Frances Ding

Frances Ding is an accomplished AI researcher currently pursuing her PhD in Machine Learning at the University of California, Berkeley. She has a strong academic background, having previously earned her Bachelor's degree in Biology and Computer Science from Harvard University and an MPhil in Machine Learning from the University of Cambridge.

Professional Experience

• AI Resident at X (the moonshot factory): Frances worked as an AI Resident from May to December 2022, focusing on biological sequence design. This role involved applying machine learning techniques to improve protein property classification and design, as well as exploring the capabilities of protein language models.12
• Research Fellow at Harvard University: Before her time at X, she conducted research on algorithmic fairness and the use of machine learning to mitigate bias due to under-representation in data.1
• Teaching Fellow: Frances served as a Teaching Fellow for a machine learning course at Harvard, enhancing her teaching and mentoring skills.1

Research Interests

Frances's research primarily centers on:

• Theoretical foundations of algorithmic fairness.
• Interpretability and robustness of machine learning systems.
• Applications of machine learning in biological contexts, particularly protein modeling.2

Education

• PhD Candidate: University of California, Berkeley (2019 - Present)
• MPhil in Machine Learning: University of Cambridge (2017 - 2018)
• Bachelor's Degree in Biology and Computer Science: Harvard University (2013 - 2017)

Frances Ding's work exemplifies a commitment to advancing the field of AI through innovative research and practical applications in biology.