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University of Toronto
Data Scientist
June 25, 2026 – Present
efficient-annotation-cookbook
March 24, 2021 – October 7, 2021
Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)
View Projectsocial-driving
January 23, 2020 – January 13, 2021
Design multi-agent environments and simple reward functions such that social driving behavior emerges
View Projectvirtualhome
March 25, 2019 – Present
API to run VirtualHome, a Multi-Agent Household Simulator
View Projectunrolled-gans
June 26, 2018 – June 27, 2018
PyTorch Implementation of Unrolled Generative Adversarial Networks
View Projectawesome-neural-programming
February 26, 2018 – July 7, 2018
A curated list of awesome neural programming resources
View Projectgail-tf
October 17, 2017 – April 23, 2018
Tensorflow implementation of generative adversarial imitation learning
View Projectdni.pytorch
May 7, 2017 – October 19, 2017
Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch
View ProjectCoGAN-tensorflow
November 30, 2016 – September 17, 2017
Implement Coupled Generative Adversarial Networks in Tensorflow
View ProjectDeep-Reinforcement-Learning-Survey
May 24, 2016 – December 23, 2017
My Exploration on Deep Reinforcement Learning Survey
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily concentrated on advanced research topics in AI/ML, particularly deep reinforcement learning and GANs. While this demonstrates deep technical interest, the lack of diversity in project types (e.g., business intelligence, data engineering, A/B testing, traditional statistical modeling) might indicate a narrower focus than typically desired for a broad Data Scientist role. The experience level is listed as 0, which contradicts the current Data Scientist role, making it difficult to accurately assess cultural fit based on career progression and team dynamics. The projects are all personal, which limits insight into collaborative work environments.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The provided data focuses solely on technical projects and a current role without details on collaboration, problem-solving approaches, or communication styles.