Good day, Balan Ramesh. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Please start by introducing your professional journey in academia.

Oh, sure.

Good evening. I'm Balan.

I completed my B.Tech in Biotechnology, uh, in 2016, uh, then I got a direct PST admit from the University of Texas at Arlington, uh, where I studied, umm.

Namban, Ramesh.

The evolution of sex chromosomes and dosage compensation.

And I and I completed my PST in 2021, and then I moved on to work with Doctor Adam Jones at the University of Idaho, where we try to understand the role of sexual selection in sex determination in sex reversed fishes like seahorses, pipe fishes or sea Dragons and so on. During my post-doc, you know, I've had the opportunity to.

Assemble new genomic resources for all the signated fishes which is not previously available. And then after completing my post doc, I'm in my current role as Assistant Professor at Mahindra University. At the Center for Life Sciences, where teaching, you know, bioinformatics and computational biology to, you know, second year and 3rd year undergraduate students. In addition, I've had the opportunity to.

You know, teach workshop in genomic data analysis to Mtech students and I have a few PS D students that I mentor as well. Thanks.

Professor Ramesh, let's evaluate your expertise step by step. First, could you walk me through your research expertise in bioinformatics, particularly in the context of medical microbiology?

OK, Medical microbiology. All right, so.

Uh, during my PhD and my post doc, uh, the broad questions that I was interested in, umm, I'm interested in is, it falls under the umbrella of evolutionary genomics. Uh, specifically during my PhD, the questions were related to the evolution of sex chromosome dosage compensation and meiotic sex chromosome inactivation. And, and some of the major players or some of the major evolutionary forces that act on this X chromosome.

Are the sexual selection which was part which which I further explored during my post doctoral training.

But sorry, but the, the whole umbrella of evolutionary genomics covers, you know, various fields like infection biology, cancer biology and and medical microbiology as well. For for, for example, if if we look at one of the current courses that I'm teaching, which is evolutionary genomics of pathogens. You know we in the course I cover.

Understood,

I go into details on how antibiotic resistance evolves in.

Pathogens like E coli, tuberculosis and how that sort of how the evolutionary trajectory varies between E coli and tuberculosis, for example.

My my focus is on evolutionary genomics, but the application is across is interdisciplinary in terms of infection biology, cancer biology and so on. So yeah, thanks.

So. In in terms of research.

Understood. Your work bridges important disciplines. How have you guided your students in connecting such broad evolutionary genomics concepts to medical microbiology in practical or lab settings?

My current PhD scholar is Inter has been working with Klebsiella pneumonia during her undergraduate as well. So when I started the position at Mahindra University last year, some of the fundamental aspects of the evolutionary trajectories like we went back to the basics on can we like broad questions like can we predict? The evolution of resistance in Klebsiella pneumonia. So we've been trying to.

Looking at all the different, uh, you know, resistant genes, uh, that are present in the plasmids and, uh, we are trying to come up with a model that can better predict, uh, the evolution of resistance or at least the path to the evolution of resistance.

Build AI ML model on.

And in terms of how am I guiding the how parts specifically involves A tiered approach wherein I make sure that the students or my student PSD scholars specifically takes courses related to.

Uh, like the introductory introduction to Python, uh, AI, ML in Python, ML workshops in Python. And then, uh, at the same time, you know, learning things and, uh, doing things are different things. So she gets an opportunity to learn what she's taking and then, you know, do it in, in the, in her research as well. And, and as my first student, you know. I I feel the moral responsibility to sort of train her.

From the basics, but she's come a long way in in the last one. Yeah, thanks.

Understood. It sounds like a robust approach to both interdisciplinary application and student mentorship. Let’s shift gears slightly. Could you explain your methodology for teaching both theoretical and laboratory bioinformatics courses effectively?

In terms of pedagogy, I've I've had training during my PhD, so as a PhD I was given stipend to be a teaching assistant where there was.

Uh, with the teaching that I got from University of Texas at Arlington, where it's a flipped classroom, uh, the materials are given to the students in advance and I try and use the, uh, class time for discussions, classroom based activities wherein we try and bring out different points of view for a similar problem. And, and I've got really good positive feedback from the students for that, uh, in terms of. Lab component I try and do it based on like a soft, like a workshop.

You know, we had, we had to clear the training given by the University of Texas and, and and also I'm a certified software carpentry instructor. So when I teach bioinformatics course in class, I try and do it.

Hands on guided approach. So we walk through certain experiments hands-on like as as a faculty, you know, I try and work a problem with them like with dummy datasets and and try and teach them how to do things. Once they feel comfortable enough, then we give them specific problems that they work independently and then we sort of evaluate them during. Routine classroom lab activities.

So.

Like a regular classroom style bioinformatics versus like a lab based bioinformatics course here.

Yeah, that's how I would teach you.

Your approach combining flipped classrooms and hands-on workshops is commendable. How do you assess your students’ grasp of theoretical bioinformatics concepts and practical lab proficiency effectively?

That's, that's a great question. I mean it, it has been really challenging in in terms of really assessing, but one of the method that.

We recently used WAS. I also teach Introduction to Python for.

Understood.

Both biotechnology and computational biology undergrads here at my current role and it's it's a common course where close to.

We gave them the opportunity to sort of train with the material like, you know, open book, you know, use of a ILLMS where allowed during the class wherein the troubleshooting techniques. That is how we sort of assist them. Like we gave them a problem, we gave them any tools that they could use and we.

To eat your students take the course and it was really difficult to sort of get a good understanding on what the student grasped. So what we did was.

Uh, ideally timed them on how fast they were able to solve the problems that seemed to work okay, but at the same time on there were some of the disadvantages with with that approach as well, uh, overall, uh, then in addition.

For uh, project based courses, uh, we have, we've had the opportunity to, you know, assign our students, come up with their own project, they try and solve it and they make a presentation during a class or the lab hours. And that has been slightly much more useful wherein students, uh, you know, added to the CV and profile. So that helps us get a good sense on whether the students really grasp the concepts or they just complete the task for the sake of completion.

So, yeah, thanks.

Your approach to combining problem-solving, open resources, and project-based evaluation is insightful. Now, moving forward, describe how you handle student evaluations and exam-related responsibilities systematically.

So being being a computational biologist and a bioinformatician, one of the luxuries that I've had is I I could automate a lot of evaluation based on the computational tools with GitHub workflows and you know, Google Classroom assessments for small in class activities and pop quizzes. Moving forward, if if the if it if the scale was really really high.

I would still uh.

Gravitate towards a problem based or a project based course wherein the access to information is pretty open, but but the problem that they choose and and sort of the and and the solution that they come up with, those would be assessed systematically using, you know, a rubric, yeah.

You've described a structured and technology-integrated approach. How do you ensure transparency and fairness in grading, especially in project-based evaluations?

Uh, it, it really, uh, based on the experience so far, uh, it, it really helps when, when we assign a project or like a activity and.

Me as a professor, when I'm open and honest about the rubric that they would be evaluated on, that really helps the students to structure their project as well and helps them align with the expectations out of a coursework or a project work or an assignment for that matter. So to ensure fairness and transparency. Making the rubric available and setting clear expectations on what is required from the project or an assignment.

Uh, is generally clearly stated, uh, and, and that has worked, uh, good for me so far, yeah.

Your emphasis on clear rubrics and upfront expectations seems well-considered. Let's now discuss guiding student projects and research. How do you balance fostering creativity while steering them toward impactful academic output?

Ah, that's a really good question. Creativity in science is really important, but overall the way I mentor my students is generally students have a clear understanding or expectations on my research profile. They they would have ideally read the research publications that I've had in the last. 5-6 years and they come with clear expectations for people to work in my one once.

They joined the.

We go through some of the foundational scientific articles in the literature and then we sort of.

Meet regularly to discuss various topics that are potentially.

New and innovative at the same time.

Sorry, sorry, I just started to cough.

Yeah. So potentially which are, you know, because we are meeting regularly, we sort of slowly refine their broad idea, broad creative ideas and refine it in a way that it is still creative, but it falls under the umbrella of the expertise that I have. And that's that's how, you know, I plan to mentor my students.

Uh, yeah. I mean, uh, I've, I've talked to, uh, fairly diverse, uh, group of students, uh, during my PhD, uh, wherein I've had students anywhere between 16, uh, who, who are really, really smart and all the way through 70, who I remember, so war veteran who completed, you know, multiple wars and still wanted to study. So I've, I've, I've had, uh, the opportunity to interact with those people and I've had the opportunity to teach such diverse.

That balance—refining creativity within your expertise—makes sense. Let’s now consider effective communication. How do you ensure clarity and structure in your teaching approach for diverse student backgrounds?

Process. In addition, one of the current courses that I'm taking also has people from diverse backgrounds. We we have it's it's introductory course to Python for biomedical data science master student. And we have students who are medical doctors, who are dental doctors, who are from computer science background and who are from biotechnology background, so catering to them. For a foundational course like Python is fairly straightforward because you assume that they.

But overall in in general, trying to sort of, you know, before starting the class, trying to have an understanding on what their background is, how diverse the class is, and then clearly saying what would be the outputs of the course and also how the course would be delivered. Sort of sets a clear expectations on what they will learn. And that sort of seems to have, you know, that I've had good feedback from the student as well.

Because the assumption is that everybody is starting from scratch. But then if people sort of pick up pace and if they gain sort of, you know, if they feel that the material is a little too easy, we sort of give them additional, you know, a little more challenging exercises if they're if they if they prefer.

It sounds like you've developed a thoughtful and adaptive teaching strategy. Now, shifting to your broader academic profile: could you highlight the most impactful research publication you've authored?

Sure. As a scientist, I would always consider that the next paper would be much more impactful. Uh, but looking back, uh, I, I would say the paper that I published in 2024.

On meotic sex chromosome you know inactivation in Tribolium beetles is is one of the most impactful ones because we did single cell RNA seek in a Tribolium beetles for the first time which.

Was never done before and there were several challenges, both technical and biological in terms of uh, uh, technical challenges being, uh, there were no marker genes to classify the cell types, which we, uh, tried to establish a method for the first time. There were no wet lab protocols on extracting single cells from Tribolium tests just before. So that was solved, uh, and in terms of, uh. Understanding the biology which was.

Whether the meiotic sex chromosome and activation was present or not was also, uh, you know, answered in the paper, uh, which was previously not answered at all. So because we solved A broader, bigger, uh, biological question and then also in the process, we saw several technical challenges. I would consider that as my most impactful work till date.

A publication addressing both pioneering biological questions and overcoming technical constraints is indeed impactful. Moving to industry-relevant contributions: have you been involved in projects or consultancy aligning with bioinformatics in medical microbiology?

Uh, not in medical microbiology, but in.

Phylogenetics, if, if that makes sense, specifically ancestry. So we've had a couple of startup founders.

Who broadly work on the ancestry reached out to us and we have offered some guidelines for them. These are still in initial stages.

They seemed to be interested in having us on board as consultants, but to be direct, I have no medical, no experience working as a consultant for any microbiology companies.

And have held the position for, uh, a year now, uh, but uh.

Understood, Professor Ramesh. Thank you for the clarification. I have gathered all necessary insights regarding your profile and expertise. Let's finalize this segment.

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

Professor Ramesh, great to connect. Let's start with an exploration of your teaching philosophy. Could you share which foundational bioinformatics concepts you most enjoy introducing to students and how you adapt your approach to ensure their understanding?

Uh, the most foundational, uh, concept that I, uh, enjoy teaching, uh, is, uh, related to, uh, alignments, uh, in, in bioinformatics. So we have global and local alignments.

It it really is a nice foundational concept, and BLAST is a tool that almost every biotechnologist uses at some point in their research career.

Is is really foundational and to sort of gain, you know, students interest over the course I've tried different things, you know, regular traditional PowerPoints. Lab based activity and then like a problem based approach. But the one thing that seems to have worked so far really well is like an in class physical paper on pen activity. So I essentially I've tried giving them like 2 sequences and I've given them.

Like, you know, like try and align this without any external tools. And for somebody who has not been exposed to blast, that's like really challenging. And then when you sort of tell them how to do it, how to deconstruct it and how to sort of work through the steps of plastic, it is really.

Fruitful to see that the the gain that ability to understand how BLAST works and yeah, that's that's the.

You know.

Foundational concept that I enjoy most teaching.

Thank you, Professor Ramesh. Your approach to teaching foundational concepts like BLAST through physical, hands-on activities is indeed intriguing and seems effective for engaging students. Could you provide an example of how you connect a complex concept from your research, such as a specific methodology or finding, to a digestible classroom activity? How do you bridge that gap to ensure students of varying backgrounds can comprehend and apply it?

Most undergrads don't really need it for their thesis work, but it is definitely something that they should be exposed to. But it becomes a necessity for masters and PST level students. And in terms of comparative genomics and transcriptomics, you know, creating various analysis pipelines using snake make.

Sure. Say for example. Snake Make is a workflow management tool. It's fairly advanced.

And Docker container is something that we do on a regular basis, but for for somebody who's getting started, this might seem like a little too intense. So the best approach that I have had is analogies because it's a pipeline development tool. What I try and do is I relate it to.

A game called Super Mario which I used to play growing up so we I create pipes out of.

With colored papers, and then there is an input and then there is an output and that goes through different colored papers which are essentially different tools and you get the final end product. So this in a way sort of helps them visualize. And then when I actually talk to them about the technical specific tools, it helps them to sort of relate a little bit more. And then when they do it in the lab, that helps them. You know the really cement those complex ideas.

Yeah.

Building on that, how do you assess whether students have truly grasped these complex concepts after implementing your teaching approach? Do you rely more on traditional assessments or do you employ more creative evaluation methods?

Uh, off late, I've mostly tried to avoid any traditional methods, which have not really been, uh, super useful with the advent of LLMS and AI. Uh, so I've had the opportunity and the liberty to sort of, uh, come up with my own or, uh, you know, uh, uh, teaching and evaluating methods.

Our

And based on the positive feedback, I've also been made, uh, the chair for, uh, pedagogy, uh, committee, uh, internally within the department. So, uh, in terms of modern or unorthodox evaluation approaches, like I said, giving them a problem and then, you know, giving them access to any resources that they want and the kind of solutions that they can implement within the given frame of time.

Is is what is evaluated and we look at how that would scale for a lot of data sets in terms of comparative genomics. Is, is that is, is, is how I try and sort of evaluate rather than restricting them to. Any sort of, you know, environment or, you know, doing a paper and pen like, you know, just a regular exam, MCQ exam. So yeah, some of the newer techniques.

Have worked slightly better and students seems seem to be more engaged.

Your focus on modern, adaptable evaluation methods—such as problem-based tasks with open access to resources—reflects a progressive approach to education, Professor Ramesh. It not only tests problem-solving skills but encourages creativity in students' responses, especially for technical areas like comparative genomics. As the chair of the pedagogy committee, how do you ensure that your evaluation techniques remain equitable and scalable across a diverse student body with varying levels of expertise?

Yeah, so being adaptable also means not being really rigid with with the schema of evaluation and then trying to adapt it to the the problem and that the student is working on trying to.

Adapt to the background of the student is is also critical. And that's where the years of the years of training and experience as as a professor and as a scholar comes into clip. And this is something that I sort of slowly train my teaching assistants on, on how to sort of, you know, draw the line between.

This is fair and this is unfair and it's not easy. It's it's really not easy.

But uh, I've I've tried to be as fair as possible.

It is evident that you approach your teaching and evaluations with a significant level of thoughtfulness and adaptability, Professor Ramesh. Ensuring fairness while considering the varied backgrounds of students is indeed a challenging balance, but it speaks highly of your commitment to inclusive and equitable education. Thank you for sharing this insight into your pedagogical philosophy.

Professor, based on the role and responsibilities expected for this position, can you outline your three-year research roadmap? Specifically, how do you plan to ensure that your work not only advances bioinformatics but also contributes to building a long-term asset for institutional growth?

Sure. My current research goals are faced and aligned. It's it's faced because the short term goal is to apply for ICMR extramural grants and DBT emerging frontiers which are cyclical and the broad areas that I would be working on are the evolution of X chromosome dosage compensation and its impact on. Various non communicable diseases like autoimmune disorders and.

Uh, cancel, uh, UH-6 biased cancer specifically, umm, and uh, uh, I would also be eligible for uh, uh, ANRFSPMECRG, our prime minister's early career research grant, which supports researchers in, in the first two years at a new place. So these, these three.

Uh, grants, uh, because they are cyclical, uh, would be the short term goal to sort of establish, uh, the lab and to train students in comparative genomics and transcriptomics. Uh, and, and, uh, in, in terms of, uh, having a sustainable research plan that aligns with the institute. There are other, you know. Area specific grants like Biogrid which is due in in a week right now.