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.

Current Teaching

• APS360 Fundamentals of AI Summer 2019, University of Toronto

Previous Teaching

• 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 Introduction to Machine Learning Winter 2018, University of Toronto (with Michael Guerzhoy)

Publications

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
[paper]

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 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.

Polychart.JS: JavaScript 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 Resume: Slightly more structured overview of what I did.

Data In Colour: A now defunct data (visualization) blog. I keep it up there still to keep links alive. I bought both domains dataincolour.com  and  dataincolor.com because I am Canadian.

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.