Yes.

Please start by introducing your professional journey in academia

I've done my undergraduation in electrical and electronics. Engineering.

Alright, so electrical and electronics engineering was your foundation — what led you from there into academia?

From your UBC Bangalore University, Visveswaraya College. So after that I joined a company. And and during that time, I prepared for the gate exam. And I had started developing a core interest in computational neuroscience.

Okay, so you moved from Visveswaraya College into industry, but started developing a core interest in computational neuroscience while preparing for GATE.

And in biomedical engineering, there was a lot of work that was happening in the healthcare. Industry. So the I read an article. At that time on how? They've used. These enter all all the. Computational Resources. To mimic one particular part of the brain. So at that time there was a lot of activity going on in trying to mimic different parts of the body. And using engineering principles to understand how our our human body. Or different organs in our human body behave. And I thought that was a very interesting, intriguing. Application of. Oh, I. You know code engineering company. So I joined IIT, I was, I cleared GATE and I joined IIT Bombay in the biomedical engineering program. Where I I pursued my masters, and then my P. PH D.

You mentioned your interest in computational neuroscience and biomedical engineering — when you're teaching foundational topics in those fields, what's one concept you find students struggle with the most, and how do you help them get past that?

So I had taken up T. Teaching duties while I was in. IIT Bombay. And what I found amongst the people. Was amongst the students, so biomedical is an interdisciplinary area. So you have. Have people coming in from different backgrounds, so you have people coming in from an engineering background. Who found understanding biological concept? See Computation, Neuroscience, and Biomedical. There's one aspect of it, but if you don't understand it, the field in which you're applying these engineering concepts, that becomes difficult. You might understand programming. You might understand data structure. But if you don't know what you're. For applying it to. That becomes a challenge. So people with an engineering background or people with a strong fundamental maths background they understand, they found difficulty understanding. Concepts on molecular biology or. Physiology. For that matter. And people with a biotech background, they had problems.

Right, so you noticed students with engineering or math backgrounds struggled to grasp the biological side, especially concepts in molecular biology or physiology. Can you give a concrete example of how you helped an engineering student overcome that challenge in your teaching?

Segments go umm. What I had. What I started practicing is. Set.

You started saying you had a practice or approach you used—can you finish that thought?

OK, so. So one example was. That, uh. Uh, I used. For instance, if you're trying to understand a system. I used concepts of. Control systems, for instance. To help them understand SO. For the for for example, let's look at a cell. If I want to explain how a. Cellular. How a cell?

Behaves in a particular manner. There are models. Where the cell is. The equivalent of a cell model is like an RC network.

So you were using control systems concepts to help explain cellular processes to engineering students—can you give a specific example of how you mapped an engineering concept onto a biological phenomenon in class?

So you used the analogy of a cell as an RC network to make it more relatable for engineering students — that's a classic modeling approach. When you do that in class, do you find it helps them connect the dots, or do they still struggle with the biological side?

Control, yeah. Yeah.

Dr., looking through your publications, your 2019 paper on computational modeling of large conductance calcium-activated potassium channels in detrusor smooth muscle cells stands out. How did your experience developing those biophysical models influence the way you teach complex bioelectricity or ion channel topics to students, especially those coming from a non-biology background?

Exactly.

Dr., considering your work in computational neuroscience and biomedical engineering, where do you see the most promising and fundable research directions over the next few years? Which specific grant agencies or funding schemes would you target to support that work?

Lot of questions. And I will go through them 1 by 1.

Lot of questions.

You said "lot of questions" — fair enough, but I do want to make sure we cover research funding since that's central to this role. If you had to pick one area in computational neuroscience or biomedical engineering that you think is both scientifically impactful and likely to attract substantial external funding in the next few years, what would you prioritize?

Mm-hmm.

So I think that your first question was how did? So I will go. With respect to funding, see, there's a lot of data that's available. You know 1 aspect of it. How? There is a lot of transcriptomics data, there's a lot of omics data, there's a lot of computational biology data that's available right now. And a lot of the challenge happens in how we process the data, how we analyze it, and if we can get any more insight. That's one aspect of it which I know is getting. Heavy importance from the industries in terms of funding. So that. That's one aspect of it In terms of the next few years? See how where I stand, I see that digital twin. Is a very impactful area. There are universities in America, in Europe.

So I think that your first question was how did? So I will go. With respect to funding, see, there's a lot of data that's available. You know 1 aspect of it. How? There is a lot of transcriptomics data, there's a lot of omics data, there's a lot of computational biology data that's available right now. And a lot of the challenge happens in how we process the data, how we analyze it, and if we can get any more insight. That's one aspect of it which I know is getting. Heavy importance from the industries in terms of funding.

Okay, let’s talk about teaching. You mentioned before how students with different backgrounds struggle in interdisciplinary fields like biomedical engineering.

You see a lot of potential in digital twin research, especially with all the available omics data and computational resources. 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?

So to answer the first question. In terms of how I would address a particular. Uh, problem when I'm catering to a. To students from varied backgrounds, is. Is is actually? Not as simple as as I can explain it in one line. But what I can say is that what the practice that I have used in the past is. That for instance, if I'm explaining a concept A. I apply. I give an example. Of that a in different different fields so I give an example in the biology field, I give an example in the in the electrical field or in the computer computer science field. Most of the time that I have seen is that when you give a very general example in order. It helps them visualize and then they can apply it suitably based on their understanding. So for instance.

When I'm. Trying to explain. Uh, cable conductance. Or how resistance is intergenic cellular resistance?

When I'm. Trying to explain. Uh, cable conductance. Or how resistance is intergenic cellular resistance? Are distributed. I use Waffle Theater.

Alright, so you start by giving multiple analogies for a single concept—one from biology,

Okay, so when you're explaining cable conductance or intercellular resistance, how do you adapt that explanation if you realize half the students are struggling with the math, while the other half are lost on the biological relevance?

Alright, so you use analogies—like Waffle Theater—to help explain distributed resistance and conductance. When you notice outcome assessment data is inconsistent across your courses, what specific steps would you take to bring those assessments in line with accreditation standards, especially given the diverse backgrounds of your students?

So I for instance see there's. So for instance, when I'm showing distributed conductance, I use the example of Imagine a waffle. Segments become one segment in the. So I give them an analogy like that. But. In some places. You they can't. You can't not learn a subject without knowing them. So they have. To put in the effort and as a. Teacher, what I do is that I always take extra. Time out. To, you know, at the beginning of the class and at the end of the class. So at the beginning of the class I. You know, recap whatever has been taught. In the previous session. To see if there's any. Lacuna And at the end of the class I summarize whatever. But when people are struggling with maths, then I.

So I for instance see there's. So for instance, when I'm showing distributed conductance, I use the example of Imagine a waffle. Segments become one segment in the. So I give them an analogy like that. But. In some places. You they can't. You can't not learn a subject without knowing them. So they have. To put in the effort and as a. Teacher, what I do is that I always take extra. Time out. To, you know, at the beginning of the class and at the end of the class. So at the beginning of the class I. You know, recap whatever has been taught. In the previous session.

Suppose a student files a formal complaint, claiming your grading on a recent exam was biased. At the same time, your department head is pressuring you to increase pass rates for your course. How would you handle this situation, balancing academic integrity with the institutional pressure?

And at the end of the class I summarize whatever. But when people are struggling with maths, then I. Yeah. So that's going to be a learning experience. I. In in the past I have not. Had that kind of a situation. But uh, going forward in, in a situation like this, definitely I will take the help of my. You know seniors because they have dealt with. Situation, but on my own at this point, what I would do is I would probably. Student down. And see where was the misunderstanding between what he expected or she expected out of the paper or out of the court? And where I feel. So at the end of each course, that's also another thing that I will be doing. Is taking feedback from the student at every periodicity. So for instance if it's a six month course I would take. Periodic review at the end of about 1.5 months.

To see where the students are, where they lack. Do they think you know XYZ needs to improve? You know these topics need to be covered in more depth then I will go. Those things again.

You mentioned earlier that digital twins and computational biology are drawing industry attention. Have you worked with any companies or external partners in these areas,

You mentioned earlier how you adapt explanations for students from different backgrounds, using analogies and targeted reviews. Say you're running a lab course where students have to build and test a basic neural recording setup. How do you ensure they grasp both the theoretical underpinnings and the hands-on technical skills in the lab?

I have.

So you make sure to get student feedback throughout the course to spot gaps and then revisit those topics—sounds pretty thorough. Shifting gears, tell me about a time you guided a student project or research effort, especially where the student struggled to define their research question or approach. What did you do to help them?

So I have handled 3 masters projects while I was doing my PhD. And so the first masters project that you know I was working with. She had a lot of. She had. The first two rather. They had a lot of difficulty grasping the. Coding language because we were. We are a computational lab. So we were using a different software that and that. They are not useful. And so for that, what I had done was I had given. The course also the the software also had its own training modules. But I also would give them like small, small exercises that try this out try that out so every. So and I would sit with them. Uh, you know, give them. Every week I would give them like a set of papers that. Then at the end of the week we would have a brainstorming session. Then within about a. Couple of weeks or three weeks they started getting the hang of the biological ***. So someone had a problem with the biology. I told them to keep reading and every time they had a problem you know they encounter. Something in the paper that they could not understand. I would sit and discuss it with them and I would make sure that you know, they they are able to assess it and, you know, process. And the ones who had a problem with the coding, I would sit and give them a small small exercise, a try and you know, develop this or do the.

So I have handled 3 masters projects while I was doing my PhD. And so the first masters project that you know I was working with. She had a lot of. She had. The first two rather. They had a lot of difficulty grasping the. Coding language because we were. We are a computational lab. So we were using a different software that and that. They are not useful. And so for that, what I had done was I had given. The course also the the software also had its own training modules. But I also would give them like small, small exercises that try this out try that out so every. So and I would sit with them. Uh, you know, give them. Every week I would give them like a set of papers that. Then at the end of the week we would have a brainstorming session. Then within about a. Couple of weeks or three weeks they started getting the hang of the biological ***. So someone had a problem with the biology. I told them to keep reading and every time they had a problem you know they encounter. Something in the paper that they could not understand. I would sit and discuss it with them and I would make sure that you know, they they are able to assess it and, you know, process. And the ones who had a problem with the coding, I would sit and give them a small small exercise, a try and you know, develop this or do the. Some very minor thing.

So for the students who struggled with coding, you gave them bite-sized exercises and regular check-ins, and for those with biology gaps, you'd sit down and work through papers together. Can you tell me about a project where you helped a student not just with technical skills, but also to shape or refine their research question into something feasible?

And even during the execution there were a lot of times. So this is 1 master student and we had we were planning to work on. Something with the, you know. Processing intracellular calcium. Oscillations. So in in the cell. There are different kinds of calcium signals and these kind of help in the mechano sensation mechano. Mechanical activity of the cells. Basically the transduction between the electrical activity and the mechanical activity. They form the bridge between the two.

So this was something that. You know, labs across the world has had not yet done so we weren't looking.

So you were guiding a master's student through a project on intracellular calcium oscillations and their role in mechanotransduction. When the student struggled to connect the biology with the computational analysis, how did you help them refine their research question so it was both scientifically meaningful and actually doable within a master's timeline?

So you were pushing into pretty uncharted territory with that calcium oscillation project, and it sounds like you and your student had to navigate a lot of ambiguity. Let’s talk about your teaching approach—when you’re running a theory-heavy course alongside a lab, how do you make sure students actually connect the abstract material with what they’re doing hands-on?

So will when the when a course has its own practical aspect of it. Then it becomes a little bit easier to understand whether they're able to grasp the concepts. Because. For instance, if I teach them. I had a quote. But I was handling the lab. Component of it. So I. There was a theory they would be taught. You know, let's see. The electrical circuits. Then they would have a lab and I would. Test them. I would first show them the circuit. I would tell them that can you analyze it? Can you tell me what? Should get. What happens? What should you put across the capacitor? What will happen? What kind of? If I put a sign here. Let's think it through. Let's do like a dry run first.

And if there are any lacuna? Immediately they are able. See it if even if they say then I'll be like rig it up. And then they'll see that that's not how. So in a practical exam, when I have create when I have conducted it. I have given. So for instance. Each person there would be like. We had a lab set of. Smallish lab and we had about 6. Six people at. So we would I had developed. Six question papers. Next question. So each of them would get like a different question paper For the. First bat. And each of them I would have them ask. Yeah.

Okay, so you walk students through a dry run before they actually build or test the circuit, asking them to predict outcomes and think through each step. When it comes to evaluating students—let's say you have a practical exam in the lab—how do you ensure your grading is objective and consistent across a large group?

And if there are any lacuna? Immediately they are able. See it if even if they say then I'll be like rig it up. And then they'll see that that's not how. So in a practical exam, when I have create when I have conducted it. I have given. So for instance. Each person there would be like. We had a lab set of. Smallish lab and we had about 6. Six people at. So we would I had developed. Six question papers.

So you actually wrote six unique question papers for your practical exam, making sure each student had a different version to work on in the lab. How did you make sure your instructions and evaluation criteria were clear enough that students with different backgrounds could all understand what was expected and feel the process was fair?

Alright, so for the first batch you were giving each student a different question paper and guiding them through the process. For courses where you're expected to evaluate students not just on technical skills but also on their ability to communicate their reasoning—how do you structure your exam or assessment to capture both aspects fairly?

So. The thing is that they are trained to. Communicate. Freely.

During the course. During the exam, one needs to be very sensitive that these students. End up with a lot of pressure.

You said your students are trained to communicate freely. For this role, clear communication is essential, both for lectures and for guiding research.

Right, you mentioned the pressure students feel during exams and the need for sensitivity. When you're evaluating under those conditions, how do you balance maintaining high academic standards with supporting students' well-being?

So to answer the first question, one complex topic that I taught to people from different backgrounds was cable theory. And also on a much larger scale. One can say is uh, processing visual signal. So I had developed a course module. On visual neuroscience. Now that that was a that was quite tricky and quite challenging.

And and in terms of explaining it to them if I had. You know it had to be done. Through tutorials.

So you developed a course module on visual neuroscience, which is definitely a challenging topic to teach across backgrounds. When you designed that course,

So you mentioned you had to use tutorials to break down complex visual neuroscience concepts for students with different backgrounds. Can you walk me through a specific tutorial or exercise you used to help students grasp a tough concept in that module?

So for instance, OK one one such example. So in terms of the visual module module, what I did was that we didn't have a a lab component. This was too big. But what I did was I gave them simulated examples of technologies that are being used right now. And these simulated examples are something that. Companies themselves have, you know. Released, but we don't get access to it or we don't have, you know, we're not. Aware of it so. So when I'm trying to show them that this is how. This is where the. Technology is See how the you know. The the difference between how you're naturalized.

So for instance, OK one one such example. So in terms of the visual module module, what I did was that we didn't have a a lab component. This was too big. But what I did was I gave them simulated examples of technologies that are being used right now. And these simulated examples are something that. Companies themselves have, you know. Released, but we don't get access to it or we don't have, you know, we're not. Aware of it so.

So you gave students simulated examples of current visual neuroscience technologies, even without a hands-on lab. How did you structure the tutorial so students could actively engage with those simulations and demonstrate their understanding, rather than just passively observing?

So you're focusing on how students notice differences in how things are naturalized or represented in the simulations. Can you give me a concrete example of an exercise or question you used in the tutorial to push students to compare those differences and articulate their understanding?

So. So one set example is. Using cable theory. So I was showing them like this is a, this is a cable and you know this is the space constant, time constant so and so forth. And I directly related that. And in terms of? Tutorial I directly related the. After that. You know. Ailments that we have, for instance multiple sclerosis.

Where there's an erosion of the mileage. So now when you have a prototype ready of a simulation.

Alright, so you used cable theory as a bridge from the mathematical model to real medical conditions like multiple sclerosis,

Okay, so you tie the simulation back to demyelinating diseases like multiple sclerosis, once students have a prototype running. When students complete that simulation, how do you assess whether they've really understood the underlying biophysics versus just getting the simulation to work?

I give them trick questions. I said OK, now if something like this were to happen, what is? First you tell me, what do you think should happen?

And then can you show me whether your simulation kind of matches with? If you're saying and if it doesn't. Which one is right? Have you?

So you use trick questions to check if students are thinking beyond just running the simulation. Switching gears a bit—when you guide students on their own research or major projects,

So you're asking students to compare what the simulation shows with what they expect, and if there's a mismatch, to figure out which is correct. How do you handle it when a student's reasoning is sound but the simulation output is unexpected—do you encourage them to trust their analysis or the simulation?

I if the reasoning is sound.

So if the student's reasoning is sound, you seem to prioritize their analysis over just trusting the simulation output.

Then I will and if the simulation does not show it. Then we sit and debug it so we go back. Yeah. So what we did was so there was this one time when our question paper. So if our question papers are very difficult, so whenever we set a question paper, we used to have this practice in the lab that we would. Give it to 2-3 students to. To do it before we actually circulate it or release it to the department.

If the students are able to do it within that hour. Given that you know less than an hour. Given that because they are, they are already familiar with the field.

Right,

So you basically use students who are already familiar with the field as a benchmark—if they can solve the paper in under an hour, then it's reasonable for the rest of the class. When it comes to guiding student projects, how do you encourage students to move from a vague interest to a specific, researchable question?

If the students are able to do it within that hour. Given that you know less than an hour. Given that because they are, they are already familiar with the field. Then we. The. So when the students approach. I first work work with them. Talk with them to see. Where is their interest? For instance, what is their background or if they've done their bachelors or masters, what topics? What are they most comfortable doing?

And on the basis of that, I tell them that OK, this is the work that we do in the lab. So I tell them that, you know, these are the kind of. Work that we do. And. You know, for instance, these in this area, if you're interested in this area, these are the questions that. You know we are trying to. Understand or it has not been understood for that there are these kind of papers so I give them like. Bits and pieces of paper that they can go through. Based on the papers, if they say that OK this one I found it interesting, this one I think I can do. Then I tell them to OK. Dig a little bit deeper, go into their citations, see if you're able to follow.

Okay, so you start by talking with students to figure out their interests and comfort zones, especially based on what they've studied before.

Alright, so you give them a selection of current questions and relevant papers,

So I gave it like I go. You know, top down approach, so if they're able to understand. Papers at this level. So sometimes a break is necessary. You know if they are losing focus. It is possible that or they're getting overwhelmed. It's possible that they have reached like a burnout state.

Possible it's it's necessary that they take a break. I understand semester and all that, but one cannot put. The students too much.

Right, so you use a top-down approach,

You recognize when students are at their limit and avoid pushing too hard,

Given that. Yeah, so I modularize the. So in in terms when I'm teaching, what I do is I. I interject. Each. Uh, slide. So I make the slides very light. They're not information heavy.

So you keep your slides light and modular, and break up the information. When you’re explaining a complex theory or concept,

And I kind of after every 2-3. I kind of give. Turn the question around. I question them. Yeah, I question them.

Every two, three slides I ask them that. OK, have you understood KSN then I. Turn the question, say that let's try a small X.

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

Mm-hmm.

Yes. So I wanted to know. What is the? What is the requirement? I mean I did see the requirement of. The team but this I believe the department is from Health Sciences and engineering. So is there a lot of focus on computer science or is there also equal amount of focus on, you know, electrical electronics?

As applied to healthcare.

Okay, good question.

Right, so your interest is in the intersection with healthcare applications. For this Assistant Professor (Research) position, the department’s current focus is primarily on computing and informatics as applied to health sciences, though there’s collaboration with areas like electrical engineering for interdisciplinary research. Anything else you’d like to know about the role or the team?

Also the role. For the rule that I have been select. Uh, I mean, I've been chosen for what is. The typically salary bracket. OK, that's fine.