I am a 5th-year Ph.D. candidate in Computer Engineering at Northeastern Unversity, supervised by Prof. Yun Raymond Fu. I am working on AI, and my research mainly focuses on LLM, Computer Vision and Anomaly Detection.

I believe in the importance of Open AI. All my first-authored papers on applied AI are open-sourced except for some of the ones within internship companies, which code can not be released due to company policies. My ultimate goal is to reach super-intelligence and AI consciousness safely, which might help human beings escape the earth and explore the universe. I believe that bio-intelligence (us human beings) is designed to unlock the super digital intelligence to find the ultimate answer: the potential higher civilization which created everything.

Education

• Northeastern University (NEU) 2020-
  Ph.D. in Computer Engineering
  Supervisor: Prof. Yun Raymond Fu
• Northeastern University (NEU) 2020-2023
  M.S. in Elctrical Engineering
  Concentration: Computer Vision Mach Learn&Alg
• Xi’an Jiaotong University (XJTU) 2016-2020
  B.S. in Mathematics and Applied Mathematics (National Honors Program)
• Georgia Institute of Technology (GaTech) 2019 Spring
  Georgia Tech School of Mathematics Visiting Honors Student Program

Working Experience

• SMILE Lab, NEU, Boston, U.S. Sep 2021- Now
  Research Assistant to Prof. Yun Raymond Fu, working on anomly detection and optimization
• Adobe, San Jose, U.S. May 2024 - Now
  AI/ML Intern - GenAI Research, mentored by Dr. Lingzhi Zhang, Dr. Qing Liu, Dr. Mang tik Chiu, Dr. Connelly Barnes, Dr. Yuqian Zhou, Dr. Sohrab Amirghodsi, Dr. Eli Shechtman and Dr. Zhe Lin
• Adobe Research, San Jose, U.S. May 2023 - Dec 2023
  Research Intern, mentored by Dr. Gang Wu, Dr. Ruiyi Zhang, Dr. Haoliang Wang and Dr. Uttaran Bhattacharya
• Mitsubishi Electric Research Laboratories (MERL), Cambridge, U.S. May 2022 - Sep 2022
  Research Intern, mentored by Dr. Kuan-Chuan Peng
• Imagine Lab, XJTU, Xi’an, China Jun 2020 - Aug 2020
  Research Assistant to Prof. Jian Sun, working on deep unfolded iterative algorithm for medical images
• FLASH Lab, GaTech, Atlanta, U.S. Jan 2019 - May 2019
  Research Assistant to Prof. Tuo Zhao, working on generative adversarial imitation learning

Selected Publications (Full list can be found on my Google Scholar, * indicates equal contribution)

Preprints

• Boosting Large Language Models with Mask Fine-Tuning
  Mingyuan Zhang*, Yue Bai*, Huan Wang, Yizhou Wang, Qihua Dong, Yun Fu
  [arXiv, Code]
• Efficient Reasoning with Hidden Thinking
  Xuan Shen, Yizhou Wang, Xiangxi Shi, Yanzhi Wang, Pu Zhao, Jiuxiang Gu
  [arXiv, Code]
• VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding
  Yizhou Wang, Ruiyi Zhang, Haoliang Wang, Uttaran Bhattacharya, Yun Fu and Gang Wu
  [arXiv]
• Explainable Anomaly Detection in Images and Videos: A Survey
  Yizhou Wang*, Dongliang Guo*, Sheng Li, Octavia Campus, and Yun Fu
  [arXiv, Code]
• Rethinking Adam: A Twofold Exponential Moving Average Approach
  Yizhou Wang*, Yue Kang*, Can Qin, Huan Wang, Yi Xu, Yulun Zhang and Yun Fu
  [arXiv, Code]

Conference Papers

• Towards Zero-shot 3D Anomaly Localization
  Yizhou Wang, Kuan-Chuan Peng, Yun Fu
  (Oral, top 8%) IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025
  [Paper, Project, Video]
• Rewrite the stars
  Xu Ma, Xiyang Dai, Yue Bai, Yizhou Wang, Yun Fu
  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  [Paper, Code]
• Don’t Judge by the Look: A Motion Coherent Augmentation for Video Recognition
  Yitian Zhang, Yue Bai, Huan Wang, Yizhou Wang, Yun Fu
  International Conference on Learning Representations (ICLR), 2024
  [Paper, Code]
• Momentum is All You Need for Data-Driven Adaptive Optimization
  Yizhou Wang*, Yue Kang*, Can Qin, Huan Wang, Yi Xu, Yulun Zhang and Yun Fu
  IEEE International Conference on Data Mining (ICDM), 2023
  [Paper, Code, Video]
• Making Reconstruction-based Method Great Again for Video Anomaly Detection
  Yizhou Wang, Can Qin, Yue Bai, Yi Xu, Xu Ma and Yun Fu
  IEEE International Conference on Data Mining (ICDM), 2022
  [arXiv, Code]
• Self-supervision Meets Adversarial Perturbation: A Novel Framework for Anomaly Detection
  Yizhou Wang, Can Qin, Rongzhe Wei, Yi Xu, Yue Bai and Yun Fu
  ACM International Conference on Information and Knowledge Management (CIKM), 2022
  [Paper, Code]
• On Computation and Generalization of Generative Adversarial Imitation Learning
  Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang and Tuo Zhao
  International Conference on Learning Representations (ICLR), 2020
  (Spotlight) NeurIPS 2019 Optimization Foundations for Reinforcement Learning Workshop
  [arXiv]

Journal Papers

• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation
  Yizhou Wang, Can Qin, Rongzhe Wei, Yi Xu, Yue Bai and Yun Fu
  IEEE Transactions on Knowledge and Data Engineering (TKDE)
  [Paper, Code]
• An unrolled Implicit Regularization Network for Joint Image and Sensitivity Estimation in Parallel MR Imaging with Convergence Guarantee
  Yan Yang*, Yizhou Wang*, Jiazhen Wang, Jian Sun and Zongben Xu
  SIAM Journal on Imaging Sciences (SIIMS), 2023
  [Paper]
• Self-Directed Online Machine Learning for Topology Optimization
  Changyu Deng, Yizhou Wang, Can Qin, Yun Fu and Wei Lu
  Nature Communications (NC), 2022
  [arXiv, Paper, Website, Tech Xplore News, Code]

Book Chapter

• Human Activity Recognition and Anomaly Detection—4th International Workshop, DL-HAR 2024, and First International Workshop, ADFM 2024, Held in Conjunction with IJCAI 2024, Jeju, South Korea, August 3–9, 2024, Revised Selected Papers
  Kuan-Chuan Peng, Yizhou Wang, Ziyue Li, Zhenghua Chen, Jianfei Yang, Sungho Suh, Min Wu
  Part of the book series: Communications in Computer and Information Science (CCIS, volume 2201)
  Included in the following conference series:
  IJCAI: International Joint Conference on Artificial Intelligence
  Conference proceedings info: IJCAI 2024.
  [Book]

Selected Presentations

• Oral report at CAS SIAM, Online, 2023
• Oral report at AI2healthcare Talk Video, Online, 2023
• Oral presentation at New England Computer Vision Workshop, MIT, Boston, MA, 2022

Selected Awards

• Best Reviewer Award of AISTAT 2025 (126 recipients worldwide), 2025
• SDM Doctoral Forum Travel Award, 2024
• IEEE ICDM Student Travel Award, 2022
• SIGIR Student Travel Grant, 2022
• Dean’s Fellowship of Northeastern University, 2020
• Outstanding graduate of XJTU, 2020
• National Zhufeng 1st Scholarship (Pilot Scheme of Top-notch Talent Cultivation in Basic Disciplines), 2019
• Special Prize in 2018 National English Competition for College Students (Top 0.1%), 2018
• XJTU Pengkang Scholarship (Top 1.5%), 2018
• National Endeavor Scholarship (Top 2%), 2017

Academic Service

Conference Session Chair

• SIAM International Conference on Data Mining (SDM), 2024: Session 10 (Text, Web, Social Media)

Workshop Organization

• the Workshop on Anomaly Detection with Foundation Models in conjunction with IJCAI 2024

Conference Reviewer

• ICML 2022, 2023, 2024, 2025
• NeurIPS 2021, 2022, 2023, 2024, 2025
• ICLR 2022, 2024, 2025
• CVPR 2022, 2023, 2024, 2025
• ICCV 2023, 2025
• ECCV 2022, 2024
• KDD 2023
• AISTATS 2025
• AAAI 2023
• IJCAI 2023, 2024
• SDM 2024
• PAKDD 2023
• ICME 2022
• ACCV 2024
• CVPR Workshop on Fair, Data-efficient, and Trusted Computer Vision, 2024, 2025
• CVPR Workshop on Multimodal Algorithmic Reasoning, 2024
• ICCV Workshop on Analysis and Modeling of Faces and Gestures, 2023
• ECCV Workshop on Unlearning and Model Editing, 2024
• ECCV Workshop on Vision With Biased or Scarce Data, 2022
• AAAI Workshop on Scalable and Efficient Artificial Intelligence Systems, 2025
• AAAI Workshop on Artificial Intelligence with Biased or Scarse Data, 2024

Journal Reviewer

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
• International Journal of Computer Vision (IJCV)
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
• Pattern Recognition (PR)
• ACM Transactions on Knowledge Discovery from Data (TKDD)
• IEEE Internet of Things Journal (IoT)
• Knowledge and Information Systems (KAIS)
• Journal of Big Data
• International Journal of Fuzzy Systems (IJFS)
• IEEE Transactions on Intelligent Vehicles (IEEE Trans. Veh. Technol.)
• IEEE Computational Intelligence Magazine (CIM)

Teaching Experience

Teaching Assistant (TA)

• EECE 7398 ST: Advances in Deep Learning, Fall 2024
• EECE 5642: Data Visualization, Northeastern University, Spring 2023 and Spring 2024
• DS 5230: Unsupervised Machine Learning, Northeastern University, Fall 2023

Contact

WeChat ID: wyzjack
Email: wyzjack990122 (at) gmail (dot) com / wang.yizhou (at) northeastern (dot) edu