Good day, Rajasekar Thota. 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?

30.

Uh, myself for doctor Rasheka Thota and uh, currently I'm working as associate professor in the Mondo University in the Department of Electrical and Electronics Engineering and coming to my education, uh.

Details And I have completed my schooling uh, within uh, RR high School uh.

In 2004.

With the percentage of 86.8% and I have completed my intermediate in the year 2006 at Arisma Junior College where the percentage of 92.5 and I have completed my B.Tech in Electrical and Electronics Engineering Institute Engineering College at 2010. And with the with the percentage of 68.9 and I have completed my Mtech in electrical power systems at Cuba College of Engineering and Technology.

Uh, with the 979 percentage and I have completed my, uh, PhD, uh, in, uh, electrical engineering at National Institute for Technology Silchar, uh, in the year 20, twenty-four.

Uh and I got UH-9 pointer CGPA in the PST and my uh coming to my uh.

Teaching experience. And I have uh, like, uh, I worked as, uh, assistant professor in JD Women's Engineering College, uh, uh, in the department of Tripoli and uh, for three years. And after that I have worked as assistant professor in Wemo Institute of Technology in the department of Tripoli. And after that I have worked as a assistant professor in Mallardi College of Engineering and Technology.

Hyderabad in the department of, uh, data Science, uh, and uh, now currently I'm working as associate professor, uh, in the department of Tripoli, uh, uh, as associate professor.

In Mohan Babu University.

Naturupathi.

This is about my like, uh, personal and, uh, educational and, uh, experience details.

Yeah.

Let’s start with assessing your technical expertise. Can you explain how artificial intelligence differs from machine learning, and provide an example of a scenario where the distinction is important?

Yeah, like, uh, artificial intelligence is uh, like, uh, it's a generic, uh, general term, uh, which includes like, let me tell you the definition of artificial intelligence. Artificial intelligence means it is a making of computers, uh, to think like a human, right, which will add more intelligence, human intelligence and as artificial intelligence included in the. Systems that things like a human so it will it will having different components.

So like, uh, mission learning is one of the component in the artificial intelligence and, uh, robotics and, uh, NLP, natural language processing and, uh, many, uh, like expert systems. Uh, so these are the comes under artificial intelligence. It is like a House of all these technologies, but machine learning is a part of artificial intelligence, which like generally.

Allows the systems to learn from the past data without being explicitly programmed.

So machine learning means, like, uh, if you, uh, if you want to predict or if you want to classify something, uh, task. So for that, or if you want to categorize or if you want to pattern recognition. So for that cases only we can use this machine learning, uh, models and, but artificial intelligence is a like, uh, like we can use in every task or like in every field, we can use this artificial intelligence. Like even optimization algorithms, nature inspired algorithms. So these also comes under artificial intelligence. Even I have used these artificial intelligence automation techniques and even machine learning models in my research work actually.

So for, in the, in my research work I have done, uh, like, uh, I can say like 2 works. One is uh, like a, uh, extraction of maximum PowerPoint from the solar, uh, PV cell, like for solar panel. So for that case, I have used a algorithms like nature inspired algorithms, optimization techniques, uh, so to extract that maximum PowerPoint from the PV panel. And even coming to the machine learning example, like I have done like energy building, energy consumption.

So for that case, I have, uh, used some past data of the, uh, next NATO data set. Uh, so with that data, pass data, I have trained the model and uh, from that I have a predicted the, uh, feature energy consumption of the. So I can I, I think it is 2 examples are sufficient to differentiate the A and machine learning.

Ya.

Can you explain how you would approach designing a laboratory course for undergraduate students that integrates AI and machine learning concepts?

Can you repeat again? I'm your voice not clear.

Certainly, Dr. Thota. How would you design a laboratory course for undergraduate students that incorporates AI and machine learning concepts?

Yeah, actually coming to like a core electrical engineering, uh, subject, uh, this is a very, very less applications are there up like very less concepts where we can use these A and machine learning because the problem is we are lack of generating like lack of getting data from the electrical side. But we can.

Do like this House Energy, like energy prediction for the energy prediction, we can uh, include like uh, then some machine learning concepts, uh, like one experiment. We can include like how to predict energy uh, like even renewables. Also you can predict solar power generation and wind, wind power generation. Also we can include in the.

We can use some wherever we can, uh, have energy management also, we can include in the elaborate, uh, along with the, uh, optimization algorithms. So we can also have some, uh, we can use this AI optimization algorithms also for different cases. So like we can add this a optimization algorithm so we can use for solving many problems in the power systems as we know like. Many problems we have solved by the optimized algorithm may last I think few decades.

Undergraduate level mission landing concepts and the A comes to the A point.

So, but this machine learning comes, uh, like, uh, now trending. So, but problem is the getting data, but, uh, better data sets are available, uh, nowadays, Uh, so we can generate, uh, the data sets also that we can keep it as an another experiments for laboratory. So through that, the students can understand how to apply the A and ML concepts on the particular electrical engineering applications.

Yeah.

How do you ensure students grasp the theoretical foundations of machine learning algorithms while applying them practically?

Yeah, actually I had an experience like I, I, I taught machine learning subject for the AML students and current institute. Yeah, because I had an experience. Even the course is like along with the practical session also like 3 hours is theory and two hours is for practical. So like students, I first we need to explain the detailed concept of the model, why it is, what it is, how it is.

And algorithm also then after that we move to like immediately after completing of the particular model explanation. If we move to the practical session that we can implement how we can implement that particular model by taking some data set. So the students can understand OK this model is working like this but the problem is students cannot aware of how it is. Doing in like internally because we just.

Uh, you call the function from the library, so start. Uh, so the students may not understand what is happening inside, so that we need to explain in the theory class itself. So once we explain like with in the theory class, how the model is working, then the students can implement in the practical session. So through this, I think it is sufficient that the students can understand how it is working and what applications we can. Like made with that model.

It’s clear that you focus on a comprehensive approach to ensure students understand both the theoretical underpinnings and practical applications. Thank you, Dr. Thota. Let us continue evaluating your involvement with research. How do you guide students in selecting and working on research projects in AI and machine learning?

Yeah, like coming to the store I have.

After teaching my machine learning subject, so in that class total semester I have observed few students who are very interested to work on machine learning projects. So I have identified them and I have prepared a group of like 3 batches now. So 3 batches currently working under me. The third year Btech students even they are very interested for multidisciplinary. Research work so even I am basically from electrical engineering and they are basically from artificial intelligence and machine learning brand.

So they are also very interested to, uh, do, uh, research under my guidance. So now 33 batches are doing uh, so I have identified a few students. So they are very interested. Thing is, uh, I have given them, uh, like solar power prediction, It is not like a, a completely technical way. Also, if we generate the data set and if we give to the students, they can do.

Prediction and all so, but thing is if we are doing some work means we should know the.

Of, uh, lateral surgery here, like why it is, but it is and how it is then only we can apply some models and if anything happens also we need to, uh, inference what, what is why it is getting results like that. So that's why I told the students, first you read the concept of solar power generation, then only you can understand why, why you are doing this project and how you are doing this project and why, what are the things you need to infer from the project. So in this way, students are. Now they're in the literature review and they are trying to acquire the concepts from the.

General papers, article, research articles. So once they understand the concept and they explain me the concepts in a proper way then I can go for the implementation because implementation part is there in gold because they already learned how to to with the data in the Python or Google collab. So thing is once they get the concepts and they can go for the implementation and maybe come up with a good results I guess.

Thank you, Dr. Thota, for describing your detailed and structured approach to guiding students in their research efforts. It’s evident you emphasize conceptual understanding and a multidisciplinary approach. Let me now evaluate another aspect relevant to this role. How do you ensure fair and comprehensive evaluation during exams while catering to diverse student capabilities?

Can you repeat again?

How do you ensure fair and comprehensive evaluation during exams while accommodating the diverse capabilities of your students?

Yeah, we can give some, uh, real time situations so that, uh.

Concepts let student knowledge also.

And we can evaluate it, uh, like through, by, uh, maybe by conducting the, by giving assignments, uh, like with this kind of things like real time situations assignments, and maybe by by assigning some projects to the students so that we can evaluate their concept levels and their project involvement.

Understood, Dr. Thota—emphasizing real-world situations, assignments, and projects ensures both depth and fairness in evaluation. Let us now proceed to the next aspect. Can you describe your approach to publishing research and collaborating with peers within academia or industry?

Can you repeat again?

Certainly, Dr. Thota. Could you describe your approach to publishing research and collaborating with peers in academia or industry?

Yeah, like, uh.

I have a list like few journals in the good, uh, impact factor journals. And, but uh, collaborating means like I, I have like I'm collaborating like with my friends, like who are similar to my uh, area. And not only with my similar area. Like I'm also collaborating with the other uh, area research area people also because machine learning is not limited to 1 area, so. Even I'm collaborating with the other researchers who are working.

In industrial oriented means, till now I have never approached industrial level but I'm just thinking once my projects if it is going well executed then I will approach any industry level and then I will collaborate with them and I will do my best level.

So we were working like on machine learning. So in, in that way we can collaborate and, uh.

Thank you, Dr. Thota. Your willingness to collaborate across disciplines and plans to extend your research to industry are noted. Let us now conclude this phase.

Professor, to begin our conversation, could you share which basic course in Artificial Intelligence or Machine Learning you find most rewarding to teach and why?

Repeat again.

Certainly, Professor. Could you share which basic course in Artificial Intelligence or Machine Learning you find most rewarding to teach and why?

Yeah, I can teach artificial intelligence subject and even I can also teach machine learning subject.

Because I already taught these two subjects, not only these two even NHS algorithms also currently I'm teaching to the students and even data science related subjects also I can teach because I have completed the NPTL courses on this subjects actually.

Understood, Professor. Thank you for sharing that.

Thank you.

Anymore.

Professor Thota, I see from your resume that your Ph.D. work focuses on the application of artificial intelligence methods for achieving near-net-zero energy buildings. Could you explain how the AI and machine learning methods you employed in your research directly contribute to practical, energy-efficient building designs?

Yeah. Actually the net zero energy building concept is mainly which utilizes the renewable energy sources available at the building location. So these renewable energy sources are like more intermittent in nature. So for that mostly building energy like buildings is generally based on. More more related and the solar rooftop solar or solar plant only.

So I have targeted, uh, mainly the, uh, solar, uh, PV panels. So what solar PV panels generally requires maximum power filter. I think problem is there. So it requires, uh, an optimization algorithm. So the a algorithms I have used to extract the maximum PowerPoint for the solar, uh, Max MPPT extraction, uh, in the, in that, that is 1 case. Uh, so if we extract the maximum power, so solar power, we can also implement the same.

Uh, wind power plant also. So these, uh, methods we can apply to extract the maximum power at the building location once one thing and another, another thing is I have, uh, uh, before, after getting the maximum was renewable energies from renewable sources, uh, we need to.

Algorithms with the for the wind energy.

Uh, we need to know what is the feature demand on the building. Also for that I have used machine learning model. So to uh, predict the, uh, energy building energy consumption. So after the for that I have used a machine learning models. And after that coming one more thing, I have used energy management because once we know the maximum power from the renewables and.

Also I have implemented multi object optimization algorithms for the for the energy management. So in this way I have used to many AI algorithms and machine learning models for the nearly net zero entry buildings.

Future demand. Now we need to manage these two such that we need to ensure that nearly net zero energy. So for that we have used for energy management.

Thank you, Professor Thota, for outlining the methodologies you've integrated into your research. Let me probe further: how do you see these AI-driven optimization and predictive algorithms being introduced effectively into a classroom setting to enhance students' understanding of energy-efficient systems?

Sorry to interrupt, we're down to the last 5 minutes.

Yeah, can you repeat again not.

Certainly, Professor Thota. How do you envision introducing these AI-driven optimization and predictive algorithms into the classroom to help students understand energy-efficient systems effectively?

Yeah, in the classroom, actually, uh, we can, uh.

This prediction we can. Yeah. Tell me.

Thank you.

You're welcome. Please continue explaining your approach to introducing these concepts in the classroom.

Yeah, generally, uh, students can, uh, use, uh, we can tell them, uh, but renewable energy concepts we know like solar power, solar, solar power plant requests, my MPPT controller. So for that we can use like.

Then we can, uh, uh, tell them to, uh, implement, uh, on the MPPT, uh, these optimization algorithms. So in such a way, like students also can understand, uh, maybe not in the classroom, maybe we can give some projects also, uh, they can, they can implement it and they can easily understand and more prediction also, like if we got the data set from the, uh, like, you know, Steve allocation, then even we can also do the prediction at the university level.

When the concepts, the MPPT concepts, we can test them and after that the conventional methods and the heuristic methods and meta heuristic optimization algorithms, we can teach them. So after teaching the DPT concept and optimization algorithms.

That maybe students can do this kind of projects in the in the classroom. We need to explain the basic thing like concepts how, how it is like how the prediction, what is the models and then they can go for the project implementation. Then it is easily understood by the students.

And that, that, uh.

Thank you, Professor Thota. You outlined a structured approach to teaching these concepts by starting with foundational knowledge and progressing toward real-world project-based learning. One final question here: How would you assess students' grasp of these advanced concepts, particularly their ability to practically apply AI algorithms to energy system problems?

Can you repeat again?

Can you repeat?

Of course. How would you assess whether students have successfully understood and can practically apply AI algorithms to energy system problems?

Yeah, once they learn any projects then we can assess the projects and if we get the any innovative like better results or novel results with the novel approaches then it can it is easily like.

Uh, easily assess that, uh students got these concepts, uh, through the, uh, like classroom teaching and as well as the project implementation process.

Understood, Professor Thota. Thank you for sharing your approach to integrating and evaluating these complex topics in an academic setting. Let us proceed.

Professor, let's discuss your research trajectory. Could you outline a 3-year research roadmap in the domain of Artificial Intelligence and Machine Learning (AI/ML), touching upon the focus areas, potential impact, and plans for funding acquisition?

Yeah, we can. I, I am planning to like target good impact factor publications in the in the past three years, maybe in the past year itself and in the like even these concepts we can apply for research funding projects also. Even I have a communicated 1 project now it is under evaluation. Now even we can apply these concepts, we can not only for net zero energy buildings, we can apply for different. Fields also that also we can. Apply for funding projects and as well as.

Like a good teaching like in to the students as well.

Understood, Professor. Your focus on high-impact publications and leveraging concepts for diverse funding applications is noteworthy. However, can you elaborate on how you plan to oversee and scale your research lab? Specifically, how do you intend to mentor junior researchers and students to ensure a consistent pipeline of quality publications while maintaining academic rigor?

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

Yeah, like, uh.

How I can uh.

Take your time, Professor. What would you like to ask?

I think that, uh, my profile is aligned with this, uh, job description. So I don't know, like, uh, how I can, uh.

Like getting into like get use these uh, uh, facilities provided by the university.

Uh, like, uh, how Again, Uh.

Use the OK nothing.

Sorry.

Yes.

No problem at all, Professor. If you don't have further questions, shall we move forward?

Professor, how would you implement an active learning model, like a Flipped Classroom, specifically for a large-enrollment course in Artificial Intelligence and Machine Learning, without relying on slides? Please be as detailed as possible.

Repeat again.

Certainly, Professor. How would you implement an active learning model, such as a Flipped Classroom, specifically in the context of a large-enrollment Artificial Intelligence and Machine Learning course, and without utilizing slides? Please provide a detailed explanation.

You mean you're asking like, uh, it is a big, uh, crowded classroom and uh, without PPT how we can teach AFL, right?

Yeah.

Yes, that's correct, Professor. Imagine teaching a large class, using an active learning approach like a Flipped Classroom, where the traditional

Please go ahead and explain how you would approach it, Professor.

Yeah, like, uh, for like teaching A and ML, it is not required PPT like even if it if the audience are very basic level. So we can teach them like how these a evolved, uh, how what is mean by a? So these concepts we can teach like with an simple examples how the agent environment, like how the mission, uh, evolved from nothing to. Now intelligent so these things we can easily explain to the audience and. So we can teach the about machine learning also like even machine learning is nothing but like prediction or classification or clustering or some other task. So we can easily explain them what is in big classification? What is in by pre regression? What is meant by clustering? So what are the different types of like supervised machine learning, unsupervised machine learning and there are different things we can explain along with the examples even it is very.

Interesting concepts to teach them and even saw audience also we'll get at, uh, in attention, uh, if we explain all these things within, uh, like a real time examples.

Understood, Professor. You're emphasizing the importance of real-time examples and conceptual explanations over relying on slides, especially for foundational topics like classification, clustering, and supervised or unsupervised learning. Let me now ask a related follow-up: in the context of this approach, what strategies would you employ to build meaningful collaborations between the university and industry partners in the domain of Artificial Intelligence and Machine Learning research? How would you engage students in these collaborations effectively?

Yeah. Wait, let's uh, like, uh, we will, uh, faster train the students that they can, uh, do some real time projects by using machine learning and activation intelligence. Uh, if those, if the students are like good in that work, then, uh, we can plan.

Related industries like that similar related one second.

Take your time, Professor. No rush. Please continue when you're ready.

Yeah, Excuse me.

Yeah.

Yeah, that students once we they implemented like any real time projects and then based on that real time projects, we can collaborate with the related industries and we'll approach them and we'll take the like maybe any consultancy projects like collaboration with the industries by explaining the approaches what we implemented in the classroom. So yeah, we'll try to impress the industry people and we'll try to collaborate with them and.