Please start by introducing your professional journey in academia.

And my area is focusing on. Securing data transmission. And as well as federal learning. My core teaching areas include data structures, operating systems. Then cloud computing, network security. And I have published. Over a 12 plus journals and the. Reputed journals. As well as attended various conferences. And uh, I also have entered the rules like.

Interesting, Professor. Since you've taught a range of foundational courses, I'm curious — is there a particular concept or topic you find especially rewarding to teach to undergraduates?

That's all about my introduction.

Right, Professor, you've worn a lot of hats — teaching, mentoring, handling placements, ERP coordination. Out of all the subjects you've taught, which foundational topic do you find most effective for sparking students' curiosity in computer science?

Uh, ask you how to protect the data? How to? Uh, give authentication. Access control. How to encrypt? Different types of encryption, Decryption algorithms? How to share the key? So these are the. I think a magical subject for the computer science learning students.

You've given a thorough overview, Professor. How do you typically introduce a challenging topic like process scheduling or encryption to students who might feel intimidated by the complexity?

For example, how to encrypt that message? For example, take care.

Professor, I noticed your thesis was on "Next-Generation Solutions for Secure Data Transmission: Improved CGAN and Federated Learning with PPGAN." Can you walk me through a time you had to explain one of those core ideas—maybe CGAN or federated learning—to an undergraduate audience in a way that really clicked for them?

Flyers. Cyber text. By encrypting all those process. So like this? I was taught with some practical problem, then how to solve that problem by using these technologies.

You referenced the Caesar cipher with a key of three when teaching encryption basics — how do you ensure students understand the limitations of such classic algorithms compared to modern cryptographic methods?

So I will explain. The Caesar cipher algorithm is the simplest CSR encryption algorithm. So it may be. Used for some basic encryptions. While advanced encryption algorithms like DES and DAYS. Slightly, I'll give some. More explanation about such modern encryption algorithms. Then I will. Explain the complexity of all algorithms. Where we can apply all those algorithms and what type of application? We can go with us. Algorithms.

That's a solid approach — comparing the complexity and application of classic versus modern encryption definitely helps students see the bigger picture. Before we wrap this section, Professor, in your experience guiding student research projects, how do you help students connect emerging topics like federated learning to practical industry problems?

Yes, definitely. I guide the students with some real world or advanced topics like Federated learning because. I used a Federated learning in my thesis and I have proven output in those phenotypic learning concept. The Council.

So instead of. The training data in a single color. Uh, remote center. We can go for further link concept where we can train the model in different devices. And that the train result may be consolidated at a single server. So this uh. Approach. Nega used for. All the machine learning algorithms, uh, are in the.

Given your work in secure data transmission and federated learning, where do you see the most fundable research directions over the next few years? What kinds of grants or industry partnerships would you actively pursue to support that agenda?

You've described federated learning's distributed model training, but for institutional sustainability, how do you see this research translating into funded projects or long-term partnerships with industry or government—what's your concrete plan to make it a revenue-generating asset for the university?

Where we can apply, where we can go for federal learning concept? And what are the advantages or benefits for that? Because in my research.

Right, Professor. You mentioned teaching core subjects like data structures and operating systems. Suppose you're running a session on data structures for 200 students, but you can't use slides or traditional lecturing — how would you make that session interactive and keep students genuinely engaged?

As well as by applying Federated learning algorithm. Instead of keeping all the data in a single central machine. This here the data will be. The system the model will be trained in the user device itself. And the trained model output will be sent to the. Server for uh. Uh, final consolidation. So I will explain that one. Why we have to go for product learning? And as well as I will do some. More examples or applications where they can go for a federal algorithms.

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?

So I will get the feedback. From the students.

Uh, I will try to improve my. Schools as well as I will identify the weak point or the parameter or in which aspect. If I try to continue my job or. Home so that that I can achieve some good results outcomes. Sai will always. Teach based on the student centric approach.

Imagine this scenario: a student files a formal complaint, alleging your grading was biased. At the same time, your department head privately urges you to improve the pass rate for that course. How would you handle this situation and navigate the tension between fairness and institutional expectations?

Given your background in secure data transmission and federated learning, do you have any industry connections or past collaborations that could translate into internship or placement opportunities for our students?

So that I I can reach. Good outcome.

Professor, let's shift to your expertise in multimedia or AI in media. Suppose you had to design a project where students analyze fake news detection using multimedia data—how would you structure the project to ensure they gain both theoretical understanding and practical skills?

Both in conceptual way as well as the practical implementation.

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

In my project I have introduced a concept called. Are you a model called Conditional Generative Adversary Network? Where the generator it. It starts to. Generate the data synthetic data based upon the real data. And the role of discriminator is. Is to identify that. Both are. The data received by the system is. Actual data are fake data generated generated. So by using. This kind of models we could able to identify whether the data is actually real data or fake data. So I will explain that project.

Advanced mechanisms like.

Alright, Dr., we're just about done. Is there anything you'd like to ask about the Assistant/Associate Professor role or the team?

What are the similar models are there? So I will explain all those models. So by applying all those models, it is possible to identify whether the news is real or fake news.

Alright, Dr., we're just about done. Is there anything you'd like to ask about the Assistant/Associate Professor role or the team?