Alan Chan

Alan Chan

PhD Student




I am a PhD student at Mila, advised by Nicolas Le Roux. Previously at Amii + UAlberta, I was an MSc student with Martha White. I currently focus on reinforcement learning and machine learning in fundamental science and social justice contexts. In addition to my research interests, I love to travel, read, and talk to new people every day! Please feel free to reach out to chat about anything!


  • Reinforcement learning
  • Machine learning for fundamental science
  • Fair, interpretable, and participatory machine learning


  • PhD in Computer Science, 2025

    Université de Montréal

  • MSc in Computing Science, 2020

    University of Alberta

  • BSc in Mathematics, 2018

    University of Alberta

Recent Papers

Approaching Ethical Impacts in AI Research: The Role of a Graduate Student

Extant societal challenges and the problematic applications of algorithmic systems so far have motivated broader consideration of the …

Inverse Policy Evaluation for Value-based Sequential Decision-making

Value-based methods for reinforcement learning lack generally applicable ways to derive behavior from a value function. Many approaches …

Training Recurrent Neural Networks Online by Learning Explicit State Variables

Recurrent neural networks (RNNs) allow an agent to construct a state-representation from a stream of experience, which is essential in …

Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning

We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that …

Automatic Prediction of Tumour Malignancy in Breast Cancer with Fractal Dimension

Breast cancer is one of the most prevalent types of cancer today in women. The main avenue of diagnosis is through manual examination …


I’m interested in (1) improving well-being and build a society more in accord with the demands of justice, (2) the development of AI to help to achieve these ends, and (3) the mitigation of the harms AI poses. I currently think a lot about:

  • Reinforcement learning (at the moment: policy optimization, exploration)
  • Interpretable, fair, and participatory machine learning
  • ML for fundamental science

Recent & Upcoming Talks

MSc Seminar

My seminar for my MSc thesis.

Problems with Fair ML

This talk will be a mostly non-technical dive into problems that I find with a lot of fair ML research today. I will begin with some …

Recent Posts


I always struggle with the languages box on applications: what do I put for Cantonese and Mandarin? I’m sure that I don’t speak them fluently or even moderately well, but I’m not a beginner either.

Talking to Strangers

My last daily chat with a stranger was several months ago. Last year, motivated mostly by the positive experiences of a friend who had done a similar challenge, I decided to find somebody new—usually a student on campus—to talk to every day.

Coping and Learning

When I was in junior high school, I had already grown a shell of isolation. When we feel lonely, especially after a prolonged period of time, it is more difficult to reach out, even accept, the social interaction we need to warm the frigidity in our hearts.

Conflicts in Categorizing

Disclaimers I use the word category quite loosely in this piece. I do not refer to the mathematical field of Category Theory, although there may be some interesting, unexplored connections there.

Reviewing and Conferences

This piece is an imaginary dialogue between me and a friend about the process of reviewing for conferences. I’m currently working through my 6 NeurIPS reviews, and in the process have generated a lot of thoughts about my experiences.