Lecturer, Center for Statistics and Machine Learning, Princeton University

Assistant Professor (status only), Dept. of Statistical Sciences, 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 at the Center for Statistics and Machine Learning at Princeton University, 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."


Currently teaching:

  • Nothing. It's summer!

Recently taught:

  • SML201 — Introduction to Data Science, Spring 2019
  • SML310 — Research Projects in Data Science, Fall 2018
  • 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
  • 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

Student projects

  • Ramaneek Gill, Twitter Hashtag Recommendation and Analysis (CSC494/495, 2015-2016)
  • Karo Castro-Wunsch, RNN and Spectral Feature Based Music Analysis and Generation (CSC492, 2016)
  • Ujash Joshi, Photo Orientation Detection with ConvNets (CSC494/CSC495, 2016)
  • Omobola Okesanjo,Demonstration in Reinforcement Learning (CSC494, Fall 2016)
  • Joshua Samson-Seltzer, Computer Vision for Camera Trap Data (GGR417, 2016-2017)
  • Sam Banning, Information Extraction from Clinical Notes (CSC494, Fall 2017)
  • Yoonsun You, Classification of Cervical Spine Fractures in CT Images (ESC494, 2018-2019)
  • Navid Korhani, Information Extraction with Small Datasets (ESC494, 2018-2019)
  • Ananya Joshi, Creating an Automated Ideological Transformer Using Moral Reframing (COS497, 2019)
  • Georgy Noarov, Collecting a Large-Scale Dataset of Fake News (2019-)

Grad students

Thi Hai Van Do (MSc in Applied Computing, University of Toronto 2018). Merchandise Classification with Machine Learning for E-commerce.

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 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).


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).