2026 PKU Workshop on
Optimization and Learning

ABOUT

Optimization and learning are deeply intertwined in modern data science and intelligent systems. Optimization provides the tools for training models, fitting structured representations, and making decisions under uncertainty, while learning brings large-scale, stochastic, nonconvex, and often distributed challenges. As models grow and deployments become more complex, progress hinges on integrating insights from optimization theory, statistical learning, and system design. This workshop brings together researchers and practitioners to discuss recent advances at the intersection of learning and optimization, spanning both theoretical foundations and emerging applications.

SPEAKERS

Tianyi Chen

Cornell University

Zhi-Quan (Tom) Luo

The Chinese University of Hong Kong, Shenzhen

Wing-Kin Ma

The Chinese University of Hong Kong

José M. F. Moura

Carnegie Mellon University

Roula Nassif

Université Côte d’Azur

Alejandro Ribeiro

University of Pennsylvania

Ali H. Sayed

École Polytechnique Fédérale de Lausanne

Anna Scaglione

Cornell University

Gesualdo Scutari

Purdue University

Vincent Y. F. Tan

National University of Singapore

Zhi Tian

George Mason University

Stefan Vlaski

Imperial College London

Lei Wu

Peking University

Jinming Xu

Zhejiang University

Kun Yuan

Peking University

ORGANIZERS

Ali H. Sayed

École Polytechnique Fédérale de Lausanne

Kun Yuan

Peking University

Zaiwen Wen

Peking University

Qing Ling

Sun Yat-sen University