The candidate has been silent for 40 seconds. Gently check in — ask if they need the question repeated, need a moment to think, or if there's a technical issue. Be encouraging and patient.

Please start by introducing your professional journey in academia

So I am biplab das. Uh, currently I'm working as assistant professor.

In cciml department. NK Pair, Institute of Engineering Technology.

Around 2 years I'm working here. In CCML department.

Alright, so you’re currently an Assistant Professor in the CCIML department at NK Pair Institute of Engineering Technology. What’s one foundational concept or topic you genuinely enjoy teaching your students, and why?

Alright, so you’re currently an Assistant Professor in the CCIML department at NK Pair Institute of Engineering Technology and have been there around two years. What’s one foundational concept or topic you genuinely enjoy teaching your students in that department?

After that I worked few months as a quality assurance engineer.

Infinite cable limited. Then I create GATE exam.

Alright, so after your bachelor's in electrical engineering, you spent some time as a quality assurance engineer. What drew you towards teaching, especially in the CCML department?

And I. I joined my masters. In Defence Institute of Advanced Technology, DDO, Pune.

After that you work few months at the JRF in Beat Spain, Hyderabad.

Okay, so after your bachelor's in electrical engineering and a stint as a quality assurance engineer, you mentioned taking the GATE exam before moving into academia. What was it about teaching in the CCML department that really appealed to you, compared to staying in industry?

Alright, so you joined the Defence Institute of Advanced Technology in Pune for your master's after working briefly in industry. In your current teaching role, is there a particular foundational concept or topic within CCML that you find especially rewarding to teach your students?

Okay, so after your master's at DIAT Pune, you mentioned working briefly as a JRF in Hyderabad. In your time teaching at NK Pair, what's one foundational concept or topic you genuinely enjoy explaining to your students in the CCIML department?

Around 2 years I'm working in that field.

My teaching interest is my computer. Deep Learning.

Alright, so you've been with the CCML department at KPR for about two years, after moving through roles in quality assurance and research. In your current teaching, is there a particular foundational concept or topic you genuinely enjoy explaining to students?

Machine learning and generative is Python programming.

Alright, so your teaching interest is in Deep Learning. When you introduce neural networks to students for the first time, what's the analogy or explanation you find works best to help them grasp the basic idea?

Alright, so your main teaching interests are deep learning, machine learning, and Python programming. When you introduce deep learning to students for the first time, what’s one analogy or teaching method you find really helps them make sense of what a neural network actually is?

Yeah, that's all about from, I said. Thank you.

Alright, so your main teaching interests are in deep learning, machine learning, and Python programming, and you mentioned generative models as a research focus. When you first introduce deep learning to students, what's one analogy or teaching method you use to help them understand the basics?

Yes.

So. Before studying deep learning, I always asked.

Like like why deep learning came into picture?

Right, you said yes, but I want to dig a little deeper. Can you walk me through a specific example of how you’ve explained a deep learning concept—like neural networks or backpropagation—to a classroom of students who are totally new to the topic?

Okay, you've covered your journey and your main teaching interests, but I need a bit more here. Can you give me a concrete example of how you actually explain a deep learning concept—like neural networks or backpropagation—in a classroom setting?

Okay, so you start by asking students why deep learning even came into the picture. When they struggle with that, how do you help them connect the limitations of traditional machine learning to the need for deep learning?

So first I start with like deep learning. How it start?

Because of that. When we have very huge amount of data. And machine learning model is unable to capture the pattern from the data.

Okay, so you start by asking students why deep learning came into the picture and highlight how machine learning handles complex problems but has limitations. Can you give me a concrete example of how you've explained a deep learning concept—like neural networks or backpropagation—to a classroom full of undergraduates?

Replanning models are capable from that. So how basically depleting models are working?

It's like how humans we are thinking. It's basically mimicking like human brains.

So you've been teaching in the CCML department for about two years and your approach is to start with the motivation for deep learning, especially the limits of traditional models with large data. That's a solid foundation. Shifting gears a bit, Professor, can you describe the classroom environment you prefer—are you more inclined towards structured lectures, hands-on labs, or discussion-based sessions for these deep learning topics?

Alright, you've explained how you introduce deep learning by focusing on the need for it when traditional machine learning can't handle massive data. Can you give me a quick example of a classroom activity or exercise you'd use to help students experience that limitation firsthand?

Like how humans? Are we thinking when to make decision? Anything.

Research, research make the planning models like similar kind of that. That's like deep learning model is consisting of so many neurons. Like humans, bodies have so many individuals.

Okay, so you help students connect deep learning to how the human brain works, especially when large data sets overwhelm traditional methods. Before we shift gears, what's one thing you do to keep a class engaged when the material gets tough or students start to lose focus?

So you're drawing a connection between how deep learning models mimic human decision-making — that's a nice way to make the concept relatable. Before we move on, when you're teaching Python programming to beginners, what's one common misconception you see students have, and how do you address it in class?

And at the end. We take decision.

Okay, so you use the analogy of neurons and talk about how deep learning mimics the way humans make decisions. Before we move forward, can you share one way you help students who are struggling with the math behind neural networks stay engaged in your class?

Deep learning model also working in same similar way.

The tech deep learning model nothing but is making human brains. How humans friends. We are thinking similarly. Where we try to make.

Alright, you mentioned your interest in generative models and deep learning. I noticed your publication on "Object Detection for Self-Driving Car in Complex Traffic Scenarios" in MATEC Web of Conferences. How do you use insights from that research to make topics like object detection more accessible to your undergraduate students?

So that also it can also think like how events you are thinking and it can make decisions.

Through data set. Uh, or depending models, uh, similar like humans, they're also try to. Learn patterns from the data set.

Okay, so you use the analogy of neurons and how deep learning mimics the human brain when explaining these concepts. I noticed your publication on "Object Detection for Self-Driving Car in Complex Traffic Scenarios" in MATEC Web of Conferences. How do you bring insights from that research project into your teaching — say, when you're covering real-world applications in your classroom?

Okay, you emphasized the analogy of neural networks mimicking the human brain, especially for students new to deep learning. I see your paper on "Object Detection for Self-Driving Car in Complex Traffic Scenarios" published in MATEC Web of Conferences—how did you adapt those research findings for classroom teaching, especially for students who hadn’t worked with real-world datasets before?

And so less it will give. I have. Have the very, very good advantage.

Alright, so you're making that connection between neural networks and human thinking for your students. I see you published "Object Detection for Self-Driving Car in Complex Traffic Scenarios" in MATEC Web of Conferences. When you've brought insights from that research into your undergraduate teaching, how do you break down the complexities of object detection so students without an advanced background can grasp the core idea?

And it can combine the complex pattern from huge amount of returns.

Alright, you've made it clear how you use the analogy of the human brain and neurons to help students relate to deep learning. Switching gears, I noticed your publication on "Object Detection for Self-Driving Car in Complex Traffic Scenarios" in MATEC Web of Conferences. How did you adapt the technical details from that research when explaining the core concepts to students who might not have a background in AI or computer vision?

Okay, so you emphasized how deep learning models mimic the structure and decision-making of the human brain, especially when handling complex data like images. I noticed your publication on "Object Detection for Self-Driving Car in Complex Traffic Scenarios" in MATEC Web of Conferences. How did you bridge the gap between your research on object detection and making the underlying concepts accessible to undergraduates or students with less technical background?

But mostly I saw, I observed that. That mostly they are using ones like foreign researcher or. The researcher using the data set. Or in Rd. scenarios of foreign. Foreign root scenarios.

But as we are Indian and our Indian Rd. scenario is totally different.

So I I try to make here like that object traditional model.

Alright, so you're highlighting how Indian road scenarios are quite unique compared to foreign datasets, which is a real gap in autonomous vehicle research. Shifting focus—grant acquisition is vital for advancing research at VIT University. What's your experience with writing and securing research grants, and how would you leverage this skill to enhance VIT’s research initiatives?

Based on. For Indian vote scenarios. Like in our Indian roads always different in complexity and as well as.

Alright, so you focused on adapting object detection models specifically for Indian road conditions, since most existing research used foreign datasets that don't match our context. When it came to getting funding for this work, how did you approach grant writing — and going forward, what types of external research grants do you think are actually most viable for advancing this kind of applied research at VIT?

Where we can see different? Come different new vehicles. Which are not present in other countries routes.

Alright, so you identified the gap that most object detection research for self-driving cars uses foreign road datasets, and you focused on Indian road scenarios, which are much more complex and unique. When it came to securing funding or research grants for this work, what specific strategies did you use, and how would you leverage your grant-writing experience to help VIT University strengthen its research profile?

Alright, so you tailored your object detection research to account for the diversity and complexity of Indian road scenarios, which is a concrete local adaptation. Let me pivot a bit—at VIT University, industry collaboration and consultancy work are strongly emphasized. Can you share how your experience with industry projects or consulting could help foster those partnerships and benefit students here?

So we have to consider our Indian Rd. scenarios.

So. In my. Did this project that is on my Mtech thesis. That I done observation personal driving car. So here I here I try to. Collect data from Indian roads.

Alright, so you highlighted how Indian road scenarios pose unique challenges for autonomous vehicles that aren't addressed by foreign datasets, and you developed object detection models tailored for that context. Grant acquisition is vital for advancing research. What is your experience with writing and securing research grants, and how would you leverage this skill to enhance VIT University's research initiatives?

Alright, you focused your MTech thesis on collecting real data from Indian roads to improve object detection for autonomous cars, which really highlights your attention to local context. Shifting gears a bit, grant acquisition is vital for advancing research. What’s been your experience with writing and securing research grants, and how would you use that skill to boost VIT University’s research initiatives?

As well as our climate, weather is different, changing as well. As our traffic also different. Also unique thing is that. In our Indian words, we can see animals.

Which is also very dangerous when car is moving or any vehicle is moving.

So. So here in my research here, I consider that. That's animals also in goats.

Alright, so you emphasized how your model accounts for unique Indian elements—rickshaws, bicycles, even animals on the road, plus local weather and traffic. For a project like that, securing funding is key to scaling up data collection and research. What experience do you have with writing and winning research grants, and if you joined VIT University, how would you use those skills to elevate our research profile and funding streams?

Alright, so you highlighted how animals on the road can be a real hazard in Indian traffic, and your model had to account for that along with the usual vehicles. Shifting to research sustainability—grant acquisition is vital for advancing research and building institutional reputation. What's your experience with writing and securing research grants, and how would you leverage that skill to enhance VIT University's research initiatives?

So that is the. Things I did.

You're right, Professor — accounting for animals on Indian roads is a unique and critical challenge. Now, thinking about sustainability, research at this scale often depends on external funding. Can you tell me about your experience with writing and securing research grants, and how you'd leverage that to enhance VIT University's research initiatives?

It is showing uh.

Alright, so you designed your model to handle not just typical vehicles but also things like rickshaws, bicycles, and even animals that are common on Indian roads. Given how challenging it is to collect that kind of tailored data, I'm curious — what was your experience like when it came to actually securing research grants or funding for this work, and how would you use your grant-writing skills to boost research capacity at VIT University?

Cortana call bridging gap.

Alright, you made your object detection model more robust by including local complexities like animals, bicycles, and rickshaws—those are definitely unique to Indian roads. Shifting gears a bit, Professor, can you tell me about your experience with securing research grants, and how you’d leverage those skills to bring more external funding to VIT University’s research programs?

Industry that it's connected. What we are considering?

Right, so you extended your object detection model to cover things like bicycles, auto-rickshaws, and even animals, making it more relevant for Indian roads. Now, given how challenging it is to collect unique data and build robust models for our environment, grant funding becomes crucial. Can you walk me through your experience with writing or securing research grants, and how you'd use that skill to boost VIT University's research initiatives?

Alright, so you really focused on making your object detection model practical for Indian roads, considering unique elements like animals, local vehicles, and unpredictable traffic. Shifting to research impact — have you ever written or secured funding through a major research grant, and if so, how would you use that experience to advance VIT University's research profile and funding streams?

Yeah, as as of now I never. Any funded proposal like that?

As that was my Mtech thesis.

Alright, so you haven't handled a funded proposal yet. You mentioned your project bridges the gap between academic research and industry needs — can you walk me through any industry collaborations or consultancy work you've done, and how that might help VIT's push for stronger ties with industry partners?

So. Now I'm I'll love to. Write those proposal in those kind of scenarios.

Alright, so you haven't written a funded proposal yet, but you're open to it in the future. Earlier you mentioned your work connects pretty closely with industry needs — have you engaged with any companies or taken on consultancy projects that might benefit VIT's push for industry-academia partnerships?

Okay, so no direct grant writing yet, but you’re interested in pursuing it. You mentioned your project bridges academic and industry needs—have you had any hands-on experience collaborating with companies or working on consultancy projects, especially in areas like autonomous vehicles or AI for real-world applications?

The public statement showing. And then try to get those funding. From different organisations.

Vocal growth or growth affect universities. Research.

Alright, given you haven’t had direct experience with grant funding yet, let’s touch on another area. You mentioned your research addresses real-world challenges and could bridge academia and industry. Can you share a concrete example of how your project or consultancy work has benefited an industry partner, or how you’d approach building those relationships at VIT?

Alright, you mentioned how visibility and public statements can help attract funding and grow the university's research profile. Shifting gears — at VIT, we place a strong emphasis on industry collaboration and career-readiness for our graduates. Can you share any concrete experience you have working with industry or consultancy projects, and how you'd use that background to benefit our students?

This project Observational autonomous vehicle. That basically I got idea. Our Indian startup is there so I Therapeutics. They are making autonomous vehicles.

So. From there only I started that. Working on that project. As well as I get consult with some engineers also who are working in swath robotics for autonomous vehicles.

Okay, so you were inspired by an Indian startup working on autonomous vehicles, and your project connected with those industry trends. Let me switch topics — at VIT, evaluating student performance is a big responsibility. How would you assess students in both theory courses and practical projects to make sure they actually meet our curriculum standards?

Precise so.

That similarly that project I. Fully discussed with here. Also in my institute where I'm working currently.

Okay, so your collaboration started with an Indian startup focused on autonomous vehicles, and you consulted with engineers from Swath Robotics. Can you give me a concrete example of how working with those industry professionals directly shaped your research methods or the way you approached your model?

Alright, so your industry connections came from consulting engineers at Swath Robotics and learning from that Indian autonomous vehicle startup. What's one thing you learned from those industry contacts that changed how you designed or approached your MTech research?

Okay, so you developed your autonomous vehicle research by connecting with engineers at Swath Robotics and discussing your ideas with colleagues at your current institute. At VIT, evaluating student performance is crucial to maintaining high academic standards. How would you approach assessing students in both coursework and practical projects to ensure they meet the university's expectations?

So so definitely if you got got selected in bit also there also I can. Sure, sure I can further.

Right, so your autonomous vehicle work really grew from exposure to Indian startups and consulting with industry engineers. Let's talk about student development — how would you evaluate student performance in both coursework and practical projects so that it lines up with VIT University's academic goals and curriculum standards?

From that industry related project and as I will try to more connect with those that industry. You are working notron was vehicle.

But that's so that I that this is also benefit and as well as our student also can work on that project.

Okay, so you got the initial idea from an Indian startup in autonomous vehicles, and you've brought in insights from engineers at Swath Robotics to enrich your project. I want to shift the focus now — at VIT, evaluating students in both coursework and practical projects is key to maintaining our curriculum standards. How would you approach student evaluation to ensure it's fair, rigorous, and aligned with the university's academic goals?

And it can can get my can overcome the bridge between industry and in. What are you really studying in deep learning also?

Given your background guiding students on industry-related projects, suppose you had to teach a large undergraduate course — let's say 200 students — but you couldn't use traditional slides or lectures. How would you design a session to keep students actively engaged with the material?

Alright, so you see a real benefit in connecting students to hands-on industry projects like your autonomous vehicle work, and you'd want to deepen those partnerships if you joined VIT. Let's imagine you have a large undergraduate class working on a similar applied project, but you can't use traditional slides or lectures — how would you actually run those sessions to keep students engaged and ensure they're learning, not just watching?

So those scenario if I want to teach them. In write that.

You were saying you’d want to teach them “in write that”—can you walk me through, step by step, what you’d actually do in a session to keep 200 students engaged if you couldn’t use slides or just lecture at them?

Where car is moving.

In the like animation, not some simul.

As we know, so that when they're seeing some videos. It's running.

Okay, so you'd start by showing a live demo of your autonomous vehicle project to the class. After that initial demo, how would you evaluate whether students are actually understanding the technical concepts, not just watching the car move?

That some working model is working. So they.

Will be always be very much interested and will be. Curiousity. Their curiosity will be more. To learn that how it's happening. Exactly if you want to do also similar kind of things.

Alright, so you're saying you'd bridge the gap between industry and academics by letting students engage with real-world projects like your autonomous vehicle work, and you'd keep evolving that partnership. Switching focus — beyond teaching and research, what role do you see yourself playing in department-level governance, like curriculum committees or program reviews?

Alright, you’d show them a working model or animation first so they can visualize the project. After that, how would you actually assess whether 200 students understood the core technical concepts — especially in a hands-on way, not just by watching the demo?

As well as. Uh, like that, uh, I can randomly in between, I can ask them questions so that they can also participate in that conversation.

So you'd use a live demo or animation to spark their curiosity and get them interested. But for accreditation, we need strong evidence that students actually achieved the learning outcomes — not just that they watched something cool. What concrete steps would you take to collect and document outcome assessment data for that class, especially if results were inconsistent across different sections?

Where do they conceit?

So you’re saying the students’ attention will be focused on the live demo, and their curiosity will drive them to learn more — but when it comes to accreditation, we have to show measurable learning, not just engagement. How would you actually assess whether the students can apply what they saw in the demo to a new technical scenario, to satisfy an accreditation review?

When they got a little bit interested.

When they are seeing that card is moving without any. Rendition, human inventation, and Travel's car is moving.

So you’d keep students focused on the demo, spark questions, and keep things interactive. But stepping outside the classroom for a moment, Professor—departmental accreditation reviews are a huge part of our quality assurance. If you joined VIT and found inconsistent outcome assessment data across courses, how would you go about fixing that?

Okay, so you'd keep things interactive by calling on students during the demo to make sure they're engaged. But, as you know, accreditation cycles can be demanding — if you joined our department and noticed that outcome assessment data was inconsistent across core courses, how would you approach fixing that?

Because we can have a good conversion.

Okay, so you’d keep the energy up with demos and questions to make sure students are engaged, but you’re also thinking about how these projects could bridge the gap between industry and academics. Let’s shift gears for a moment — at a university like VIT, department-level governance is a big deal. Beyond teaching and research, what role do you see yourself playing in things like curriculum committees or program reviews?

Okay, so you see that active class discussions and student participation during demos really build engagement and help students learn. But stepping back to the bigger picture, Professor — when a department faces inconsistent outcome assessment data across courses, how would you help get everyone aligned and make sure we're meeting accreditation requirements day-to-day?

Select that we can engage students.

Alright, so you believe that real-world demos and interactive discussion are much more engaging than traditional slides, and you’d use those to spark curiosity and keep students focused. But accreditation is about more than engagement — it requires documented, measurable learning outcomes across all courses. Suppose you joined our department and found that assessment data for student outcomes was inconsistent across faculty. How would you go about fixing that?

Yes, Sir. Then when you're taking assessment those things, so those will be like one day. Preparation. Umm.

We have to make sure that when we're designing that course or designing the assessment. To make sure that we we have to make the balance. Like so that soon will be more curiosity curious about that.

So you're saying you'd treat assessment as a focused exercise, preparing materials and ensuring students are ready for it in advance. But day to day, accreditation means collecting consistent evidence — like rubrics, mapped outcomes, or shared marking schemes — not just prepping for a single test. Have you actually worked with formal outcome-based assessment processes before, where you had to align grades and evaluations to a published curriculum or accreditation framework?

Middle 30%, some middle level NDP questions and may rest of them some difficult level questions. Like that we can. Like balance that. So that. They'll also be more. Engage on that they will also be. Think when you're writing the assessment.

Like 20%. Some basic theory.

Okay, you’re focused on making assessments engaging and balanced, but real accreditation work means tracking outcomes with clear rubrics and documented evidence, not just relying on student curiosity. Let’s shift gears — beyond teaching and research, what role do you see yourself playing in department-level governance, like curriculum committees or program reviews?

Alright, Professor, let's shift to something every faculty member faces. Imagine a student files a formal complaint, saying your grading was biased — at the same time, your department head is pressuring you to raise pass rates in your course. How would you handle that situation?

So that that through that with through. We can understand the real understanding of students.

Okay, so you're saying you’d structure the assessment with a mix of basic, intermediate, and tough questions to keep students engaged and thinking. But if your department head pushes back, arguing that your approach makes it too hard for students to pass and wants you to make the exam easier, how would you handle that while still upholding academic standards?

Also, after getting the assessment, when we see. That students are lagging in somewhere.

Alright, so you're trying to balance assessments with a mix of question difficulties to keep students engaged and test real understanding. Let me give you an ethical scenario, Professor: imagine a student files a formal complaint that your grading was biased, but at the same time, your department head is urging you to raise pass rates for the course. How would you handle that situation?

Okay, so you're saying that after the assessment, if you notice students are lagging, you'd use that information to adjust your approach. Earlier you mentioned engaging students with real-world demos and discussions. Now, let me give you a scenario: imagine a colleague asks you to co-author a research paper, but you notice some of their data seems questionable or possibly manipulated. What would you do in that situation?

In real life some videos or somebody will examples or some. Projects Who's already done?

Alright, so you’d use real-world examples and projects to address gaps when students struggle on assessments, aiming to make the material more concrete. Let’s switch gears, Professor — grant acquisition is vital for advancing research. What’s your experience with writing and securing research grants, and how would you leverage that skill to enhance VIT University’s research initiatives?

Play so we can see then the examples like Netflix, Amazon. Who's there daily or daily using?

How they're getting recommendation? From the Amazon, Netflix.

Okay, so you’re showing them recommendation systems like Netflix and Amazon to make the theory more tangible — that’s clear. But let’s address an ethical tension, Professor: imagine a student formally complains that your grading was biased, and at the same time your department head is urging you to raise pass rates for accreditation reasons. What would you actually do in that situation?

Right, so you're connecting the recommendation systems students use every day to the underlying theory—good way to make it feel relevant and real. Now, let's tackle a scenario that comes up in academia: suppose you’re collaborating on a research paper with a colleague, and you start to notice that some of the data they’ve provided doesn’t quite add up. What would you actually do in that situation?

Whenever I'm opening their Netflix or Amazon or any web series, any. And web platform.

So like that they will engage and they will rectify themselves.

Right, so you're grounding student understanding with examples they encounter every day, like Netflix recommendations, to clarify the theory behind the tech. Now, Professor, imagine a situation: a student files a formal complaint, saying your grading is biased. At the same time, your department head is urging you to increase pass rates. How would you handle that tension between academic integrity and institutional pressure?

Alright, so you're saying when students see familiar examples like recommendations on Netflix or Amazon, it helps them connect theory to their real lives and stay engaged. Let me give you a scenario that tests your approach to ethics and diplomacy, Professor: imagine a student files a formal complaint saying your grading is unfair, but at the same time your department head is pushing you to raise pass rates. How would you handle that situation?

So there will be more. I'm not really curious about that, how they're getting those. Condition so like that their learning will also be in. More different way.

And we can understand also that they are now really understanding in proper.

Right, so you’re saying students will be more engaged and bridge gaps in understanding when you connect the theory to real-world tools they actually use, like Netflix for recommendation systems. Let’s take that and put you in a scenario: imagine a student formally complains that your grading is biased, but at the same time your department head is pressuring you to raise pass rates. How would you handle that tension between upholding academic standards and the institutional push for higher success rates?

Right, so you’re focused on using real-world scenarios to deepen students’ understanding, especially when they struggle — and you’re watching for those gaps in your assessment data. Now, suppose you’re asked to raise average pass rates by your department head, but you also receive a formal complaint from a student who feels your grading was biased. How would you navigate the tension between maintaining academic standards and meeting institutional pressure?

So. The student uh. Obviously shouldn't always be always like a student. There will be they always try to like blaming others or blaming others for their failure or blaming management or blaming faculties for their some kind of failure happening whenever they have. But then. We tried to. Neetu Con convinced them that. Understand them.

That you are getting lace marks or you are getting. Uh, two other friends.

So you’d try to listen and understand the student’s side, recognizing that sometimes complaints are more about frustration than real bias. But say the department head insists you make your grading easier to boost pass rates, even if that means lowering standards. How would you handle that situation, Professor?

You getting less marks because there will be some reasons.

If you you can see. In general. If for that reason. We can ask talk them personally.

So you’d try to help the student see why their results might differ from their friends’, and explain your grading. But say your department head pushes you to make the grading easier to boost pass rates, and you feel that’d compromise integrity — how would you respond in that situation?

Based on that topics where he got less Marks and we can. Try to tell them that if he he want to get good marks on those questions. They have to write in that way. They have to think the question in that way.

So you’d address the student’s concerns directly, and explain why their marks might differ from their peers, focusing on the reasons behind the grades. Now, if your department head pushes back and insists you need to boost pass rates even if it means relaxing your grading standards, how do you respond?

Okay, let's shift to how you connect academia and industry. Given your background, have you worked with any companies or industry partners in a way that helped students gain exposure to real-world problems — maybe through internships, guest lectures, or project collaborations?

Because when we ask some real world questions or. Umm, some practical level questions?

Okay, so you’d handle a student’s complaint by talking with them one-on-one, asking questions about the topics they struggled with, and helping them understand how their answers could be improved. Can you give me a concrete example from your past experience where you connected students with an industry partner, or brought in a real company project for students to work on?

OK, so some. Students who have good concept or good depth concept on that topic, so they can write that on their own language in a different way.

So you’d sit down with the student, talk through the topics where they lost marks, and show them what a strong answer would look like, using practical or real-world examples. Have you ever partnered with any companies or industry professionals to actually bring those kinds of real-world projects or case studies into your classroom at VIT, or at your previous institution?

In good areas, but in some people who have average understanding, average understanding of that concept.

Alright, so you'd use one-on-one conversations and targeted questions to help students reflect on their answers and understand how strong responses are built, especially using practical examples. Have you ever partnered with an industry organization, or brought in a real-world project from a company for your students to work on directly?

Not able to describe that. Therefore describe their full understanding or full knowledge on the particular topics.

So you’re distinguishing between students who have deep conceptual understanding and those who are more average, and you’re using real-world and practical questions to gauge that. In your own experience, have you worked directly with industry partners—maybe on consultancy projects or research collaborations—that helped bring that kind of industry exposure to your students?

So like that we can make them believe. The two right that questions or get good marks on that particular part.

Okay, so you’re saying students with deeper understanding can express concepts in their own words and handle practical questions well, while others might struggle to show their full grasp. Have you ever worked directly with an industry partner or brought a real-world company’s project into your classroom to help bridge that gap for your students?

They appear to understand the basic concept in depth.

Then only. They can able to answer proper way and they can. Get good marks for that person also.

Alright, so you’re saying by showing students the connection between how recommendations work on platforms like Netflix or Amazon and the concepts in your course, you help them see the link between theory and real-world tech — and that can motivate them to learn more deeply. Now, have you ever actually worked with any tech companies or industry partners to bring real-world projects or problems into your classroom?

Okay, so you're saying when students show only average understanding, you focus on helping them express their knowledge more clearly, especially around practical topics. Shifting gears a bit — do you have any direct experience working with industry partners or consulting on real-world projects, and if so, how could you use those connections to help your students at VIT University?

Like this, they can understand that faculty was not biased together another. Other students. Faculties are biased for the students. Answering the way other students are.

Alright, so you're saying by helping students connect their understanding to real-world scenarios, especially when they struggle to articulate their knowledge, you can guide them toward deeper learning. Shifting to industry connections — have you ever collaborated with companies or worked on industry projects that could help your students gain practical exposure or open up internship opportunities?

Answering and attempting that questions. There is a things we have to make them believe and. Yeah, I think it will work.

Alright, you’re saying that by being transparent about how grading works, and helping students see that deeper understanding leads to better answers, you can reduce perceptions of faculty bias. Let’s shift to industry alignment — in your field, have you had any direct engagement with industry, like consulting projects or partnerships, that you could bring into the classroom to help students connect theory with real-world practice?

Alright, so you believe that making grading criteria transparent and helping students connect their understanding to real-world questions reduces complaints about bias. Shifting gears a bit — have you had any direct experience working with industry partners or on consultancy projects, and if so, how would you use those connections to create more industry-relevant opportunities for students at VIT University?

So. I'm just around 2 years I'm working.

So I tried to connect with some industries and also some industry project was there, but those are very small.

Alright, thanks for being direct about your current experience level. Now, I want to check on a few core skills for this Assistant Professor role. Let's start with emerging technologies — say you're teaching an introductory AI course and you need to design a lab session using a simple machine learning model. How would you structure that lab to make the concepts clear for students who have never coded before?

As I know that PIT have good industry connection institute collaboration. That's the reason I wanted to join. And there if I get selected definitely I would try to connect with them.

But definitely if I would get opportunity.

Okay, so you’ve had a couple of years in the field and some small industry project exposure, but not much hands-on consultancy yet. Shifting gears, let’s talk about teaching — when you’re running a theory course with a lab component, how do you make sure students actually connect what they learn in lectures to what they’re doing in the lab?

And to to bring more projects from them so that then I can solve some projects for them so that they will also understand that that is benefited that industry. So like. Like that they can. Call Ashwin. Also what? Doing project interview. They could get the chance to work on that industry also. Like that, we can attract and make a. Relationship with the industry.

Okay, so you’ve had a couple of years in the field and some small industry project exposure, but not much hands-on consultancy yet. Shifting gears, let’s talk about teaching — when you’re running a theory class and a corresponding lab, how do you make sure students actually connect what they do in the lab to the concepts from your lectures?

Okay, so you’re clear that you haven’t done major consultancy yet, but you see the value in those industry connections and want to leverage them if you join VIT. Switching up — when you’re teaching both theory and lab courses, how do you make sure students really grasp the underlying principles, not just the steps in the manual?

So for that reason, a good collaboration needed with the institution also, you know.

Okay, so you see value in leveraging industry relationships to bring real projects into the classroom, even if your direct experience is still building. Switching topics — when you’re teaching both theory and lab, how do you make sure students who struggle with practical application still grasp the core concepts?

The CPI institution industry will not believe to come.

So. That's the really main thing. For reason I wanted to join the bit so that I can. We make the opportunity to connect with more industries. And can. And connect them with our students. So that as well as company will get benefited as well as our university will get benefited.

Okay, so you see clear value in building trust with students by showing that grading is based on understanding, not just wording, and that faculty aren't biased. Switching things up — when it comes to guiding student research or major projects, how would you help a student who’s struggling to pick a research topic that’s both manageable and innovative?

Alright, so you see strong institutional support as key to building industry trust and partnerships, and you’d want to leverage that. Switching gears, you mentioned earlier about teaching both theory and lab courses — walk me through how you structure a lab session so that students with different levels of understanding can all stay engaged and actually learn by doing.

As well as the student also will get benefit.

Alright, so you see joining VIT as a way to build those industry relationships and open up opportunities for students and the institution. Let’s talk about your teaching approach — when you’re running both theory and lab classes, how do you make sure students who are struggling with the core concepts don’t get left behind during the practical sessions?

Alright, so you see those industry collaborations as a way for everyone to benefit — students, the university, and the companies themselves. Let’s shift a bit. When you’re teaching both theory and lab courses, how do you make sure students who struggle with the concepts in theory can still succeed in the practical or lab component?

What The thing is that until unless you don't have weight. Good basic concept.

Off duty. So it's very difficult to create some state of that model or very good solution for. Remove all projects.

Alright, so you’re pointing out that in AI, practical application really depends on a solid grasp of the basics. Let’s try something different — imagine you’re evaluating a set of student exams and you suspect a few students have very similar answers that might indicate copying. How do you handle grading and follow-up in that situation?

But when? We observed that. In theory, when we are starting. Concept those things. But most things we forgets most most of the times when you are not revised that much.

Alright, so you're saying without a strong grasp of theory, it's tough for students to build effective AI solutions in practice. Switching topics — you mentioned student evaluation earlier. Can you describe a time when you had to grade or evaluate students on an assignment or exam, and how you made sure your grading was consistent and fair across the whole class?

But what? Common practical lab cases. When you make really some models there you can face real. Problems.

Okay, so you're noticing that without regular revision, students tend to lose the thread on theory, which then impacts their ability to build solid practical models in AI. Say you've got a student who's doing well in labs but consistently underperforms in theory exams — how would you address that with them?

Suppose the machine learning model like the like object direction model is there for deep learning.

Right, so you’re highlighting how hands-on labs in AI can reveal the gaps that students might have missed during theory classes, especially if they haven’t kept up with revision. In your courses, when you spot a student who’s strong in practical work but still struggling with the underlying theory, what’s your strategy for helping them bridge that gap?

So when you are learning that how object edition is happening getting bounding box classifying them.

Okay, so you’re highlighting how those collaborations could help students and industries both, but you haven’t had a chance to do larger projects yet. Switching gears now — tell me about your research background in your PhD. What was your specific area of focus, and how do you see it shaping your teaching or future work?

Okay, so you’re describing how, in AI especially, students need a strong foundation in theory or their practical work—like building object detection models—will fall short, and hands-on labs can highlight those gaps. What strategies do you use in class to help students bridge that gap when they’re struggling with the theoretical parts but need to succeed in the practical labs?

Music balance data imbalance is there.

So when training the model. Maybe some learning was not even popular we can understand. I will learn some hyperparameter tuning.

Those thing in theory also we learn, but that time we're not able to understand properly. But maybe some student can understand.

Alright, you’re describing how industry partnership can benefit everyone, but you haven’t had the chance to work on large projects yet. I want to switch topics — you mentioned earlier you’ve been involved in student evaluation and exam duties. Can you walk me through exactly how you handle grading a batch of student lab reports to keep things fair and consistent?

But most of the students when they come into lab. So rather than they are not understanding theory that much popular way also. When solving the lab or solving the seeing the problems when entering the model and deploying the model.

Right, so you’re saying that when students are building something hands-on, like an object detection model, the real-world problems—like missing data or data imbalance—really surface and force them to apply what they learned in theory, especially things like hyperparameter tuning. When you notice a student still can’t bridge that gap between the theory and actually making the model work, what steps do you take to help them figure it out?

Okay, so you’re saying that when students actually build something, like an object detection model, they come face-to-face with missing data, imbalanced datasets, and realize that theory alone isn’t enough—those practical struggles really cement the concepts. Thinking about your role in project guidance, how do you help a student who’s stuck on a research question that doesn’t seem to have a clear answer in the literature?

When the seeing the performance. Is reducing. By changing some parameters.

They can understand OK, so this is the call hyper tuning. That's why learning rate is matters there that much that's. Query optimizer. Choosing the optimizer is also. Replace a very crucial role.

Right, so you're saying that for most students, even if theory isn't their strong suit, getting hands-on with building and deploying models actually helps them understand those concepts much better. One thing I want to check—when it comes to evaluating students, especially in labs, how do you make sure your grading is fair for both students who are stronger in theory and those who excel at practical work?

Alright, you just described how students who don’t fully grasp theory often get that “aha” moment once they actually see things break or improve in the lab—especially with issues like missing data or imbalanced datasets in AI. Switching gears, how do you typically evaluate students’ work in your courses, especially when it comes to balancing theory exams and practical project performance?

While you're making a model. But while reading theory may be some concept they missed.

Alright, so you’ve seen students really internalize theory once they get hands-on and actually tune models or see results shift as they experiment. Shifting gears — when you’re evaluating students, especially in lab exams or project reviews, how do you ensure the assessment is fair and accurately reflects their understanding, not just their results?

Training the model they understand OK why Adam optimizer is more. When attacked, optimism. Compared to others.

You’re describing how students might miss some theory in class but, once they see the impact of, say, changing the optimizer or the learning rate during a lab, those concepts finally click. Now, thinking about student evaluation — when you have to grade a practical exam where a student’s model doesn’t work as expected, how do you fairly assess their performance?

And like loss function.

When do we use binary cross binary cross entropy loss function? When?

Right, so when students actually train and deploy the models themselves, that's when optimizer choices and parameter tuning really start to make sense—often more than just reading about them. Let’s talk about student evaluation for a moment. When you’re grading projects or exams, what’s your process for making sure your evaluation is fair and consistent across all students?

Right, so you’re saying that students might skip over some concepts in theory, but once they’re hands-on—like tuning optimizers or loss functions—they actually see what those choices do and it clicks. Now, thinking about student evaluations, when you’re grading a final project or lab report, how do you ensure your grading is fair and consistent across the whole class?

When do you want win absoluted?

Right, so when students actually have to pick a loss function—like binary cross entropy or categorical cross entropy—they finally see the impact in real training, even if they missed it in lectures. Switching topics, when you're evaluating students in an exam setting, how do you design questions to make sure you're really testing both their conceptual understanding and their ability to apply what they've learned?

So you’re saying students really get why things like the Adam optimizer or binary cross-entropy matter only once they hit a wall in the lab, even if the theory didn’t click before. Shifting gears, how do you handle evaluating students fairly when some do well on projects but struggle with written exams, or vice versa?

That in the theory what there may be some lagging. Portion in lab. Those questions will be cleared properly. And they are also feeling very too much comfortable later on in through devotion also.

Atomic can relate properly.

Okay, so you’re saying students only fully grasp concepts like loss functions, optimizers, and hyperparameter tuning when they hit real-world issues in the lab—like data imbalance or missing values—rather than just through theory. Let’s shift a bit: when you’re guiding student research projects, how do you help them move from a vague idea to a focused, high-quality project that’s publishable or industry-relevant?

Okay, so you’re saying that when students have those gaps in theory, actually struggling through the labs helps reinforce and clarify the concepts—especially when they hit walls with things like optimizers, loss functions, or learning rates. Shifting gears, walk me through how you evaluate students fairly during exams or lab assessments when some students might grasp things better in practice and others in theory—how do you structure your evaluation to account for both?

If some some students. Are missing some theory 12 points also.

So you notice some students light up when asked certain questions, while others miss theoretical details. Shifting to your own teaching—when you're guiding student projects or research, how do you help students bridge those gaps between theory and practical application in their project work?

So if these that students is. Highlighting that particular problem or highlighting that how particular problem would come and how they could solve they can solution given it.

So based on that we can observe that we can understand that yeah, this that student have the understanding.

So you're saying if a student at least highlights the right problem and outlines how they'd approach the solution, that's what you look for—even if they miss some theory details. Can you give me a concrete example of a lab or exam question where this approach helped you distinguish between students who really understood the material and those who were just memorizing?

That this problem arises for that problem.

And they should solve in that way.

Alright, you’re saying that even if a student misses some theory points, if they can identify the core of a problem and outline their approach, that shows real understanding. On a different note, can you share an example of how you’ve guided a student through a research project—maybe when they were stuck or unsure how to proceed?

Alright, so you’re really focused on whether students can pinpoint the actual problem and propose a solution—that’s how you’re gauging their understanding, not just rote theory. Switching tracks now, tell me about a time you guided a student or group through a research project—how did you help them move from a vague idea to a well-structured research plan?

Alright, so you're watching for whether students can pinpoint the core of a problem and suggest a solution, not just recite theory. Switching topics now—can you share an example of guiding a student through a project where they got stuck technically, and describe how you helped them get past that without just giving away the answer?

I really have the capability to solve that particular problem.

Alright, so you're saying you can tell a student really understands the material—even if their answer is brief—when they can point to the core of a problem and outline how they'd actually solve it. Switching topics, Professor, can you tell me about a time you guided a student through a research project from the initial idea all the way to publication? What was your approach to mentoring them through setbacks?

So like that we can. Maxwell more weighted to that student also.

Right, so you're saying that even if a student's answer is brief, as long as they've identified the core problem and offered a solution, you give them credit for genuine understanding. Switching gears—can you share a time when you guided a student project or research effort, and the student hit a roadblock in applying emerging technologies, like AI, IoT, or Cyber Security? What was your approach to helping them get unstuck?

Yeah, so.

So mostly I worked with the student in artificial intelligence field.

Alright, so when you notice a student's answer is concise but nails the key concepts, you make sure their understanding is recognized in your grading. Now, thinking about your teaching style—when you're explaining a complex topic in class, how do you ensure your communication is clear and structured enough that students with varying backgrounds can all follow along?

And they are like students when they're writing projects.

Doing the projects so they face so so different different issues.

You mentioned working closely with students in artificial intelligence—can you give me a specific example where a student got stuck implementing something in AI, like a neural network architecture or model evaluation, and describe exactly how you helped them work through that obstacle?

Suppose they are training the model and they are seeing that accuracy is very less.

Alright, so you’ve worked with students doing projects in artificial intelligence—when you guided them through those projects and they ran into obstacles, like maybe their model wasn’t converging or they struggled to implement a specific algorithm, can you give me a concrete example of how you helped them move forward?

So when they stuck and they thought how we can improve that.

Okay, so you’ve worked with students in artificial intelligence projects and seen them run into all kinds of issues. Can you give me a concrete example—maybe a time when a student struggled with implementing a machine learning algorithm, and what specific steps you took to help them break through that problem?

So some silly mistake they did that they forgot. They they mostly they forget their basics. And they forgot that our AI model mostly.

Alright, so you've seen students run into model accuracy issues during their AI projects and had to help them troubleshoot. Shifting to a different area—tell me about your experience with industry projects or consultancy work. Have you ever been involved in a project outside academia, and if so, what was your specific role?

So when your students hit low accuracy training models in AI projects, you help them troubleshoot and improve the approach. Shifting focus a bit, tell me about your direct experience with industry projects or consultancy—what's one example where your academic expertise actually influenced or changed the outcome of a real-world industry engagement?

A learnt from Data First. Hyperformity learning rate. Those will come secondary.

So when your students forget the basics during model training, you catch that quickly and help them refocus on fundamentals to improve results. Now, thinking about your own background—can you talk about a time you were involved in an industry project or consultancy, and how that experience influenced your teaching or research approach?

1st is our datas. Be a proper oil structured. Annotatorship should not have any noise.

Alright, so when your students get stuck on low accuracy in AI projects, you help them step back and look at fundamentals—like making sure their data is correct before tweaking hyperparameters. Shifting topics now—can you talk about your direct experience with industry projects or consultancy work, especially anything where you applied your academic knowledge to solve real-world problems?

That's why you. You're getting less accuracy.

Alright, so you notice when students focus too much on hyperparameters before checking if their dataset is clean and well-structured—that's a common pitfall. Switching topics now—tell me about a time you directly contributed to an industry project or consultancy, and what your specific role was in bridging academic knowledge with practical application.

So Yep. To first overcome. Reduce those noises from the data set.

Alright, so you emphasized how students often overlook data quality when troubleshooting AI model accuracy, and you guide them to focus on clean, well-structured data before adjusting other parameters. Now, thinking about your teaching—how do you structure your theory and lab sessions to make sure students really grasp both the concepts and practical skills?

Able to see the data set is imbalanced or not.

Okay, so you guide students to look at their dataset quality first before they start adjusting things like learning rate, and you help them see where their fundamentals might be off. Shifting topics a bit, can you give me an example of a theory course you've taught, and how you balanced theory with practical lab sessions to help students really grasp the material?

Once or data set is fine. Then only you should focus on later on hyperparameter tuning.

Alright, so you've seen students run into issues with imbalanced or noisy datasets in AI projects, and you help them focus on cleaning and structuring their data before anything else. Switching topics now—how do you approach teaching theory courses versus laboratory courses, especially when you have a wide range of student abilities in the same class?

Alright, so you guide students to focus on their dataset first when model accuracy is low, making sure it's clean and balanced before tweaking anything else. Shifting topics a bit—can you walk me through how you structure your theory and lab classes to make sure students understand both the underlying concepts and their practical applications?

Yeah. Yeah, we can.

I could you see like that.

Okay, so you help students realize when their low AI model accuracy is caused by things like noisy or imbalanced data, not just parameter tuning. Shifting to teaching, when you run theory or lab courses, how do you structure a topic so that students who learn differently—say, more visually or more hands-on—can all grasp the main concepts?

Right, so you help students realize when their low accuracy in AI projects is due to issues like noisy or imbalanced data, and you walk them through evaluating and cleaning their datasets before worrying about things like hyperparameters. Switching topics—when you're teaching theory or lab courses, how do you structure your sessions to make sure students not only grasp the concepts but can apply them hands-on?

So in theory, mostly. I first festival I used to be given them some lecture about 14-15 minutes.

And I randomly ask questions from them.

Alright, so you kick off theory sessions with a focused lecture of around fifteen minutes. For your lab courses—how do you design experiments or hands-on work so students actually connect the practical with the theory you've just covered?

What could be? The answer for that?

Alright, so you keep your lectures short, then ask students questions to keep them engaged. When it comes to student evaluation—like grading exams or assignments—what's your process to make sure your grading is fair and consistent?

Would somebody world examples or real world scenarios I try to?

Alright, so you break up your theory lectures with active questioning to check understanding. When it comes to grading, what's your process for marking exams or assignments to make sure you're fair and consistent across all students?

Who who keep on? On their field.

Okay, so you use real-world examples and randomly quiz students during your lectures to keep them thinking. When it comes to evaluating students—grading their exams or lab work—how do you make sure your assessment is fair and actually measures what they've learned?

And they can and. Trying. They're trying to. Solve that question.

Through that. Their understanding would be better as well as. With real world how it's connecting that also they can understand.

Alright, you like to keep students engaged with questions and connect content to their real-world interests. Shifting gears—when you're guiding students working on their final year projects or research, how do you help them choose topics and stay on track through the whole process?

Alright, so you break up your theory lectures with a short talk and then engage students by asking random questions about what you've covered, sometimes tying those to real-world scenarios. For your lab sessions, how do you design experiments or practical tasks to make sure students connect the theory to hands-on skills?

And in lab. I always try to give them that.

Okay, so you keep them engaged with real-world examples and encourage them to connect theory to practice by solving scenario-based questions. When it comes to evaluating students—say, for grading exams or lab reports—what's your process to make sure you're being both fair and effective?

Suppose state captain train on observation.

They can give them freedom. You can download any data set. Any kind of data set.

Okay, so you make sure in lab sessions to connect activities with what they're learning in theory and encourage them to solve problems hands-on. Shifting gears, when you evaluate student work—like grading exams or lab submissions—how do you make sure your grading is fair and consistent?

Alright, you were describing how in your lab sessions you give students practical observation-based exercises to reinforce the theory. Shifting gears a bit—have you ever supervised student research projects or guided them through publishing in journals? What strategies do you use to help them move from just completing the project to actually producing work that's publication-ready?

For particular problem you can make out. So like that way. I'll make sure that they will engage on those.

Okay, so you give your students flexibility to choose and download any dataset for their lab work, which encourages independent exploration. Thinking about your own research background—can you share how your doctoral work prepared you to guide students in publishing their research in reputable journals?

Solving the project proper way.

So that they can. I can understand it better way.

Also. They are feeling like that thing. They are doing. Not. We are teaching or anything that they should feel like that.

Okay, so you encourage students to pick their own datasets and craft problems around them in your lab sessions—that's a good way to build ownership and engagement. Switching gears, when you're guiding students through larger research projects or theses, how do you help them narrow down a topic and structure their methodology, especially if they're struggling to focus their research question?

Alright, so you give your students a lot of autonomy in labs, letting them choose datasets and design projects to match the concepts they're learning—that's a solid approach to building engagement. Shifting focus, can you describe a time when you guided a student project or research effort from start to finish, and what specific strategies you used to help the student overcome challenges along the way?

They are doing that model and after making the model they will be very happy. So they will get more curious to make the next model.

Alright, so you give your students plenty of freedom in labs to choose datasets and approach problems in their own way—that definitely encourages deeper engagement and understanding. Now, thinking about your experience in industry projects or consultancy—can you share a concrete example of a real-world project you worked on, and what role you played in its execution?

Alright, so you're focused on making students feel like they're genuinely doing the work themselves, not just following instructions from you—that's a solid teaching philosophy. Switching gears, when it comes to evaluating student work, like grading exams or lab assignments, what's your approach to making sure your evaluations are fair and consistent across the whole class?

Like that I try to do.

Alright, so you're focused on making sure students feel genuine ownership of their work, and you give them the freedom to pick datasets and drive their own projects in labs. Going a different direction—what's your process for evaluating student performance, both in exams and through ongoing assessment during the semester?

Alright, so you focus on creating an environment where students feel real ownership of their learning, especially through hands-on labs and making their own models. Switching gears, Professor, can you tell me about a time you guided a student project or research effort—what was your approach to mentoring them through the process?

Yes, so before doing any project. I first of all tell them. That you choose your project you see surrounding your college or universities. You can feel. What problem solution? You continue to. Once they will take one week time. To find out the problem statement.

Then they will come then I told them. Go through the research papers.

There will be so many plenty of researches and. For different event.

Okay, so you have your students identify real-world problems from their surroundings before starting a project and give them a week to define the problem statement. When it comes to actually evaluating students—like for exams or practicals—how do you ensure your grading is fair and truly reflects each student’s understanding?

Okay, so you give students time to identify a real-world problem, then have them dive into research papers to ground their project. Shifting focus a bit—when it comes to evaluating student work, especially exams or lab submissions, what's your process for grading fairly and consistently across a large group?

Papers or. Is there a public statement?

Okay, so after guiding them through reading research papers, you give students time to really investigate existing work before starting their own. Let me ask—when you're teaching a theory-heavy course, how do you structure your lectures to make sure students stay engaged and actually understand the key concepts?

Then they I suggested them to read those papers.

Alright, so you want your students to feel like they're building something themselves and get that satisfaction after seeing their models work. Shifting the focus a bit—can you tell me about your own experience working on industry projects or consultancy? What was a concrete project you contributed to, and what was your specific role?

Alright, so after your students define the problem and read through the relevant research, you encourage them to approach the project solution themselves. Shifting focus a bit—Professor, can you describe how you evaluate students’ work, especially when it comes to theory-heavy courses versus practical labs?

How? What? What kind of models they used?

Okay, so you make sure your students dig into research papers, understand the gaps, and analyze how previous authors approached their problems before starting their own work. Shifting focus, Professor, can you give me a concrete example of how you’ve evaluated student work—what criteria or process do you use when grading an exam or assessing a project?

That they would have. Great idea.

I used to follow. Then later on we can. Convert the project also to. Make a good paper.

Okay, so you guide your students to identify research gaps by studying what’s already been done, then encourage them to add something new to their projects. Switching topics—Professor, can you describe your experience with industry projects or consultancy, and how that’s influenced your teaching or research?

Right, so you guide your students to turn their project work into publishable research if it's strong enough. Now tell me, how do you approach evaluating student work and exams—what's your process for making sure grading is fair and consistent?

On Vibe.

No, I tried. To complete it also always. I tried to evaluate. Based on their understanding, based on their concept.

Okay, so you mentioned lab exams—can you share a concrete example of how you design a lab exam to accurately assess both practical skills and deeper understanding, not just rote execution?

Suppose they solve a makeup project. I tried to understand that.

Okay, so you're focused on evaluating students based on their actual understanding, not just whether they finish the task. Can you give me a concrete example of a lab exam you designed—how did you structure it to check both practical skills and deeper conceptual grasp?

Why they use that loss function?

So you try to evaluate based on their conceptual understanding rather than just whether they completed the task. Can you give me a concrete example of a lab exam you've designed—how did you structure it to reveal actual depth of understanding, not just rote execution?

Who have been release understanding I can tell them less marks later on I try to.

Alright, so you focus on why students chose a particular model or loss function, not just what they built. Switching gears, Professor, can you walk me through how you structure a theory class to keep students engaged—what do you do if you notice the energy dropping?

Into this subject also when they're writing, so they're also I used to evaluate like that. Have to see if under the core concept.

Alright, so you adjust marks based on how well students actually understand their approach, not just if they get the right answer. Now, switching over—can you tell me about a time you guided students through a research project and helped them publish in a reputable journal? What was your specific role in shaping the paper?

To answer the particular question. If they're answering the particular concept.

Okay, so you really emphasize understanding the core concepts behind a student’s answer, not just the final result. Let’s switch gears—can you give me an example of how you’ve guided students through a research project, from topic selection to publication? What’s your step-by-step approach for mentoring them along that path?

You can give them boot marks.

Alright, so you really focus on whether students have grasped the core concepts when evaluating their written answers. Shifting gears, Professor, can you walk me through a time you guided students working on a research project—how did you help them move from initial idea to an actual publication?

Alright, so you’re really focused on whether students have actually understood the core concept when they answer. Switching gears now—tell me about a time you guided students through a full research project, from initial topic selection to publication. What was your role at each stage?

And future scope everything I just guided them.

Okay, so your role as a mentor spans everything from guiding literature review to future scope and model evaluation. Now, Professor, can you share an example of an industry project or consultancy you’ve been involved with—what was your contribution and how did you balance that with your academic responsibilities?

First, they come up with literature review.

So you have students check in with you at every stage, starting from literature review — you’re pretty hands-on as a mentor. Can you give me an example of a specific challenge a student faced during model building, and how you guided them through it?

And really that are. They are really. Find out those gaps or not that also I personally verify. They've gone through going. 2 Newspapers.

Papers. The date.

Okay, so you walk students through every phase, from reviewing literature to discussing the future scope, and you have them check in at each step. Outside of academic research, Professor, have you been involved in any industry projects or consultancy work where you applied your expertise directly?

With different different test cases. Then if I see really that model is working on the. Uh, proper way?

Can I? It depends on the giving them feedback if I found any. Yeah, problem is there in that model. I tried to tell them they can. And train again that model with some more epochs and they can change some parameters to. Work on those part where the model is not working properly.

Okay, so you make sure the students are actually identifying real research gaps in the literature and not just collecting papers for the sake of it. Now, switching gears, Professor, how do you decide which emerging technologies—like AI or IoT—are worth teaching in your courses, given how fast the field moves?

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

Also like we are seeing average vision is saying based on different models, we are saying like that.

So you personally check how the students' models perform and give them feedback on where to tweak parameters or retrain if needed — that's quite hands-on. Alright, we're wrapping up. Is there anything you'd like to ask about the role or the team?

You’re talking about evaluating models by comparing average performance across different versions — that kind of benchmarking is crucial. Alright, we’re wrapping up. Is there anything you’d like to ask about the role or the team?

Thoroughly. Also whatever mistake is happening, I try to tell them. That you come and it is your mistake you have to solve this way.

I'll evaluate the paper.

Alright, so you personally review the completed papers and evaluate them after guiding the students through each research phase. We're at the end of our conversation, Professor. Is there anything you'd like to ask about the role or the team?

I want to come on, although everything is fine, then we'll process with the. Publications and we send to some good reputated. Conference or general so you could do.

Like that?

Alright, so you hold your students accountable for fixing their own mistakes and make sure they understand your feedback, not just follow instructions blindly. We're wrapping up, Professor. Is there anything you'd like to ask about the Assistant Professor role or the team?

Alright, so you’re really making sure students are responsible for correcting their own mistakes and understand your feedback, not just implementing it blindly. We’re at the end of our conversation, Professor. Is there anything you’d like to ask about the role or the team before we wrap up?

Till now I. I did some conferences, IEEE conferences and also some conferences which are. I'm going to publish in Springes.

Alright, so you’re really making sure students are responsible for correcting their own mistakes and understand your feedback, not just implementing it blindly. We’re at the end of our conversation, Professor. Is there anything you’d like to ask about the role or the team before we wrap up?

Like that I I did like that shortly. I just had to guide them in. From project to table.

Alright, so you’ve actually published with your students at IEEE conferences and are aiming for Springer journals as well. We’re at the end of our conversation, Professor. Is there anything you’d like to ask about the role or the team?

Alright, so you’ve actually published with your students at IEEE conferences and are aiming for Springer journals as well. We’re at the end of our conversation, Professor. Is there anything you’d like to ask about the role or the team before we wrap up?

I I would love to join in CC IML department.