Eric Frankel

Hello! I'm a second year Ph.D. student at the University of Washington, where I am co-advised by Sewoong Oh and Lillian Ratliff. I am fortunate to be supported by the NSF Graduate Research Fellowship. I am currently on leave and a Member of Technical Staff at Treeline AI.

Previously, I graduated from Stanford University with a B.S. with Honors in Mathematics and a M.S. in Statistics, advised by Emmanuel Candès. I was also affiliated with the Stanford AI Lab and worked with Chelsea Finn and Ehsan Adeli.

Github     G. Scholar     LinkedIn     Twitter

ericsf [at] cs [dot] washington [dot] edu

2026

Glad to have contributed to OpenThoughts, accepted as an oral at ICLR 2026

2025

Two papers accepted to ICML 2025

OpenThoughts: Data Recipes for Reasoning Models

Etash Guha*, Ryan Marten*, ..., Eric Frankel, ..., et al.
International Conference on Learning Representations (ICLR) 2026 (Oral)
PDF

S4S: Solving for a Diffusion Model Solver

Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh
International Conference on Machine Learning (ICML) 2025
PDF

Finite-Time Convergence Rates in Stochastic Stackelberg Games with Smooth Algorithmic Agents

Eric Frankel, Kshitij Kulkarni, Dmitriy Drusvyatskiy, Sewoong Oh, Lillian J. Ratliff
International Conference on Machine Learning (ICML) 2025
PDF  •   Code

Conformal Reasoning: Uncertainty Estimation in Interactive Environments

Eric Frankel, Shuyue Stella Li, Lillian J. Ratliff, Yulia Tsvetkov, Sewoong Oh, Pang Wei Koh
NeurIPS 2024 Workshop on Statistical Frontiers in LLMs and Foundation Models
PDF

JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments

Roberto Martín-Martín, Mihir Patel, Hamid Rezatofighi, Abhijeet Shenoi, Eric Frankel, JunYoung Gwak, Amir Sadeghian, Silvio Savarese
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023
PDF  •   Webpage

2026

Frontiers of Flows for Generative AI, CMU — "S4S: Solving for a Diffusion Model Solver"

2026

Reviewer for COLM 2026

2023

Course Assistant, CS 230: Deep Learning, Stanford University

2023

Course Assistant, CS 224N: NLP with Deep Learning, Stanford University

2022

Course Assistant, CS 330: Deep Multi-Task and Meta Learning, Stanford University