I'm a PhD Student in the Machine Learning Group at the University of Toronto and the Vector Institute. My supervisor is Rich Zemel. I'm primarily interested in how to learn better and fairer algorithmic decision-making systems. My interests include fairness, causal inference, and generative modelling.
Excited to have co-organized and program chair the inaugural Pan-Canadian Self-Organizing Conference on Machine Learning (PC-SOCMLx)! This event is for Canadian graduate students in machine learning to meet each other and develop a research community. Thanks to the Vector Institute, Mila, Amii, CIFAR, and Facebook for their support in making this event happen. The feedback from the event was great and we're looking forward to the next one!
Excited to be attending the NBER Economics of Artificial Intelligence Conference, hosted at the University of Toronto, along with the NBER Economics of AI Young Scholars Workshop beforehand. Looking forward to meeting lots of interesting people from far outside my research area!
Causal Modeling for Fairness in Dynamical Systems is on arXiv! We discuss how to model standard problems of long-term unfairness in systems using causal graphical models, and demonstrate the advantages of this approach through detailed case studies.
Excited to be attending The Summer Institute on AI and Society, jointly hosted by CIFAR, AMII, and UCLA Law! Looking forward to meeting interesting people from across a range of disciplines.
Our paper "Detecting Extrapolation with Influence Functions" (with James Atwood and Alex D'Amour) was accepted as a contributed talk to the Workshop on Uncertainty and Robustness in Deep Learning at ICML 2019!
I'm very excited to be in Kigali, Rwanda until the end of May, to teach a course on Privacy and Fairness in Machine Learning at the African Master's in Machine Intelligence, along with Elliot Creager, Toni Pitassi, and Rich Zemel.
I'll be working as a research intern at Google Brain in Cambridge until mid-May, under the supervision of Alex D'Amour, thinking about causal inference and out-of-distribution detection.
In Princeton this week! I'm giving a talk at 12:20pm in the CS building, room 201 on Tuesday.
Fairness Through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data was accepted to FAT* 2019! See the list of accepted papers here. I'll also be presenting it at the Workshop on Ethical, Social, and Governance Issues in AI in December.
I attended the ML Fairness Workshop at Google in Cambridge, MA, USA. I was excited to meet a number of very interesting people doing research in fair ML - thanks to Google for hosting!
Fairness Through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data is on arXiv! We propose using causal inference and generative modeling to better learn from historically biased datasets.
Our code for Learning Adversarially Fair and Transferable Representations is on Github - thanks to my collaborator Elliot Creager for all his hard work on this code.
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer was accepted to NIPS 2018!
Learning Adversarially Fair and Transferable Representations: ICML 2018 (Oral)
Fairness in Machine Learning: Princess Margaret Hospital Summer Series 2018
Fairness in Machine Learning: An Overview: U of T Undergraduate AI Group (UAIG) AI Day, 2017
Stochastic Variational Inference: Tutorial for CSC412/2506: Probabilistic Learning and Reasong
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models: paper presentation for CSC2539, Topics in Computer Vision: Visual Recognition with Text
Differentially Private Recommender Systems: paper presentation for CSC2419, Topics in Cryptography: Algorithms & Complexity in Private Data Analysis
I'm also a musician - I love writing, singing, and playing music. I put out an album in August 2018 - if you're interested, check it out on Spotify or see my my website for more info! A couple of years ago I wrote the songs for a musical in the Toronto Fringe Festival. I also play jazz piano and love to improvise.
Email: lastname (at) cs (dot) toronto (dot) edu