Open Positions

My work is on probabilistic AI, which includes probabilistic machine learning (esp. Bayesian), decision-making under uncertainty (Bayesian optimization, bandits, tree search, etc.), and their applications in AI4Science (chemistry, biology, etc.). Specifically, I focus on the foundation of AI (think math, proofs, algorithms), but motivated by applications. Successful candidates will have high alignment with this, as shown by their transcripts, CVs, past experiences, and interview results.

Funded Positions

PhD (for candidates with thesis-based MSc)
  1. See Direct-Entry PhD requirements below. They must be fulfilled. Additional requirements below.
  2. You have a very clear, vivid motivation for obtaining a PhD.
  3. You are highly disciplined and reliable.
  4. You have at least a first-author conference (not workshop!) submission (doesn’t have to be accepted yet!) in ML venues like AISTATS, UAI, NeurIPS, ICML, and ICLR.
  5. You have at least one reference letter from a researcher/prof in ML who publishes regularly in the above venues.
Direct-Entry PhD (for candidates with BSc)

I invite academically successful final-year CS/Math/Stats BSc students who are highly motivated to do research to apply. In this stream, you start as an MSc Thesis student. After one year, there will be an evaluation process, and a successful student will be converted seamlessly into a PhD student for the next three years (in total, four years). Otherwise, the student continues with MSc Thesis for the next 2 terms (in total, five terms—1.5 years).

Requirements

  1. Well-defined motivation on why you want to do a PhD.
    • E.g., because you want to be a researcher, a prof, etc.
  2. Outstanding soft-skills:
    • Reliability, coachability, collegiality, discipline.
    • These and the previous points are just as important as the technical requirements below.
  3. Outstanding math and CS coverage & performance during your undergrad.
    • Linear algebra: Vectors, matrices, etc.
    • Multivariable calculus: Gradients, Jacobians, Hessians, etc.
    • Probability and statistics: Probability space, sample space, events, random variables, probability distributions.
    • Computer science: Data structures, algorithms, discrete mathematics, database, coding, etc.
  4. Good machine learning coverage in your undergrad.
    • Regression, classification, bias-variance, regularization, etc.
    • Coverage of more advanced topics in ML is a bonus.
  5. Research experience in machine learning, e.g., industry/lab internships and thesis.
    • Experience in writing research proposals or papers is a huge plus.

For Current Western Students

MSc Course-Based Directed Study

I have a couple of slots for Winter 2027. Note that this is only for current MSc Course-Based at Western.

Requirements

  1. DS 9000 (Introduction to Machine Learning) OR CS 9548 (Foundations of Machine Learning) AND
  2. CS 9553 (Deep Learning for Computer Vision) OR CS 9539 (Reinforcement Learning) OR CS 9840 (Probabilistic Generative AI)
CS/DS BSc Honours Thesis

Unfortunately I do not have undergrad supervision slots this year (Fall 2026 / Winter 2027). I will take several students for Fall 2027 / Winter 2028. You can apply for either CS 4490Z or DS 4999Z.

Requirements

  1. DS 3000 (Introduction to Machine Learning) AND
  2. CS 4452 (Deep Learning for Computer Vision) OR CS 4453 (Reinforcement Learning) AND
  3. Must also did well in MA 1600 (Linear Algebra) AND CS 1501 (Calculus) AND SS 2857 (Probability and Stats)

Students who have taken or is concurrently (seriously) taking CS 4451 are preferred.

Diversity, Equity, and Inclusion Statement

We are committed to building an inclusive environment that values diverse backgrounds, perspectives, and experiences. Applications from women, minorities, LGBTQ+ individuals, and those with non-traditional paths are encouraged.