Majid Kazemian named 2026 University Faculty Scholar for research in genomics and molecular immunology

Majid Kazemian, professor in Purdue University’s Departments of Biochemistry and Computer Science, has been named a 2026 University Faculty Scholar in recognition of his research on gene regulation in virus-associated cancers, autoimmune disorders and infectious diseases.

Q&A with Majid Kazemain

How would you define your research background?

I've always been interested in using computation to understand complex systems. My research training began in computer science, whereas an undergraduate I worked on image processing and pattern recognition. During my master's training in robotics and artificial intelligence (AI), I was introduced to biologically inspired computing such as neural networks and genetic algorithms, which sparked my interest in computational biology and, ultimately, changed the direction of my career.

I completed my Ph.D. in Computer Science at the University of Illinois Urbana-Champaign, where I developed computational methods to identify gene enhancers and model their role in gene regulation. While I enjoyed building predictive models, I increasingly wanted to understand the biology behind them. That led me to pursue postdoctoral training at the National Institutes of Health, where I combined large-scale genomic data with molecular immunology to study host-pathogen interactions. During that time, I also gained extensive experimental training, allowing me to validate computational predictions directly at the bench.

What key projects have shaped your research and its direction?

Several projects have shaped the direction of my research. During my Ph.D., I studied how a single cell develops into a complex organism by building computational models of gene regulation in the developing fruit fly embryo. This work showed how networks of transcription factors and gene enhancers establish the body’s developmental blueprint, sparking my interest in the mechanisms that regulate gene expression.

During my postdoctoral training, I shifted to immunology and human disease, where I developed computational approaches to uncover previously uncharacterized transcribed regions of the human genome, including novel cancer-associated transcripts. Through close collaborations with immunologists, I also helped define gene regulatory pathways controlling immune function while gaining the experimental expertise needed to directly test computational predictions at the bench.

Since starting my independent career, my research has focused on understanding how gene regulatory programs are rewired in infectious diseases and cancer. By integrating computational modeling with experimental validation, my laboratory has shown how the cancer-causing Epstein–Barr virus directly reshapes host gene regulation and has uncovered previously unrecognized mechanisms that control inflammatory responses. These include identifying vitamin D as a brake on excessive T-cell inflammation and revealing how uncontrolled complement activation contributes to severe lung inflammation.

In my lab, we are interested in looking at how immune system is related across all infectious diseases. For example, if you have too much of immune activation, you get an autoimmunity; if you have too little, you can get cancer. This balance is determined by these molecular and gene regulatory mechanisms that are important to understand.

What’s the most exciting aspect of your research?

What excites me most is that we are moving from simply describing biological systems to predicting and directing their behavior. For much of my career, we've used computational models to understand how gene regulation works. Today, advances in genomics, machine learning and experimental technologies are making it possible to ask a much more ambitious question: can we predict how a cell will respond to a disruption and then design interventions that move it toward a healthier state?

Equally exciting is that we can test these ideas experimentally. Having a laboratory that combines computational modeling with molecular biology allows us to rapidly move from a computational prediction to a biological mechanism.

What motivates your work?

There's something incredibly rewarding about seeing a pattern emerge from a dataset, testing it experimentally, and realizing you've uncovered a biological mechanism that wasn't known before.

What makes this especially meaningful is that those discoveries can ultimately improve our understanding of diseases like cancer, autoimmunity and infectious diseases. Even though fundamental research is often a long journey, each new insight has the potential to open the door to better diagnostics or therapies.

What’s next for you?

I'm excited about bringing predictive and generative AI into biology in a way that is tightly integrated with experimentation. My goal is to develop models that not only explain how gene regulatory programs change in cancer and infectious diseases but also predict how those programs can be therapeutically reprogrammed. At the same time, we're expanding the experimental side of the laboratory to investigate several new lead regulators of the immune system and understand their potential as therapeutic targets.

One project currently under revisions that we expect to have a significant impact focuses on gene regulation and identifying new regulators that control how genes are expressed. Our research has centered on regions of the genome called enhancers, which promote gene activity. We discovered that some enhancers also generate novel genes from themselves. We identified one such enhancer RNA located next to the well-known cancer gene KRAS and found that it plays a direct role in regulating KRAS expression. To determine whether this enhancer RNA could serve as a therapeutic target, we developed a mouse model lacking the genomic region. These animals showed an increased susceptibility to infection, with similar biology occurs in humans. This ongoing research is extremely exciting, and we are now trying to figure out whether we could use this as another target for not only infections but also cancers associated with KRAS.

In what ways has Purdue’s College of Agriculture contributed to your success?

One of the things I've appreciated most about Purdue University’s College of Agriculture is its commitment to interdisciplinary science. My research brings together computation, molecular biology, immunology and genomics, and the college has embraced that breadth through its strong support of the Purdue One Health initiative.

Recognizing that challenges such as infectious diseases, cancer and emerging pathogens require collaboration across disciplines has created an environment where my research naturally fits and where collaborations are encouraged. That philosophy has been backed by meaningful support.

Programs such as AgSEED and AI Fusion have enabled researchers to pursue new research directions and establish collaborations that would have been difficult through traditional funding mechanisms alone. Being in the biochemistry department has also connected me with outstanding colleagues across plant, animal and human health, reinforcing the idea that advances in one area often translate to discoveries in another.