Jiaming Zeng

Jiaming Zeng

Senior Machine Learning Researcher

AKASA

Stanford University

Biography

Jiaming Zeng is a Senior 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 Senior 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.

Interests
  • Natural Language Processing
  • Interpretability
  • Causal Inference
  • Clinical Informatics
  • AI Ethics
Education
  • PhD in Management Science and Engineering, 2021

    Stanford University

  • MEng in Management Science and Engineering, 2018

    Stanford University

  • BSc in Mathematics with Computer Science, 2015

    Massachusetts Institute of Technology

Recent Publications

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(2023). Stress-Testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society.

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(2022). Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society.

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(2022). Downstream Fairness Caveats with Synthetic Healthcare Data. arXiv.

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(2020). Cancer treatment classification with electronic medical health records (student abstract). Proceedings of the AAAI Conference on Artificial Intelligence.

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(2020). Developing a machine learning tool for dynamic cancer treatment strategies. Proceedings of the AAAI Conference on Artificial Intelligence.

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Accomplish­ments