I am an Associate Professor at the University of Toronto (UofT), leading the StatsLE (Statistics, Learning, and Engineering) group. I am broadly interested in statistics + AI, with a focus on leveraging statistics to make AI reliable and trustworthy. Motivated by challenges in the industrial sector, my interests extend to ensemble learning, transfer learning, GenAI, and reinforcement learning. I am also interested in AI for tech, finance, and science. We always welcome industry collaborations, so feel free to reach out!
Prior to my tenure at UofT, I was an associate research scholar at Princeton University. I obtained my PhD from the University of North Carolina at Chapel Hill (UNC-CH) and my BS in SCGY from the University of Science and Technology of China (USTC).
In addition to my faculty role, I also serve as an associate editor for Electronic Journal of Statistics (EJS) and as an area chair for ML conferences such as AISTATS, COLT, and UAI.
I am currently on leave from UofT, and may be slow to respond to emails. You can find herein my perspectives on the future of statistics as a discipline and possible future research directions, albeit in Chinese. Fundamentally, I champion problem- and data-centric approaches in statistics and AI, striving to drive tangible advancements in practice for the betterment of society and the progress of humanity. Additionally, I am a strong advocate for openreview.
Hiring interns in 2024! We are currently hiring 1-2 PhD student research interns in generative AI. Preferring candidates in visual generative models and welcoming those in LMMs. 20/40 hours per week; 8-month+ contract with the target of publishing. Join us anytime from now. Send me an email with CV and a short intro of yourself.
We are hiring! We are currently looking for PhD students with strong theoretical or engineering abilities. We also have multiple positions for research interns and visiting students. Please see here in English and here in Chinese.
Postdoc opportunities: We have a postdoctoral fellow position joint with The Hu Lab. Please also check Schmidt AI postdoc fellowship, Banting postdoc fellowship, SGS provost postdoctoral fellowship, UTSC provost postdoctoral fellowship, arts and science fellowship, UTSC postdoc fellowship. All fellowships require sponsorships, with some mandating faculty nominations and early application deadlines as early as July of the preceding year. Feel free to contact me for details.
Recommendation letters: If you are looking for information on recommendation letters, please refer to here.
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Let's talk stats: A@B with A=qiang.sun B=utoronto.ca (academic, on leave), or C@D with C=qsunstats D=gmail.com (non-academic).
Address: Hydro 9042, 700 University Ave, M5G 1Z5; or IC 478, 1095 Military Trail, M1C 5J9.
Recent News
[05/2024] Plenary talk on Stats + Trustworthy AI @ STATSTRO 2024.
[04/2024] Check out the final version of our Wassertein SSL paper.
[04/2024] Hengchao's paper on The mixtures of geodesic factor analyzers received the 2024 IMS Hannan Graduate Student Travel Award. Congratulations!
[03/2024] Our paper Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed awards was accepted to TMLR.
[03/2024] Buxin's paper on The exact risks of reference panel-based regularized estimators received the 2024 Statistical Learning and Data Science (SLDS) Student Travel Award. Congratulations!
[03/2024] Our paper Quadratic matrix factorization with applications to manifold learning was accepted to TPAMI.
[03/2024] Fireside chat about Stats and AI on 03/17. Here is a summary.
[01/2024] Our paper The exact risks of reference panel-based regularized estimators was posted on arXiv.
[01/2024] Our paper Rethinking self-supervision learning was accepted to ICLR 2024.
[01/2023] 1W-MINDS seminar (moved to March). Here is my recorded talk.
[10/2023] New version of the paper Do we need to estimate the variance in robust mean estimation? (Self-tuned robust mean estimators) was posted. The objective function proposed in this paper was referred to as the Sun-Huber objective by later works; see Holland (2023).
[10/2023] AI Talks.
[10/2023] Brown Bag Seminar.
[08/2023] Our paper Ensemble linear interpolators: The role of ensembling is now avaialble at arXiv. Bagging achieves a stabler performance in the weak signal-to-noise ratio regime while being consistent in strong signal-to-noise ratio regime. I refer to this ability as "algorithmic adaptivity". This notion of adaptivity holds the promise to explain "Why do some algorithms always outperform other seemingly optimal algorithms?" and serves a possible route to reliable/trustwothy AI.
[07/2023] ICML 2023.
[06/2023] PC@NeurIPS 2023.
[06/2023] Nuffield Econometrics Seminar at Oxford.
[05/2023] New paper on "Fast global convergence of gradient descent for low-rank matrix approximation" posted.
[05/2023] New version of the paper on "Self-tuned robust mean estimators (Do we need to estimate the variance in robust mean estimation?)". The objective function proposed in this paper was referred to as the Sun-Huber objective by later works; see Holland (2023).
[05/2023] Industry tech talk.
[05/2023] New paper on "Directional diffusion models".
[05/2023] New paper with Shuoguang on "Online generalized sparse regression: How does overparametrization help?".
[04/2023] The "Sketched ridgeless linear regression: The role of downsampling" paper has been accepted to ICML 2023. Congratulations to Xin, Yicheng, and Siyue!
[03/2023] Check our recent work on variance-aware robust reinforcement learnig, [arXiv]. Xiang is the first to come up with robust bandit and RL algorithms with tight variance-aware (instance-dependent) regrets!
[03/2023] Brown Bag Seminar.
[02/2023] Check our recent work on the statistical roles of downsampling: "Sketched ridgeless linear regression: The role of downsampling". Xin, Yicheng, and Siyue view downsampling as a dual of overparametrization, which then motivates new methods and theories!
[01/2023] I am serving as ACs for UAI 2023 and COLT 2023, and on the PC of KDD 2023 - ADS. Please consider submitting your best work at UAI 2023 and COLT 2023.
[10/2022] We are organizing an online reading seminar on (Reinforcement) Learning + X. Send me a message if interested.
[08/2022] I am serving as an AC for AISTATS 2023. Please consider submitting your best work at AISTATS 2023.
[07/2022] Together with Xingdong Feng, Chengchun Shi, Fan Zhou and Hongtu Zhu, we are organizing an online conference on ``From Statistics to Artificial Intelligence: Reinforcement Learning" at Shanghai this summer from 07/09 - 07/12. Thanks to SUFE, ARL and Mingshi for the support! Here is our conference flyer in Chinese and our conference flyer in English.
[04/2022] Talk at UNC-CH for the James E. Grizzle Distinguished Alumni Award from UNC-CH.
[04/2022] Congratulations to Dylan who successfully defended his thesis!
[03/2022] We are organizing an online reading seminar on Reinforcement Learning + X. Send me a message if interested.
[02/2022] New paper accepted to JASA.
[01/2022] I am serving on the PC for COLT 2022. Please consider submitting your best work at COLT 2022.
[12/2021] New paper accepted to Information and Inference.
[10/2021] I am happy to receive the James E. Grizzle Distinguished Alumni Award from UNC-CH.
[08/2021] New paper accepted to Integrating Materials and Manufacturing Innovation.
[07/2021] I am happy to serve as an area chair for AISTATS 2022. Please consider submitting your best work at http://aistats.org/aistats2022/.
[06/2021] New papers accepted to Electronic Journal of Statistics and Journal of Manufacturing Process.
[01/2021] I am happy to serve as a PC member for COLT 2021.
[11/2020] Congratulations to Dylan for passing the comprehensive exam.
[08/2020] New paper accepted to Journal of Machine Learning Research: Hoeffding's inequality for general Markov chains and its applications to statistical learning.
[06/2020] New paper accepted to Journal of Econometrics: Bayesian factor-adjusted sparse regression.
[06/2020] Congratulations to Anna Little for accepting an offer to join University of Utah as an assistant professor in the Department of Mathematics.
[05/2020] Congratulations to Qi Wang for accepting an offer to join Lanzhou University as an associate professor in the School of Life Sciences.
[05/2020] Congratulations to Rui Mao for receiving the prestigious IMS Hannan Graduate Student Award.
[04/2020] New paper accepted to JASA.
[03/2020] We are happy to receive the New Frontiers in Research Fund on machine learning for inverse material design.
[03/2020] New paper accepted to Nature Communications: Metagenome-wide association of gut microbiome features for schizophrenia.
[01/2020] New paper posted: Bayesian high-dimensional linear regression with generic spike-and-slab priors.
[01/2020] Read this nice commentary article about our recent paper on "Adaptive Huber regression": https://eranraviv.com/adaptive-huber-regression/.
[10/2019] Welcome to Mohamad Elmasri, who joins us as a postdoctoral fellow from MILA.
[09/2019] Welcome to Yicheng Zeng, who joins us as a postdoctoral fellow from HKBU.
[07/2019] New papers accepted to Molecular Psychiatry and FEBS Letters.
[07/2019] New paper posted to arXiv: Robust convex clustering: How does fusion penalty enhance robustness?
[05/2019] Invited talk at UCSD.
[04/2019] Research day: http://www.fields.utoronto.ca/activities/18-19/stats-research-day.
[04/2019] Invited talks at UW Seattle, MSU, UIUC, UW Madison.
[03/2019] New paper accepted to Statistical Science: User-friendly covariance estimation for heavy-tailed distributions.
[03/2019] New paper posted on arXiv: Bayesian factor-adjusted sparse regression.