W.I.S.E. Wednesday Spotlight: Dr. Rengasayee Veeraragavan, Biomedical Engineering Professor at The Ohio State University

Dr. Rengasayee Veeraragavan is a biomedical engineer and microscopist whose research investigates how nanoscale protein organization shapes electrical signaling in the heart. His interdisciplinary journey spans chemical engineering, cardiac physiology, mathematical modeling, and advanced microscopy, reflecting his belief that complex biological problems require diverse tools. He shares the challenges that shaped his path, his passion for solving scientific puzzles, and his advice to embrace curiosity, persistence, and confidence in the face of uncertainty.

Interview Transcript:

Question #1: Can you please introduce yourself and your professional and academic journey as well?

My name is Rengasayee Veeraragavan or Sai, and I usually describe myself as a microscopist, physiologist, and biomedical engineer. Those labels didn’t happen all at once but were collected over time. My academic journey has involved several pivots, and I see these pivots as features, not flaws. 

 Intending to pursue a career in life science research, I began my training with an undergraduate degree in chemical engineering. That decision was intentional. At the time, I noticed that most people working in life sciences were deeply specialized in biology and therefore attacked problems with broadly similar approaches. I thought differently. I figured I could always learn biology later, but engineering would give me a robust way of thinking about messy, complex systems. We are, after all, bags of heat and mass transport. Understanding transport phenomena, thermodynamics, and system-level thinking seemed like a powerful foundation for life science. 

 After that, I pursued a PhD in biomedical engineering, specifically focused on cardiac physiology. During that time, I used high-speed imaging to observe electrical signal propagation in the heart. I also began leveraging circuit theory and electrical engineering principles to build my understanding of cardiac physiology. 

 When we ran into problems that couldn’t be solved with experimental tools alone, I pivoted them again. I did a postdoctoral fellowship in mathematical modeling, working on stochastic and multiscale computational modeling approaches to understand cardiac processes. This experience helped me recognize the critical need for quantitative data on cardiac nanostructure in disease. So, making yet another pivot, I completed another postdoc in a microscopy-heavy lab, where I trained in super-resolution microscopy and quantitative image analysis. 

 From the outside, it may look like I switched fields multiple times. But every shift was driven by one central question: how does one heart cell communicate electrically with its neighbor? That question requires tools from engineering, mathematics, imaging, computation, and physiology. Each pivot was helping me collect the tools necessary to solve that puzzle.

Question #2: What inspired you to pursue a career in STEM and teaching?

I’ve never wanted to do anything other than science. That curiosity predates most of my memories. I’ve always been fascinated by living systems, such as understanding what makes plants grow, what makes animals move, and how molecules interact. But what truly shaped my intellectual direction was my interest in nonlinear dynamics. 

 In high school, I read Chaos by James Gleick, and it profoundly influenced me. I realized that nonlinear dynamics could explain an extraordinary range of phenomena; from how a single molecule responds to a photon, to how coastlines erode, to how faucets drip. The same mathematical principles appeared everywhere. That specifically fascinated me. 

 When I turned toward biology, I asked myself what system would allow me to explore those ideas meaningfully. The brain is fascinating, but it’s difficult to define what it is trying to accomplish at any given moment. The heart, on the other hand, has a clear objective: it is a pump. It must activate its cells in a precise sequence, repeatedly and reliably. 

 I also came across examples where transitions from healthy heart rhythms to arrhythmias, such as ventricular fibrillation, exhibited nonlinear behaviors, period doubling, bifurcations, and chaotic dynamics, exactly the patterns I had been studying mathematically. That intersection of mathematics and physiology felt perfect. 

 At the end of the day, I’m just someone who enjoys solving puzzles. I like being challenged by problems that look solvable but resist easy answers. Science allows me to pursue that instinct at scale. 

Question #3: How would you describe your research to someone unfamiliar with it?

The short version is that I study how biological building blocks assemble into functional machines in the heart. Here’s the longer version. Think of proteins as Lego blocks. If you rearrange the same Lego bricks into a different structure without changing any blocks, you create something entirely different. Biology behaves similarly. My central question asks if we keep the same proteins in the same amounts but rearrange them structurally. What changes? 

 For decades, biology has focused heavily on molecular abundance, such as how much of a protein is present, whether a gene is upregulated or downregulated. And clinical imaging gives us large-scale views of tissues and organs. But neither approach tells us how molecules are organized at the nanoscale. What we’ve learned over time is that many chronic diseases don’t begin with changes in protein quantity. They often begin with rearrangements of what is already there. The same proteins, in the same amounts, are assembled differently. So, my research focuses on detecting, quantifying, and understanding these nanoscale rearrangements. I want to know how structural organizations correlate with function and dysfunction in the heart. Ultimately, if we can understand harmful rearrangements, we may be able to correct them.

Question #4: What technical tools and skills do you use, and how did you develop them?

My skillset evolved intentionally through pivots. I never wanted to be limited to one discipline. Biology is inherently multiscale and Multiphysics, but you cannot solve those problems with expertise in a single domain. Because of this, I started focusing on different avenues.  

 I started programming in fourth grade. Initially, I thought programming would help me create digital comic books. That was the motivation. But once I started coding, I realized how powerful it was for modeling and data analysis. That interest has stayed with me. 

 Chemical engineering gave me tools in transport phenomena and systems thinking. During my PhD, I used high-speed cameras to observe electrical propagation in cardiac tissue. I applied circuit theory concepts to biological systems. When we encountered phenomena that we suspected were nanoscale in origin but couldn’t observe directly, I pivoted to mathematical modeling. I learned stochastic methods and multiscale modeling approaches. 

 Later, I trained in super-resolution microscopy, including single-molecule localization techniques. That introduced computational challenges such as analyzing millions of coordinate points in continuous space. The first laptop I used for analysis overheated and failed, because my inefficient code pushed it so hard. That forced me to deepen my understanding of algorithm optimization and computer science fundamentals. 

 Throughout my career, I’ve maintained a mindset: if the tool I need doesn’t exist, I build it. If I enter a new field, I buy high school and undergraduate textbooks and rebuild my understanding from the basics. That habit has allowed me to transition across disciplines with confidence.

Question #5: Can you share a moment from your research that surprised you or changed your perspective?

One turning point came during my PhD when we discovered a phenomenon we couldn’t explain. The data suggested something important was happening, but our existing tools were insufficient to uncover the mechanism. At first, that was frustrating. But eventually, I realized the problem wasn’t the question but was the lack of appropriate tools. That realization fundamentally changed my perspective. Rather than abandoning the question, I chose to acquire new tools.  

The model I wanted to build at that time was probably fifteen years ahead of the available structural data. So I pivoted into mathematical modeling first, and later into microscopy to collect structural information that didn’t yet exist. That experience taught me that science is often limited not by imagination, but by instrumentation. Sometimes progress requires patience. Sometimes it requires building the tools yourself.

Question #6: What challenges have you faced, and how did you overcome them?

I approach challenges knowing that they are the constant, that they are the rule, not the exceptions. Early in graduate school, I performed a Western blot experiment that worked perfectly the first time. After that, it failed repeatedly for months. I ran it three to six times per week, troubleshooting reagents, equipment, and protocols. It was exhausting. What kept me going was my perspective. To me, it was a puzzle. I don’t quit puzzles, because they’re fun. If my motivation had been external validation or career advancement, I might have walked away. But curiosity and the desire for fun kept me engaged. 

 Later, transitioning into a mathematics department was another challenge. I was experimentally trained and suddenly immersed in theory. I had to relearn how to regulate my workday. I had to laboriously break down for myself concepts others treated as shorthand. That’s when I developed the habit of returning to foundational textbooks whenever I enter a new field. 

 Each challenge forced growth in directions I hadn’t anticipated. And in hindsight, those moments were the most transformative. 

Question #7: What advice would you give to someone who lacks confidence in pursuing STEM?

First, STEM isn’t just about careers. Science literacy improves everyday life. It helps you make better decisions about health, safety, risk, and even routine activities. Knowing mechanics can prevent injury. Understanding chemistry helps you interpret nutrition labels. Science makes life more navigable. Second, you cannot know everything. You can’t drink the entire ocean. So don’t focus on drinking it ALL. Drink as much as your curiosity demands. Let your appetite define your learning. Finally, shame is useless. Saying “I don’t know” is not a weakness, but a superpower. Every time you say it, you’re standing at the edge of learning something new. No one is universally competent at everything. Expertise is contextual. Confidence doesn’t come from knowing everything. It comes from being comfortable not knowing and choosing to learn.