This is the homepage of Lisa Zhang.
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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
Past courses and archived course websites
- CSC311 Introduction to Machine Learning
- CSC324 Programming Languages
- CSC263 Data Structure and Analysis
- CSC413 Neural Networks and Deep Learning
- CSC338 Numerical Methods
- CSC321 Neural Networks and Machine Learning
- CSC290 Communication Skills for Computer Scientists
- APS360 Fundamentals of AI
- CSC108 Introduction to Programming
- Summer 2018 (St. George) with Mark Kazakevich
- CSC411/2515 Introduction to Machine Learning
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
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 SIGCSE 2023 (to appear)
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
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] 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.