Faculty Spotlight: Can Firtina on the Future of Bioinformatics and Real-Time Genomic Analysis

Can Firtina, an assistant professor in the Department of Computer Science at the University of Maryland, studies how to efficiently analyze and interpret biological data through algorithms, hardware design, and artificial intelligence. His research lies at the intersection of bioinformatics and computer architecture, where he aims to make genomic analysis accessible, portable, and integrated into everyday life.

In this Q&A, Firtina discusses his journey into computer science, the challenges of analyzing biological data, his current research directions, and the collaborative opportunities that drew him to the University of Maryland.

Was there a defining moment that shaped your career path into computer science?

There were two. The first was during my undergraduate research in bioinformatics. I worked with a professor at Bilkent University who gave me a project that required analyzing a large volume of biological data efficiently. That experience helped me understand the importance of balancing memory usage and processing speed, especially when results could affect health outcomes. It sparked my interest in bioinformatics and large-scale data analysis.

The second came at the start of my Ph.D. when my advisor encouraged me to think about memory beyond just storage—considering bandwidth, latency, and technology choices. That changed how I viewed hardware’s role in computation and led me to combine algorithm design with hardware design to accelerate data analysis.

Can you tell me about your research focus and what drew you to this field?

My work centers on analyzing biological data such as DNA, RNA, and proteins. I focus on genomic data analysis because of its importance in understanding diseases and developing treatments.

The biggest challenges are data size and noise. Large datasets require speed, while noisy data demands accuracy. My research explores how to make analysis both efficient and precise. Beyond the technical side, the field’s potential impact on health and discovery keeps me motivated.

What are you currently working on, and what excites you most about it?

I’m building my lab and shaping its long-term goals. The vision is to make genomic data analysis part of daily life through portable and efficient systems. Imagine devices capable of sequencing and analyzing DNA in real time, providing continuous health feedback or identifying plants and bacteria in the environment.

To achieve this, we focus on three areas: creating algorithms that handle noise and speed; co-designing hardware and software to minimize energy and latency; and developing AI systems that adapt as genomic databases grow. Together, these efforts aim to make biological analysis faster, smarter, and more accessible.

What is one challenge you’ve encountered in your research, and how have you approached it?

In bioinformatics, researchers often build new tools from scratch to test ideas, but that can be inefficient. Early in my career, I realized that integrating new ideas into existing tools allows faster progress and broader adoption.

Now, I encourage students to build upon established frameworks instead of starting over. It saves time, promotes collaboration, and helps the community adopt new ideas more readily.

How does your work connect with or contribute to the broader computer science community and society?

One application is real-time biological analysis for healthcare and environmental monitoring. For example, we collaborated with a hospital in Switzerland that wanted to use our system during surgeries to detect bacterial contamination in real time. This could help prevent severe infections like sepsis.

Another example is agriculture. Drones equipped with efficient analysis systems could monitor soil and crops or detect plant diseases. Similar technology could assess bacteria levels in rivers or lakes, offering real-time environmental insights.

What inspired you to join the University of Maryland?

The department’s size and diversity make it ideal for interdisciplinary collaboration. My research intersects bioinformatics, computer architecture, and AI—all areas represented strongly at Maryland.

I was also drawn to the university’s proximity to the National Institutes of Health and other research centers, which provides opportunities for partnerships that connect computing with health and life sciences.

If you could give one piece of advice to students interested in your area of research, what would it be?

Test your ideas early and share them with the community. Progress comes from experimentation and feedback.

More broadly, stay open-minded and receptive to constructive criticism. Even familiar material can reveal new insights as you grow. Approach feedback as an opportunity to learn—it’s essential for developing as a researcher.

—Story by Samuel Malede Zewdu, CS Communications