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Yash Chainani

Ph.D. candidate | computational biologist | cheminformatician | machine learning scientist
Northwestern University & Joint BioEnergy Institute | previously ML intern at Tatta Bio



About Me

Hi! I am a final-year PhD candidate working at the intersection of machine learning, computational biology, and cheminformatics.

My research broadbly focuses on making ML models more trustworthy and robust in the face of unreliable data - using synthetic data, data augmentation, multimodal contrastive learning and reinforcement learning. Unlike natural language, where large, high-quality datasets are abundant, biology and chemistry often suffer from data scarcity and noise, which makes developing methods that perform well in low-data and/ or imperfect-data regimes especially critical. Most recently, I’ve become particularly interested in leveraging these techniques to build large reasoning models for biology and chemistry. You can find all of my existing publications and select talks on my google scholar page.

Prior to starting my PhD, I completed my BS in Chemical and Biomolecular Engineering at UC Berkeley’s College of Chemistry. While at Berkeley, I took several courses in data science and machine learning, which catalyzed my jump to working on problems in AI4Science when I started graduate school at the department of Chemical and Biological Engineering at Northwestern University. At Northwestern, I am fortunate to be co-advised by two brilliant scientists, Linda Broadbelt and Keith Tyo. I am also a member of the Center for Synthetic Biology at Northwestern and of the new pathway development group at the Joint BioEnergy Institute at Lawrence Berkeley National Laboratory.

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