations, do you have any active industry connections that could translate into internship or placement opportunities for students at VIT?