Office: TBD Email: guerzhoy at cs.toronto.edu

Appointment calendar — to set up a meeting, please consult my public calendar (Google log-in required) and send an email with proposed times.

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."

Teaching

Currently teaching

ESC180 — Introduction to Computer Programming

Recently taught

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
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 (UofT CSC494/495, 2015-2016)
Karo Castro-Wunsch, RNN and Spectral Feature Based Music Analysis and Generation (UofT CSC492, 2016)
Ujash Joshi, Photo Orientation Detection with ConvNets (UofT CSC494/CSC495, 2016)
Omobola Okesanjo,Demonstration in Reinforcement Learning (UofT CSC494, Fall 2016)
Joshua Samson-Seltzer, Computer Vision for Camera Trap Data (UofT GGR417, 2016-2017)
Sam Banning, Information Extraction from Clinical Notes (UofT CSC494, Fall 2017)
Yoonsun You, Classification of Cervical Spine Fractures in CT Images (UofT EngSci thesis, 2018-2019)
Navid Korhani, Information Extraction with Small Datasets (UofT EngSci thesis, 2018-2019)
Ananya Joshi, Creating an Automated Ideological Transformer Using Moral Reframing (Princeton undergrad thesis, 2019)
Georgy Noarov, Collecting a Large-Scale Dataset of Fake News (Princeton CSML certificate project 2019-2020)
Preeti Iyer, Understanding Urban Mobility in NYC with Latent Factor Models (Princeton undergrad thesis, 2020)
Max Piasevoli, Generative Adversarial Networks for Model Checking (Princeton COS498, 2020)
Niranjan Shankar, Text Classification for Small Datasets (Princeton COS498, 2020)
Daniel K. Chae, Generating Novel Hypotheses from Complex Models of Clinical Data (Princeton COS498, 2020)
Claire S. Lee, Classifying and Understanding Affective States in Bipolar Disorder from Video (Princeton COS498, 2020-)
Ryan Lee, Investigating the Double Descent Curve (Princeton Math junior paper, 2020)
Vydhourie Thiyageswaran, Pretraining Transformer Architectures for Q-learning (Princeton CSML IW)
Vydhourie Thiyageswaran, May Jiang, and Anthony Hein, Developing an agent for Gin Rummy (Princeton summer work)
Coco Zhang, Guided backpropagation and non-monotonic output functions (UofT EngSci thesis, 2020-2021)
Calvin Tan, Pretraining to speed up Q-learning for card games with large feature spaces (UofT EngSci thesis, 2020-2021)
Theodore Block, The Triple Descent Curve when Training Single-Hidden-Layer Neural Networks (UofT EngSci thesis, 2020)
Daniel Pinheiro Leal, Pretraining to speed up reinforcement learning for control tasks (UofT EngSci thesis, 2020-2021)

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.

Automatically Solving SAT/TOEFL Synonym Questions with Computational Linguistics, presented at Nifty Assignments at SIGCSE 2017. Used in CPSC231 at the University of Calgary (2017) and in CS2230 at the University of Iowa (2017). Originally designed for CSC180 (2010-2016), with Jackie C.K. Cheung and François Pitt.
Understanding How Recurrent Neural Networks Model Text, presented at Model AI Assignments at EAAI 2018. Used in CSC321 at the University of Toronto (2017) and at Hacettepe University in CMP722 (2017) and CMP784 (2018). Originally designed for CSC321 (2016), with Renjie Liao.
Neural Networks for Face Classification with TensorFlow, presented at Model AI Assignments at EAAI 2018. Used in part at Udacity as part of the Self-Driving Car Engineer Nanodegree (2016-). Some materials used in 67103 at the Hebrew University of Jersualem (2017) and in AMTH/CPSC 663b at Yale University (2018). Used at Hacettepe University in BBM406, CMP722, and CMP784 (2017-2018). Used at the University of Toronto in ECE521 (2018). Used by the Intel AI Academy (Week 6). Partially used in id2223 at KTH (2018). Originally designed for CSC321 and CSC411 (2016-2017).
AI Engines for Pong, presented at Nifty Assignments at SIGCSE 2018 and posted on NCWIT's EngageCSEdu. Used in part in the Deep Learning and Artificial Intelligence program at LMU Munich (2019). Originally designed for CSC180 and for contests at the University of Toronto (2014-2016). Used in COMP 202 at McGill University (2020).
Building a Fake News Detector, presented at Model AI Assignments at EAAI 2019. Used in part in CSC411/2515 at the University of Toronto (Fall 2018). Originally designed for CSC411/2515 (Winter 2018), with Lisa Zhang.
Predicting and Preventing Deaths in the ICU: Designing and Analyzing an AI System, presented at Model AI Assignments at EAAI 2020. Originally designed for SML 201 (Spring 2019), with Stephen Keeley.
Auditing the COMPAS Recidivism Risk Assessment Tool, presented at the Tips, Techniques, and Courseware session at ITiCSE 2020. Originally designed for SML 201 (Spring 2019), with Claire S. Lee and Jeremy Du.

Conferences

I am co-chair of the Symposium on Educational Advances in Artificial Intelligence (EAAI) for 2021 (collocated with AAAI in Vancouver in Feb. 2021) and 2022 (TBD).

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!