Jiaming Zeng is a Machine Learning Researcher at AKASA. She received her PhD from Stanford University working with Ross Shachter, Susan Athey, and Daniel Rubin. Her research focused on employing causal models and machine learning to develop interpretable models for medical decision making. While an undergrad at MIT, she worked with Cynthia Rudin to develop interpretable machine learning models for predicting prisoner recidivism.

She did her postdoc at IBM Research’s Computational Health group, working on identifying and mitigating bias present in clinical data. Previously, she worked as an AI Resident at X, the Moonshot Factory (formerly Google X) to increase sustainable fishing and protect the ocean. She was also an AI Research intern on the NVIDIA AI Infrastructure team, working on practical ways to capture uncertainty in neural networks. Currently as a ML Researcher AKASA, she leads the research and development efforts for pretraining, finetuning, and evaluting LLMs for automating clinical workflow.

Jiaming’s research has been published in channels such as Nature Communications, JCO Clinical Informatics, NeurIPS, etc. Her work has also been featured in various news channels. In her freetime, she enjoys reading, writing, being out in nature, and learning about other cultures.