Eric Zhao

Eric Zhao

I'm a 4th year PhD candidate in CS at UC Berkeley, where I'm advised by Michael I. Jordan and Nika Haghtalab. I'm a Google PhD Fellow, a NSF Graduate Research Fellow, and affiliated with the Berkeley AI Research Lab (BAIR).

My current research is on understanding how to effectively apply language models to problems where inference-time compute budgets are large (>1B tokens per problem) and verification is non-trivial. Previously, I worked at the intersection of machine learning and game theory, studying the alignment challenges inherent in multi-objective forecasting and multi-distribution learning.

I received my B.S. from Caltech in 2020 and previously interned with Google Research, Nvidia Research, and Salesforce Research.

Selected Works

(α-β) denotes when authors are ordered alphabetically.

  • Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi
  • Truthfulness of Decision-Theoretic Calibration Measures (α-β) Mingda Qiao, Eric Zhao
    PDF Blog COLT 2025
  • From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning Eric Zhao, Pranjal Awasthi, Nika Haghtalab
    PDF Blog Preprint, Feb 2025
  • On-Demand Sampling: Learning Optimally from Multiple Distributions (α-β) Nika Haghtalab, Michael I. Jordan, Eric Zhao
    PDF Blog NeurIPS 2022
    Neurips Outstanding Paper Award

Selected Awards

  • Google PhD Fellowship (2024)
  • NSF Graduate Research Fellowship (2023)
  • NeurIPS Best Paper Award (2022)

Contact

eric.zh@berkeley.edu