Computational Modelling plays a crtical role in understanding disease dynamics and how they spread within and across populations in order to come up with intervention strategies.
Kiran Kumari, our next pathbreaker, Postdoctoral Fellow at the Max Planck Institute for Infection Biology (Germany), develops mathematical models to study the transmission of infectious diseases such as COVID-19 and influenza.
Kiran talks to Shyam Krishnamurthy from The Interview Portal about one of her most memorable and rewarding projects during her PhD, when she developed a computational model to study chromatin organization, which could provide insights into diseases like cancer.
For students, your career may not follow a straight path, and that’s okay. Sometimes, the skills and experiences you gain from one field can be applied to others in unexpected ways.
Kiran, Your background?
I grew up in Ranchi, Jharkhand, and completed my schooling there. My father had a government job, and my mother was a school teacher. Because of this, education was always encouraged in my home. Since I didn’t come from a business background, I knew early on that I had to build a career for myself. However, at that time, I had no idea that I would go on to earn a PhD. My only focus was to study hard, get good marks, and secure a good job.
Coming from a smaller town, extracurricular activities and public speaking were not emphasized in school. After completing my schooling, I joined BIT Sindri in Jharkhand for my BTech, and later pursued my MTech from ISM Dhanbad. This meant that for most of my early education, I stayed within Jharkhand.
During my master’s program, I realized that I had a growing interest in research. I found myself enjoying even the small tasks assigned by my supervisor and was fully focused on my work. This passion led me to pursue a PhD at IIT Bombay. Moving to Mumbai was a turning point, as it gradually helped me understand and shape my career path. Initially, I had no clear idea that this was what I wanted to do, but with each step, my interests and goals became clearer.
What did you do for graduation/post graduation?
For my graduation, I pursued a BTech in Chemical Engineering from BIT Sindri, Jharkhand. After that, I completed my MTech in Chemical Engineering from ISM Dhanbad (now IIT Dhanbad). During my postgraduate studies, I developed an interest in research, which eventually led me to pursue a PhD in Computational Biology from the IITB-Monash Research Academy, a joint program between IIT Bombay, India, and Monash University, Australia.
What were some of the key influences that led you to such an offbeat, unconventional, and unique career in Computational Biology?
My career path was shaped by a combination of curiosity, influential mentors, and key turning points in my academic journey.
Key Influencers & Mentors
During my master’s program at ISM Dhanbad, I developed a strong interest in research. My supervisor played a crucial role in encouraging me to think critically and approach problems systematically.
During my master’s, I had the opportunity to conduct research at CSIR-NML, Jamshedpur, which had advanced equipment, making it an ideal choice for my work. Interestingly, while attending seminars at CSIR-NML, I had the chance to listen to lectures by visiting professors from Monash University. Through interactions with them, I learned about the joint PhD program between IIT Bombay and Monash University. Their suggestion to explore this program ultimately shaped my next career step, leading me to pursue my PhD there.
Later, during my PhD at IIT Bombay, I was fortunate to work under Prof. Ranjith Padinhateeri and Prof. Ravi Jagadeeshan, who provided invaluable guidance and helped shape my research skills. Their mentorship inspired me to pursue a career in computational biology.
Can you explain how you made the transition from Chemical Engineering to Computational Biology?
I have always been fascinated by biological processes and wanted to explore them further. With a master’s degree in Chemical Engineering, I saw an opportunity to apply my expertise in simulations and polymer modeling to biological systems. While searching for a suitable project, I was fortunate to find one at the IITB-Monash Research Academy, which offered the perfect blend of chemical engineering from Monash University and biosciences from IIT Bombay. This interdisciplinary approach aligned perfectly with my interests and research goals.
During my PhD, I worked on modeling chromatin organization (Chromatin is a complex of DNA and proteins that forms chromosomes in the nucleus of a cell), which introduced me to the intersection of biology, mathematics, and computer science. This interdisciplinary approach fascinated me and solidified my interest in theoretical and computational research.
Another defining moment was my postdoctoral research at the University of Texas at Austin, where I explored chromatin organization in cancer cells. This experience broadened my understanding of biological systems and motivated me to apply computational techniques to study infectious diseases, leading me to my current role at the Max Planck Institute for Infection Biology.
Throughout my journey, each step helped refine my interests, and the exposure to different fields and mentors played a crucial role in shaping my career in computational biology and epidemiology.
How did you plan the steps to get into the career you wanted? Or how did you make a transition to a new career? Tell us about your career path
My career path evolved gradually, shaped by curiosity and a willingness to explore new opportunities rather than a rigid plan. While I began in chemical engineering, my growing interest in research led me toward computational biology, and later into infectious disease modeling. Initially, I focused on learning and excelling in my studies without a specific career goal in mind. However, during my master’s program at ISM Dhanbad, I developed an interest in problem-solving and data analysis through small research projects, which motivated me to pursue a PhD.
I earned my PhD in Computational Biology from IITB-Monash Research Academy, a joint program between IIT Bombay and Monash University. My research focused on chromatin organization, where I developed computational models to study DNA structures inside cells. This experience introduced me to interdisciplinary research and gave me the skills to work with mathematical models and simulations. After completing my PhD, I moved to the University of Texas at Austin for a postdoctoral fellowship, where I applied computational biology to study chromatin organization in cancer cells. This transition allowed me to expand my expertise and gain a deeper understanding of the biological applications of computational methods.
After working at UT Austin for a couple of years, I transitioned into infectious disease modeling and moved to Germany to join the Max Planck Institute for Infection Biology. In my current postdoctoral research, I develop mathematical models to study the transmission of infectious diseases such as COVID-19 and influenza. This shift was driven by my interest in applying computational techniques to pressing global health challenges.
Networking and mentorship played a crucial role in my career progression. The professors and mentors I worked with during my PhD and postdoctoral research guided me toward new opportunities and helped me refine my research interests. Attending conferences, workshops, and collaborating with experts in different fields also expanded my understanding of potential career paths.
My journey from chemical engineering to computational biology and later into epidemiology was not a predetermined path, but rather a continuous process of exploration and learning. Each step helped me build new skills, refine my research focus, and discover exciting ways to apply computational methods to real-world problems.
How did you get your first break?
My first break came during my master’s program at ISM Dhanbad when I was introduced to research through small projects assigned by my professors. While I initially pursued chemical engineering with the goal of securing a stable job, these research experiences sparked my curiosity about computational modeling. I found myself deeply engaged in problem-solving and data analysis, which motivated me to explore research opportunities further.
The real turning point came when I was accepted into the IITB-Monash Research Academy for my PhD. This joint PhD program between IIT Bombay and Monash University provided me with exposure to international research, advanced computational techniques, and interdisciplinary collaboration. Under the guidance of experienced mentors, I developed computational models to study chromatin organization, which became a defining moment in my career. This experience not only strengthened my technical skills but also shaped my interest in applying computational biology to complex biological problems.
What were some of the challenges you faced? How did you address them?
Throughout my career, I have faced several challenges, each of which has helped me grow and adapt. One of the biggest challenges was transitioning from chemical engineering to computational biology during my PhD. Since my background was in a different field, I had to invest significant time in learning programming, mathematical modeling, and biological concepts. To overcome this, I took online courses, sought guidance from my advisors, and practiced coding regularly until I became proficient in computational techniques.
Another major challenge was moving abroad for my PhD and postdoctoral research. Adjusting to a new academic culture, managing research independently, and adapting to life in different countries was overwhelming at times. However, I built a support network by connecting with fellow researchers, seeking mentorship, and maintaining a balanced routine to stay focused on my goals.
The third challenge was switching research fields from chromatin organization to infectious disease modeling. Although I had experience in computational biology, epidemiology was a new area for me. To bridge the gap, I collaborated with experts, read extensively, and gradually gained the necessary skills to work in this field. This transition reinforced my ability to adapt and apply computational techniques to different biological problems.
Each challenge has taught me resilience, adaptability, and the importance of continuous learning, ultimately shaping my career in a meaningful way.
Where do you work now? What problems do you solve?
I am currently a Postdoctoral Fellow at the Max Planck Institute for Infection Biology in Germany, where I develop mathematical models to study the transmission of infectious diseases such as COVID-19 and influenza. My research helps in understanding how diseases spread in different populations and how interventions, such as vaccination or quarantine, can affect their transmission.
What skills are needed for your role? How did you acquire the skills?
To do this work, I rely on a combination of computational modeling, statistical analysis, and epidemiological knowledge. I acquired these skills gradually over the years—during my PhD, I learned programming, mathematical modeling, and data analysis, which were essential for my research on chromatin organization. When I transitioned to infectious disease modeling, I took the initiative to learn epidemiology by reading research papers, taking online courses, and collaborating with experts in the field.
What’s a typical day like?
A typical day in my job involves a mix of coding, data analysis, literature review, and discussions with colleagues. I spend time writing and debugging mathematical models, analyzing data from disease outbreaks, and writing research papers. I also attend meetings, present my findings, and collaborate with researchers from different backgrounds to improve our models.
What is it you love about this job?
What I love most about this job is its real-world impact—the models I develop contribute to a better understanding of disease dynamics and help shape public health policies. I also enjoy the intellectual challenge of solving complex problems, the flexibility of academic research, and the opportunity to collaborate with experts in different fields. The combination of science, computation, and public health makes my work both exciting and meaningful.
How does your work benefit society?
My work benefits society by helping to understand and predict the spread of infectious diseases such as COVID-19 and influenza. By developing mathematical models, I analyze how diseases transmit in different populations and evaluate the effectiveness of interventions like vaccination, quarantine, and public health policies. These models provide data-driven insights that can guide policymakers in making informed decisions to control outbreaks and minimize their impact on communities.
In addition, my research contributes to scientific advancements in epidemiology, improving our understanding of how different factors—such as mutations in viruses, population movement, and immunity levels—affect disease transmission. By collaborating with other researchers and public health experts, my work helps develop better strategies for disease prevention and control, ultimately protecting lives and improving global health.
Tell us an example of a specific memorable work you did that is very close to you!
One of the most memorable and rewarding projects I worked on was during my PhD, when I developed a computational model to study chromatin organization. Chromatin plays a crucial role in gene expression, and understanding its organization can provide insights into diseases like cancer. This project was particularly close to me because it involved both complex mathematical modeling and biological understanding—two areas that I was passionate about but initially unfamiliar with.
The challenge was reconstructing the 3D structure of chromatin from its linear DNA sequence and predicting how changes in its configuration might impact cellular functions. I spent countless hours refining the model, analyzing the data, and interpreting the results. One of the most satisfying moments was when the model successfully replicated real experimental observations, providing new insights into chromatin behavior.
This project had a significant impact on my career because it not only gave me the confidence to tackle complex biological questions using computational methods but also opened doors to my postdoctoral work, where I could apply these skills to different areas like infectious disease modeling. The experience taught me the value of perseverance, creativity, and interdisciplinary collaboration, making it a pivotal moment in my career.
Your advice to students based on your experience?
Based on my experience, my advice to students would be to stay curious, be adaptable, and embrace continuous learning. It’s important to explore different fields and not be afraid to step out of your comfort zone. Your career may not follow a straight path, and that’s okay. Sometimes, the skills and experiences you gain from one field can be applied to others in unexpected ways.
I would also encourage you to seek out mentorship early on. Surround yourself with people who can guide you, challenge you, and provide valuable feedback. Whether it’s professors, industry professionals, or fellow students, learning from others can help you make better decisions and grow in your career.
Additionally, don’t be afraid to make mistakes or face setbacks. Failure is often the best teacher, and perseverance is key. Learn from your mistakes, adapt your approach, and keep moving forward.
Lastly, try to balance your academic pursuits with personal well-being. While hard work and dedication are important, taking care of yourself—both physically and mentally—ensures you can sustain your passion and productivity in the long run.
Future Plans?
My future plans involve continuing to explore and contribute to the field of computational biology and infectious disease modeling. In the immediate term, I aim to further expand my research at the Max Planck Institute for Infection Biology by developing more sophisticated models to better understand the dynamics of infectious diseases, particularly in the context of emerging pathogens and pandemics.
I also plan to deepen my expertise in epidemiology and its intersection with computational techniques, applying these insights to global health challenges. Long-term, I aspire to transition into an academic or research-focused role where I can lead projects, mentor young researchers, and contribute to the development of new computational methods that can aid in tackling public health issues.
Ultimately, my goal is to combine my research experience with a focus on disease prevention and policy-making, contributing to both scientific knowledge and practical solutions to improve public health on a global scale.
Linkedin: https://www.linkedin.com/in/kiran-asha-kumari/Google scholar: https://scholar.google.com/citations?user=xMKmSYEAAAAJ&hl=en