Our lab focuses on developing computational methods to understand complex biological systems. We combine machine learning, molecular modeling, and experimental validation to tackle challenging problems in biology and medicine.
Research Directions
Machine Learning for Biology
How can we develop new machine learning methods tailored for biological data?
We develop deep learning architectures and algorithms specifically designed for biological sequences, structures, and networks. Our methods handle the unique challenges of biological data including noise, missing values, and complex dependencies.
Key areas:
- Generative models for molecular design
- Interpretable AI for biological insights
Precision Medicine
How can we personalize treatments based on individual molecular profiles?
We combine genomic, structural and molecular biology, and clinical data to predict treatment responses for patient stratification.
Key areas:
- Treatment response prediction
- Clinical decision support
Current Projects
Project 1: Deep Learning for Drug Discovery
We’re developing new deep learning models that can predict drug-target interactions and optimize molecular properties.
Project 2: Interpretable AI for Biology
We’re working on making predictive and generative models more interpretable for biological applications, allowing researchers to understand not just predictions but also the biological mechanisms behind them.
