Good day, Shivakumar. 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?

Yes, Arjun. So thank you for introducing yourself.

I'm Doctor Shiva Kumar, currently working as a post doctoral research Fellow at Bohan University of Science and Technology, South Korea.

So I completed my PhD in Computer aided drug discovery.

Uh from National Institute of Technology, Karnataka, India in in the year 2024 and I may interdisciplinary researcher.

At the interface of chemistry, especially medicine, chemistry a cancer, bioinformatics and.

Computational data analysis and computer-aided drug discovery.

So if I talked about my research achievements so far, I have published.

20 research articles and two book chapters in the like.

Q1 and Q2 international journals.

And if I talk about this teaching and research experience, I.

One year of teaching experience at the post doctorate level and I have one year of research experience as a Post Doctor Research Fellow at Postech South Korea.

And along with my research accomplishment, so I have so far have conducted 50 plus online and offline workshops and I have gave a lot of invited talks through those.

Workshops I have mentored 5000 plus students and researchers so far and along with that.

I have.

Prepared many questions, paper, evaluated their thesis answer script, grading system, all those kind of things. Apart from that, if I talk about my primary research area, so I am uh, mainly interested in cancer bioinformatics with emphasis on like cancer genomics, transcriptomics and integrated omic data analysis.

To find out the biomarker and driver mutations.

Especially in case of cancer drug discovery.

Yes. So during my PhD work, actually I have worked in the area of like medical chemistry and computer aided drug discovery. But along with my actual work, I did a lot of collaborative works and in that I have handled 1 project which is mainly related to the cancer bioinformatic work. So there what we have did, we have collected the clinical data from a few hospitals by maintaining the ethical rules and we have performed various cancer genomics and.

Thank you for the detailed overview, Professor Kumar. Based on your academic background and research accomplishments, let's delve deeper into relevant areas of expertise. First, describe a specific instance in your research on cancer bioinformatics where integrating omics data provided unexpected insights or results. How did you handle the multidisciplinary challenges involved?

Speak a transcriptomics analysis. So mainly instead of performing individual genomics and like transcriptomics, we focused on the holistic approach especially which is a focus on like integrated omics data analysis. So we have taken collected the sample data of like around 100 to 200 patients from the up.

Well established clinic and we have evaluated the data to find out the to study exactly to analyse how exactly the cancer is initiating, how the propagation is taking place, how the angiogenesis is taking place, how the metasys is taking place. We have studied complete mechanism. So during that project so by using the multi ohmic data and based on that we have design. So once we have understood what is the exact mechanism so we have designed some drug molecules some.

Molecules so which can act as a anti cancer and then we have performed some computational studies, in silico studies like molecular docking, MD simulation and.

DfT calculation, all those kind of things.

It sounds like a substantial interdisciplinary effort integrating clinical data, computational approaches, and biomolecular studies. Your focus on mechanism-driven drug discovery using multi-omics is impressive. From that project, can you elaborate on how you ensured the reliability of the omics data you analyzed, especially given the complexity of cancer genomics and transcriptomics?

Pardon. Can you repeat your question?

Of course, Professor Kumar. Could you elaborate on how you ensured the reliability of the omics data you analyzed, particularly given the inherent complexity and variability of cancer-related genomics and transcriptomics?

Yes, yes. So in our research, what we have, did we have primary analyzed these?

High throughput cancer genomics and transcriptomics data. So which are like inherently complex due to the heterogeneity and the scale.

So, which includes like some RNA sequence data, bulk RNA sequence data to study their differential gene expression between the tumor and the.

Normal samples. So that is what we have did. So we have followed 2 approach. 1 is like bulk RNA sequence data, another one is a single RNA sequence data. So from that we have analyzed how exactly the mutation is taking place. So what are the nuclear types exactly which part of the the Zine is undergoing the mutation?

So we have did that one. So whether we have focused on the whether it is like genetic mutation or whether it is a epigenetic mutation. So once we got the information from the genetic whether it is genetic or epigenetic mutation, then we have dive deeper into that. So depending on that we have studied what kind of proteins.

But let's consider in our project we found that. So it is kind of a epigenetic type of mutation. So epigenetic means either it may be due to the.

Uh, DNA methylation or it is due to the histone modification, right? So then we have analyzed it.

Modification then we found that whatever the data we have taken in that data, it is uh, whatever the changes in the uh, gene sequence, this is arising due to the uh, chemical changes to the histone proteins. So we have find out that according to that, we have find out what is the mechanism based on that we have design some ligand molecules.

Which chemical involvement processes involved in that, whether it is due to the DNA methylation or whether it is due to the histone?

Your approach demonstrates a thorough methodology, leveraging multiple layers of omics data to pinpoint both genetic and epigenetic mutations. The depth of your analysis, particularly unraveling epigenetic mechanisms like histone modifications, reflects strong research precision. Let's now pivot slightly. Given your experience in teaching and conducting numerous workshops, how do you structure a complex topic like cancer bioinformatics in a way that undergraduate or early-stage graduate students can grasp effectively?

Well, so regarding this teaching and to maintain a proper curriculum and to follow a proper approach, especially at the undergraduate and the postgraduate level. So first I will ground the student in the biological constant like what cancer is at the molecular level and why genomics and transcriptomics matter.

Next I will introduce the core data type like DNA, RNA and epigenetic marks using the visual models rather than the algorithms.

And only after this foundation.

I will move that computation to various computational tools starting with like high high level workflow instead of the coding directly.

So for workshops like, I will emphasize on the hands on learning with like clear, uh, cancer datasets, I will demonstrate them by using the various softwares and tools. Then how to handle those data instead of just giving the theoretical lecture, I will give them hands on training. So how to use that data for the way in the research like that, I will follow that approach. Instead of just giving the theoretical content, I will follow 50 + 50. So 50 like tutorial 50% and 50% I will follow the hands on approach. So.

Wish whatever the topic it may be either it may be the undergraduate level, postgraduate level. In the beginning I will consider that they are very beginner to the concept. Then I will start from the very fundamentals. First I will cover the fundamentals. Once they are good in the fundamentals then I will go for the application oriented concepts.

Your structured approach, starting with foundational concepts and progressing to application-driven hands-on learning, is pedagogically sound. It balances theoretical clarity with practical engagement, especially critical for a complex field like cancer bioinformatics. Since you've mentioned mentoring over 5,000 students and conducting workshops, could you share how you evaluate the effectiveness of your teaching methods? Specifically, how do you gauge whether participants or students have successfully learned and internalized the material?

Well, so as I mentioned earlier, so far have conducted like 50 plus workshops, some workshops in online mode and few workshops in offline mode and few through some invited lectures and usually for each workshop I will get around 70 to 100 participants.

And what I will do, I will, whatever the let's consider, I'm going to teach them molecular docking or it may be the MD simulation, or either it may be the DfT calculation, or it may be the RNA sequence data analysis, or it may be the single RNA sequence data analysis, whatever it may be. So usually I will divide the workshop in such a way that my first lecture will be completely the basic and theoretical part. And the second part will be the download and installation of the software and the basic features of the software. And the next part, I will go to that step by step.

So usually whatever the workshop I will conduct it is like in a hands on training mode.

Performing that actual task and once the workshop is done, after each session, I will conduct the quiz. So I will conduct a quiz consisting of the 10 multiple choice question and multiple select questions. Based on that, I will test their theoretical knowledge whether they are understanding it or not. That is the one thing I will use to evaluate the whether the students are understanding it or not. And the second way is in between my lecture, in between my hands and tutorial. So I will ask few.

Questions. So I will test them whether they are following me or not, whether they're understanding it me or not. I will ask those things some question. And after that at the end of workshop, I will conduct the one full, full length twist.

To test their knowledge after that after each hands on training, I will give the uh, exercise for them. So we give the exercise for them. So they need to perform and they have to submit that project further before they joining the next session. So that is another way of testing whether they are understanding it or if, if they are getting any kind of technicality issue or subject issues. So we are going to clarify in the next session. And the next thing is. Many of the participants, they have published their papers based on our.

Projects. So what on which topic? We have conducted the workshop, they have utilized that concept in their research and they have published the research papers, review articles and book chapters on those concepts. So that is how I I will evaluate the students.

Your multi-layered evaluation process, combining theoretical quizzes, real-time questioning, practical assignments, and subsequent follow-up discussions, demonstrates a thoughtful and rigorous approach to teaching. The added measure of tracking how students utilize the learned concepts in their own research and publications further highlights the effectiveness of your mentoring. Moving forward, your focus on hands-on engagement is clear. Could you share how you strike the right balance between guiding students and allowing them autonomy during complex exercises like RNA-sequence data analysis or molecular docking experiments?

See, whenever we give the hands-on training, either it may be like orange sequence data analysis or it may be the molecular docking, whatever it is okay. So I will maintain the balance through like independent by using like independent model so that evolve as a.

Yes.

A student gained confidence, so initially I provide the structure guidance like clear workflow checkpoints, the expected outcomes.

So that the student will understand the logic of the analysis.

Without being overwhelmed by the some technical details. So during early stages of RNA sequence analysis or like molecular docking. So I demonstrate them the critical steps such as like the quality control.

So the parameter selection, how to select the various parameters, then how to interpret the results. So we'll go step by step.

OK, so as the exercise progresses I deliberately reduce the intervention so student will make the independent decision on the tool choice like parameter turning and the biological interpretation.

So while I act as if facilitator rather than the director.

OK, so this approach ensures the student will develop problem solving skills, analytical independence and the confidence. Apart from that, if I want to explain by step by step, let's consider I'm going to demonstrate them how to perform the molecular docking.

Then what I will do first I will give a basic lecture on the molecular docking. Then I will demonstrate them how to download and install the software. Then we'll during the molecular docking, we follow the each steps step by step. So what are the there are multiple steps which are involved in the molecular docking. The first step is the selection of the protein and the ligand molecule. So once your ligand molecule is ready, once your protein is ready, now we have to prepare our protein molecule and the ligand molecule. So to prepare the protein molecules again we have to follow various steps, right? So we have to check that.

Whether the protein contain any missing residues or missing atoms with the protein contain any missing residues or atom, we need to prepare that protein properly by using the model. There are various models are there. So by using anyone model either I can use a homology modeling or like alpha whole modeling anything I can prepare the protein structure. So through that I need to add those missing residue. So after that what I need to do I need to check that so whether the charge is distributed properly or not. So if it is not distributed properly I have to add the.

Properly after that I have to I have to check that whether all hydrogens are added or not. So usually we will do this like polar hydrogen. So because during molecular docking only the polar hydrogens are involved in the docking process. So that's why by following those methods I will demonstrate them how to prepare the protein. Once the preparation of protein is done, now I will demonstrate them how to prepare the ligand molecule.

OK. So by using the various software because there are multiple tools are there to prepare the protein and ligands. Let's consider I will use the MGL tools or product phenom.

Once the protein and ligand is prepared, then what is the next step? So I will, uh, prepare the grid box. So the purpose of creating the grid box is to, uh, specify the, the active sites of my protein. So by, uh, designing the grid box, I'm telling to the software that so this is the active sites of my protein. So try to bind my ligand molecule exactly at that position. So I am giving the instruction to the software through the creating the grid box. So once the grid box is created, then I will perform the molecular docking. So once the molecular docking.

Is done, I'm going to perform the analysis to how the interactions are taking place, what is the binding energy I'm getting. So like that similarly same procedure I will follow for either the RNA sequence data or either it is genomics or transcriptomics. So we'll go a step by step.

Thank you, Professor Kumar. Your methodical, step-by-step teaching model—starting from foundational understanding to progressively hands-on autonomy—demonstrates your commitment to student learning. Your structured breakdown of processes like molecular docking and how you integrate pedagogical scaffolding with active facilitation is commendable. By guiding students through practical aspects like protein preparation, grid box creation, and interaction analysis while allowing autonomy over time fosters both technical skill and critical thinking. Next, could you discuss one of your most impactful research publications related to cancer bioinformatics? What challenges did you face, and how did you overcome them?

Yes, perfect.

So, so far have like published 4 research articles based on especially, especially based on the cancer drug discovery. OK, so one of my most research contribution involves the applying the computational and bioinformatic approaches to identify the potential therapeutic target under drug candidate in the cancer-related pathways. So I especially using the so integrated. Molecular docking, AD meeting analysis and.

Omics driven analysis. So the primary challenge I was faced was the the biological data.

So which integrating the the diverse data sets such as UH gene expression profiles, mutation information and the protein ligand interaction data while maintaining the biological relevance?

OK. And the heterogeneous city in the data?

The another major challenge I was faced was the translating the computational prediction into the meaningful biological insights rather than producing the isolated in silico research.

So I addressed these challenges by adapting A systematic multi level validation strategy. Pass validating findings across independent datasets by applying stringent statistical thresholds.

And conceptualizing results through pathways and functional analysis. So this ensures the robotness, reproducibility and the translational relevance of the study.

Your approach highlights a critical balance between computational prediction and biological relevance in cancer drug discovery. The systematic multi-level validation, stringent statistical thresholds, and pathway functional analysis reflect a rigorous methodology for achieving reliable, impactful results. Given your extensive experience in both research and mentorship, how do you ensure your approach adapts when mentoring students at different levels of expertise—ranging from beginners to advanced researchers?

Yes, that. So regarding your questions like to how to maintain the continuity between the beginners and the advanced level?

Show I adapt by mentoring the approach by using a uh.

Individualized mentoring framework.

So that aligns expectations with the student experience and the research maturity.

Let's consider I will talk into three different ways as a beginners intermediate level and the advanced researchers for beginners.

I focus on building strong fundamentals like basic biology, computational concepts, a structured workflows while providing the close guidance and frequency.

Frequent feedbacks so the emphasis is on the clarity, confidence and the skill acquisition. For intermediate student, I shift towards the problem based mentoring, encouraging them to troubleshoot, interpret results independently and critically read literature while I provide the statistic direction rather than the step-by-step instruction.

In case of advanced researchers, my role become that as a scientific collaborator.

I challenge assumptions, refine hypothesis and support.

A grant writing and publication strategies ensuring the the develop intellectual independence and leadership as a researchers. So this is my approach in case of beginner level, intermediate level and the advanced level research.

Your approach of tailoring mentorship based on the research maturity of students is exemplary. By focusing on foundational clarity for beginners, problem-based learning for intermediates, and intellectual independence for advanced researchers, you are fostering growth at every level. Let's now discuss another aspect. In terms of student evaluation and exam duties, could you detail how you balance fairness, rigor, and constructive feedback when grading assessments or projects?

Well, during the evaluation it is very.

Necessary to maintain the uh.

Unity in all the candidate right? So the fairness matters a lot. So I balance this fairness, the regardless and the constructive feedback by using a transparency.

Uh, then criterion based, uh evaluation. So combined with the meaningful feedback loops.

So first I define the clear.

Rubrics that specify the expectations for the conceptual understanding.

Are like methodological correctness, data interpretation and scientific communication. This ensures consistency and fairness across the student.

Secondly, I maintain academic rigor by assessing.

Not just the final results, but the reasoning process, reproducibility and the awareness of limitations particularly impudent in the bioinformatic projects.

So thirdly, I will provide constructive.

Actionable feedbacks, highlighting the strength and offering the specific guidance for their improvement, so whenever it is possible.

I hello revisions and follow-up discussions to assess men become a learning tool rather than just a purely A judgmental exercise. So these are the approaches I will follow to balance the fairness regardless and constructive feedback.

Your emphasis on transparency, criterion-based evaluation, and actionable feedback ensures a fair and rigorous assessment process. Treating evaluations as an opportunity for learning—rather than just a judgment—demonstrates your commitment to student development and academic integrity. As your expertise spans both research and teaching, how do you integrate your research findings into your classroom teaching to enrich the curriculum, particularly in the context of cancer bioinformatics?

Well, uh, very good question. So that is very, very important. So instead of focusing so slowly on the research or, or independently on the teaching. So it is very important to focus on both research and teaching and to integrate them. So that is what we need in today's, uh.

Era OK, so to integrate my research into a teaching by closing a gap between the theory and real world application. I regularly incorporate the real datasets and the case studies and the workflow derived from my own research.

Into a lectures practical sessions. For example, let's consider I use anonymized cancer genomics or docking datasets to demonstrate how the hypothesis are.

Formed, tested and validated in a active research.

I also discussed the research challenges like negative research and the limitations which helps students to understand the iterative nature of the science. So this approach not only enriches the curriculum but also expenses students to current research.

And practice and encourage them for the critical thinking approach and motivate them to view the classroom learning as a directly relevant to the cutting edge cancer bioinformatic research. So just instead of teaching them as a the content which is mentioned in the textbook during the lecture itself, I will take the data which I got from the my research. Then I will give them the.

Practical learning experience. That is how I am going to integrate my.

Research with the teaching.

Your approach to integrating research into teaching through real-world data, case studies, and addressing research challenges fosters a deep connection between theory and practice. This not only enhances student engagement but also inspires them to think critically and pursue innovative research. Finally, to address another important area, could you highlight a specific instance where you guided a student or mentored a researcher through a particularly challenging project or concept? How did you support them in overcoming those challenges?

Yes, right. So during my PhD work on post-opolar studies work, I have mentored more than 10 students especially for their master thesis. And during my post doc I have mentored some PhD scholars. So those who are who are working in the area of like bioinformatics and the drug discovery. So during my PhD work.

A few students those who are doing their master thesis research work, they face problem during the synthesis part.

So along with the computational knowledge, I have expertise in the synthesis also. They are facing problem to design the molecule to design this scheme, scheme to prepare that molecule and the proper procedure. So then I helped them to design the proper molecule based on their computational knowledge to by performing some in silico studies and bioinformatics studies based on that knowledge. In silico studies knowledge how they can prepare the molecule. Then once they have prepared the molecule then how to design the proper scheme to synthesize that molecule once the scheme is ready.

To synthesize the molecules. So these things have taught them during their master thesis that is the one incident and the second instant is during my post doctoral studies, one of the student is working in the area of like drug discovery especially they have focused on the tuberculosis drug discovery and he is facing the problem to perform the MD simulation. He done the molecular docking. But once he done the molecule docking, once he got the complex, he was facing problem to perform the MD simulation. Because during the MD simulation what happens?

Uh, then what kind of procedure they need to follow?

He, he was getting some error. It is telling that some residues are missing, some items are missing. So he was not able to add those missing items and residues. So I, I guided them how to add those missing resolution missing items. So what are the things we need to keep in mind during while performing the MD simulation, otherwise what kind of issues we'll face? So did that one also. So along with that. One more instance during my master student so one student is working on like.

So in the year like 21, so they have started early for the anti cancer drug, sorry, anti COVID drug discovery. And during that time also they're facing a lot of problem to find the suitable target. So because it is very important by whenever you're going to design any drug molecule, so it first, the first thing we need to know is we have to find out the the mechanism behind that disease. So once you have find out the mechanism, we have to know that what kind of protein, which protein is exactly involved in that mechanism. So finding the proper protein, the target.

Anti COVID drug discovery.

The receptor is very very important and but they are not able to find it out the.

A proper target. So I guided them. What are the things we need to consider in mind while deciding a target for any kind of disease? So these are the instances.

I can mention.

Thank you for sharing, Professor Kumar. These instances reflect your versatility as a mentor—offering guidance across computational techniques, synthesis challenges, and target identification for various diseases. Your hands-on and systematic support not only helped students overcome technical barriers but also enriched their understanding of critical processes in drug discovery and bioinformatics. This concludes the core of my assessment. Is there anything additional you'd like to highlight about your qualifications or approach before we wrap up?

Time is up! Ending the interview now.