Sotabase

Career

· Visiting Faculty Researcher, Google Brain2022–2022
· Assistant Professor of Computer Science and Data Science, New York University2022–
· Senior Researcher II, Waabi Innovation2021–2021
· Senior Research Scientist I, Uber Advanced Technologies Group (ATG)2018–2021
· Research Scientist II, Uber Advanced Technologies Group (ATG)2017–2018
· PhD in Computer Science, University of Toronto2017–2021
· Research Intern, Twitter2016–2016
· Research Intern in Google Brain and Image Understanding, Google2015–2015
· MS in Computer Science, University of Toronto2015–2017
· SWE Intern in StreetView, Google2014–2014
· SDET Intern in Visual Studio, Microsoft2013–2013
· B.A.Sc. in Engineering Science, ECE Option, University of Toronto2011–2015

Publications (118)

Learning to Reweight Examples for Robust Deep Learning
International Conference on Machine Learning · 2018
1,591
cited
Meta-Learning for Semi-Supervised Few-Shot Classification
International Conference on Learning Representations · 2018
1,401
cited
Exploring Models and Data for Image Question Answering
Neural Information Processing Systems · 2015
750
cited
The Reversible Residual Network: Backpropagation Without Storing Activations
Neural Information Processing Systems · 2017
618
cited
End-to-End Instance Segmentation with Recurrent Attention
Computer Vision and Pattern Recognition · 2016
308
cited
Graph HyperNetworks for Neural Architecture Search
International Conference on Learning Representations · 2018
296
cited
Physically Realizable Adversarial Examples for LiDAR Object Detection
Computer Vision and Pattern Recognition · 2020
280
cited
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
European Conference on Computer Vision · 2020
226
cited
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Computer Vision and Pattern Recognition · 2021
217
cited
SBNet: Sparse Blocks Network for Fast Inference
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2018
188
cited
Incremental Few-Shot Learning with Attention Attractor Networks
Neural Information Processing Systems · 2018
187
cited
Image Question Answering: A Visual Semantic Embedding Model and a New Dataset
arXiv.org · 2015
153
cited
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
International Conference on Learning Representations · 2018
145
cited
SceneGen: Learning to Generate Realistic Traffic Scenes
Computer Vision and Pattern Recognition · 2021
133
cited
End-to-end Contextual Perception and Prediction with Interaction Transformer
IEEE/RJS International Conference on Intelligent RObots and Systems · 2020
130
cited
Identifying Unknown Instances for Autonomous Driving
Conference on Robot Learning · 2019
124
cited
A General Framework for Inference-time Scaling and Steering of Diffusion Models
International Conference on Machine Learning · 2025
114
cited
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
Conference on Robot Learning · 2021
99
cited
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
IEEE/RJS International Conference on Intelligent RObots and Systems · 2019
98
cited
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
International Conference on Learning Representations · 2016
83
cited
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