And I completed my masters. In Computer science and Engineering from Anna University. And I hold my bachelors in Information technology from. And I have worked in various engineering colleges. Like Panimala Engineering College. And RMT Engineering College, Chennai. And where I gained a good experience in both academic and teaching responsibilities. I took various subjects during my tenure like structured and Object oriented programming. Digital principles and system design. Then cryptography and network security. And then operating system. In the data structures also I handled. Uh, the associated laboratories as well. And about my research interest. My research interest lies in. Data predicting, modeling and data-driven application in agro environmental systems. That's it. Thank you.

Actually, after my ME, I started my career as a lecturer. Uh, in uh, escape engineering college, which is, which is actually located in Thiruvananallai. And later I joined as an assistant professor in RMD Engineering college. And then where I worked for two years. And later I joined as an assistant professor in particular engineering college. And then? Then later I decided to pursue my PhD, so I had an opportunity to. To question my PhD in VIT Vellore Institute of Technology, Vellore. So I joined as a research scholar. By the year 2020 during actually during the time of COVID. And then after that, uh. And by the year 2022, I had an opportunity to work as an Assistant Professor junior. In the School of Computer Science and Engineering. At the same institute.

You've taught a range of core courses and labs over your career. Out of all the subjects you've handled—like data structures, cryptography, or system design—which one do you find most rewarding to teach, and why?

That's it, That's it about my career.

You’ve had a pretty diverse journey and been exposed to a lot of academic environments. When it comes to running a lab—like for data structures or system design—how do you make sure students actually understand the concepts, not just follow instructions?

Right, you've handled both lectures and labs across colleges. When you're teaching, especially in a lab setting, how do you make sure students actually understand and not just follow instructions blindly?

So actually for the lab, actually we have both the theory as well as the practical hands on session, right? So in the theory classes, I just conceptually explained the. Things like. And during lab, uh, session, I'll give you basically. Uh, the application oriented commissioner's and scenario so that it is easy for them to practically understand how the particular subject is applicable.

Got it — sounds like you use real-world scenarios to help them connect theory with practice. Can you give me a concrete example of an application-oriented lab assignment you've designed, maybe for data structures or cryptography?

Like rather in in for example, in a data structures we. Let us take the basic data structures like queue. So I'll give you like, uh, the real time scenario where the queue is actually applicable. Like for example, in, uh, printing document, we actually apply the queue scenario, right? So I'll give you the real time scenario where we are using the.

Professor, I noticed your recent publication "Efficient hyperparameter tuning for precise precipitation prediction using EHPC framework" in Water Resources Management. How have you used insights from that research when teaching machine learning or data modeling to your students—especially those at the undergraduate level who may not have a strong math background?

Like I think machine learning and data modeling they require to have not an advanced mathematical background as of my knowledge. I'm telling like they must have a sound knowledge in statistical oriented things. So first let them understand the statistical oriented mathematical background so that it's easy for them to cope up with the machine learning as well as. What about the data analytics concepts?

That makes sense—you focus on building a statistical foundation first. Can you recall a time when a student really struggled to grasp a core concept from your precipitation prediction research, and explain how you helped them bridge that gap without oversimplifying the science?

Yes, like precipitation prediction. It is actually like the rainfall prediction. So firstly let them understand the things with the help of the datasets. So that they clearly understand about the domain. Like how, where and the forecasting of rainfall is utilized within. Later they'll understand about the conceptual oriented things.

Okay, so you use real datasets to ground the concept for them first. When it comes to evaluating their understanding—especially for these application-focused topics—what methods or assessments have you found most effective to judge whether a student has truly internalized the material?

Assessment like we'll give you the case studies. As a questionnaire is so let them response like how they are. Uh, answering the particular case study so that we can, we will assess the students evaluation. We, we evaluate the students performance through those assessment like we will make sure whether they submitted the solution for that particular case studies.

Case please.

Professor, focusing on your research in data-driven modeling for agro-environmental systems—where do you see the most fundable directions emerging over the next few years? Which funding agencies or grant mechanisms would you specifically target to support a research group in this area?

Professor, focusing on your research in data-driven modeling for agro-environmental systems—where do you see the most fundable directions emerging over the next few years? Which funding agencies or grant programs would you specifically target to support such work?

Actually agro environmental system like in future like we. Just incorporate like artificial intelligence. And the machine learning like how? Even though normally like. And presently, like the farmers, they don't, they don't have much understanding about the technical side. So we, we. Work on that, bridging this particular gap. Like how we are going to make them understand the technical side so that they can able to incorporate those things. To achieve a better. Predictive quality of Agromedical. Product.

Professor, considering your background in both theory and lab courses, suppose you're tasked with teaching a large "Introduction to Artificial Intelligence" class without using slides. How would you design a session that keeps 200 students actively engaged and ensures they truly grasp the concepts?

Like first we just introduced the artificial intelligence with the scope of the subject, like how, why and we required to study the subject. Then later like we'll carry out the concept with the real world scenarios so that it's easy for them to relate how way and where we are applying the artificial intelligence concept. So it's easy for them to understand practically.

Accreditation cycles can be pretty demanding. Say you join our department and find that outcome assessment data is inconsistent across courses—how would you go about fixing that?

Department finding outcomes. Assessment data inconsistency promise.

No, I don't have any idea right now like that.

Professor, let's consider a real situation: imagine a student formally complains that your grading was unfair, but at the same time your department head is urging you to raise pass rates for accreditation reasons. How would you handle that tension between maintaining academic integrity and responding to institutional pressure?

No problem. Let's shift to a real dilemma: imagine a colleague asks you to co-author a research paper, but you notice some of their data looks questionable. What would you actually do in that situation?

I can't. I can't clearly get that point.

Sorry, sorry I can't get the point clearly like.

Alright, I'd like to shift to some core qualifications for this role. Can you walk me through a specific project or experience where you applied multimedia or AI techniques in a media context—not just theory, but actually building or deploying something?

Specific project experience where we applied AI technique night. Like a technique, it's actually the machine learning is a part of artificial intelligence. So we apply machine learning technique for predicting things. So actually my research is on focusing on predictive modeling like. The first work is about predicting the, uh, crop yield, and then the second was work is about predicting the air quality, like how the environment will affect the, uh, productivity of agriculture. Then the third work is clubbing together how the crop yield will improvise based on the environmental factor.

That's pretty high-level—when you were building your crop yield prediction model, what was your own role in that project? Were you leading the research, handling the data preprocessing, or working on the model design?

Like in the in the machine learning pipelining, my focus is on how we are going to optimize the ML model. That too, particularly the predictive model. So I just uh, tried a various. For example, my first first objective is to work on ensembling model. Then later I'll work on like optimization strategies like since we have various optimization strategies to hyper. To tune the hyperparameters of. Of the model. So the second work is to focus on how we are going to tune the hyperparameters of the model in order to achieve the best result. And the third work is to about feature selection, how we are going to select the feature relevant feature according to. The particular domain.

Right, that's clear—you focused on optimizing, ensembling, and feature selection. Switching gears, do you hold a PhD in a relevant specialization, and if so, what was your dissertation topic?

Like I. Like while I'm working with the second objective, I feel like it's a most most. Challenging one. Why? Because. Like, uh, I, it's I used to various model and then, uh, hyper tuned the each and every parameters of the model and then evaluate those model with the various, uh, optimization strategies. It does not only, uh, help me to achieve the decide a result, but also give me the deepen understanding of machine learning model, particularly in optimization of machine learning model.

Alright. Let me clarify—do you currently hold a PhD in a relevant field, and if so, what specific area was your dissertation focused on?

Actually my area is like a predictive modeling. And data-driven application where we apply that predictive modeling in an agro environmental system, not only an agricultural but also consider the environmental factors.

Got it—so your focus was on predictive modeling in agro-environmental systems. Have you published your research in any reputed journals, or are you currently working on submissions?

Yes, I actually submitted and my two of my SCSC index paper got already submitted and published. And then two more paper I published in Scopus index to journal. Relevant to my PhD work and recently. Uh, two of my SCA indexed paper has been. Accepted for publication. Both are one is the queue queue 2 journal and another one is the queue three journal.

Right. Have you ever guided student research projects or mentored students working on independent studies? If yes, what's one example of how you helped a student navigate a difficult research or project challenge?

Actually, before my PhD, I just guided the UG students in my earlier experience like before 2020. Like, uh. I guided a few students, undergraduate students there. I think their work is not that much complicated that before emerging technologies like AL and data analytics. Right now I don't have any experience like in guiding students relevant to research in future I focusing on both. Collaborating and doing research with students.

Okay, thanks for clarifying. Have you ever worked on industry projects or consultancy assignments—maybe with a company or external organization—where your research or technical skills were applied directly to solve a real-world problem?

So actually after 2020, since I was busy with my PhD research then in future actually. I'm going to plan uh to get a funded projects as well as planning to. Uh, file a pattern. Right now I don't have any experience. Relevant to the funded project as well as patents.

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

Actually, before explaining about the role, I just. Wanted to tell my experience, this is the first time like I'm attending an interview equipment with an artificial intelligence it's like. Very new experience for me.

That's interesting to hear—AI-based interviews are becoming more common, but it can definitely feel a bit different the first time. Do you have any questions about the Assistant/Associate Professor role itself or about the team you'd potentially join?

The questions about I don't because already I am a part of this organization, so from 2020, so I. Don't have much questions because already I aware of the organization requirements as well as responsibilities as well as the standards, academic standards within the institution. And. I don't have right now. I don't have any questions.