This is the homepage of Lisa Zhang.

Me In a Nutshell

I am currently an Assistant Professor, Teaching Stream (CLTA) at the Department of Mathematical & Computational Sciences, University of Toronto Mississauga. My office is located at DH3078.

I held many roles during my career: startup founder, data scientist, machine learning researcher, pure math student, and now a computer science teacher. I am passionate about machine learning, teaching education, writing, and still have a soft spot for great data visualization and nerdy humour.

My current research interest is in the intersection of Computer Science Education and Machine Learning. More specifically, I am interested in using Machine Learning techniques to understand student code.

Current Teaching

Previous Teaching


Analyzing CS1 Student Code Using Code Embeddings

Robert Bazzocchi, Micah Flemming, Lisa Zhang
SIGCSE 2020 Technical Symposium Poster To appear

Model AI Assignments: Gesture Recognition using Convolutional Neural Networks

Lisa Zhang, Bibin Sebastian
EAAI 2020 To appear

AI Education Matters: Building a Fake News Detector

Michael Guerzhoy, Lisa Zhang, Georgy Noarov
AI Matters, Volume 5, Issue 3. September 2019 [paper]

First-order miniKanren representation: Great for tooling and search

Gregory Rosenblatt, Lisa Zhang, William E. Byrd, Matthew Might
ICFP miniKanren Workshop 2019 [paper]

Experience Report: Mini Guest Lectures in a CS1 Course via Video Conferencing

Lisa Zhang, Michelle Craig, Mark Kazakevich, Joseph Jay Williams
CompEd 2019 [paper]

Model AI Assignments: Building a Fake News Detector

Michael Guerzhoy, Lisa Zhang
EAAI 2019 [repository]

Neural Guided Constraint Logic Programming for Program Synthesis

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
NeurIPS 2018 [paper] [github] [workshop]

MSC Thesis

Leveraging Constraint Logic Programming for Neural Network Guided Program Synthesis
Supervisors: Richard Zemel, Raquel Urtasun

Reviving and Improving Recurrent Back-Propagation

Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel
ICML 2018 [arxiv]

Inference in probabilistic graphical models by Graph Neural Networks

KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow
ICLR Workshop Track 2018 [paper]

Learning deep structured active contours end-to-end

Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun
CVPR 2018 [arxiv]

Invited Talks

Using GraphX and Pregel on Browsing History to Discover Purchase Intent

Lisa Zhang
Spark Summit East 2016 [link]

Old Projects

Tiny EpiphanyThis is my blog, formerly known as "A Notebook". I write about whatever comes to mind, technical and not.

PolychartData visualization software that connects directly to your database, and helps you explore the data using drag-and-drop.

Polychart.JSJavaScript library built on top of RaphaelJS. My take on the Grammar of Graphics and how to handle interactions. The way data transformations are handled are interesting here too.

My ResumeSlightly more structured overview of what I did.

Data In ColourA now defunct data (visualization) blog. I keep it up there still to keep links alive. I bought both domains  and because I am Canadian.


You can email me at lczhang [at] cs [dot] toronto [dot] edu. If you are emailing me regarding a course, please include the course code in the email subject. Please mention if you are a current or past student.