Student Spotlight - Mohammad Nahian Abrar

Mohammad Nahian Abrar

Student, PhD

1. The “Spark”

What initially sparked your interest in this research area, and what problem are you most passionate about solving?

What initially drew me to this area was realizing that statistics could have a direct impact on human health. As I learned more about genetics, epigenetics, and omics data, I became fascinated by how molecular signals can be connected to disease risk and early detection. What especially stayed with me was the idea that prevention is often more powerful than cure. If we can identify reliable genetic or epigenetic biomarkers early enough, we may help predict disease risk, guide interventions, and ultimately improve patient outcomes before conditions become severe.

2. The “Journey”

Can you share a key moment or challenge in your research—an “aha!” discovery or a hurdle you overcame—and how you navigated it?

A key challenge in my PhD was working with rare binary outcomes in high-dimensional omics data. There are good methods for rare-event problems and good methods for high-dimensional data, but I realized that very few approaches handled both challenges well at the same time. That was the moment I thought, “There has to be a better way.”

I spent a long time reading existing methods, testing ideas, running simulations, and watching many models fail before anything became promising. There were plenty of late-night debugging moments where the ideas and code seemed more stubborn to shape up. The most rewarding moment was seeing the methods finally produce stable and meaningful results. It reminded me that research is not usually one big “aha” moment; sometimes it is a long series of small corrections, failed attempts, and persistence until the work begins to make sense.

3. The “Big Picture”

How do you see your research impacting the real world or contributing to your field in the next few years?

My PhD work taught me that behind every complex biomedical data, there may be signals that can change how we understand and prevent disease. In the next few years, I want to apply this experience to major chronic diseases such as cardiovascular disease and cancer.

Long term, I hope to build a biomedical research lab where statisticians, physicians, and public health researchers work together to connect mathematical logic, biological mechanisms with clinical reality and public health need. For me, the big picture is simple: use data not just to explain disease, but to help prevent it earlier.

4. The “Inspiration”

Who has influenced your research path (a teacher, scientist, mentor, or even a fictional character), and what is one thing you learned from them?

The person who has influenced my research journey the most is my doctoral advisor, Dr. Yu Jiang. When I started my Ph.D., I had ambition and curiosity, but I was still figuring out how to navigate the world of research and where to begin. She taught me not just academics, but how to think like a researcher—how to break down a difficult problem, organize ideas, ask better questions, and work through challenges step by step.

What I admire most about her is the balance she maintained between guidance and trust. She was patient when I struggled, gave me the space to grow independently, and consistently encouraged me during important moments—whether it was presenting at conferences, writing papers, or defending my dissertation. One of the biggest lessons I learned from her is that good research is not about knowing all the answers immediately; it is about being patient, being curious and humble, and trusting the process. That mindset has shaped me into a more confident and passionate researcher.

5. The “Personal Touch”

What is one unique skill or non-academic hobby that supports your research or keeps you motivated?

One non-academic passion that keeps me grounded is soccer. I’m very competitive when it comes to playing, and I genuinely enjoy both the teamwork and strategy behind the game. Research can sometimes feel like a long process with unexpected setbacks, and soccer reminds me that progress often comes from persistence, adapting quickly, and learning from mistakes—whether it is a missed goal or a model that refuses to cooperate. It also gives me a healthy way to disconnect and recharge. I like to joke that sometimes a stubborn statistical problem just needs ninety minutes of soccer before the solution decides to show up.