About Me
I am a research scientist at OpenAI working on ChatGPT. I work on the training and alignment of large language models.
Previous I was a PhD student at the Department of Computer Science at Stanford University advised by Stefano Ermon, where I worked on uncertainty quantification and generative models.
Contact: szhao at openai dot com
Selected Publications
For a full list of publications sorted by topic see here
Comparing Distributions by Measuring Differences that Affect Decision Making
Shengjia Zhao*, Abhishek Sinha*, Yutong He*, Aidan Perreault, Jiaming Song, Stefano Ermon [openreview] (ICLR’22 Outstanding paper award 0.15%)Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
Shengjia Zhao, Michael P Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon [arXiv] (Neurips’2021)Reliable Decisions with Threshold Calibration
Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon (Neurips’2021)Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Shengjia Zhao, Stefano Ermon [arXiv] (AISTATS’2021 Oral 3.1%)Improved Autoregressive Modeling with Distribution Smoothing
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon (ICLR’2021 Oral 1.8%) openreviewIndividual Calibration with Randomized Forecast
Shengjia Zhao, Tengyu Ma, Stefano Ermon [arXiv] (ICML’2020)A framework for Sample Efficient Interval Estimation with Control Variates
Shengjia Zhao, Christopher Yeh, Stefano Ermon [arXiv] (AISTATS’2020)A Theory of Usable Information under Computational Constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon [arXiv] (ICLR’2020 Oral 1.9%)InfoVAE: Balancing Learning and Inference in Variational Autoencoders
Shengjia Zhao, Jiaming Song, Stefano Ermon [arXiv] (AAAI’2019)Adaptive Antithetic Sampling for Variance Reduction
Hongyu Ren*, Shengjia Zhao*, Stefano Ermon [paper] (ICML’2019)Learning Neural PDE Solvers with Convergence Guarantees
Jun-Ting Hsieh*, Shengjia Zhao*, Lucia Mirabella, Stefano Ermon [arXiv] (ICLR’2019)Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao*, Hongyu Ren*, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon [arXiv] (NeurIPS’2018 Spotlight 3%)A Lagrangian Perspective on Latent Variable Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon [arXiv] (UAI’2018 Oral 8.6%)A-NICE-MC: Adversarial Training for MCMC
Jiaming Song, Shengjia Zhao, Stefano Ermon [arXiv] [code] (NeurIPS’2017)Learning Hierarchical Features from Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon [arXiv] (ICML’2017)Adaptive Concentration Inequalities for Sequential Decision Problems
Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon [pdf] (NeurIPS’2016)
Awards and Fellowships
- ICLR’2022 Outstanding Paper Award (2022)
- JP Morgan PhD Fellowship (2019)
- Qualcomm Innovation Fellowship (QInF) (2018)
- Qualcomm Scholarship (2016)
- Google Excellence Scholarship (2015)
Teaching and Services
- Reviewer: NeurIPS (2017, 2019, 2020, 2021), ICLR (2019, 2020, 2021), ICML (2019, 2020, 2021)
- Organizer: Information Theory and Machine Learning (ITML) Workshop (NeurIPS’2019)
- Teaching: CS228 Head TA (2019 and 2021)