Nocturne 2023 lyrics

Assistant Professor, Teaching Stream, Division of Engineering Science and Dept. of Mechanical and Industrial Engineering, University of Toronto

Affiliate Scientist, Li Ka Shing Knowledge Institute, St. Michael's Hospital, collaborating with LKS-CHART


I work in machine learning focusing on ML applications to patient chart data, computer vision, and applied statistics and take on data science consulting projects. I teach in the Division of Engineering Science at the University of Toronto, and collaborate with the Li Ka Shing Centre for Healthcare Research, Analytics, and Training at St. Michael's Hospital in Toronto.

My last name is pronounced ger-JOY, with a hard "g", and with the "J" pronounced like the "s" in "measure."

Note to prospective students: I am happy to discuss collaboration opportunities with current UofT students. I get a lot of email from prospective students form outside of the University of Toronto. I am not able to help students from outside the University of Toronto get admitted and not able to asses applicant files. Please see here and here for application information.


Currently teaching

Recently taught

  • ECE324 — Machine Intelligence, Software, and Neural Networks, Winter 2022, Winter 2023
  • ESC180 — Introduction to Computer Programming, Fall 2009/2010/2014-2016, 2020-2022
  • ESC190 — Algorithms and Data Structures, Winter 2021, 2023
  • MIE490 — Mechanical and Industrial Engineering Capstone, Fall-Winter 2022-2023
  • SML201 — Introduction to Data Science, Spring 2020
  • SML480 — Pedagogy of Data Science (NEW!), Spring 2020
  • SML310 — Research Projects in Data Science, Fall 2019, Fall 2018
  • SML201 — Introduction to Data Science, Spring 2019
  • CSC411/CSC2515 — Machine Learning and Data Mining, Winter 2018 (local cached copy)
  • CSC411 — Machine Learning and Data Mining, Winter 2017
  • CSC180 — Introduction to Computer Programming, Fall 2014/2015/2016/2020
  • STA303/STA1002 — Methods of Data Analysis II, Summer 2016
  • C4M — Computing for Medicine, Winter-Summer 2016
  • CSC321 — Introduction to Neural Networks and Machine Learning, Winter 2016 (won the CSSU award for excellence in teaching)
  • CSC320 — Introduction to Visual Computing, Winter 2015
  • CSC165 — Mathematical Expression and Reasoning for Computer Science, Summer 2014

Grad students

  • Kexin Chen, Using Social Media Data to Explore Complex Disease, M. Eng. project, 2023
  • Zixuan Wan, Enabling Automatic Checking of Empirical Standards Criteria for Academic Publishers, M. Eng project, 2023-2024
  • Hauze Xu, Text mining API for automated keywords extraction and tag analysis. Mitacs Business Partnership internship, 2023-2024 M.Eng
  • Vijaykumar Maraviya, A Peer Reviewer Recommender System. MEng in Mechanical and Industrial Engeering project, University of Toronto, 2021 (co-supervised with Eldan Cohen).
  • Danting Ada Dong, Object Detection and Image Segmentation for Receipt Images. MSc in Applied Computing, University of Toronto, 2021 (co-supervised with Lueder Kahrs).
  • Elisa Du, Enriched Understanding of Retail Receipts for Personalized Financial Insights. MSc in Applied Computing, University of Toronto, 2021 (co-supervised with Rohan Alexander).
  • Thi Hai Van Do, Merchandise Classification with Machine Learning for E-commerce. MSc in Applied Computing, University of Toronto, 2018.

Undergraduate student projects

  • Sajjad Hashemi, Improving the cost function of variational autoencoders for material design, Argonne National Laboratory project, 2023-
  • Sophie Lee, Designing a Challenging Test Set for LLMs, EngSci thesis, 2023-2024
  • Haani Ahmed, Speeding Up Reinforcement Learning by Selecting Pretrained Models using Guided Backpropagation, EngSci thesis, 2023-2024
  • Juho Kim, Exploring the "Honour Culture" Theory Using Social Media Data, EngSci thesis, 2023-2024
  • Kailyn Yoon, Exploring the Moral Foundations Theory Using Social Media Data, EngSci thesis, 2023-2024
  • Jasmine Zhang, Robustness in the context of text classification with Transformers, EngSci thesis, 2023-2024
  • Yiping Wang, Interpretability of ViT when Colour is an Important Cue, EngSci thesis, 2023-2024
  • Cameron Smith, Interpretability of ConvNets when Colour is an Important Cue, EngSci thesis, 2023-2024
  • Amy Saranchuk, Interpretability for Self-Supervised Models, EngSci thesis, 2023-2024
  • Jason Zuo, How do Transformers Use (And Don't Use) Positional Encoding?, EngSci thesis, 2023-2024
  • Tracy Qian, WGANs for Model Checking of Hierarchical Models for Small Datasets, EngSci thesis, 2023-2024
  • Niko Marinkovich, Analyzing the Observed Phenomenon that Computer Chess Beat Humans EngSci Much Earlier than Computer Go, EngSci thesis, 2023-204
  • Kamron Zaidi, Machine Learning of Understanding Brilliant Moves in Board Games, EngSci thesis, 2023-2024
  • Sadam Alabed Alrazak, Talal Alaeddine, Kevin Karam, Omar Samman, Ahmad Khater, Data Collection on Youth Sports Participation in Canada, MIE capstone project 2023-2024 (client: Sasha Gollish, Faculty of Kinesiology, University of Toronto)
  • Yichen Mao, Michael Simunec, Jiaming Li, Mavis Chen, Qingyi Xia, Designing, Building, and Validating an App for Coaching Athletes at the University of Toronto, MIE capstone project 2023-2024 (client: Sasha Gollish, Faculty of Kinesiology, University of Toronto)
  • Pavlos Constas, Vikram Rawal, Matthew Honorio Oliveira, Andreas Constas, Aditya Khan, Kaison Cheung, Najma Sultani, Carrie Chen, Micol Altomare, Michael Akzam, Jiacheng Chen, Vhea He, Lauren Altomare, Heraa Murqi, Asad Khan, Nimit Amikumar Bhanshali, Youssef Rachad, Toward A Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency, summer research project
  • Ian Wu and Youssef Rachad, Developing materials for introductory computer science courses, work-study students, 2023
  • Jackson Kaunismaa, Mechanistic Interpretation of Neural Networks, work-study researcher student, 2023
  • Mustajab Azam, Sajjad Hashemi, Shahzeb Ahmed, Variational Autoencoders for Material Design, MIE Capstone Project, 2023
  • Sajjad Hashemi, Variational Autoencoders for Material Design (with Noah Paulson)
  • Jackson Kaunismaa, Mechanistic Interpretation of Neural Networks (independent project, 2022-2023)
  • Shardul Ghuge, Multivariate Time Series Forecasting of Patient’s Health using LSTM (UofT EngSci thesis, 2022-2023)
  • Jonathan Spraggett, Sim2Real Reinforcement Learning for Soccer skills (UofT EngSci thesis, 2022-2023)
  • Noelle Lim, Using Machine Learning Transformers to Detect Comorbid Anxiety and ADHD (UofT EngSci thesis, 2022-2023)
  • Vo Yoang, Investigating How Meaning is Stored and Manipulated by Transformers (UofT EngSci thesis, 2022-2023)
  • George Saad, Using Pre-Training to Speed Up Deep Reinforcement Learning (UofT EngSci thesis, 2022-2023)
  • Michael Ruan, Improving Sample Efficiency of Deep Reinforcement Learning With State Representation Learning (UofT EngSci thesis, 2021-2022)
  • Morgan Sun, Generating Novel Hypotheses from Complex Models of Clinical Data (UofT EngSci thesis, 2021-2022)
  • Jonathan Spragett, Learning robot soccer-ball-kicking behaviour with reinforcement learning (UofT EngSci thesis, 2021-2022)
  • Eric Wang, Learning robot walking behaviour with reinforcement learning (UofT EngSci thesis, 2021-2022)
  • Yize Zhao, Improving content-based video retrieval (UofT EngSci thesis, 2021-2022)
  • Chi-Chung Cheung, Learning to play Go by playing on smaller Go boards (UofT EngSci thesis, 2021-2022)
  • Daniel Pinheiro Leal, Pretraining to speed up reinforcement learning for control tasks (UofT EngSci thesis, 2020-2021)
  • Theodore Block, The Triple Descent Curve when Training Single-Hidden-Layer Neural Networks (UofT EngSci thesis, 2020)
  • Calvin Tan, Pretraining to speed up Q-learning for card games with large feature spaces (UofT EngSci thesis, 2020-2021)
  • Coco Zhang, Guided backpropagation and non-monotonic output functions (UofT EngSci thesis, 2020-2021)
  • Vydhourie Thiyageswaran, May Jiang, and Anthony Hein, Developing an agent for Gin Rummy (Princeton summer work)
  • Vydhourie Thiyageswaran, Pretraining Transformer Architectures for Q-learning (Princeton CSML IW)
  • Ryan Lee, Investigating the Double Descent Curve (Princeton Math junior paper, 2020)
  • Claire S. Lee, Classifying and Understanding Affective States in Bipolar Disorder from Video (Princeton COS498, 2020-)
  • Daniel K. Chae, Generating Novel Hypotheses from Complex Models of Clinical Data (Princeton COS498, 2020)
  • Niranjan Shankar, Text Classification for Small Datasets (Princeton COS498, 2020)
  • Max Piasevoli, Generative Adversarial Networks for Model Checking (Princeton COS498, 2020)
  • Preeti Iyer, Understanding Urban Mobility in NYC with Latent Factor Models (Princeton undergrad thesis, 2020)
  • Georgy Noarov, Collecting a Large-Scale Dataset of Fake News (Princeton CSML certificate project 2019-2020)
  • Ananya Joshi, Creating an Automated Ideological Transformer Using Moral Reframing (Princeton undergrad thesis, 2019)
  • Navid Korhani, Information Extraction with Small Datasets (UofT EngSci thesis, 2018-2019)
  • Yoonsun You, Classification of Cervical Spine Fractures in CT Images (UofT EngSci thesis, 2018-2019)
  • Sam Banning, Information Extraction from Clinical Notes (UofT CSC494, Fall 2017)
  • Joshua Samson-Seltzer, Computer Vision for Camera Trap Data (UofT GGR417, 2016-2017)
  • Omobola Okesanjo, Demonstration in Reinforcement Learning (UofT CSC494, Fall 2016)
  • Ujash Joshi, Photo Orientation Detection with ConvNets (UofT CSC494/CSC495, 2016)
  • Karo Castro-Wunsch, RNN and Spectral Feature Based Music Analysis and Generation (UofT CSC492, 2016)
  • Ramaneek Gill, Twitter Hashtag Recommendation and Analysis (UofT CSC494/495, 2015-2016)

My assignments around the web

I enjoy creating and sharing my assignments. I sometimes enjoy Googling my name to see who uses them.


I am co-chair of the Symposium on Educational Advances in Artificial Intelligence (EAAI) for 2021 (virtual conference co=located with AAAI in 2021) and co-located with AAAI in Vancouver, BC in 2022.

I am on the Program Committee of the Toronto Machine Learning Summit (2017-), the Canadian Conference on Artificial Intelligence (2018-) and the Symposium on Educational Advances in Artificial Intelligence (Model AI Assignments track and Diversity and Inclusion in AI Education track 2020-). Submit your stuff!

Recent media mentions

Quoted in MIT Technology Review (Jun. 2017) on teaching machine learning with TensorFlow and on the TensorFlow ecosystem; the story also appeared in Business Insider (Jul. 2017); quoted in The Varsity (Feb. 2017) on AI for literature search and on careers in machine learning and data science; quoted in The Cannon on "curving" and grading policies; Computing for Medicine profiled in UofT News (Mar. 2016).


Come to the CSML Reading Group

AlexNet implementation+weights in TensorFlow

The UofT Data Science Team

Tournament for Pong AIs, 2016 (the 2015 tournament).

Advice to students about asking for reference letters

The Course Webpage Wiki — please contribute!

Just for fun

Back in grad school, I used to co-ordinate the weekly CSGSBS cookie breaks.

Derandomizing Bogosort: A Very Serious Webpage.

Re: Your Grades at the NΨ 2017 Nocturne Talent Show (lyrics).

How Deep is Your Love with Gradient Descent (note the lyrics: "How deep is your love? I really need to learn.") Source code.