Statistics & Optimization for Trustworthy AI

Fall 2025 courses: EECS 498 Foundations of LLMs (syllabus), EECS 553 Machine Learning (syllabus)

News:

• I will serve as a Senior Area Chair for NeurIPS 2025
• Recent preprints:
  • Continuous Chain of Thought Enables Parallel Exploration and Reasoning
  • When and How Unlabeled Data Provably Improve In-Context Learning
  • Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement
  • Attention with Trained Embeddings Provably Selects Important Tokens
• Recent papers:
  • Everything Everywhere All at Once, ICML 2025 spotlight
  • Test-Time Training Provably Improves Transformers as In-context Learners, ICML 2025
  • High-dimensional Analysis of Knowledge Distillation, ICLR 2025 spotlight
  • Provable Benefits of Task-Specific Prompts for In-context Learning, AISTATS 2025
  • AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments, CVPR 2025
• New award from Amazon Research on Foundation Model Development
• 2 papers will appear at AAAI 2025
  • On the Power of Convolution Augmented Transformer (Oral presentation)
  • TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
• We are presenting 4 papers at NeurIPS 2024
  • Selective Attention: Enhancing Transformer through Principled Context Control
  • Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
  • Fine-grained Analysis of In-context Linear Estimation
  • CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
• Congrats to Mingchen on his graduation and joining Meta as a Research Scientist!
• Congrats to our 2023 interns who will pursue their PhD studies in UC Berkeley, Harvard, and UIUC!
• Two papers at ICML 2024: Self-Attention <=> Markov Models and Can Mamba Learn How to Learn?
• New course on Foundations of Large Language Models: syllabus (including Piazza and logistics)
• New awards from NSF and ONR: We kickstarted two exciting projects to advance the theoretical and algorithmic foundations of LLMs, transformers, and their compositional learning capabilities.
• Two papers at AISTATS 2024
  • “Mechanics of Next Token Prediction with Self-Attention”, Y. Li, Y. Huang, M.E. Ildiz, A.S. Rawat, S.O.
  • “Inverse Scaling and Emergence in Multitask Representations“, M.E. Ildiz, Z. Zhao, S.O.
• Two papers at AAAI 2024 and one paper at WACV 2024
  • Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
  • Effective Restoration of Source Knowledge in Continual Test Time Adaptation
• Invited talks at USC, INFORMS, Yale, Google NYC, and Harvard on our works on transformer theory
• Two papers at NeurIPS 2023
  • Max-Margin Token Selection in Attention Mechanism, spotlight paper!
  • Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs
• Grateful for the Adobe Data Science Research award!
• Our new works develop the optimization foundations of Transformers via SVM connection
  • Transformers as Support Vector Machines => Twitter thread
  • Max-Margin Token Selection in Attention Mechanism, NeurIPS’23
• Two papers at ICML 2023: Transformers as Algorithms and On the Role of Attention in Prompt-tuning
• Two papers at AAAI 2023: Provable Pathways and Long Horizon Bandits

We are grateful for our research sponsors