Welcome Alperen

Transfer Learning for Engineering Biology

Tell us a bit about yourself…

I’m a computer scientist who loves using machine learning and AI to solve complex biological problems. My journey began with a master’s in computer engineering, where I discovered my passion for computational biology by working on enzyme function prediction. During my PhD, I focused on developing AI-driven methods for predicting drug-target interactions and molecular properties. I’ve always enjoyed collaborating in interdisciplinary teams and presenting my research at international conferences, and I’m excited to apply my skills to solving real-world challenges.

How did you end up working on the CYBER project?

I’ve always been interested in combining AI with biology, and the CYBER project felt like a perfect fit for that. While working at EMBL’s European Bioinformatics Institute, I developed machine learning models for drug discovery, which led me to explore their applications in synthetic biology, particularly in designing and optimizing biological systems. When I heard about the CYBER team’s mission to reprogram cyanobacteria for environmental solutions, I knew it was something I wanted to be part of. Joining the team at the University of Edinburgh lets me bring my experience in computational biology to an exciting project with real-world potential.

What are you most excited about in your role?

I’m really excited about the opportunity to work with such a diverse team of scientists and engineers. There’s something incredibly motivating about being part of a project that blends biology and AI to tackle big environmental challenges. I’m looking forward to using machine learning techniques to optimize biological systems in new ways. It’s exciting to think about the potential breakthroughs we could make, especially in area like sustainability.

What do you think is going to be the toughest challenge for CYBER?

I think one of the hardest parts will be integrating biology with computational models, especially since biological systems can be unpredictable and messy. Translating the complexity of these systems into something that machine learning models can effectively handle is no easy task. And, of course, taking our ideas from the lab to real-world applications will be a big challenge. But that’s also part of what makes the project so interesting—it’s about finding ways to innovate while still staying grounded in practical solutions.

Where do you see yourself heading to in the future?

In the future, I hope to continue working at the intersection of AI and biology, perhaps leading my own research group one day. I’m really passionate about how AI can revolutionize fields like drug discovery and synthetic biology. Whether in academia or industry, I want to keep pushing the boundaries of what’s possible with computational biology, solving real-world problems along the way.