About
Hello :) I am Erica, currently a Ph.D. candidate in Operations Research in the Department of Management Science & Engineering (MS&E) at Stanford University, co-advised by Prof. Jose H. Blanchet (MS&E) and Prof. Mert Pilanci (Electrical Engineering). My work has been shaped by close collaborations with Prof. James Zou and Prof. Robert Tibshirani. I am grateful to be supported by the Stanford Graduate Fellowship (SGF) in Sciences and Engineering via the Koret Foundation and a PhD Fellowship from Jump Trading in the AI/ML track. At Stanford, I am affiliated with Stanford Center for AI Safety and Advanced Financial Technologies Laboratory (AFTLab).
As part of my Ph.D., I completed a co-enrolled M.S. degree in Electrical Engineering, specializing in Control & Optimization. Prior to Stanford, I earned a dual B.A. in Mathematics and Statistics from Columbia College, Columbia University, graduating summa cum laude with honors from both departments.
This past summer (2025), I interned as an Applied Scientist working on Agentic AI with Amazon Science at the Bellevue office. This summer (2026), I interned at Two Sigma as a Quantitative Researcher working on post-training for alpha modeling at the New York headquarters.
News
Excited to share that I will be back at Google Ventures giving a talk on building verifiers for agentic markets at the San Francisco headquarters on Oct. 1st, 2026!
Check out out latest work: TERMS-Bench and leaderboard! TERMS-Bench introduces a new way to evaluate agentic capabilities in non- or semi-verifiable domains, where structure is loose and no native verifier exists: constructing the environment itself as the verifier. We focus on agentic negotiation, including commercial extensions such as stateful agentic procurement chains, and evaluate the most capable high-reasoning models from major providers as of May 2026.
Excited to share that I received the Jump Trading Fellowship in the AI/ML track (2026), supporting my research on reliable modern learning and agentic AI systems.
Check out our latest work: Statsformer! Statsformer bridges LLM guidance with statistical rigor, delivering provable safety guarantees that mitigate performance degradation from LLM hallucinations while consistently outperforming strong AutoML baselines (e.g., AutoGluon, LLM-Agent–style systems).
Research
I am a researcher building at the intersection of foundation models, agentic systems, and markets. I began as a theorist and statistician, and still draw heavily on optimization, probability, and statistical learning, but increasingly my work centers on a broader question: what structures do intelligent systems need in order to learn, act, and interact reliably in messy real-world environments?
My recent work spans post-training under noisy learning signals, verifier-based evaluation for semi- and non-verifiable domains, LLM-integrated learning with formal guarantees, and multi-agent systems for strategic and economic interaction. Across these settings, I am especially interested in long-horizon, strategic domains, where the clean verification story of mathematics and coding breaks down, and where progress may require reasoning from first principles about the structures beyond engineering more harnesses.
Philosophically, I’m inspired by mathematician Hans Hahn’s view of mathematics as a precise, elegantly constructed conceptual framework: one that enables us to abstract information and perform tautological transformations to uncover fundamental laws governing our world [1]. What I find beautiful in this view is that it captures both the power and the limit of formal languages such as mathematics: they let us make hidden structure explicit, but they can only take us as far as the framework itself allows. Some of the questions I find most interesting begin at that boundary: when progress requires not just solving within a framework, but building a better one.
Feel free to reach out if you’re interested in my work 🙂
Selected Industry Projects
- Negotiation Agent for the Amazon Marketplace
Patent pending · Amazon Science, Bellevue · 2025Designed and built end-to-end agentic AI system for strategic price negotiation in a real-world marketplace environment under business constraints.Position: Applied Scientist Intern · Role: Research and system development lead · Status: Pilot testing
[Post]
Open-Source Projects
- TERMS-Bench: A Diagnostic Benchmark for LLM Negotiation Agents
Open-source · Evaluation benchmark · HAI <> Stanford Engineering · 2026Creator; development and engineering lead. - OpenThoughts-Agent: Data Receipe for Agents
Open-source · Benchmark · Stanford, Daytona, Oumi, Bespoke Labs, and Laude Institute · 2026Contributor; data creation and curation.
Scholarly Works
- TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate
Erica Zhang, Fangzhao Zhang, Aneesh Pappu, Batu El, Jose Blanchet, Suan Athey, Jiashuo Liu, James Zou
arXiv Preprint (2026)
[PDF] · [arXiv] · [Codes - Coming soon] · [Leaderboard] - OpenThoughts-Agent: Data Recipes for Agentic Models
Negin Raoof, Richard Zhuang, Marianna Nezhurina, Etash Kumar Guha, Atula Tejaswi, Ryan Marten, Charlie F. Ruan, …, Erica Zhang, …, Jenia Jitsev, Alex Dimakis, Benjamin Feuer, Ludwig Schmidt
arXiv Preprint (2026)
[PDF] · [arXiv] · [Codes] - Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration
Erica Zhang*, Naomi Sagasn*, Danny Tse, Fangzhao Zhang, Mert Pilanci, Jose Blanchet
Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS), 2026
[PDF] · [arXiv] · [Codes] - Optimizer-Induced Mode Connectivity: From AdamW to Muon
Fangzhao Zhang*, Sungyoon Kim*, Erica Zhang, Yiqi Jiang, Mert Pilanci
Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS), 2026
[PDF] · [arXiv] · [Codes] - Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
Erica Zhang*, Fangzhao Zhang*, Mert Pilanci
International Conference on Machine Learning (ICML), 2025
[PDF] · [arXiv] · [Codes] - LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization
Erica Zhang*, Naomi Sagan*, Ryan Goto*, Jurik Mutter, Nick Phillips, Ash Alizadeh, Kangwook Lee, Jose Blanchet, Mert Pilanci, Robert Tibshirani
arXiv Preprint (2025)
[PDF] · [arXiv] · [Codes] - Empirical martingale projections via the adapted Wasserstein distance
Jose Blanchet, Johannes Wiesel, Erica Zhang, Zhenyuan Zhang
Annals of Applied Probability, AAP2239, 2025
[PDF] · [arXiv] · [Codes] - HieroLM: Egyptian Hieroglyph Recovery with Next Word Prediction Language Model
Xuheng Cai, Erica Zhang
LaTeCH-CLfL 2025 @ NAACL 2025
[PDF] · [arXiv] · [Codes] - An optimal transport-based characterization of convex order
Johannes Wiesel, Erica Zhang
Dependence Modeling 11 (1)
[PDF] · [arXiv] · [Codes] - Convex Order and Arbitrage
Erica Zhang
arXiv Preprint
[PDF] · [arXiv]
Invited Talks
- TERMS-Bench: Building Verifiers for Agentic Markets
Private Research Night, Google Ventures, San Francisco · Oct. 1st, 2026 Learning When to Trust LLM Priors: Reliable Prediction with Statistical Guarantees
Jump AI Symposium, Jump Trading, New York City · May 28, 2026Benchmarking and Evaluation for Semi-Verifiable Domains: A Case Study of Agentic Negotiation
Frontier Research Series, Google Ventures, San Francisco · May 20, 2026- TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate
Open Model Benchmarks, Google DeepMind, San Francisco · May 6, 2026 · [Slides]
Academic Service
- Co-chair & Organizer, Optimization under Uncertainty cluster at the INFORMS Annual Meeting, San Francisco, November 1–4, 2026.
- Referee, NeurIPS, 2026.
- Referee, Management Science, 2025.
Honors
- Jump Trading Fellowship in AI/ML, Jump Trading (2026).
- Stanford Graduate Fellowship (SGF) in Sciences and Engineering, Stanford University (2023)
- Phi Beta Kappa at Columbia College Delta Chapters, Columbia University (2023)
- Departmental Honors in Mathematics, Columbia University (2023)
- Departmental Honors in Statistics, Columbia University (2023)
- Nexus Fellowship, The D. E. Shaw Group (2021)