About

Hello :) I am Erica, currently a Ph.D. candidate in Operations Research within the Department of Management Science & Engineering (MS&E) at Stanford University. I am fortunate to be co-advised by Prof. Jose H. Blanchet (MS&E) and Prof. Mert Pilanci (Electrical Engineering). I am also 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 recently completed a co-enrolled M.S. degree in Electrical Engineering (2025), 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. I am excited to share that I will be joining Two Sigma this summer (2026) as a Quantitative Research Intern in AI and ML at the New York headquarters.

News

I’m honored to be co-organizing and chairing, with Lukas Fiechtner, a session under the Optimization under Uncertainty cluster at the INFORMS Annual Meeting in San Francisco, November 1–4, 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

Beginning as a theorist and statistician, I draw on classical tools from optimization and probability to tackle modern challenges in machine learning, particularly in large-scale, high-dimensional settings where sample complexity and statistical rigor matter most.

Currently, my research bridges foundation models and agentic systems with statistical rigor and safety. I develop LLM-integrated learning and decision-making agentic systems with formal guarantees that mitigate failure modes such as hallucination. My goal is to move beyond heuristic LLM augmentation toward systems that are robust, interpretable, and provably safe.

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]. As I continue my journey as a researcher, I hope to uncover more of these hidden structures within learning systems through the lenses of optimization and statistical theory and push the frontiers of what we can rigorously understand and design in machine learning.

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 · 2025
  
  Designed 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]

Scholarly Works

• Statsformer: Validated Ensemble Learning with LLM-Derived Semantic Priors
  Erica Zhang^*, Naomi Sagan^*, Danny Tse, Fangzhao Zhang, Mert Pilanci, Jose Blanchet
  arXiv Preprint (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]

Academic Service

• Referee, Management Science, 2025.

Honors

• Jump Trading Fellowship in AI/ML (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)

References