Sotabase

Career

· Assistant Professor, University of California, Los Angeles (UCLA)2021–
· Intern, Microsoft Research, Redmond2018–
· Intern, OpenAI2017–
· Doctor of Philosophy - PhD Artificial Intelligence, Stanford University2015–2020
· Co-founder & CTO, Inception
· Bachelor of Science - BS, Computer Science and Engineering, Indian Institute of Technology Delhi
· Research Scientist, Meta
· Assistant Professor, UCLA
· Postdoctoral Training, University of California, Berkeley

Publications (134)

node2vec: Scalable Feature Learning for Networks
Knowledge Discovery and Data Mining · 2016
11,801
cited
Decision Transformer: Reinforcement Learning via Sequence Modeling
Neural Information Processing Systems · 2021
2,039
cited
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Nature · 2020
824
cited
ClimaX: A foundation model for weather and climate
International Conference on Machine Learning · 2023
376
cited
Permutation Invariant Graph Generation via Score-Based Generative Modeling
International Conference on Artificial Intelligence and Statistics · 2020
335
cited
Graphite: Iterative Generative Modeling of Graphs
International Conference on Machine Learning · 2018
322
cited
A Deep Hybrid Model for Weather Forecasting
Knowledge Discovery and Data Mining · 2015
271
cited
Online Decision Transformer
International Conference on Machine Learning · 2022
243
cited
Pretrained Transformers as Universal Computation Engines
arXiv.org · 2021
230
cited
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
AAAI Conference on Artificial Intelligence · 2017
216
cited
Stochastic Optimization of Sorting Networks via Continuous Relaxations
International Conference on Learning Representations · 2019
199
cited
Learning Controllable Fair Representations
International Conference on Artificial Intelligence and Statistics · 2018
184
cited
Frame Averaging for Invariant and Equivariant Network Design
International Conference on Learning Representations · 2021
171
cited
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Neural Information Processing Systems · 2022
168
cited
Fair Generative Modeling via Weak Supervision
International Conference on Machine Learning · 2019
155
cited
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
International Conference on Machine Learning · 2022
137
cited
Learning Policy Representations in Multiagent Systems
International Conference on Machine Learning · 2018
135
cited
Neural Joint Source-Channel Coding
International Conference on Machine Learning · 2018
133
cited
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
DGS@ICLR · 2019
130
cited
Frozen Pretrained Transformers as Universal Computation Engines
AAAI Conference on Artificial Intelligence · 2022
122
cited
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