Chengzhi Mao

Assistant Professor, Department of Computer Science, Rutgers University
chengzhi.mao@rutgers.edu Google Scholar
Chengzhi Mao headshot

Brief Bio

I am an Assistant Professor in the Department of Computer Science at Rutgers University. Previously, I have been a Research Scientist at Google, working on Gemini and GenAI related research. I completed my Ph.D. at Columbia University in 2023, advised by Carl Vondrick and Junfeng Yang. I got my BS at Tsinghua University in 2018, advised by Yuan Shen.

My research goal is to build reliable multimodal reasoning models that can serve as the “brain” of robots and computer agents. I aim to develop AI systems that integrate vision, language, action, and world knowledge to reason, plan, and act safely and smoothly in open-world environments. More broadly, my work seeks to advance trustworthy foundation models that can understand complex multimodal contexts, make robust decisions, and collaborate effectively with humans in the physical world.

I am actively recruiting highly self-motivated Ph.D. students, research interns, and postdocs. If you’re interested, please email me with your CV. Interest and experience in robotics, multimodal reasoning, or generative models will be a plus.

My lab owns 32 Blackwell RTX Pro GPUs in addition to our Nvidia, AMD cloud compute.

News

Students

Yang Li — PhD Student Yuan Qing — PhD Student Yibin Wang — PhD Student Zirui Zhang — PhD Student Shilong Xiang — Research Assistant

Selected Papers

2026
StochasT : Learning with Stochastic Turn Depth for Visual Instruction Tuning thumbnail

StochasT : Learning with Stochastic Turn Depth for Visual Instruction Tuning

Yuan Qing, Chengzhi Mao, Boqing Gong
ECCV 2026 — paper
APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model thumbnail

APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model

Yuanjie Lu, Beichen Wang, Zhengqi Wu, Yang Li, Xiaomin Lin, Chengzhi Mao, Xuesu Xiao
IROS 2026 — paper
S2COPE: Self-Supervised Concept Discovery via Preference Learning thumbnail

S2COPE: Self-Supervised Concept Discovery via Preference Learning

Shilong Xiang, Zirui Zhang, Chengzhi Mao
arXiv — paper / project page
Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification thumbnail

Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification

Qihao Liu, Chengzhi Mao, Yaojie Liu, Alan Yuille, Wen-Sheng Chu
CVPR'26 Highlight — paper / project page
LACE: Lattice Attention for Cross-Thread Exploration thumbnail

LACE: Lattice Attention for Cross-Thread Exploration

Yang Li, Zirui Zhang, Yang Liu, Chengzhi Mao
arXiv 2026 — paper
R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning thumbnail

R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning

Zirui Zhang, Haoyu Dong, Kexin Pei, Chengzhi Mao
CVPR 2026 — paper / code / project page
Mull-Tokens: Modality-Agnostic Latent Thinking thumbnail

Mull-Tokens: Modality-Agnostic Latent Thinking

Arijit Ray, Ahmed Abdelkader, Chengzhi Mao, Bryan Plummer, Kate Saenko, Ranjay Krishna, Leonidas Guibas, Wen-Sheng Chu
CVPR 2026 Findings — paper / code / project page
Language Instructed Vision Embeddings for Controllable and Generalizable Perception thumbnail

Language Instructed Vision Embeddings for Controllable and Generalizable Perception

Chengzhi Mao, Xudong Lin, Wen-Sheng Chu
ICLR 2026 — paper
2025
Latent Adversarial Reflection for LLM Jailbreaking thumbnail

Latent Adversarial Reflection for LLM Jailbreaking

Ran Li, Hao Wang, Chengzhi Mao
NeurIPS 2025 — paper
Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision thumbnail

Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision

Chenshuang Zhang, Kang Zhang, Joon Son Chung, In So Kweon, Junmo Kim, Chengzhi Mao
NeurIPS 2025 — paper
EDITLORD: Learning Code Transformation Rules for Code Editing thumbnail

EDITLORD: Learning Code Transformation Rules for Code Editing

Weichen Li, Albert Jan, Baishakhi Ray, Junfeng Yang, Chengzhi Mao, Kexin Pei
ICML 2025 — paper
Diversity Helps Jailbreak Large Language Models thumbnail

Diversity Helps Jailbreak Large Language Models

Weiliang Zhao, Daniel Ben-Levi, Wei Hao, Junfeng Yang, Chengzhi Mao
NAACL 2025 (Oral) — paper
2024
SelfIE: Self-Interpretation of Large Language Model Embeddings thumbnail

SelfIE: Self-Interpretation of Large Language Model Embeddings

Haozhe Chen, Carl Vondrick, Chengzhi Mao
ICML 2024 — project
Raidar: geneRative AI Detection viA Rewriting thumbnail

Raidar: geneRative AI Detection viA Rewriting

Chengzhi Mao, Carl Vondrick, Hao Wang, Junfeng Yang
ICLR 2024 — paper
2023
Doubly Right Object Recognition: A Why Prompt for Visual Rationales thumbnail

Doubly Right Object Recognition: A Why Prompt for Visual Rationales

Chengzhi Mao, Revant Teotia, Amrutha Sundar, Sachit Menon, Junfeng Yang, Xin Wang, Carl Vondrick
CVPR 2023 — arXiv / dataset / code
Shadows Shed Light on 3D Objects thumbnail

Shadows Shed Light on 3D Objects

Ruoshi Liu, Sachit Menon, Chengzhi Mao, Dennis Park, Simon Stent, Carl Vondrick
CVPR 2023 — arXiv / dataset / code
2022
Real-Time Neural Voice Camouflage thumbnail

Real-Time Neural Voice Camouflage

Mia Chiquier, Chengzhi Mao, Carl Vondrick
ICLR 2022 (Oral) — arXiv / code / Science / talk
Discrete Representations Strengthen Vision Transformer Robustness thumbnail

Discrete Representations Strengthen Vision Transformer Robustness

Chengzhi Mao, Lu Jiang, Mostafa Dehghani, Carl Vondrick, Rahul Sukthankar, Irfan Essa
ICLR 2022 — arXiv / code / cite / talk
2021
Adversarial Attacks are Reversible with Natural Supervision thumbnail

Adversarial Attacks are Reversible with Natural Supervision

Chengzhi Mao, Mia Chiquier, Hao Wang, Junfeng Yang, Carl Vondrick
ICCV 2021 — arXiv / code / cite / talk
Generative Interventions for Causal Learning thumbnail

Generative Interventions for Causal Learning

Chengzhi Mao, Amogh Gupta*, Vikram Nitin*, Baishakhi Ray, Shuran Song, Junfeng Yang, Carl Vondrick
CVPR 2021 — arXiv / code / cite / talk

Teaching

Contact

Department of Computer Science, Rutgers University
Email: chengzhi.mao@rutgers.edu

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