Openings

StatsLE Lab | Statistics, Learning, and Engineering

StatsLE Openings

Before you proceed, please read here. 

Our group is looking for students, visitors, postdocs, and industry collaborators. We are particularly looking for students with strong engineering abilities. When contacting me, please use the email subject "Prospective intern/visiting student/PhD (pick one): Your Name - Your Affiliation".  Please read the instructions before getting in touch. I travel frequently, and I apologize in advance if I am slow in responding to your emails. 

• Prospective PhDs: I typically take 1-2 PhD students per year. We are mainly looking for students on GenAI and trustworthy AI.  Before contacting me, please read the followings carefully. 
  1. I value students' research ethics, hardworking, and independence over all other merits. 

  2. You are expected to be strong in either theory or engineering. Please note that you only need to be strong in either of the two. 

  3. Theoretically strong means that you have solid foundation in analysis and probability. You should be familiar with measure concentration, empirical process theory, or mean field asymptotics (one of the three). Below are some references. 

     • Measure concentration: First 5 chapters of High Dimensional Statstics (HDS) by Wainwright. In addition, you can also refer to Probability in High Dimension by van Handel or High Dimensional Probability (HDP) by Vershynin. 

     • Empirical process theory by Pollard.

     • Mean-field asymptotics: Please also read the first two chapters of  Random Matrix Theory for Machine Learning (RMT4ML) and related application chapters. You can learn more on equilibrium statistical mechanisms by referring to Statistical Mechanism of Lattice Systems (SMLS) . 

  4. Computationally strong means that you have strong deep learning engineering abilities. You are expected to have a paper in top venues.  Below are some references.

     • Reference: Dive into Deep Learning.  Get familiar with DDP. 

     • You should be reasonably familiar with general deep leanring and generative AI.

     • You should have a specific research area, such as model merging, that you are interested in. Please be specific.

     • You are expected to have worked on an open-source project such as FACIL.

  5. As we continue to explore new areas that may be influential in practice, you are also welcome to pursue your own research directions. 

  6. If you are interested in joining StatsLE, the best way is to get in touch early and do some research projects with us. This works as a trial period to see if we are a good fit.  We usually take PhDs from our own interns; see research interns below for details. 
  7. When preparing your applications, you should apply to the graduate program in statistics. In your cover letter and personal/research statement, please state how your research interests align with mine.

• Research interns: We take a few research interns each year to prepare them for PhD applications, and usually take our own PhD candidates from this pool too. Research interns are often externally funded, for example, by their own colleges or by CSC. We often can not provide funding unless in exceptional cases. When  contacting us for such opportunities, please

  1. briefly describe yourself including education background, research experience and achievements, programming/theoretical skills; 

  2. attach your resume (including your publications, ranking/GPA, and anything that is important) with a pdf file, and a Github link for your open source projects;

  3. attach a one-page research statement and your best papers.  

• PhD research interns: From time to time, we may have funded opportunities for funded PhD research interns. Please check our updates frequently. 

  1. Starting from Summer 2024, we are 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 in top ML/CV/NLP venues. Join us anytime from now. Send me an email with CV and a short intro of yourself. 

• Postdoc fellows: I may have openings for postdoctoral fellows. When inquiring about such positions, please send your CV along with a one-page research proposal and your best 2-3 papers in PDFs. Joint supervisions are possible. There are also open calls for postdoctoral positions at UofT: 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. Feel free to contact me for details. 

  1. We currently have a postdoctoral fellow position, joint with The Hu Lab, for generative AI and/or their applications to health science.

• Visitors whose research interests closely align with mine are welcome to contact me with your CV, representative papers, and a research plan. I can not provide fund to visitors.

• Industry collaborators are welcome to get in touch for potential collaborations. 

Some details about our ongoing research

If you are interested in joining our group as interns and students, please read the followings carefully (updating...). 

• Trustworthy and Reliable AI:  We are interested in using statistics to understand AI and then make AI reliable and trustworthy. For example, we can make AI more reliable by protecting the models/algorithms from random noise (and stats is really good at understating noise!) and other conditions such as background/lab effects, adversarial noise, and nonstationarity. 

• Generative AI: We are interested in developing frontier generative models in vision and making geneartive models trustworhty such as uncertainty quantification and calibration. 

• Large language models (LLMs) and RL:  We are also interested in LLM alignment and making LLMs trustworthy. 

Please consider to take STAD80, if

you are a UofT undergraduate or master student looking for some research experience and independent research projects for the first time. This course uses independent research projects as part of the evaluation. If you think you have a compelling story though, for example, you are an ICPC world finalist (strong in engineering), feel free to reach out. I apologize in advance if I am slow in responding to your emails. 

Tips for Graduate Students

So long, and thanks for all the tips by Witten D

Checklists for Stat-ML PhD students by Ramdas A

Efficient Writing by Marron JS

Write Statistics Right by Little R

Style and grammar tips for biostatistics and statistics students by Little R

How to be a modern scientist: https://leanpub.com/modernscientist

Grinding PhD: https://www.goodreads.com/en/book/show/15731248-the-phd-grind