Michael Zhang

I’m Michael Zhang, a final-year PhD student at the University of Toronto and Vector Institute, where I am fortunate to be supervised by Jimmy Ba and supported by the NSERC CGS Fellowship and the Schwartz Reisman Graduate Fellowship.

I'm interested in improving our understanding of neural networks and the algorithms we use to train them (more specifics below). I did my undergrad and Master's at the University of California, Berkeley and have previously done internships at Tesla, Google Research, and LinkedIn.

You can contact me at [first name] @cs.toronto.edu.

CV  /  Google Scholar  /  Twitter  /  Substack

• Dec 2023: We released a new preprint on using language models for hyperparameter tuning; we'll present this at the Neurips FMDM workshop. Arxiv link. Happy to chat about this direction!
• Dec 2023: I gave an interview about AI Safety and related research directions at Toronto, which was featured in an U of T news article. Link
• July 2023: Presenting work on language model+prompting-based approach for answering course questions at ITiCSE in Finland.
• May 2023: I'm grateful to receive the Schwartz Reisman Institute graduate fellowship!
• Apr 2023: We released a new preprint on a prompt boosting algorithm with large language models: arxiv.
• Feb 2023: Multi-Rate VAE received an oral presentation (top-5% of accepted papers) at ICLR.
  
• Jan 2023: I am an instructor for CSC311: Intro to Machine Learning this semester!

Some topics I am currently interested in:
-Improving our understanding of deep neural network optimization
-Can we make hyperparameter tuning easier (e.g. more efficient and automated)?
-How can we develop current and future AI models that are more likely to be socially positive?
-AI Safety and interdisciplinary thinking about technology (e.g. through SRI).

A full list of publications is on Google Scholar.

We propose and evaluate a question-answering system that uses decomposed prompting to classify and answer student questions on a course discussion board.

We propose Multi-Rate VAE (MR-VAE), a hypernetwork which is capable of learning multiple VAEs with different rates in a single training run.

Autoregressive models are better for learning dynamics models than standard feedforward models in the fixed dataset setting.

We investigate and analyze why many neural networks have the loss decreasing monotonically in weight space from initialization to final solution.

Framework and aggregation rules for combining the preferences of multiple agents with noisy views of some ground truth.

Optimization algorithm that speeds up training by using a search direction generated by multiple steps of an inner optimizer.

Approach for tackling sparse reward tasks by generating goals of intermediate difficulty.

Framework for safely transferring policies learning in simulation to the real world.

I enjoy teaching and am grateful to have had the opportunity to teach a variety of different subjects in computer science.

Lecturing

Toronto CSC311: Introduction to Machine Learning: Winter 2023

Teaching Assistant

Toronto CSC311: Introduction to Machine Learning : Fall 2021 (head TA)
Toronto CSC2547: Introduction to Reinforcement Learning : Winter 2021
Toronto CSC421/2516: Neural Networks and Deep Learning : Winter 2019
Toronto CSC2541: Deep Reinforcement Learning : Fall 2018
UC Berkeley CS170: Introduction to Algorithms - Spring 2018
UC Berkeley CS189: Introduction to Machine Learning - Fall 2017
UC Berkeley CS189: Introduction to Machine Learning - Spring 2017
UC Berkeley CS70: Discrete Math and Probability - Fall 2016
UC Berkeley CS70: Discrete Math and Probability - Spring 2016

At Berkeley, I was awarded the Outstanding Graduate Student Instructor award.

I try to adhere to the principle "journey before destination" (Brandon Sanderson). Some activities I enjoy: basketball, running, exploring new places, reading, improv.

2020 Book Recs

Template from here!