Ada Fang

About

Ada Fang is pursuing a PhD in Chemistry at Harvard University, where she is a Kempner Graduate Fellow and a member of Professor Marinka Zitnik’s research group. Her research involves developing machine learning models to understand complex chemical biology relationships, enabling the testing of novel hypotheses in previously unexplored systems. Her primary focus lies in leveraging geometric deep learning and graph neural networks for the effective utilization of large experimental datasets. Additionally, she is dedicated to employing AI to assist scientists in formulating and testing innovative hypotheses. Prior to joining the Kempner, Fang completed a Bachelor of Science (Honors) at the University of Sydney, Australia, majoring in mathematics and chemistry. 

Research Focus

Fang’s current research focuses on chemical biology, geometric machine learning, AI for science, and lies at the intersection of chemistry and ML/AI. She tackles the largely unexplored universe of drug-like molecules, estimated at 10^60, using machine learning to understand complex chemical biology relationships. Her approach leverages large experimental datasets for modeling molecules through geometric deep learning and equivariant neural networks. She pursues the development of models that are chemically grounded by integrating scientific inductive biases. Ada is also focused on using AI to empower scientists, particularly in generating novel hypotheses and conducting in silico experiments. She acknowledges the challenges and opportunities in AI for science, emphasizing the importance of unified datasets, benchmarks, experimental validation, and close collaboration with scientists. Overall, she is excited to integrate AI as a powerful tool for researchers to use for scientific discovery.