Interpreting Genomic Data Through Computation

By Maeve Smillie

Junior Faculty Spotlight: Pawel Przytycki

As a 2025 Junior Faculty Fellow at the Hariri Institute for Computing, Pawel Przytycki, PhD, applies computational thinking to some of biology’s most complex questions. An Assistant Professor in the Faculty of Computing & Data Sciences and core member of the Bioinformatics Program, Przytycki develops interpretable computational models to make sense of massive genomic datasets. His work focuses on how subtle changes in gene regulation within the noncoding genome drive differences in development and disease, transforming vast amounts of raw data into actionable biological insight.

In this Q&A, Przytycki discusses his research and how the fellowship fuels his work.

Can you describe your research focus and its applications?
My work aims to apply my formal training in computer science toward analyzing and understanding large genomic datasets with a particular interest in the role of the noncoding genome in development and disease. I develop interpretable models rooted in a deep understanding of networks, graph theory, and statistics. A guiding principle of my research is that simple models with intuitive methods for combining data often lead to the most biologically meaningful results.

How did you become interested in this? Was there something that inspired this area of interest?
We are living in an age of large-scale genomics data. In 2000, the first human genome was sequenced, and since then incredible breakthroughs in high-throughput genetic sequencing technology have made it possible to sequence huge numbers of genomes. While these data are incredibly exciting, extracting biological meaning from the sheer quantity of heterogeneous data being generated requires cutting-edge computational methods. For people with a background in computer science, this looks like a problem in need of algorithmic solutions!

What are the main goals or objectives of your research?
It is becoming increasingly clear that complex genetic diseases manifest differently in each person, tissue, and even cell, and that many of these differences are driven by subtle changes in gene regulation. Vast amounts of multi-modal data are therefore needed to understand how these diseases function and progress. As the scale and complexity of data available continues to grow, cutting-edge methods at the intersection of computer science and genomics are necessary for extracting biological meaning and making actionable predictions. The long term outcome of this research is improving our ability to understand the progression of diseases allowing for earlier interventions.

Has there been a recent development or finding that you find particularly exciting?
The most significant and exciting development in data-driven genomics over the last decade is that the cost of sequencing is actually decreasing faster than Moore’s law, meaning the abundance of data is outpacing the improvement in computational power to analyze it.

What do you feel is most rewarding about your work, either as a professor or researcher?
Mentoring students is the most rewarding aspect of both being a professor and researcher. There is no better feeling than working through a major roadblock with a student and then seeing the magic of what happens when they take things from there.

How do you plan on using this fellowship opportunity?
I look forward to being a part of the Hariri community and exploring how data-driven methods intersect and differ across different computational fields.

Learn more about Przytycki’s work here.