Weiran Huang
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Weiran Huang (黄维然) |
Our lab is actively seeking self-motivated graduate students, as well as visitors and postdocs, who are interested in joining our lab.
If you're interested, please email me your CV. Before reaching out, please carefully read our latest guidelines (招生须知).
About Me
I am an Associate Professor at the Qing Yuan Research Institute of Shanghai Jiao Tong University, and the Principal Investigator of the Foundational and Applied Machine Intelligence (FAMI) Lab. I also serve as a Research Consultant at Shanghai AI Lab. My research interests include machine learning, encompassing both the theory and applications of algorithms, such as pre-training and foundation models, data-efficient fine-tuning, continual learning, and multimodal learning. I earned my PhD in computer science from the Institute for Interdisciplinary Information Sciences of Tsinghua University, where I had the honor of being supervised by Turing Award Laureate Prof. Andrew Yao and IEEE Fellow Prof. Wei Chen. Prior to that, I received my bachelor's degree in Electronic Engineering from Tsinghua University. Additionally, I was a visiting scholar hosted by Prof. Yaron Singer at Harvard SEAS.
Latest News
Apr 2024: I am honored to have been chosen as one of the 17 Microsoft Research Asia StarTrack Scholars for the year 2024 from around the globe!
Dec 2023: Our team achieved first place in the Foundation Model Prompting for Medical Image Classification Challenge 2023 at the NeurIPS 2023 Competition!
Oct 2023: Our paper “When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method” was among the ICCV 2023 Best Paper Finalists (Top 0.2%)!
Sep 2023: Our paper “DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation” has been accepted by NeurIPS 2023!
Sep 2023: Our paper “FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning” has been accepted by NeurIPS 2023!
Sep 2023: I am honored to have been selected as one of the 26 recipients of 2023 CCF-Baidu Open Fund!
Aug 2023: Our paper “InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4” has been covered by AI media Jiqi Zhixin (机器之心)!
Jul 2023: Our paper “When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method” has been accepted by ICCV 2023 as an oral presentation!
Apr 2023: Our paper “Rethinking Weak Supervision in Helping Contrastive Representation Learning” has been accepted by ICML 2023!
Jan 2023: Our paper “Towards the Generalization of Contrastive Self-Supervised Learning” has been accepted by ICLR 2023!
Jan 2023: Our paper “ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations” has been accepted by ICLR 2023!
Jan 2023: Our paper “Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding” has been accepted by ICLR 2023!
Dec 2022: I am excited to join the faculty at Shanghai Jiao Tong University as an Associate Professor!
Education & Work Experience
2012–2018: PhD in Computer Science, Tsinghua University (Advisor: Andrew Yao)
2009–2012: BSc in Electronic Engineering, Tsinghua University
2008–2009: Fundamental Science Class (数理基科班), Tsinghua University
2022/12–Present: Associate Professor at Shanghai Jiao Tong University
2024/04–Present: Research Consultant at Shanghai AI Lab
2018/07–2022/11: Research Scientist at Huawei Noah's Ark Lab (Director: Jun Yao)
2018/03–2018/06: Research Intern at Microsoft Research Asia (Mentor: Wei Chen)
2017/11–2018/02: Visiting Scholar at Harvard University (Advisor: Yaron Singer)
2015/08–2017/10: Research Intern at Microsoft Research Asia (Mentor: Wei Chen)
Awards & Honors
The 1st Winner Prize, Foundation Model Prompting for Medical Image Classification Challenge, NeurIPS 2023 Competition, 2023.
ICCV 2023 Best Paper Finalist for “When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method”, 2023.
Excellence in the Microsoft Research Asia Internship Program, 2018.
Outstanding Graduate Student, 2018.
Gold medal in the 24th Chinese Physics Olympiad (CPhO), Member of National Training Team (国家集训队, 31 people in China), 2007.
Teaching
CS7336: Machine Learning
CS7352: Advanced Neural Network Theory and Application
CS3968: Machine Learning Theory
Services
Reviewer: ICML (2022-2024), NeurIPS (2019-2023), ICLR (2022-2024).
Sponsors
We are deeply grateful to the following sponsors for their generous support of our research, listed in no particular order.
