Sean Segal

I'm currently a senior research scientist at Waabi, where we work on building the next generation of self-driving technology. I'm also a PhD candidate in the Machine Learning Group at the University of Toronto supervised by Prof. Raquel Urtasun.

Previously, I was a research scientist at Uber ATG R&D and a software engineering intern at Facebook and RetailMeNot Inc. I obtained my Masters in Computer Science at University of Toronto in 2020, supervised by Prof. Raquel Urtasun. Prior to that, I did my Bachelor's at Brown University studying Computer Science and Economics.

Email  /  GitHub  /  Google Scholar  /  LinkedIn  /  Twitter

profile photo

Publications

project image

Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes


Sean Segal, Nishanth Kumar, Sergio Casas, Wenyuan Zeng, Mengye Ren, Jingkang Wang, Raquel Urtasun
Conference on Robot Learning (CoRL), 2021
arXiv / pdf

Introduces fine-grained active selection via partial labeling for efficient labeling for perception and prediction.

project image

Diverse Complexity Measures for Dataset Curation in Self-Driving


Abbas Sadat, Sean Segal, Sergio Casas, James Tu, Bin Yang, Raquel Urtasun, Ersin Yumer
International Conference on Intelligent Robots and Systems (IROS), 2021
arXiv / pdf

Model-agnostic complexity measures for dataset curation across autonomy tasks.

project image

Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs


Sean Segal, Eric Kee, Wenjie Luo, Abbas Sadat, Ersin Yumer, Raquel Urtasun
Conference on Robot Learning (CoRL), 2020 (Oral, Best Paper Presentation Finalist)
arXiv / pdf / video / live presentation

Spatio-temporal tagging of self-driving scenes from raw sensor data.

project image

End-to-end Contextual Perception and Prediction with Interaction Transformer


Lingyun Li, Bin Yang, Ming Liang, Wenyuan Zeng, Mengye Ren, Sean Segal, Raquel Urtasun
International Conference on Intelligent Robots and Systems (IROS), 2020 (Oral)
arXiv / pdf

Adapting the Transformer to model multi-agent interactions in trajectory prediction.

project image

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction


Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun
Conference on Robot Learning (CoRL), 2019
arXiv / pdf / video

Multimodal, long-range pedestrian prediction through probabilistic occupancy maps.


Projects

Here are some projects I've worked on over the years. These include coursework, side projects, and work I've done during internships.

project image

Carla RL Gym


GitHub

OpenAI Gym environment for the CARLA self-driving simulator with implementations of multiple common RL baselines (e.g., A2C, ACKTR, PPO). Originally written as part of a final project for Prof. Jimmy Ba’s deep reinforcement learning course. Now has 100+ stars and 30+ forks on Github!

project image

Javascript Imports LSP Server


GitHub

An LSP server for Javascript imports, providing Diagnostics, Code Actions and Autocompletion for Javascript Imports in any LSP-compatible IDE. Written during my time at Facebook on the Nuclide team. Most of the code is open source and can be found here.

project image

T.I.N.C is not Catan


GitHub

Fully functional Settlers of Catan emulator online. Also offers advanced economic features including decimal resource counts and dynamic port exchange rates based on resource supply & demand. Now has 130+ stars and 15+ forks on Github!

project image

Simplex Solver


GitHub

Solves Linear Programs in Python using the Simplex Method. Originally written as an assignment for Optimization Methods for Finance course at Brown University.

Talks

project image

CVPR 2021 Tutorial: All About Self-Driving (Section 7: Datasets, Metrics and Evaluation)


Our lab put on a full-day tutorial at CVPR 2021 covering all aspects of self-driving. My talk covered an overview of self-driving datasets, modern dataset curation techniques, and the evaluation of self-driving models. Check out the full tutorial with video recordings here!

Teaching

Brown University

Spring 2018 - Head TA for CS1420 (Machine Learning) with Prof. Michael Littman

Fall 2017 - TA for CS1470 (Deep Learning) with Prof. Eugene Charniak

Spring 2017 - TA for CS0320 (Software Engineering) with Prof. John Jannoti

Fall 2016 - TA for CS0330 (Computer Systems) with Prof. Tom Doeppner

Spring 2016 - TA for CS0004 (Scientific Computing) with Prof. Dan Potter


Design and source code from Jon Barron's website