PHYSICAL AI · WORLD MODELS

Haoyu Lei 雷浩瑀

HUAWEI · Intelligent Automotive Solution (IAS) BU
Autonomous Driving Solution (ADS) Research

I have joined HUAWEI’s Intelligent Automotive Solution (IAS) BU, Autonomous Driving Solution (ADS) Research Department at Oct 2026.

My research focuses on Physical AI Foundation Models for embodied AI and autonomous driving.

I received my Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong (CUHK) in July 2026, advised by Prof. Farzan Farnia. My research interests include diffusion-based generative models, machine learning theory, and trustworthy AI (Robustness, Fairness, and Interpretability).

I received my B.Sc. in Mathematics and Information Engineering, a double-major program at CUHK, as part of the ELITE Stream. I was fortunate to be advised by Prof. Chandra Nair and Prof. Anthony Man-Cho So.

World ModelsEmbodied AI & Autonomous DrivingDiffusion ModelsMachine Learning TheoryTrustworthy AI

Feel free to email me about internships or research collaborations!

News

2026.10

I have joined HUAWEI's IAS BU, ADS Research Department!

2026.07

I am honored to have graduated with a Ph.D. from the Department of Computer Science and Engineering at CUHK!

2026.03

Our paper is accepted by ICML 2026!

2026.03

Our paper is accepted by ISIT 2026!

2026.01

Our paper is honored to be selected by AAAI 2026 ML4Wireless Best Paper Award!

Earlier news (11)
2025.11

Two papers are accepted by AAAI 2026 & AAAI 2026 ML4Wireless!

2025.09

Two papers are accepted by NeurIPS 2025!

2025.07

Our paper is accepted by ACMMM 2025!

2025.06

Join Huawei Hong Kong Research Center, Theory Lab as a research intern!

2025.06

Honored to be selected as the Outstanding Intern in Noah's Ark Lab!

2025.01

Our paper is accepted by AAAI 2025 GenPlan Workshop (Spotlight)!

2025.01

Our paper is accepted by ICLR 2025!

2024.07

Join Huawei Noah's Ark Lab as a research intern!

2024.04

Our paper is accepted by UAI 2024!

2022.08

Start the journey as a Ph.D. student at CSE, CUHK!

2021.05

Our paper is accepted by ICASSP 2021!

Selected Publications

* Equal contribution

Overview of Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance2026

ICML 2026

Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance

Jingwei Zhang, Haoyu Lei, Zijin Feng, Jiacheng Sun, Farzan Farnia

We propose a training-free Semantic-Aware Kernel Entropy (SAKE) guidance method for diffusion LLMs to enhance the reasoning ability.

Overview of On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations2026

ISIT 2026

On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations

Haoyu Lei*, Mohammad Jalali*, Chin Wa Lau, Farzan Farnia

We theoretically and numerically study the lens of robustness to distributional shifts at the channel output.

Overview of Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes2026

AAAI 2026 ML4Wireless (Best Paper Award)

Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes

Haoyu Lei, Chin Wa Lau, Kaiwen Zhou, Nian Guo, Farzan Farnia

We propose a highly-efficient 1-NFE consistency flow model for decoding tasks in Error Correction Codes with superior performance.

Overview of Boosting Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation2026

AAAI 2026

Boosting Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

Haoyu Lei, Kaiwen Zhou, Yinchuan Li, Zhitang Chen, Farzan Farnia

We propose a general energy-guided sampling framework during inference time that enhances both the cross-scale and cross-problem generalization capabilities of diffusion-based NCO solvers without requiring additional training.

Overview of SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Scores2025

NeurIPS 2025

SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Scores

Mohammad Jalali*, Haoyu Lei*, Amin Gohari, Farzan Farnia (*Equal Contribution)

We propose the Scalable Prompt-Aware Renyi Kernel Entropy Diversity Guidance (SPARKE) method to improve prompt-aware diversity in text-to-image diffusion models, allowing large-scale generation settings with linear inference computational overhead.

Overview of Boosting the visual interpretability of CLIP via adversarial fine-tuning2025

ICLR 2025

Boosting the visual interpretability of CLIP via adversarial fine-tuning

Shizhan Gong, Haoyu Lei, Qi Dou, Farzan Farnia

We propose an unsupervised adversarial fine-tuning (AFT) with norm-regularization to enhance the visual interpretability of CLIP.

Overview of On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms2024

UAI 2024

On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms

Haoyu Lei, Amin Gohari, Farzan Farnia

We theoretically show that Inductive Biases widely exist in DP-based fair supervised learning algorithms, and further propose a distributionally robust optimization framework to mitigate the biases.

Education & Experience

Education

Experience

Honors & Awards

2025

Huawei Noah's Ark Lab Outstanding Intern

2022

Postgraduate Studentship Scholarship Scheme

2021

Chung Chi College Departmental Prize (Graduation Top 10%)

2019 - 2021

Dean's List (Academic Year Top 10%)

2019 - 2021

Chung Chi College Class Scholarship (College Top 10%)

2019 - 2021

ELITE Stream Scholarship

Teaching

2025-26 Fall

AIST3010/ESTR3114 Numerical Optimization (Undergraduate)

2024-25 Spring

CSCI5130 Game Theory (Postgraduate)

2023-24 Spring

CSCI5030 Machine Learning Theory (Postgraduate)

2023-24 Fall

ENGG2760A Probability for Engineers (Undergraduate)

2022-23 Spring

ENGG1003SG Digital Literacy and Computational Thinking (Undergraduate)

2022-23 Fall

AIST3010 Numerical Optimization (Undergraduate)

Academic Service

ICML Golden Reviewer, ICML, NeurIPS, ICLR, AAAI, UAI, TMLR, ISIT Reviewer

Languages & Skills

Languages: English (Advanced), Cantonese (Intermediate), Putonghua (Native)

Programming Skills: Python (Main), Pytorch (Main), Matlab, R, C, C++, C#, LaTeX

Contact

Feel free to email me about internships or research collaborations!