Hello.

Please start by introducing your professional journey in academia

Hi, I'm Aarti. I have completed my PhD in mathematics. I have a deep knowledge in machine learning and. Data analysis.

Hi, I'm Aarti. I have completed my PhD in mathematics. I have a deep knowledge in machine learning and. Data analysis. I am passionate about UH combined learning. And I, uh, if I am also contribute.

Hi, I'm Aarti. I have completed my PhD in mathematics. I have a deep knowledge in machine learning and. Data analysis. I am passionate about UH combined learning. And I, uh, if I am also contribute. In research. Academics and. Career development.

Alright, so you've done your PhD in mathematics and you've got expertise in machine learning and data analysis. Is there anything else about your academic journey you'd like to add, or does that sum it up?

Okay, so you mentioned you're passionate about combined learning. Can you describe what your ideal classroom environment looks like when you're teaching an undergraduate mathematics course?

Alright, Dr. Aarti, you mentioned being

Hello.

Okay, Dr. Aarti, you talked about your PhD in mathematics and your passion for combined learning. When you’re teaching an undergraduate math course, what’s one foundational concept you especially enjoy introducing to new students?

I can introduce. Basic concept in machine learning. How the machine learning works in real life applications? How mathematics contribute in machine learning? And data analysis. And the importance of mathematics.

I can introduce. Basic concept in machine learning. How the machine learning works in real life applications? How mathematics contribute in machine learning? And data analysis. And the importance of mathematics. Then I can use that techniques in real life applications.

I can introduce. Basic concept in machine learning. How the machine learning works in real life applications? How mathematics contribute in machine learning? And data analysis. And the importance of mathematics. Then I can use that techniques in real life applications. Can you encourage students to do small? Projects.

I can introduce. Basic concept in machine learning. How the machine learning works in real life applications? How mathematics contribute in machine learning? And data analysis. And the importance of mathematics. Then I can use that techniques in real life applications. Can you encourage students to do small? Projects. In machine learning and data analysis.

Okay, so you'd introduce students to how machine learning is grounded in mathematics and show real-life applications. Can you walk me through how you'd explain the link between a mathematical concept—like linear algebra or probability—and a practical machine learning algorithm to undergraduates?

Alright, so you'd introduce students to how machine learning is grounded in mathematics and show real-life applications. Can you walk me through how you'd explain the link between a mathematical concept—like linear algebra or probability—and a specific machine learning algorithm, to a group of undergrads?

Alright, so you focus on showing students how math underpins machine learning and you encourage hands-on projects. When you introduce a real-life application, say, predicting house prices, how do you help students who are struggling to connect the math theory to the practical coding part?

So you'd start by connecting mathematics to real-life machine learning problems and encourage students to try out small projects. When you guide these students on their first machine learning project, what specific steps do you take to make sure they really understand the math behind the algorithm, not just the code?

Uh, first I will introduce the basic concepts in machine learning. Then then I can encourage them by doing small projects like. Uh, first we have to understand. The Max behind machine learning. And the types of machine learning? What is supervised learning? What is unsupervised learning? Then based upon the project title, we can. We can develop some. Predictions. And give output.

Uh, first I will introduce the basic concepts in machine learning. Then then I can encourage them by doing small projects like. Uh, first we have to understand. The Max behind machine learning. And the types of machine learning? What is supervised learning? What is unsupervised learning? Then based upon the project title, we can. We can develop some. Predictions. And give output. Then I will teach them. How to collect the data? Because. Because lot of data are there.

Uh, first I will introduce the basic concepts in machine learning. Then then I can encourage them by doing small projects like. Uh, first we have to understand. The Max behind machine learning. And the types of machine learning? What is supervised learning? What is unsupervised learning? Then based upon the project title, we can. We can develop some. Predictions. And give output. Then I will teach them. How to collect the data? Because. Because lot of data are there. But we have to collect the data appropriate to our uh. Prediction. So we have first main thing we have to collect the data. And find the error.

Uh, first I will introduce the basic concepts in machine learning. Then then I can encourage them by doing small projects like. Uh, first we have to understand. The Max behind machine learning. And the types of machine learning? What is supervised learning? What is unsupervised learning? Then based upon the project title, we can. We can develop some. Predictions. And give output. Then I will teach them. How to collect the data? Because. Because lot of data are there. But we have to collect the data appropriate to our uh. Prediction.

Uh, first I will introduce the basic concepts in machine learning. Then then I can encourage them by doing small projects like. Uh, first we have to understand. The Max behind machine learning. And the types of machine learning? What is supervised learning? What is unsupervised learning? Then based upon the project title, we can. We can develop some. Predictions. And give output. Then I will teach them. How to collect the data? Because. Because lot of data are there. But we have to collect the data appropriate to our uh. Prediction. So we have first main thing we have to collect the data. And find the error. Then we can apply machine learning models methods. Then we can do small projects at first.

Alright, so you start by making sure students grasp the math underlying machine learning, then you walk them through project-based predictions. I noticed on your CV you completed a PhD thesis on advanced statistical methods applied to data analysis. Can you tell me about a time you had to explain one of those advanced methods—maybe something from your thesis—to students who had only a basic statistics background?

Alright, Dr. Aarthee, I noticed your PhD research was on "An Expert Crime Decision Support System to Analyse Crimes using Mixture Models." When you teach undergraduates about mixture models or related statistical methods, how do you break down those advanced ideas for students who might not have a strong background in statistics?

Okay, so you focus on making sure students understand the math—like the difference between supervised and unsupervised learning—and you help them collect the right data for their projects. When a student is struggling to grasp why a particular algorithm needs, say, normalization of data, how do you break down that concept so it really clicks for them?

Okay, so you walk students through the basics, help them distinguish between types of machine learning, and emphasize data collection and error analysis for their projects. Dr. Aarti, I noticed your PhD research focused on "An Expert Crime Decision Support System to Analyse Crimes using Mixture Models." When you teach undergraduates about mixture models or related statistical methods, how do you adapt those advanced concepts for students who may only have a basic math background?

Okay, so you emphasize starting with the basics, helping students understand key distinctions like supervised and unsupervised learning, and guiding them through data collection before they build models. Dr. Aarti, I noticed your PhD work was on “An Expert Crime Decision Support System to Analyse Crimes using Mixture Models.” When you teach undergraduates about something as advanced as mixture models, how do you break down that topic so it’s accessible to students who might not have a strong mathematical background?

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system.

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system. So I mentioned a mixture models. If I teach undergraduate students. First I will specify the. Uh, one topic like supervised learning.

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system. So I mentioned a mixture models. If I teach undergraduate students. First I will specify the. Uh, one topic like supervised learning. Teen supervised learning We we use label data.

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system. So I mentioned a mixture models. If I teach undergraduate students. First I will specify the. Uh, one topic like supervised learning. Teen supervised learning We we use label data. In supervised learning, a lot of methods are there.

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system. So I mentioned a mixture models. If I teach undergraduate students. First I will specify the. Uh, one topic like supervised learning. Teen supervised learning We we use label data. In supervised learning, a lot of methods are there. Uh, that is, uh, support pet or Misha, uh. Support vector.

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system. So I mentioned a mixture models. If I teach undergraduate students. First I will specify the. Uh, one topic like supervised learning. Teen supervised learning We we use label data. In supervised learning, a lot of methods are there. Uh, that is, uh, support pet or Misha, uh. Support vector. Then linear regression. Like that? First I will focus on one type. And ask them.

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system. So I mentioned a mixture models. If I teach undergraduate students. First I will specify the. Uh, one topic like supervised learning. Teen supervised learning We we use label data. In supervised learning, a lot of methods are there. Uh, that is, uh, support pet or Misha, uh. Support vector. Then linear regression. Like that? First I will focus on one type. And ask them. 2UH-2 Steady well. And apply the basic methods. And will give will get small predictions.

Okay, so your work with mixture models ties together multiple types of learning and recommender systems. Shifting gears a bit, I saw you also contributed to a project on data analysis for supply chain management—how did you connect your research expertise to teaching students about practical industry applications in that area?

Yeah, I do research in. Mixture models. Why I mentioned mixture models means? It's mixed up with supervised learning, unsupervised learning, and recommendation system. So I mentioned a mixture models. If I teach undergraduate students. First I will specify the. Uh, one topic like supervised learning. Teen supervised learning We we use label data. In supervised learning, a lot of methods are there. Uh, that is, uh, support pet or Misha, uh. Support vector. Then linear regression. Like that? First I will focus on one type. And ask them. 2UH-2 Steady well. And apply the basic methods. And will give will get small predictions. Then I will go to another topic. So the student. I'm not confused.

Alright, so your research with mixture models bridges supervised, unsupervised, and recommender systems, and you'd begin by focusing on one concept at a time for undergraduates. I also noticed you contributed to a data analysis project in supply chain management—how do you use your experience from that project to design classroom activities or examples that help students see the practical impact of mathematics outside academia?

Okay, so your work with mixture models bridges supervised, unsupervised, and recommender systems, and when you teach undergraduates you’d introduce one concept at a time, like labeled data in supervised learning. I also saw you contributed to a project on data analysis for supply chain management. Can you tell me how you’d use that experience to help students connect abstract mathematical theories to practical industry problems?

Alright, so you’d pick one core topic—like supervised learning—and really focus on that when introducing mixture models to undergrads. Switching gears, I also saw you contributed to a project on data analysis in supply chain management. How did your work there influence the way you teach mathematical modeling or applied statistics to students?

Alright, so when you introduce mixture models to undergraduates, you focus first on supervised learning and labeled data, then move to specific algorithms. Let me switch tracks for a moment — your CV notes research experience in data analysis applied to supply chain problems. How has that applied research influenced the way you design classroom projects or case studies for students?

Now, Dr. Aarthee, looking at your research in data analysis and machine learning—where do you see the most fundable research directions over the next three years? What kinds of grants or external funding opportunities would you target, given your expertise?

Alright, so when you introduce mixture models, you focus first on supervised learning and labeled data, then move to specific algorithms like support vector machines and linear regression. Now, Dr. Aarti, looking at your research in data analysis and machine learning—where do you see the most fundable research directions over the next three years? What kinds of grants or external funding would you target for your work?

So you break down mixture models by starting with one concept, like supervised learning, and gradually layering on others so students aren't overwhelmed. Let's talk about your own research for a moment—where do you see the most fundable directions in your work over the next few years? Which grants or external sources would you target to support that research?

My research based. Crime Analysis. Particularly. Woman crime and child trafficking. Umm, et cetera.

My research based. Crime Analysis. Particularly. Woman crime and child trafficking. Umm, et cetera. So I will get. Uh, fund based on. Woman crime like. Excerpt uh, then, uh, DST like that. There are, uh, many government agencies.

My research based. Crime Analysis. Particularly. Woman crime and child trafficking. Umm, et cetera. So I will get. Uh, fund based on. Woman crime like. Excerpt uh, then, uh, DST like that. There are, uh, many government agencies. Also provide fund. For the particular research.

So your research focus is on crime analysis, especially issues like women’s safety and child trafficking. For ambitious topics like these, which specific grants or funding agencies would you pursue to help scale your work, and how would you structure a research group to make the most impact?

My research based. Crime Analysis. Particularly. Woman crime and child trafficking. Umm, et cetera. So I will get. Uh, fund based on. Woman crime like. Excerpt uh, then, uh, DST like that. There are, uh, many government agencies. Also provide fund. For the particular research. A based on women crime. So I will definitely get. Funded project under. Government.

My research based. Crime Analysis. Particularly. Woman crime and child trafficking. Umm, et cetera. So I will get. Uh, fund based on. Woman crime like. Excerpt uh, then, uh, DST like that. There are, uh, many government agencies. Also provide fund. For the particular research. A based on women crime. So I will definitely get. Funded project under. Government. Government-related funds.

Okay, so you’re looking at government agencies like DST to fund research on women’s safety and child trafficking. Given that focus, how would you build a research group around these topics at VIT University, and what strategies would you use to foster interdisciplinary collaboration with other departments?

Alright, so you're looking at government agencies like DST to fund research on women’s safety and child trafficking. Given that focus, how would you build a research group around these topics at VIT University, and what would your strategy be to keep attracting external funding as your work grows?

You're focused on crime analysis, especially on issues like women and child safety, and you've mentioned agencies like DST for funding. Let me shift a bit—VIT University values high-impact research and publications in top journals. How would you align your research agenda with this expectation, and what steps would you take to boost both your group's visibility and the institution's scholarly output?

So you’d pursue government funding, especially from agencies like DST, for your research on women’s crime and child trafficking. Thinking

Yeah, my research based. Crime analysis Woman crime analysis.

Yeah, my research based. Crime analysis Woman crime analysis. So I I used a machine learning algorithm in my. Research I still. Started my research in 2016.

Yeah, my research based. Crime analysis Woman crime analysis. So I I used a machine learning algorithm in my. Research I still. Started my research in 2016. Though I'm a mathematics student. I, I, uh, took up a challenge and. Work on machine learning models. In 2016, only that is a emerging. Topic Machine Learning and. So I. I learned. I learnt basic thing in machine learning. And give predictions. Now it's growing a lot. Grow growing day by.

Right, your focus is on crime analysis, especially women’s crime. Since VIT University values high-impact research and publications, how would you align your research agenda to contribute meaningfully to reputed journals and the institution’s scholarly output?

Yeah, my research based. Crime analysis Woman crime analysis. So I I used a machine learning algorithm in my. Research I still. Started my research in 2016. Though I'm a mathematics student. I, I, uh, took up a challenge and. Work on machine learning models. In 2016, only that is a emerging. Topic Machine Learning and. So I. I learned. I learnt basic thing in machine learning. And give predictions.

Yeah, my research based. Crime analysis Woman crime analysis. So I I used a machine learning algorithm in my. Research I still. Started my research in 2016. Though I'm a mathematics student. I, I, uh, took up a challenge and. Work on machine learning models. In 2016, only that is a emerging. Topic Machine Learning and. So I. I learned. I learnt basic thing in machine learning. And give predictions. Now it's growing a lot. Grow growing day by. So so I. Do different, uh. Model prediction using mathematics.

Yeah, my research based. Crime analysis Woman crime analysis. So I I used a machine learning algorithm in my. Research I still. Started my research in 2016. Though I'm a mathematics student. I, I, uh, took up a challenge and. Work on machine learning models. In 2016, only that is a emerging. Topic Machine Learning and. So I. I learned. I learnt basic thing in machine learning. And give predictions. Now it's growing a lot. Grow growing day by. So so I. Do different, uh. Model prediction using mathematics. Then apply new new techniques. And apply for high impact journals.

Dr. Aarti, let's shift to your teaching—imagine you're assigned a large introductory mathematics class, say, 200 students. If you couldn't use slides or a blackboard, how would you run a session to keep those students genuinely engaged and learning the material?

Yeah, my research based. Crime analysis Woman crime analysis. So I I used a machine learning algorithm in my. Research I still. Started my research in 2016. Though I'm a mathematics student. I, I, uh, took up a challenge and. Work on machine learning models. In 2016, only that is a emerging. Topic Machine Learning and. So I. I learned. I learnt basic thing in machine learning. And give predictions. Now it's growing a lot. Grow growing day by. So so I. Do different, uh. Model prediction using mathematics. Then apply new new techniques. And apply for high impact journals. Definitely I will. Uh. Published in high-impact journal in future.

Alright, you really emphasized how you had to learn machine learning from scratch as a mathematics scholar and then applied it to crime analysis. Let’s shift to your teaching: imagine you’re assigned a large introductory mathematics class—say, 200 students—and you can’t use slides or a blackboard. How would you run a session that actually keeps those students engaged and participating?

Alright, you took up machine learning back in 2016, applied it to crime analysis, and have seen the field evolve rapidly. Now, if you were teaching a large undergraduate class at VIT—let’s say 200 students—and couldn’t use slides or a blackboard, how would you keep those students genuinely engaged during a session on a topic like mixture models?

You really highlighted how you dove into machine learning as a math scholar and have kept evolving your research since 2016. Shifting a bit, Dr. Aarti, if you were tasked with fostering interdisciplinary collaboration at VIT—especially leveraging your strengths in supply chain management and advanced statistical methods—what concrete initiative would you propose to get different departments working together?

Alright, you really emphasized how you had to learn machine learning from scratch as a mathematics scholar and then applied it to crime analysis. Let’s shift to your teaching: imagine you’re assigned a large undergraduate class—let’s say 200 students—at VIT. If you couldn’t use slides or a blackboard, how would you keep those students engaged and actually learning during a session?

Alright, you really emphasized how you took on machine learning as a mathematics scholar and applied it to crime analysis, and you’re aiming to publish in high-impact journals. Let’s shift to teaching: if you had a large undergraduate math class—say, 200 students—and you couldn’t use slides or the blackboard, how would you keep everyone actively engaged and make sure they’re learning, not just listening?

Yeah, when I take a class. I want to ensure whether. All all uh. Uh, listening first. Because. Thing was important more than learning.

Yeah, when I take a class. I want to ensure whether. All all uh. Uh, listening first. Because. Thing was important more than learning. 1st I have to. Encourage all the students to listen my class.

Yeah, when I take a class. I want to ensure whether. All all uh. Uh, listening first. Because. Thing was important more than learning. 1st I have to. Encourage all the students to listen my class. And I will try to. Uh, teach the subjects. Little bit interesting, interestingly. So the student Start learning. First they will listen and slowly they will start uh, they will get some interest and Start learning.

Yeah, when I take a class. I want to ensure whether. All all uh. Uh, listening first. Because. Thing was important more than learning. 1st I have to. Encourage all the students to listen my class. And I will try to. Uh, teach the subjects. Little bit interesting, interestingly. So the student Start learning. First they will listen and slowly they will start uh, they will get some interest and Start learning. Then we have to connect the. Subject with. Practical application means. They've some more interest in the class. And they will start. Listening. If if they first listen means. Gradually, they will get interest. And they will. Start learning.

You put a lot of emphasis on making sure students are actually listening first. Say you notice some students are clearly distracted or disengaged—how would you pull them back in and make the session interactive, given you can't rely on technology or slides?

You really focus on making sure students are actually listening. But say you notice a bunch of students clearly aren’t paying attention—maybe they're on their phones or just zoning out. How do you pull them back in and make the session interactive, especially without slides or the board?

So you focus on making your classes interesting to get students listening first, hoping that’ll spark their motivation to learn. How do you actually check if your approach is working—what do you use to assess whether students are both engaged and understanding the material?

So you really focus on getting students to listen first, then try to spark their interest with practical applications. Let’s say you notice a group of students is clearly distracted—maybe on their phones or chatting. How would you pull them back in and make the session interactive for them?

Now if I uh, if I notice some students were uh, using uh, mobile phone. Chatting first, uh. I. Scold them, uh. Directly. First I will. I will teach them the importance of. Learning then. I asked them. So first I. With the as the request. Please don't use mobile phone.

Now if I uh, if I notice some students were uh, using uh, mobile phone. Chatting first, uh. I. Scold them, uh. Directly. First I will. I will teach them the importance of. Learning then. I asked them. So first I. With the as the request. Please don't use mobile phone. Coach chatting inside the class. If you are not. Interested in in particular subject means. Just. Simplicity and. Uh, watch. Then I will. I will. Tell them the. Importance of. The particular topic.

Now if I uh, if I notice some students were uh, using uh, mobile phone. Chatting first, uh. I. Scold them, uh. Directly. First I will. I will teach them the importance of. Learning then. I asked them. So first I. With the as the request. Please don't use mobile phone. Coach chatting inside the class. If you are not. Interested in in particular subject means. Just. Simplicity and. Uh, watch. Then I will. I will. Tell them the. Importance of. The particular topic. And ask that group of students. Some. Or I will give some work. If I if I do that. Regularly means. All other students will be aware. If I, uh, if they. If they do group chat or. Mobile chatting or chatting or distracting the class means.

Now if I uh, if I notice some students were uh, using uh, mobile phone. Chatting first, uh. I. Scold them, uh. Directly. First I will. I will teach them the importance of. Learning then. I asked them. So first I. With the as the request. Please don't use mobile phone. Coach chatting inside the class. If you are not. Interested in in particular subject means. Just. Simplicity and. Uh, watch. Then I will. I will. Tell them the. Importance of. The particular topic. And ask that group of students. Some. Or I will give some work. If I if I do that. Regularly means. All other students will be aware. If I, uh, if they. If they do group chat or. Mobile chatting or chatting or distracting the class means. They will get. Uh, punishment. Not like. Uh, management. Like some. Homework or project? I will give them like that. So. All the students. Uh, I think. Should, uh, listen the clans? I will work with. The faculty members. Because I am. Manu Jony so surely. I want I need a help.

Now if I uh, if I notice some students were uh, using uh, mobile phone. Chatting first, uh. I. Scold them, uh. Directly. First I will. I will teach them the importance of. Learning then. I asked them. So first I. With the as the request. Please don't use mobile phone. Coach chatting inside the class. If you are not. Interested in in particular subject means. Just. Simplicity and. Uh, watch. Then I will. I will. Tell them the. Importance of. The particular topic. And ask that group of students. Some. Or I will give some work. If I if I do that. Regularly means. All other students will be aware. If I, uh, if they. If they do group chat or. Mobile chatting or chatting or distracting the class means. They will get. Uh, punishment. Not like. Uh, management.

Now if I uh, if I notice some students were uh, using uh, mobile phone. Chatting first, uh. I. Scold them, uh. Directly. First I will. I will teach them the importance of. Learning then. I asked them. So first I. With the as the request. Please don't use mobile phone. Coach chatting inside the class. If you are not. Interested in in particular subject means. Just. Simplicity and. Uh, watch. Then I will. I will. Tell them the. Importance of. The particular topic. And ask that group of students. Some. Or I will give some work. If I if I do that. Regularly means. All other students will be aware. If I, uh, if they. If they do group chat or. Mobile chatting or chatting or distracting the class means. They will get. Uh, punishment. Not like. Uh, management. Like some. Homework or project?

Right, so you’d start with a direct request and try to get them to see the value in paying attention. Before we wrap up, how do you actually figure out if your teaching methods are working—what do you use to check if students are really understanding the material, not just memorizing it?

Right, so you’d start by directly addressing the distraction, then try to appeal to the importance of the topic and encourage them to listen. When you try this approach, how do you actually check if your teaching is working—do you use quizzes, discussions, or something else to see if students are really learning?

Okay, so you’d start by requesting the students put their phones away and try to help them see why the topic matters, maybe assigning them a task to keep them involved. Shifting gears — accreditation cycles can be pretty demanding. If you joined our department and found that outcome assessment data was inconsistent across courses, how would you approach fixing that?

Right, so you’d start by directly addressing the distraction, try to appeal to the importance of the topic, and sometimes assign work as a consequence. Shifting gears — 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?

Right, so you address distractions directly, explain why the topic matters, and sometimes give extra assignments to keep things on track. 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?

I will work with. The faculty members. Because I am. Manu Jony so surely. I want I need a help. With my faculty members. And I will collaborate. Uh, with them. And I will find. What are the? Data inconsistent in our department.

I will work with. The faculty members. Because I am. Manu Jony so surely. I want I need a help. With my faculty members. And I will collaborate. Uh, with them. And I will find. What are the? Data inconsistent in our department. So I will do it. So group activity.

I will work with. The faculty members. Because I am. Manu Jony so surely. I want I need a help. With my faculty members. And I will collaborate. Uh, with them. And I will find. What are the? Data inconsistent in our department. So I will do it. So group activity. And I will, uh. Find 2. How to sort out the? That incorrect. Direct data in our department.

I will work with. The faculty members. Because I am. Manu Jony so surely. I want I need a help. With my faculty members. And I will collaborate. Uh, with them. And I will find. What are the? Data inconsistent in our department. So I will do it. So group activity. And I will, uh. Find 2. How to sort out the? That incorrect. Direct data in our department. Uh, surely we will correct them. With the help of our senior faculty members.

Alright, you’d start by collaborating with faculty since you’re new to the department—sounds like you see teamwork as key. Day-to-day, what would you actually do to make sure outcome assessment data gets standardized across all courses? Would you set up new processes, or rely on existing systems?

I will work with. The faculty members. Because I am. Manu Jony so surely. I want I need a help. With my faculty members. And I will collaborate. Uh, with them. And I will find. What are the? Data inconsistent in our department. So I will do it. So group activity. And I will, uh. Find 2. How to sort out the? That incorrect. Direct data in our department. Uh, surely we will correct them. With the help of our senior faculty members. And my coworker.

Alright, Dr. Aarti, you’d look for support from other faculty and collaborate to spot data inconsistencies. Beyond teamwork, day-to-day, what actual steps would you take to make sure outcome assessment data gets collected accurately and on time?

Alright, so if you found inconsistencies you’d approach it as a group activity with your colleagues. Before we wrap this section, how would you propose fostering interdisciplinary collaboration between departments here at VIT University, especially using your expertise in supply chain management and advanced statistical methods?

Alright, so you'd start by collaborating with faculty since you're new to the department—sounds like you see teamwork as key. Day-to-day, what would you actually do to make sure outcome assessment data gets collected accurately and stays consistent across all courses?

Okay, so you'd rely on collaboration and group effort with your faculty colleagues to tackle the data inconsistencies. Dr. Aarti, day-to-day, what would you actually do yourself to make sure outcome assessment data gets collected accurately and consistently every semester?

Alright, let's shift gears to something that comes up in academic life. Imagine a student files a formal complaint, saying your grading was biased, but at the same time your department head is pressuring you to improve pass rates. How would you navigate that situation?

I will conduct a weekly. Test to the students.

I will conduct a weekly. Test to the students. Then I will. Give us sample grade to them weekly wise. So they will know.

I will conduct a weekly. Test to the students. Then I will. Give us sample grade to them weekly wise. So they will know. How? Recording, uh. Or how I am correcting their uh. Marks and records. And give Rick grades.

I will conduct a weekly. Test to the students. Then I will. Give us sample grade to them weekly wise. So they will know. How? Recording, uh. Or how I am correcting their uh. Marks and records. And give Rick grades. So they will get some basic idea.

Okay, so your first instinct is to run weekly tests to ensure fairness in grading. But let’s say your department head pushes back, insisting that pass rates need to go up even if it means adjusting assessment standards. How would you respond if you felt that would compromise academic integrity?

I will conduct a weekly. Test to the students. Then I will. Give us sample grade to them weekly wise. So they will know. How? Recording, uh. Or how I am correcting their uh. Marks and records. And give Rick grades. So they will get some basic idea. If you write. Like this means. This faculty member will give some. Marks based upon the. The formulas like that. So they will get some clear idea. About the grading system. Of my teaching.

So your plan is to conduct weekly tests and share sample grades so students see how they're assessed. But if your department head disagrees and insists you raise pass rates even if it means adjusting your grading standards, how would you handle that pressure while keeping your academic integrity?

I will conduct a weekly. Test to the students. Then I will. Give us sample grade to them weekly wise. So they will know. How? Recording, uh. Or how I am correcting their uh. Marks and records. And give Rick grades. So they will get some basic idea. If you write. Like this means. This faculty member will give some. Marks based upon the. The formulas like that. So they will get some clear idea. About the grading system. Of my teaching. Uh, so. Uh, if so. If my. If my superior asked them to. Pressuring about my. Pause. I will ensure. The test. And this and make the. Student to understand.

I will conduct a weekly. Test to the students. Then I will. Give us sample grade to them weekly wise. So they will know. How? Recording, uh. Or how I am correcting their uh. Marks and records. And give Rick grades. So they will get some basic idea. If you write. Like this means. This faculty member will give some. Marks based upon the. The formulas like that. So they will get some clear idea. About the grading system. Of my teaching. Uh, so. Uh, if so. If my. If my superior asked them to. Pressuring about my. Pause. I will ensure. The test. And this and make the. Student to understand. Uh, how the grade system goes on?

So your approach is to provide frequent assessments and transparent grading so students understand the process. Dr. Aarti, if your department head still insists that you raise pass rates regardless of student performance, even after you explain your method, what would you do then?

So you'd use weekly sample grades to show your process and help students understand how they're being assessed. But if your department head still insists on raising pass rates, even if it means relaxing your grading standards, what would you do?

You make the grading process transparent by sharing regular assessments and showing students exactly how their work is evaluated. Let's say your department head still insists that you raise pass rates, even if it means relaxing some of your grading standards. How would you respond to that kind of pressure?

Okay, so you’d handle it by making your grading process transparent with weekly assessments. Let’s push a little—if your department head disagrees and insists you relax your standards to boost pass rates, how would you respond?

Okay, so you’d use regular assessments and transparency to address concerns, and you’d collaborate with colleagues if there’s data inconsistency. Let’s close this section with something practical: if a colleague asks you to co-author a paper and you notice some of their data seems questionable, how would you handle that situation?

Yeah, first of all I will handle. Situation in a friendly manner.

Yeah, first of all I will handle. Situation in a friendly manner. First I will discuss with my. Uh, discuss with my faculty member.

Yeah, first of all I will handle. Situation in a friendly manner. First I will discuss with my. Uh, discuss with my faculty member. About the paper. About the paper and. Data. I asked them. Them itself. To find the. Um, any error or modification in the paper? You would still find and tell me. Let me know. Uh, like that? I will tell. They will. Revise the paper. And they will, surely they will say some correction means at that time I will point out.

Yeah, first of all I will handle. Situation in a friendly manner. First I will discuss with my. Uh, discuss with my faculty member. About the paper. About the paper and. Data. I asked them. Them itself. To find the. Um, any error or modification in the paper? You would still find and tell me. Let me know. Uh, like that? I will tell. They will. Revise the paper. And they will, surely they will say some correction means at that time I will point out. With the point out with the request like. Uh, please see your data is inconsistency. If they correct them.

So you’d approach it in a friendly way first. But if your colleague insists on including that questionable data, even after you raise your concern, what would you actually do?

Yeah, first of all I will handle. Situation in a friendly manner. First I will discuss with my. Uh, discuss with my faculty member. About the paper. About the paper and. Data. I asked them. Them itself. To find the. Um, any error or modification in the paper? You would still find and tell me. Let me know. Uh, like that? I will tell. They will. Revise the paper. And they will, surely they will say some correction means at that time I will point out. With the point out with the request like. Uh, please see your data is inconsistency. If they correct them. We can, surely. Deliver paper. Present a paper. Or communicate a paper in a better way in a better journal. High impact journal. So, uh, please consider your uh. Error in data. And uh, please modify that and correct it.

Yeah, first of all I will handle. Situation in a friendly manner. First I will discuss with my. Uh, discuss with my faculty member. About the paper. About the paper and. Data. I asked them. Them itself. To find the. Um, any error or modification in the paper? You would still find and tell me. Let me know. Uh, like that? I will tell. They will. Revise the paper. And they will, surely they will say some correction means at that time I will point out. With the point out with the request like. Uh, please see your data is inconsistency. If they correct them. We can, surely. Deliver paper. Present a paper. Or communicate a paper in a better way in a better journal. High impact journal. So, uh, please consider your uh. Error in data.

Yeah, first of all I will handle. Situation in a friendly manner. First I will discuss with my. Uh, discuss with my faculty member. About the paper. About the paper and. Data. I asked them. Them itself. To find the. Um, any error or modification in the paper? You would still find and tell me. Let me know. Uh, like that? I will tell. They will. Revise the paper. And they will, surely they will say some correction means at that time I will point out. With the point out with the request like. Uh, please see your data is inconsistency. If they correct them. We can, surely. Deliver paper. Present a paper. Or communicate a paper in a better way in a better journal. High impact journal. So, uh, please consider your uh. Error in data. And uh, please modify that and correct it. Uh, we can, surely. Published in. Published in High. Impact.

Yeah, first of all I will handle. Situation in a friendly manner. First I will discuss with my. Uh, discuss with my faculty member. About the paper. About the paper and. Data. I asked them. Them itself. To find the. Um, any error or modification in the paper? You would still find and tell me. Let me know. Uh, like that? I will tell. They will. Revise the paper. And they will, surely they will say some correction means at that time I will point out. With the point out with the request like. Uh, please see your data is inconsistency. If they correct them. We can, surely. Deliver paper. Present a paper. Or communicate a paper in a better way in a better journal. High impact journal. So, uh, please consider your uh. Error in data. And uh, please modify that and correct it. Uh, we can, surely. Published in. Published in High. Impact. Like that? I will handle that situation.

So you’d start with a friendly conversation and raise your concerns with your colleague directly. What if they refuse to change the data and still want you to be a co-author—what would you actually do in that situation?

Since your research is rooted in crime analysis and machine learning, do you have any connections with industry or government agencies that could help your students get real-world project experience or internships related to these areas?

You mentioned that you’d handle questionable data with a colleague by talking it through and asking them to check for errors—sounds collaborative. Since your research is rooted in crime analysis and machine learning, do you have any connections with industry or government agencies that could help your students get real-world project experience or internships?

Alright, so you’d start by talking with your colleague, encourage them to review their data, and only move forward once you’re comfortable with the integrity of the results. Since your research is rooted in crime analysis and machine learning, do you have any connections with industry or government agencies that could help your students get real-world project experience or internships?

So, your approach is to handle data issues with colleagues diplomatically and focus on collaboration and integrity before moving forward with research publications. Since your research deals with crime analysis and machine learning, do you currently have any industry or government connections that could open the door for your students to work on real-world projects or internships?

Alright, Dr., so you’d address data issues with colleagues directly and focus on collaboration before moving forward. Since your research is rooted in crime analysis and machine learning, do you have any connections with industry or government agencies that could help your students get real-world project experience or internships?

Alright, so you approach data integrity with colleagues by being diplomatic and focusing on improving the paper together. Since your research centers on crime analysis and machine learning, what connections—if any—do you have with industry or government agencies that might help your students get hands-on experience with real-world projects?

Since my research, uh, research is based on crime analysis.

Since my research, uh, research is based on crime analysis. And there is a lot of. Field work. Maybe the? Uh, nowadays, uh, crime increasing day by day.

Since my research, uh, research is based on crime analysis. And there is a lot of. Field work. Maybe the? Uh, nowadays, uh, crime increasing day by day. We can approach government. Officials.

Alright, Dr., let's talk about your background with supply chain management, advanced statistics, and AI. Imagine you're advising a student team that's trying to predict demand in a complex supply chain using machine learning. What's one mathematical or statistical pitfall you'd warn them about before they start modeling?

Since my research, uh, research is based on crime analysis. And there is a lot of. Field work. Maybe the? Uh, nowadays, uh, crime increasing day by day. We can approach government. Officials. Like headquarters of. Police headquarters.

Since my research, uh, research is based on crime analysis. And there is a lot of. Field work. Maybe the? Uh, nowadays, uh, crime increasing day by day. We can approach government. Officials. Like headquarters of. Police headquarters. And we can get. Some manual data.

Since my research, uh, research is based on crime analysis. And there is a lot of. Field work. Maybe the? Uh, nowadays, uh, crime increasing day by day. We can approach government. Officials. Like headquarters of. Police headquarters. And we can get. Some manual data. We can encourage students to get some. Manual data with that higher end. This office for. Research.

Since my research, uh, research is based on crime analysis. And there is a lot of. Field work. Maybe the? Uh, nowadays, uh, crime increasing day by day. We can approach government. Officials. Like headquarters of. Police headquarters. And we can get. Some manual data. We can encourage students to get some. Manual data with that higher end. This office for. Research. If they provide means. Uh, will take hands-on data.

Okay, so you mentioned a lot of field work connects your research to real-world trends in crime. Shifting gears, when you teach theory-heavy math courses, how do you make sure students actually grasp the abstract concepts, not just memorize formulas?

So you see a lot of real-world field work and possible government connections because of your research focus. Tell me about a time when you guided students on a research project, especially where they had to handle messy or incomplete data—how did you help them structure their work and actually get to a meaningful result?

Okay, so you see a path for connecting with police headquarters and government officials for field work, which could open up real-world project opportunities for your students. Shifting to teaching theory versus labs—when you run a lab for a math-heavy subject, how do you structure it so students really understand the connection between the calculations and the real-world application?

Alright, so you're getting manual data from police headquarters and connecting with government officials for field work. Thinking about your background in industry projects or consultancy—can you give me an example where you worked with an external organization or company and how that experience influenced your teaching or research?

Okay, so you see value in students doing manual data collection through your contacts with police headquarters. Let's talk about advanced statistics—when you're teaching a course that covers both classical inference and newer machine learning methods, how do you help students understand when to use traditional statistical techniques versus more modern AI approaches?

So you’d have students collect manual data through your connections with police headquarters, which gives them real exposure to how field work happens. Shifting gears, can you walk me through a time you taught a theory-heavy math course—how did you make sure students actually understood the foundational proofs, not just memorized them?

In that uh. We can approach.

Okay, so you’d approach it as a friendly conversation first, then point out the data issues and ask your colleague to address them so you can move forward with the publication. Now, thinking about industry experience—have you worked on any consultancy projects or collaborated with companies where you applied your mathematical or analytical skills outside of academia?

Ah, no, I didn't work. For any consultancy. Project or collaborated with. Any companies?

Ah, no, I didn't work. For any consultancy. Project or collaborated with. Any companies? Till now. In future. Surely I will. Collaborate with. Government. Uh. Official since my project is based upon. Crime Analysis.

Ah, no, I didn't work. For any consultancy. Project or collaborated with. Any companies? Till now. In future. Surely I will. Collaborate with. Government. Uh. Official since my project is based upon. Crime Analysis. So I will get. Some collaborated with uh. Government officials.

Ah, no, I didn't work. For any consultancy. Project or collaborated with. Any companies? Till now. In future. Surely I will. Collaborate with. Government. Uh. Official since my project is based upon. Crime Analysis. So I will get. Some collaborated with uh. Government officials. And I will. Do my work.

Alright, so you haven't done consultancy or industry collaborations. Let’s look at your teaching style—when you need to explain a concept that students consistently struggle with, like Bayes’ Theorem or Markov chains, what’s your go-to approach to make it stick for them?

Ah, no, I didn't work. For any consultancy. Project or collaborated with. Any companies? Till now. In future. Surely I will. Collaborate with. Government. Uh. Official since my project is based upon. Crime Analysis. So I will get. Some collaborated with uh. Government officials. And I will. Do my work. Positively well.

Thanks for being upfront about not having consultancy or industry collaborations yet. Since you mentioned your research dives into both advanced mathematics and machine learning for crime analysis, can you give me a concrete example of how you explain a complex statistical method—like principal component analysis or logistic regression—to students who don't have a strong math background?

Alright, Dr., I hear you haven't done consultancy or industry collaborations yet, but you're planning to connect more with government officials as your crime analysis research progresses. Since you've mentioned publishing and aiming for high-impact journals, can you share which journals you've already published in, or if not, what your publication strategy is for getting into reputed journals?

Alright, since you haven't done consultancy or industry projects yet but plan to work with government officials, let's focus on your teaching. When you're covering advanced statistical methods in your courses—say, introducing something like principal component analysis—how do you break down the intuition and math so students with weaker backgrounds can actually follow along?

Right, so you haven’t worked on consultancy or industry projects yet, but you’re looking to collaborate with government officials as your research moves forward. Let’s talk about your teaching style—when you’re covering advanced statistical methods in your classes, how do you ensure students grasp both the intuition and the technical rigor behind something like principal component analysis, rather than just following the steps?

A principal component tunnel is. Supervised learning method.

Hmm, actually, principal component analysis—or PCA—is an unsupervised learning technique, not supervised. But moving along, since you’ve spent time working with machine learning in your research, how do you help students bridge the gap between just applying algorithms and actually understanding the mathematical foundations behind them?

A principal component tunnel is. Supervised learning method. Sorry, unsupervised learning method. In that we can collect the data. Using uh. And we can group the data. And we can, finally. Get the. Prediction.

A principal component tunnel is. Supervised learning method. Sorry, unsupervised learning method. In that we can collect the data. Using uh. And we can group the data. And we can, finally. Get the. Prediction. Uh. Teach the students about the. Basic concepts of principal component analysis.

A principal component tunnel is. Supervised learning method. Sorry, unsupervised learning method. In that we can collect the data. Using uh. And we can group the data. And we can, finally. Get the. Prediction. Uh. Teach the students about the. Basic concepts of principal component analysis. And we have two, uh. We have to identify. Which data will be? To the principal component tunnel. We should, uh, we should not blindly. Apply any data to any method. 1st we have to. Fit the data to the particular method. Then only we get. We'll we will get the. Preferred I want to know what type of. I will get if I'm selected.

Alright, we're wrapping up. Is there anything you'd like to ask about the role or the team?

Alright, Dr., we're wrapping up. Is there anything you'd like to ask about the role or the team?

Sure, Dr. If you're selected, you'd be teaching and mentoring students in mathematics and emerging technologies, handling classroom and lab sessions, evaluating students, guiding research projects, and contributing to research and publications. There are also opportunities to help with curriculum development and departmental responsibilities. Anything else you'd like to ask?

Active if I'm selected. I will. I'm very adaptable.