Good day, Dr. Ganesh Govindarajan. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

I completed my PhD from IIT Madras, then I went a post doc in a pre university of Bosano and also I worked as a another postdoc in Florida International University in other Research Center.

Please start by introducing your professional journey in academia.

So my basically I'm a multidisciplinary researcher, I work on a PhD from applied mathematics and postdocs are on shared control system like your robot interactions and currently I was working in Applied Research Center in Florida International University.

Is an implementation of algorithm and working in robotics.

Thank you for sharing that context, Professor. Let's proceed with some targeted questions to assess your expertise and experiences across the relevant areas for this role. Starting with computational modeling, could you explain how you've applied computational modeling techniques in your recent work, specifically in interdisciplinary areas like shared control systems or robotics?

So the computation modeling. Currently I implemented the algorithms in the like.

UH particle filtering algorithm implemented in the uh robots like uh implementing the particle filtering and the UH algorithm in the dog robots. So I works on the mathematical formulations for the particle filtering algorithm and also I works on the computational things how the particle filtering, how the weight waging and resuppling techniques is focused on the particle filtering algorithm?

So that are the computational approach which I worked on the uploaded international city and another computation approach which I works on the Italy is a free human robot interactions like how the robots can able to integrate the like how the controls can computational things which is like controls which is implemented in the. Robots and how the human can handle interface with the controls. That is what I working on this.

You provided some interesting applications of computational modeling. Regarding your work on particle filtering algorithms and its applications in robotics, how do you evaluate the computational efficiency or performance of these algorithms when deployed in real-world robotic systems?

What?

Can you speak?

Certainly, Professor. Regarding your work on particle filtering algorithms, how do you assess their computational efficiency or performance when these algorithms are used in real-world robotic systems?

In real world applications purpose only we are designing the computational things like I'm I'm really told I'm working on the basic applied mathematics and applied implementation of applied not only for the computational things, it's for applied things applied in a real time purpose. So the particle filtering works and computationally very nice and before after implementing the.

Actually.

Umm, uh, like uh, before implementation in the real time I came, I left the job for my personal issues.

Ah, I worked on the evolutionary reinforcement algorithm in a smart material for the camouflage robots. Like a robot can go at a different depth inside the water, so the water pressure and the top side is very high and low bottom side is very low like a pressure is very high. So all the material can't able to withstand that high pressure regions. So the using the EA technique the material. Changes at a different pressures.

Understood, Professor. Thank you for clarifying that. Let us now transition to discussing your experience with AI and machine learning applications in materials science and manufacturing. Could you share an example where you applied AI or ML techniques in those domains?

Same is can able to applicable for the space industries also.

Yeah.

Hello.

Yes, Professor. Could you elaborate on how the evolutionary reinforcement algorithm was implemented to adapt the material properties under such extreme conditions? Specifically, how did you simulate or validate the material's behavior for both underwater and space environments?

So actually for evolutionary algorithm A works for the different environmental conditions. So it if it changes with the different environment and the materials properties will change using the reinforcement learning.

At a different environment conditions. So in order to integrate those things, we formulated the mathematical formulations so that formulations finally integrated with the evolutionary enforcement downloading and finally it is integrated.

Those things can able to implement it in the uh.

Space industries also.

OK.

I always go for the go for the complex from applied mathematics to the computational things. First I make the students to solve applied mathematics and then we go for the practical applications.

That’s a structured approach, Professor—building from the fundamentals of applied mathematics to practical applications. When teaching this way, how do you assess whether your students are grasping both the theoretical and practical aspects effectively?

Oh.

Can you what is? What's your question?

My question is: When you teach students, how do you evaluate whether they are understanding both the theoretical concepts and the practical applications effectively?

Make them to evaluate, uh, make them to give the new concept and, uh, uh, to work on the as individual, uh, giving the different, different, uh, uh.

Theoretical approach and practical approach make the students to train according to that.

Yes.

Now, regarding your ability to guide student research projects, can you share an example of a research project you supervised and how you supported the student in achieving their objectives?

I don't understand what you say.

Let me clarify. Could you provide an example of a student research project you supervised? How did you help the student reach their research goals?

Uh, actually I guided the students, master students. So I, I gave the all the fundamental things what they needed for the.

What the students need for the executing the task so and store. I'll explain the fundamental concept and make the students to works independently if the step by step and also I will monitor the each step how it is works.

Yeah.

Hello.

Yes, Professor. Let's move to your experience with research publications. Could you highlight one of your notable publications and explain the significance of its contributions to the field?

Yeah, that is what I explained. I have a publication in evolutionary algorithm reinforcement learning for the different environmental conditions, how the material will changes with respect to the environmental condition if the material is going.

Like 200 meters, like inside the water, the material due to the different pressures and different environmental conditions, the material changes its properties and also if it is.

Uh, go for the space industries in kind of material to the different materials can able to implement. So that is a very challenging and task which I worked on that. And also I worked on the gradient descent algorithm and the optimization technique looks.

Yeah.

Could you share an example where your expertise was applied in an industry project or consultancy, specifically focusing on its impact or outcome?

Currently I'm working on the element controls in the industries for the chemical industries, how the chemical process industries can be useful according to that. So I'm just exploring what is happening, how the LLMS works.

Thank you, Professor. With your insights on these various facets, I believe we've covered a comprehensive spectrum of your skills and expertise. Let me confirm everything on my end. One moment.

Professor, could you tell me about a foundational concept in your field that you particularly enjoy teaching and how you typically engage students with it?

I particularly enjoyed the teaching of uh uh, algorithms, mathematical approach of algorithms and uh.

And also the computational aspect computational modeling like mathematical approach of computational modeling.

Alright, yeah, kind of uh, integrating the mathematics with the computational level teach.

OK.

Professor Govindarajan, let us examine the connections between your research and your teaching. I noticed your work on the project titled "Autonomous Surface Robot for Algae Estimation," which employs AI gradient descent-based search and real-time visualization. Can you explain how you might use elements of this project to develop or design a course module for students in computational modeling? Specifically, how might such a module balance theoretical foundations and practical applications?

While doing the practical application we faced a lot of the problems how to implement it, how to integrate the algorithms in the practical robots. So I make the students to learn more about the theoretical like computational aspects and finally I will give the demo for how the practical works will works. So before going practical also we just tested some of the computational.

Aspects how the practical will outcome will comes I can able to show them.

Yeah.

Understood. How would you ensure that students are able to effectively transition from understanding the theoretical aspects to successfully implementing them practically? Could you provide an example of an activity or assignment you might design for this purpose?

Based on this how the students will understand those basic fundamental concept, I will iterate the problem to higher level problems and final task is to implementation the practical approach.

No, actually I will start. I'll make the students to work from the basic concept like theoretical mathematical concepts and based on this.

That’s a structured approach—starting from foundational mathematical concepts, iteratively advancing to complex problems, and culminating in practical implementation. For your course, if some students struggle with this transition, particularly from theory to hands-on tasks, how would you support them? Would you incorporate any supplementary resources, mentoring, or alternative methods?

Uh, by mentoring, monitoring the each task of the students, how it is the works step by step, uh, monitoring the HN tags. So step by step, uh task implementation instruments I can do.

Hello.

I'm not able to hear it what he said.

Hello.

Hello.

Can you hear?

Yeah, right now I can able to hear you.

Yes, I can hear you clearly. Can you hear me?

Great. I was asking about your project on "IoT-Enabled Semantic Mapping" using LIDAR and AI segmentation. How would you introduce and simplify this advanced topic for students in a classroom setting?

So initially for uh, AI based schematic mapping, uh, there is a lot of uh, datasets are available. I make the students works for the, uh, works on the model, uh, small, small models based on the small, small models. Like I teach the students how, how the big systems works. So initially the nowadays a lot of source of the models is available in the.

A lot of the source of model is available.

In the from the Internet source. So I will train the students to works on the model and understand how the model works. Based on this I will make the students to works on the AI integrated semantic.

So.

Things to works on major task.

OK.

By monitoring like, uh, each and every students and uh, to have A to make the to explain in front of me, uh, how it is works.

How do you evaluate whether students have truly understood the connection between these smaller models and the larger, integrated systems?

And make the students to monitor by simply monitoring the students and make the make them to explain in front of me how the students will.

Yeah.

Thank you, Professor. That gives a clear picture of how you bridge research and teaching. Allow me a moment.

Professor, could you provide an overview of your 3-year research roadmap, particularly focusing on how you would align it with the goal of advancing institutional rankings and securing external funding?

Yeah, he works on the new topics like digital twins and LLM and controls. I apply for the fundings for the Indian government and other DS, DS and.

And also I works for the, I apply for the international fund also because I have an international exposure so I can work for the international department.

Understood, Professor. With your expertise in emerging areas like digital twins, LLM, and controls, how will you ensure the scalability of your research laboratory? Specifically, how do you plan to mentor junior researchers to maintain a consistent pipeline of high-quality publications?

I already have a experience in publications for high quality publications, so if I organize the lab, I make the strollers. I will hire the junior engineer or junior assistants to focus on this and finally make the students to publish in high quality papers.

I already have an international collaborations like in Italy and US, so after joining in the Inver institute, I can able to collaborate such as possible.

Like implementing their small, small task like generating them, uh, like, uh, instead of uh, going for the slides, make the students to works on the some theoretical approach. And uh, uh, after completing the theoretical approach, the students has to do the practical things. So the camp is students can able to grab the knowledge immediately without going for the slides and uh, traditional approach.

Professor, let us explore your strategies in teaching. For a large-enrollment computational modelling course, how would you implement an active learning model, like the Flipped Classroom, ensuring it is effective while not relying on traditional slides during class sessions?

Understood, Professor. It sounds like you favor having students engage directly by working on theoretical concepts first and then applying them practically to reinforce their understanding. Can you detail how you would structure this, for example, over a week? Specifically, what resources or activities would you provide before the class session, during the class session, and after the session to ensure they stay engaged and achieve the intended learning outcomes?

In class section I will tell fundamentals like basical concept of basic mathematical modeling in outside like if they go outside they need to give the homework task like.

Some of the computational tasks, homework task and also in in institute they have to do some practical things how it is works.

I will monitor the each and step by step.

I will give the 50%, uh, sorry, uh, 50% uh, theoretical, uh, gradings and 50% practical gradings. So the students, umm, in the 50% of, uh, theoretical grading, how much the students understand the fundamentals and basic mathematics. And also another 50% of practical, how the students can able to from the gained knowledge of the theoretical, how the students can able to implement in the practical that I will give them more, more according to that.

Thank you for elaborating, Professor. You mentioned assigning computational homework tasks and practical exercises. How would you then assess whether students truly grasp the concepts, ensuring they have developed the expected competencies without relying solely on standardized testing?

Thank you for explaining, Professor. It seems your balanced grading strategy places emphasis equally on theoretical understanding and practical application. I see the structure. We'll proceed now.

Professor, given the emphasis on accreditation and institutional building in this role, could you outline a systematic approach to improve the departmental metric of research output per faculty to meet high-level accreditation standards?

What what is your question?

Professor, my question is: How would you approach systematically improving the research output per faculty in your department to align with high-level accreditation standards?

Uh, like, uh, just to collaborating with the faculties and, uh, to implement the new things and understanding the digital links. And also I works, uh, make the students to works. So I make the makers to faculties. I just collaborated with the faculties to, for the publications and the sharing the labs and, uh, getting some more input from the experience faculties and for the obligations for a high factor.

Kind of those things.

Thank you, Professor. Collaboration and shared resources are indeed significant strategies. Following that, how do you balance the administrative demands, such as preparing for accreditation audits, with maintaining your own consistent research output?

I already have experience of UH balanced UH while I'm doing PhD. I was assistant warden so I can able to uh maintain the UH.

Like economic activities apart from that.

Leaders, leadership roles. So I in the in the during my assistant bottom rules, I monitor the full control of the hostels and also give the instruction to the clerical Staffs and giving the outcome to the warden like professors that that's what what could occur during my PhDs. So it is a simple task I can able to do according to the now if I joined during our consult also I can do.

I see. Leadership and multitasking during your Ph.D. certainly reflect your capability to balance responsibilities effectively. Lastly, could you share any specific experience you've had serving on internal committees that directly impacted quality audit results or accreditation processes? How did you contribute?

What is the role? Is a professor or assistant professor or what is what kind of role? What is the main contribution for the research?

Our scheduled time for the interview is now up. Before we conclude, do you have any questions related to the role or the company?

OK.

Can you hear me?

Yes, I can hear you, Professor. Let’s begin.

Tell me.

Professor, let us start with an ethical scenario. Suppose you are faced with an allegation from a student regarding grading bias, which conflicts with the institutional goal of maintaining a certain pass percentage. How would you resolve this situation effectively?

Actually I will give the marks to the students because nowadays students taking the some of the difficult task like ending the life. I will give the good grades to the students.

Minimum grades so I make the students too happy.

I understand your concern regarding student well-being, but academic integrity is vital in ensuring that grades accurately reflect a student's performance. How would you balance compassion for students with the ethical responsibility to maintain fair and unbiased grading practices?

I will make the students to repeat if they are completely don't know about the topics like courses. Make the students to works for the next next semester.

Actually, uh.

To works for the make the students to work for the next semesters if the if the student is completely bad and that course.

Understood, Professor. Now, related to professional boundaries, let me ask you this: how would you design laboratory sessions to complement theoretical instruction in computational modeling and create a cohesive, impactful learning experience for students?

Like works on the large scale like most of the GPUs and also works make the make the students to works.

I just make the students to implement in GPU.

Noted, Professor. Following up—if a disagreement arises in a public committee forum between you and institutional leadership regarding such an approach, how would you handle it while maintaining professionalism and defending your academic stance?

I want to go against any of the leadership, uh persons in case I will say what my perspective, uh.

According to that, that's all I can be able to do.

Thank you for sharing, Professor. Let us move forward.

Dr. Govindarajan, let's focus on bridging academia and the professional industry. How would you concretely leverage your professional network to create robust internship or core-sector employment pipelines for students in computational modeling?

I already have a collaboration with the industries, so I send the students, uh, for the interns, uh, to works on the projects because I'm doing the, some of the consultancy tasks to the industries, so.

Uh, I will make the students to works according to that.

That approach aligns well with industry involvement. To modernize the curriculum further, how would you integrate laboratory sessions with theoretical instruction in computational modeling courses to create a cohesive and impactful learning experience for VIT University students?

So the for the VAT university students, for any students, I works for the. Initially I go for the applied mathematical things for the computational problems and after the students understanding the applied mathematics and also the computational concepts, I will go for the practical things. Many many task like implementation of the.

Practical things by using the computational approach. So after solving up the students all the many many tasks of the practical, they will go for the major task.

For the publications.

Understood. Shifting focus to consultancy, what specific strategies do you employ to secure industrial consultancy projects that generate revenue and align with VIT University's emphasis on high-value funded projects?

Time is up! Ending the interview now.

OK.