This is the homepage of Lisa Zhang.


Me In a Nutshell

I am an Assistant Professor, Teaching Stream at the Department of Mathematical & Computational Sciences, University of Toronto Mississauga. My office is located at DH3072.

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

I am a member of the CS Education Research Group at the University of Toronto.


Past courses and archived course websites

Undergraduate Research/Project Courses

I frequently work with undergraduate computer science students at the University of Toronto Mississauga on research or implementation projects as part of CSC398/492/493 Independent Study Courses and Research Opportunity Programs. Prof. Larry Zhang has a very informative FAQ about these project courses. Courses with me tend to have more of a research slant.

Most (but not all) students I worked with have taken a programming languages or machine learning course with me and have done very well. The project idea can come from either you or me. To get a sense of the type of projects that I supervise, please scroll through the list of publications below. Almost all of the recent papers/posters/workshops are collaborations with students.

New (Summer 2023): I will be running a reading course on dependent types, reading the book "The Little Typer" by Daniel P. Friedman and David Thrane Christiansen. If you are interested, please express your interest here:


Publications in CS/AI Education

Just-In-Time Prerequisite Review for a Machine Learning Course Lisa Zhang, Sonya Allin The Western Canadian Conference on Computing Education (2023) [to appear]
Embedding and Scaling Writing Instruction Across First- and Second-Year Computer Science Courses Lisa Zhang, Bogdan Simion, Michael Kaler, Amna Liaqat, Daniel Dick, Andi Bergen, Michael Miljanovic, Andrew Petersen Proceedings of the 54th ACM Technical Symposium on Computer Science Education (2023) [paper] Best Paper - Experience Reports and Tools Track
Exploring Common Writing Issues in Upper-Year Computer Science Rehmat Munir, Francesco Strafforello, Niveditha Kani, Michael Kaler, Bogdan Simion, Lisa Zhang Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (2022) [paper]
Additional Evidence for the Prevalence of the Impostor Phenomenon in Computing Angela Zavaleta Bernuy, Anna Ly, Brian Harrington, Michael Liut, Andrew Petersen, Sadia Sharmin, Lisa Zhang Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (2022) [paper]
AI Education Matters: Text Denoising Autoencoder for News Headlines Lisa Zhang, Pouria Fewzee, Charbel Feghali AI Matters, Volume 7, Issue 1. September 2021 [paper]
Model AI Assignments: Text Denoising Autoencoder for News Headlines Lisa Zhang, Pouria Fewzee EAAI 2021 [repo][article]
Model AI Assignments: Gesture Recognition using Convolutional Neural Networks Lisa Zhang, Bibin Sebastian EAAI 2020 [repo] [article]
AI Education Matters: Building a Fake News Detector Michael Guerzhoy, Lisa Zhang, Georgy Noarov AI Matters, Volume 5, Issue 3. September 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] [ article]

Posters/Workshops in CS/AI Education

Student Reactions to Bots on Course Q&A Platform Yu-Chieh Wu, Andrew Petersen, Lisa Zhang ITiCSE 2022 Poster [paper]
Using Deep Learning to Localize Errors in Student Code Submissions Shion Fujimori, Mohamed Harmanani, Owais Siddiqui, Lisa Zhang SIGCSE 2022 Technical Symposium Poster [paper]
CS1 Programming Feedback with Bug Localization. Lucas Roy, Haotian Yang, Lisa Zhang The 6th SPLICE Workshop at L@S (2020) [paper]
Recommending Personalized Review Questions using Collaborative Filtering Zain Kazmi, Wafiqah Raisa, Harsh Jhunjhunwala, Lisa Zhang The 6th SPLICE Workshop at L@S (2020) [paper]
Analyzing CS1 Student Code Using Code Embeddings Robert Bazzocchi, Micah Flemming, Lisa Zhang SIGCSE 2020 Technical Symposium Poster [paper] [poster]

Programing Languages Workshop

Fail Fast and Profile on: Towards a miniKanren Profiler Sloan Chochinov, Daksh Malhotra, Gregory Rosenblatt, Matthew Might, Lisa Zhang. miniKanren Workshop 2022 [paper]
Universal Quantification and Implication in miniKanren. Ende Jin, Gregory Rosenblatt, Matthew Might, Lisa Zhang. miniKanren Workshop 2021 [paper and talk]
Relational Floating-Point Arithmetics. Lucas Sandre, Malaika Zaidi, Lisa Zhang miniKanren Workshop 2021 [paper and talk]
A Relational Interpreter for Synthesizing JavaScript Artem Chirkov, Gregory Rosenblatt, Matthew Might, Lisa Zhang miniKanren Workshop 2020 [paper] [talk]
First-order miniKanren representation: Great for tooling and search Gregory Rosenblatt, Lisa Zhang, William E. Byrd, Matthew Might miniKanren Workshop 2019 [paper]

Machine Learning

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]
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]


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.