Let's start with my self introduction. So myself, Doctor K Jagdish. And I'm working as an associate professor.

Sure. So from the year of 2015 I started my teaching career. So in the year of 2015, I have joined Aditya Institute of Technology as an Assistant Professor in the Department of Electronics and Communication Engineering. And my journey started with academic side and also in 2017 it started with the research field. In 2017, I have registered for PhD that is doctored as a philosophy in the field of medical image processing. And it started with the artificial intelligence and also machine learning algorithms to segment and classify the lung cancer cells. So it took. About six years of journey. And finally by 2020 third November 1st I have submitted my refunded my Bible voice with.

Appreciate the introduction, Professor. When you're teaching an introductory course, what's one topic or concept you especially enjoy helping students grasp, and why?

Interesting, Professor. You mentioned using AI and machine learning for medical image processing, especially in segmenting and classifying lung cancer cells. When you introduce complex topics like this to students, how do you make sure that those with less technical background aren't left behind?

From the transition of the electronics and communication field to artificial intelligence and data science department, it gave me a much explorable and wide range of data science expertise, especially in the data science fundamental subjects and also digital circuits, embedded systems and so on.

Right, that’s a substantial transition. When you first started teaching data science courses, what did you find most challenging about helping students shift their thinking from traditional electronics concepts to AI and machine learning ideas?

Yeah. So coming to the transition of electronics to the an machine learning. So machine learning which includes different phases of algorithms which is imaginary and also different types perspectives. So the basic things with the help of electronic components how they are connected with the IoT. And how they can take their own decisions in time being so it makes me curious to. Uh, think about. The transition of electronics field to a. So students may. Who are in the field of electronics, they are also familiar with the AI algorithms, especially in the Python programming so that. The impact of electronics field in the AI can move them or take them to the higher heights.

That’s pretty high-level — I’m curious, when you’re teaching those fundamentals, do you have a particular method or analogy you use to help students really grasp how AI differs from traditional embedded systems?

So that's a much drastical change or drastical difference between the AI and how they handle with the traditional embedded system. So traditional embedded systems is whatever the software programming are the programs which we are. So using the software it should be embedded with the help of a a bus like feature so to be. Implementer are integrated in the hardware component, so once it it takes much for example, the interface between the software and hardware takes much uh, issues. For example, uh, traditional setup methods or any uh, lag between the time time duration. Are any errors? So to overcome all these with the help of AI which is automatically they can embedded with the software and. Hardware so that they, uh, hardware systems which is connected with the cloud. They can work themselves with the help of a. So it is a it is a drastical change between the traditional embedded systems and AI.

Okay, Professor, I hear your comparison. Given your experience bridging electronics and AI, do you find students are more engaged when you use real-world examples, or do they respond better to hands-on lab work?

Exactly, they respond much better in the hands on lab work. For for example, if the traditional teaching method or blackboard teaching is uh, it's getting bored when the physical components are really, uh, help hand, hand over so to the particular person or a particular student. So when they touch and when they practically feed the transition between the uh. Communication of signals to the, uh, the hardware they can also, uh, to be make interest. Chat with the help of a hardware components. They are much better and much clearer. Understandable with the hardware. To the lab component compared to the theoretical class.

Professor, I noticed your recent publication in the IETE Journal of Research—“Volumetric Analysis of Lung Tumor Staging and Classification using the Deep Learning Model.” When you teach advanced AI or medical imaging modules, how do you adapt complex research like this for students who may not have a strong background in either deep learning or medical data?

Sure. So. Based upon their interest, so those who are interested in the medical feed are they can able to collect the medical data with the help of the knowledge of a algorithm. That is, who are specifically, uh, well versed with the algorithms. Based upon the applications and the industry field, they can change over the data. Not only about the medical field, they can also connect with other data data set which they are collected. So it is not only the predicted or it only not the bound with the medical data. They can also apply the particular a algorithm 3 in the another field, for example communication networks and so on.

Let’s turn to research productivity and funding. Given your work in medical image processing and AI, where do you see the most fundable research directions over the next three years? What kinds of grants or external funding would you target?

So the my my main focus is to apply grant uh. From the ICMR. So. That is Indian council of medical research, so with the help of medical analysis and medical imaging techniques Background. So I wish to, uh, predict the early stages of lung cancer cells so that it will be, uh. Society worth helpful to the pay end users. So that my first and foremost wish is to get some grant about from the ICMR or any other medical related Indian association or Indian government norms. So that it will be helpful. For the doctors as well as the sufficient. So who are in trouble with the analysis for, for example in the technical field of a algorithm handling and also the image processing techniques?

Let's shift to your teaching approach. Suppose you have a large undergraduate class—say, 200 students—on an AI foundation topic. But you can't use slides or traditional lecturing. How would you structure a session to keep students engaged and ensure they actually interact with the material?

So with the crowd of 200 students I have to use the PowerPoint method. That is with the I have to use the ICT classroom since if I am using the traditional method it it won't be reachable to the end. For example 200 students is adaptable means it can't. Uh, get any concentration with the help of blackboard that is traditional teaching method. So with the help of PPD presentation or or related subjects related to the video. Or any video links I can engage the 200 crowd? With that ability, uh. For understanding and also their interest towards the subject. For example I can give any activity based learning. So different PPTS are different structures are different images which is included in PPDS they. They can create many concentration ideas. And it can also improve the uh.

Professor, let's imagine you can't use any slides, videos, or online materials at all—just the classroom space and whatever's in it. How would you get those 200 students actively involved and ensure they're actually learning, not just passively sitting there?

So I I. They are with their own login. I can create any. Quiz uh quiz items or any online Google forms to enroll there or give their answers or poll their votes. In between the classes I can create some polls with the understanding I can create 2-3 quizzes or activities which can. Attract the 200 crop.

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?

Coming to the aggregation purpose. That they will list out some of the features, for example the criteria wise. They will have some different. Aspects that is. Files which we have to keep in. And based upon the aggregation we have. To encourage outcome based teaching as OBT method also. So what are the requirements? To be handled or to be maintained in the department, I will render my full support.

Imagine this situation: a student files a formal complaint alleging your grading was biased, but at the same time, your department head is pressuring you to improve pass rates. How would you navigate that tension while maintaining academic integrity?

First of all, I have to maintain my academic integrity. So I will try to explain or I will try to make clear to my head of the Department about the situation and the importance of the particular course which they are. Taken so once the credit is simply given. The importance of that, uh subject or subject material cannot be understood by that particular student category, so for my better understanding. I will, uh spoke or I will make convenience ahead of the department. Or else I will put to there. This track.

Then this is the case.

Given your expertise in both electronics and AI, do you have connections with industry partners that could help translate classroom learning into internships or placement opportunities for your students?

Given your background, how do you handle student evaluation and exam duties—especially if you have to grade both technical reports and hands-on lab work in the same course?

Yeah, sure. So when same courses to be handled are the same laboratories handled for many times, it will be well versed and I will be the subject expertise. So that it helps me to evaluate. So. Category, domain and how to have? For previous understood. And so based on that the evaluation must be done and also how the evaluation techniques especially the. So based upon all these. So when it comes to the hands on, we have to be very keen that. It is clearly hot, and it is more important than the theoretical subject. To the hands on hands on it, it should be. Evaluation.

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

So far I have been in the field of examination cell and also the placement coordinator site. So what are I? It is new to. For you, for me, to this university. So what are the roles to be followed for me? Or what are the roles I have to take care? When I join.