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 DH3068.
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.
- APS360 Fundamentals of AI Summer 2019, University of Toronto
- CSC290 Communication Skills for Computer Science Winter 2019, University of Toronto Mississauga
- CSC338 Numerical Methods Winter 2019, University of Toronto Mississauga
- APS360 Fundamentals of AI Winter 2019, University of Toronto
- CSC324 Programming Languages Fall 2018, University of Toronto Mississauga (with Daniel Zingaro)
- CSC290 Communication Skills for Computer Science Fall 2018, University of Toronto Mississauga
- CSC108 Introduction to Programming Summer 2018, University of Toronto (with Mark Kazakevich)
- CSC411/2515 Introduction to Machine Learning Winter 2018, University of Toronto (with Michael Guerzhoy)
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
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]
Using GraphX and Pregel on Browsing History to Discover Purchase Intent
Spark Summit East 2016 [link]
Tiny Epiphany: This is my blog, formerly known as "A Notebook". I write about whatever comes to mind, technical and not.
Polychart: Data visualization software that connects directly to your database, and helps you explore the data using drag-and-drop.
My Resume: Slightly more structured overview of what I did.
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.