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 DH3078.

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.

Teaching

In the upcoming year 2022-2023, I will be teaching:

CSC324 (fall 2022) with Prof. Louisa Harutyunyan
CSC311 (fall 2022) with Prof. Sonya Allin
CSC413 (winter 2023) with Prof. Florian Shkurti

Past courses and archived course websites

CSC311 Introduction to Machine Learning
- Fall 2021 (UTM) with Anthony Bonner
CSC263 Data Structure and Analysis
- Winter 2022 (UTM) with Tingting Zhu andSamar Sabie
- Winter 2021 (UTM) with Sushant Sachdeva andMichael Miljanovic
CSC413 Neural Networks and Deep Learning
- Winter 2022 (UTM) with Florian Shkurti
- Winter 2021 (UTM) with Florian Shkurti
CSC338 Numerical Methods
CSC324 Programming Languages
- Fall 2021 (UTM)
- Fall 2020 (UTM) with Andi Bergen
- Fall 2019 (UTM)
- Fall 2018 (UTM) with Daniel Zingaro
CSC321 Neural Networks and Machine Learning
- Winter 2020 (UTM) with Pouria Fewzee
CSC290 Communication Skills for Computer Scientists
APS360 Fundamentals of AI
- Summer 2019 (St. George)
- Winter 2019 (St. George)
CSC108 Introduction to Programming
- Summer 2018 (St. George) with Mark Kazakevich
CSC411/2515 Introduction to Machine Learning
- Winter 2018 (St. George) with Michael Guerzhoy

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.

I am hoping to supervise several student projects in Winter 2023 on projects related to:

Machine Learning education: identifying factors that contributes to student success in a machine learning course; identifying ideas that machine learning studends find challenging
PiazzaBot: Using machine learning to help the course staff answer Piazza questions more quickly.
Evaluation of computer science writing interventions: Statistical evaluation the result of writing instruction and feedback in first- and second-year computer science.
Student-lead implemenation projects in machine learning and/or programming languages.

If you are interested, please reach out with a description of the project you are interested in, your unofficial transcript, and your resume if you have one. Please reach out earlier rather than later if you can, so I can plan ahead.

Publications

Publications in CS/AI Education

Exploring Common Writing Issues in Upper-Year Computer Science Rehmat Munir, Francesco Strafforello, Niveditha Kani, Michael Kaler, Bogdan Simion, Lisa Zhang SIGCSE 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 SIGCSE 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

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

Universal Quantification and Implication in miniKanren. Ende Jin, Gregory Rosenblatt, Matthew Might, Lisa Zhang. miniKanren Workshop 2021 [paper and talk]

Universal Quantification and Implication in miniKanren. 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]

Contact

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.