Yeah, sure. Let's start.

Please start by introducing your professional journey in academia

So my name is Inait Ali. I have recently completed my. PhD. From NIT Srinagar. I've started my career in science. Basically from UH bachelor's degree at SP College Srinagar. To be specific, bachelor's in science. After that I moved. To University of Kashmir and I. Received masters in mathematics. And after that I prepared for a year and. Past UGC NET sheriff exam. It's All India rank. 105 And after that I got admission for. PhD And after that I got admission for. PhD. One mathematical essence I did not teach. Undergraduates as such, but I have had the chance to teach UH engineering students UH.

So my name is Inait Ali. I have recently completed my. PhD. From NIT Srinagar. I've started my career in science. Basically from UH bachelor's degree at SP College Srinagar. To be specific, bachelor's in science. After that I moved. To University of Kashmir and I. Received masters in mathematics. And after that I prepared for a year and. Past UGC NET sheriff exam.

So you mentioned starting with a strong science foundation. What’s one mathematical concept you enjoy teaching to undergraduates, and why does it resonate with you?

About. Discrete Mathematics.

Alright, so you’ve taught engineering students rather than undergraduates.

So you focused on Discrete Mathematics with engineering students. What aspect of Discrete Mathematics did you find most engaging to explain,

Since I, I.

You started to say "since I, I..." — sounds like you were about to share something. Go on.

Yeah, as I was saying that, uh, let's say, uh. Or introducing covering of graphs.

Alright, so you’re introducing the concept of covering of graphs. How did you approach explaining that idea to students who might not have a strong mathematical intuition—did you use any practical examples or analogies to help them grasp it?

In discrete mathematics and before introduced in coloring, we. Suppose uh. We. Yeah, I used to. I used the practical analogy of covering a map so that adjacent countries or adjacent reachers would not get the same color.

In discrete mathematics and before introduced in coloring, we. Suppose uh. We. Yeah, I used to. I used the practical analogy of covering a map so that adjacent countries or adjacent reachers would not get the same color. And we have to use. Minimum as minimum colors as possible.

Alright, Dr., I noticed your publication on L(2,1)-coloring of zero-divisor graphs of finite commutative rings in the Indian Journal of Pure and Applied Mathematics. How did that particular research shape the way you teach graph theory or algebraic concepts to students—did you bring any insights or examples from your work into the classroom?

Okay,

And then it was the starting idea. And then as a application to that. Yeah, I, I. With that particular insight would be that I used the analogy of assigning frequencies to radio networks in such a ways so that. Uh, radio transmitter which are nearby do not interfere with each other. I gave that. Problem in the class and then as an application I introduced the formal notation of L tone covering.

That is how I introduced the the students to my research.

Right,

So you introduced your research to students by connecting the map-coloring analogy to real-world frequency assignment problems—using that as an application before getting into formal L(2,1)-covering. Did you notice students engaging more when you brought in examples from your own research?

Hello.

Given your research in graph theory and its applications, where do you see the most fundable directions emerging over the next few years? Which granting agencies or calls would you realistically target to support a research group in this area?

I would say expander graphs and network theory. This is particularly hot topic in AS application For application point of view outside.

I would say expander graphs and network theory.

So you see expander graphs and network theory as the most fundable directions. Which specific agencies or grant calls—like SERB, DST, or international programs—do you think would be most likely to support this kind of research, and how would you position your proposal to stand out?

You mentioned earlier how you use map-coloring and frequency assignment analogies to introduce graph concepts. Suppose you had to teach a class of 200 engineering students without slides or traditional lectures. How would you structure a session to keep them actively engaged and ensure they’re really wrestling with the material, not just listening?

So yeah, before. Please, uh, ask. One question at a time. So that. They can answer uh. With that, good, uh. Thinking about it. You are asking about me. How would you I uh. Get a proposal. Basically I will start from uh, Ramanujan graphs, uh, why They are the best expanders we can get. How their use is in? Error correction, error correction codes and etcetera. And this there is an open problem in Ramanujan graphs which has been open. Almost for let's say 20 to 30 years now. And I'm working today to crack their problem.

Okay, so you'd start with Ramanujan graphs and their applications in error correction, highlighting open problems to spark interest.

So that. Would be basic umm. Basically a good proposal. Both SERP and DST can uh. Be acceptable for that. Because of the application part. I basically would ask a question. Basically a simple question there will take a problem. So that students will engage in formalizing various methods to solve that question, like I said. For example, assigning frequencies to a radio networks in a city such that No2 radio network networks which are. Fairly close to each other, do not interfere with each other. And I would ask the students, can you propose a method so that we have to use less less bandwidth at the same time?

On the radio networks do not interfere with each other or some problem like that. So that students think their own way about.

Alright, so you’d frame the class around a concrete problem—like minimizing bandwidth for radio networks—and get students actively proposing methods to solve it. When you’re evaluating their answers or solutions in a large group,

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?

Uh, sorry, I didn't get the question. Can you please repeat that?

No problem, Dr. Here’s the question again: 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?

Uh, what do you mean by accreditation cycles Or, uh, incus and inconsistent? What do you mean by that exactly?

Sure, let me clarify. Accreditation cycles are the periodic reviews by bodies like NBA or NAAC to ensure our department meets standards for teaching, assessment, and outcomes. When I say “inconsistent outcome assessment data,” I mean that records showing how well students achieve course objectives aren’t uniform—some courses have detailed evidence, others don’t. How would you address this inconsistency so we’re ready for audits and maintain credibility?

I have no idea about this since I'm new to this field, but let's see. If students do not uh, some students do not uh, perform good in some course or do not achieve their. Learning goals we have to prepare for remediation cycles and. Go for uh. Course I mean.

Special. Prize courses in such a way so that students would be more interesting and students would do some work on it. On their own, maybe publish a small paper?

Okay, so you’re thinking about remediation and reviewing courses when students don’t meet learning goals. Let’s shift focus—when you’ve been part of curriculum development before, what was your specific role in making sure course content and assessments matched accreditation standards, and how would you apply that experience to VIT’s programs?

Alright, so you’d design courses to spark student interest and maybe get them publishing small papers. When you were developing these courses, what steps did you take to make sure the assessments and learning objectives actually lined up with national accreditation standards—like NBA or NAAC—not just student engagement?

I'm not see that, uh, what? Uh, NBA and NAC? What, uh, they are up to and we will, uh, design the course accordingly. I guess that's fair, no.

Let’s shift gears. Imagine this scenario: a student files a formal complaint, claiming your grading is biased, while at the same time your department head is pressuring you to raise pass rates. How would you navigate that situation and make sure both academic integrity and fairness are maintained?

Yeah, that's a very good question that I have had in mind for a very long time. Students sometimes do tend to think about that. I mean navigating situation where you are skis it between a student's formal. Complete man and departmental pressure.

Yeah, that's a very good question that I have had in mind for a very long time. Students sometimes do tend to think about that. I mean navigating situation where you are skis it between a student's formal. Complete man and departmental pressure. And transparency data I mean.

Right, you’ve thought about being caught between a student’s formal complaint and departmental pressure. What would your first step be, practically, if you received that complaint and still wanted to uphold your grading standards?

The immediate audit established the facts. Review the rubric. Check the. Would you please ******* let me finish?

Right, so you’re focusing on transparency, but I want to push a bit further—imagine the department head insists that you adjust your grading to improve pass rates, even if it means relaxing your usual standards. What would you actually do in that moment?

You were saying—you’d audit and review the rubric, and check something, but I didn’t catch the rest. Go on.

I mean, for the buys I would uh. Invite third party, let's say, and focus on the work, not the student and transparency. Would be my shield and for the uh. Managing the departmental pressure. I mean instead of just saying no, show them the data upgrades are low, providing evidence of why.

I mean, for the buys I would uh. Invite third party, let's say, and focus on the work, not the student and transparency. Would be my shield and for the uh. Managing the departmental pressure.

You’ve described a pretty thoughtful approach to handling bias and departmental pressure—thanks for breaking that down. Now, shifting to industry alignment: given your background in mathematics, do you have any industry connections or experience that could help students secure internships or placements, or bring real-world problems into your classroom?

Right,

For example. Below attendance message prerequisites so. Good thing about getting Intel students getting internship. I mean bridging the gap between abstract mathematical research industry application is one of the most effective ways. To boost student engagement. I mean, I would, uh, get them into cyber, uh, security and cryptography, telecommunications, logistics and operation research.

Okay, so you’d encourage students to pursue internships and projects in fields like cybersecurity, cryptography, telecommunications, and operations research to bridge theory and application.

And uh, I mean, I mean I will be using my academic collaboration for internships. Uh, let's say joint supervise and uh, the social labs. I haven't had that experience in my past life, but. I have a help students before. Best certain things so that, uh, they may be. Umm getting a real world job for example at my time in an IT Srinagar. A prime example of that would be development of, I mean. And getting them to. D and develop various coding languages which are used. Train for various AI models.

I mean, there is that, but uh, I don't have any formal. Uh, experience about that?

Alright, you mentioned using your academic collaborations and joint supervision to help students, especially during your time at NIT Srinagar,

Alright, so you haven’t had formal experience with industry partnerships, but you’ve helped students with real-world skills, like coding for AI models. Given that, how would you guide students in projects or research related to advanced statistical methods and supply chain optimization, especially within an interdisciplinary framework?

Question because, uh. Uh, doing research from application point of view is all about doing interdisciplinary research.

You’re saying interdisciplinary research is key for applying advanced statistical methods and supply chain optimization.

I and uh, I have had the chance of going interdisciplinary, interdisciplinary, uh, interdisciplinary research, uh. I mean, I don't know why Turtle Research builds the foundation. Industry ready placement outcomes are driven by the ability of the student to model and simulate.

For example. Example my Google guide students to build AI models or optimize algorithms using Python or Matlab which I know. Having students complete a project on, for example.

Okay, so you’re highlighting that the foundation for industry-ready outcomes is giving students the ability to model and simulate, especially through interdisciplinary work.

Alright, so you guide students to build A I models or optimize algorithms using Python or Matlab, and you’ve had them complete real-world projects. Let’s move to curriculum development—Professor, how have you contributed to designing or updating courses to meet accreditation standards, and how would you approach aligning with VIT’s specific program objectives?

Uh, first let me explain the. Question about supply channel algorithms I think. Fully prepared to lead to surgeon supply chain optimization and advances.

The statistical methods I mean my background in graph theory and combinatorial optimization is the mathematical engine.

You were about to explain your approach to leading students in supply chain optimization and advances, but you didn’t finish your thought. Go ahead—how would you prepare students for that kind of project, step by step?

Alright, your background in graph theory and combinatorial optimization gives a solid mathematical foundation for supply chain work. Let’s turn to curriculum design—how have you previously contributed to developing or updating courses to meet accreditation standards, and how would you approach aligning with VIT’s specific program objectives?

And which objectives are we specifically? Talking about of uh, VITI don't know about uh. Uh, learning objectives of AIT, I mean. Hello, I think they are frozen.

Right, you’re not sure about VIT’s specific learning objectives. That’s fair—most universities set clear goals around employability, interdisciplinary skills, and applied research. If you were tasked with updating a mathematics curriculum to meet accreditation standards, what steps would you take to ensure transparency and academic rigor in student assessment?

Uh, that's a pretty good question. Uh. I mean for about the curriculum objectives, I have to refine the curriculum structure in such a way. It is both academically rigorous and it should be also industry relevant. I mean we should have active learning objectives from theory to objective for example and its impact. Then we should have interdisciplinary curricula map.

Let's say linear algebra to singular value decomposition. Yeah. And then, uh, we should have disciplinary security, let's say Python or Matlab. Me finish, you **********.

Alright, you emphasized the need for a curriculum structure that’s both academically rigorous and industry relevant, with active learning objectives and an interdisciplinary map. How would you ensure fair and transparent assessment methods while maintaining academic rigor in your mathematics courses, especially for classroom evaluations and exam responsibilities at VIT?

You mentioned moving from linear algebra to singular value decomposition, and then connecting that to disciplinary skills like Python or Matlab. You didn’t finish your thought—can you walk me through how you’d make sure classroom evaluations, assignments, and exams are fair and transparent, especially when balancing those practical skills with academic rigor?

Yeah, about, uh, this. Linear algebra and singular decomposition. I would. And then disciplinary would be Python And Matlab and it's applied research would be AI image compression. Using. Extended value decomposition to reduce noise in digital photography and etc. And that's a good thing.

Yeah, about, uh, this. Linear algebra and singular decomposition. I would. And then disciplinary would be Python And Matlab and it's applied research would be AI image compression.

So you’d have students take linear algebra concepts like singular value decomposition and apply them in Python or Matlab, for research projects like A I image compression. When it comes to assessing those projects and exams, how do you make sure your grading is transparent and students understand the criteria—do you use rubrics, peer review, or something else?

Alright, let’s talk about your core skills.

And about the question you are asking. I'm in. To maintain the regular I would separate conceptual and derivation. Competition. A single exam or assignment should be divided into. Feed the string buckets. Let's see bucket. One should be theoretical foundations about why this happens. And bucket 2 should be algorithmic implementation of the same and then there should be. There should be interpretation and sensitivity.

You break exams into buckets: theory, algorithmic implementation,

And then there should also be transparent, uh, classroom evaluation. Uh. I mean to help my students, uh. To get into deep tech supply chain applications, my strategy would be simple, uh like I will define. What first the deep application we should be getting into the student?

You’re saying your strategy starts with defining the target DeepTech application for the student. What’s next—how do you help them take advanced statistical concepts and actually implement them in code or a model, especially if they’re struggling with the practical side?

Let's see. I mean, uh, in 26 the global supply.

Alright, you started to mention the global supply context in 2016—sounds like you were about to tie that into helping students apply statistical methods in practice. Go ahead, can you finish your thought?

Yeah, as I was saying, let's see, I would. Briefly explain how I would use that. I mean if if we have to design A logistic networks for a deep tech. Drone delivery startup. We will first use the math there.

That is, they use community optimization to define the optimal locations for charging hubs and extra.

So, if you were guiding students to design a logistics network for a DeepTech drone delivery startup and starting from the mathematical modeling—how would you help them move from abstract graph theory or optimization models to actually coding up a working simulation or prototype?

Deep Tech Clearwood P Implement anti destructive in using our graph model to simulate 30% node failure.

Got it.

You’d have students simulate node failures in their graph models, like a 30 percent failure rate, to test the resilience of supply chain networks. When guiding these projects, how do you help students interpret the statistical results and connect them to practical improvements—do you have a structured method for teaching that analysis?

Deep Tech Clearwood P Implement anti destructive in using our graph model to simulate 30% node failure. And uh. Outfook outcome word for. I wish you partner the the students with Indian deep star deep tech startups specifically which are those in. Space tech can defense logistics. Uh, to bring unsolved problems into the classroom. Students not just solve a text problem, they solve the bottleneck for a company.

So you’d partner students with real Indian DeepTech startups in areas like space tech and defense logistics, bringing unsolved industry problems into the classroom so students tackle genuine bottlenecks. When it comes to structuring your teaching approach for these projects, how do you make sure your communication is clear and the learning objectives are well-structured for students from diverse backgrounds?

I mean instead of paper proof and the output output of my teaching should be. Let's see if we start something. Should be Python based simulation, for example using. Network X or π mode to model the combinatorial optimization problem.

Let's say last mile delivery. The account, uh, that accounts for real time traffic and fuel constraints.

You mentioned using NetworkX or PiMode for Python-based simulations as the output of student projects, instead of traditional paper proofs.

You brought up modeling last mile delivery with real-time traffic and fuel constraints. How do you incorporate student evaluation and exam duties into these types of hands-on, open-ended projects to ensure fairness and consistency?

You are awesome, Mia. Very different kind of questions. At the same time, I do not know which one to answer. I mean to bridge the gap between traditional mathematical rigor and, uh, modern deep applications like last amount delivery. The proof shouldn't be discarded, it should be evolved. And the goal is to move the student from a static understanding of a theorem to dynamic applications of an algorithm.

You’re saying the aim is to evolve mathematical proof into dynamic algorithmic application, moving students from static theory to active modeling and simulation. Switching gears—can you briefly tell me about your research publications in reputed journals and how they relate to your teaching or industry experience?

And the prohibit method. While teaching complex topic like vehicle routing problems or community optimization, the classroom communication follows should follow four stage production like. Formal definition. Then we would go. Chorum and formal proof and relation. My publication which I. I'm very proud of being. Algebraic computer citizen existence of perfect and total perfect course. And graphs. And it is published in a very good journal. Journal of Algeria, two weeks. And then we as you know, perfect codes and total perfect codes, uh, are used in. Error correction. And Harry?

Is also used in networks. They have a pretty. Much applications.

Alright, so your publication on algebraic codes and their application to error correction in graphs shows a strong theoretical foundation. Do you also have experience guiding student research projects or collaborating with industry on consultancy,

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

No, I do not need to ask up anything about the role. I mean, but uh, this way of interviewing is not good for. I'm your five got 6 questions on a single goal and and you don't let me finish every time.