I am an assistant professor in the ECE department at UC Riverside.

My research focuses on machine learning, reinforcement learning, and foundation models, with an emphasis on developing efficient and reliable learning algorithms and systems for large-scale, multimodal problems. My current interests include:

• Interactive learning: leveraging foundation models to design efficient and adaptive algorithms and agents for interactive environments.

• Test-time training and scaling: enhancing the performance and robustness of pretrained models during inference, especially on novel or unseen tasks.

Previously, I received my Ph.D. in Computer Sciences from the University of Wisconsin–Madison, where I was advised by Robert Nowak. During my Ph.D., I worked on theoretical foundations of interactive machine learning, including active learning and contextual bandits. I now aim to incorporate these algorithmic insights into the design of practical AI systems — see examples below.

Selected Recent Papers

• Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Models
  Yinglun Zhu, Jiancheng Zhang, and Fuzhi Tang
  Preprint (under review) 2025, [Blog] [Code]

• Online Finetuning Decision Transformers with Pure RL Gradients
  Junkai Luo and Yinglun Zhu
  Preprint (under review) 2025

• Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
  Bowen Zuo and Yinglun Zhu
  Preprint (under review) 2025

• Towards Multimodal Active Learning: Efficient Learning with Limited Data Pairs
  Jiancheng Zhang and Yinglun Zhu
  Preprint (under review) 2025

• LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems
  Yufei Li, Zexin Li, Yinglun Zhu, Cong Liu
  Real-Time Systems Symposium (RTSS) 2025
  ★ Outstanding Paper Award

• Efficient Sequential Decision Making with Large Language Models
  Dingyang Chen, Qi Zhang, and Yinglun Zhu
  Conference on Empirical Methods in Natural Language Processing (EMNLP) 2024, [Code]

Selected Older Papers [Full List]

• Active Learning with Neural Networks: Insights from Nonparametric Statistics
  Yinglun Zhu and Robert Nowak
  Conference on Neural Information Processing Systems (NeurIPS) 2022

• Efficient Active Learning with Abstention
  Yinglun Zhu and Robert Nowak
  Conference on Neural Information Processing Systems (NeurIPS) 2022

• Contextual Bandits with Large Action Spaces: Made Practical
  Yinglun Zhu, Dylan Foster, John Langford, and Paul Mineiro
  International Conference on Machine Learning (ICML) 2022, [Code]
  ★ Incorporated into the leading machine learning library Vowpal Wabbit; see here for instructions

• Contextual Bandits with Smooth Regret: Computational Efficiency in Continuous Action Spaces
  Yinglun Zhu and Paul Mineiro
  International Conference on Machine Learning (ICML) 2022, [Code]
  ★ Full Oral Presentation (top 2.1%)

• Near Instance Optimal Model Selection for Pure Exploration Linear Bandits
  Yinglun Zhu, Julian Katz-Samuels, and Robert Nowak
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, [Code]

• Pareto Optimal Model Selection in Linear Bandits
  Yinglun Zhu and Robert Nowak
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, [Code]

• Pure Exploration in Kernel and Neural Bandits
  Yinglun Zhu\(^\star\), Dongruo Zhou\(^\star\), Ruoxi Jiang\(^\star\), Quanquan Gu, Rebecca Willett, and Robert Nowak
  Conference on Neural Information Processing Systems (NeurIPS) 2021, [Code]

• On Regret with Multiple Best Arms
  Yinglun Zhu and Robert Nowak
  Conference on Neural Information Processing Systems (NeurIPS) 2020, [Code]

• Robust Outlier Arm Identification
  Yinglun Zhu, Sumeet Katariya, and Robert Nowak
  International Conference on Machine Learning (ICML) 2020, [Code]

Dissertation

• Interactive Machine Learning: From Theory to Scale
  Yinglun Zhu
  Ph.D. Dissertation, University of Wisconsin–Madison, 2023

Group

Ph.D. Students: Jiancheng Zhang (Fall 2024 - present), Bowen Zuo (Fall 2024 - present)

Prospective Students: If you are interested in joining my lab, please apply to the ECE or CSE Ph.D. program at UC Riverside and list me as a potential advisor.

Teaching

• Spring 2025: EE 260 Large Models and Advances in AI

• Winter 2025: EE 114 Probability, Random Variables, and Random Processes in Electrical Engineering

• Fall 2024: EE/CS 228 Introduction to Deep Learning

• Spring 2024: EE 260 Large Models and Advances in AI

• Fall 2023: EE/CS 228 Introduction to Deep Learning