Sotabase

Career

· Visiting Research Fellow, Citadel2022–2022
· ML and AI Lead, EchoMark2022–
· Vice President, Product, Apex.AI2019–2021
· Software Engineer, Google2018–2019
· Assistant Professor, University of Chicago2018–
· PhD Candidate, Computer Science, University of California, Berkeley2013–2018
· BS, Electrical Engineering and Computer Sciences, University of California, Berkeley2008–2012
· VP, Product, Concentric AI
· Visiting Research Fellow, Simons Institute for the Theory of Computing
· Assistant Professor, SKAI Institute
· Product and Engineering leader, Stanford and Berkeley startups
· MS in Computer Science, University of California, Berkeley
· MS in Electrical Engineering, University of California, Berkeley
· MBA, University of California, Berkeley - Walter A. Haas School of Business

Publications (97)

Data Cleaning: Overview and Emerging Challenges
SIGMOD Conference · 2016
517
cited
Communication-Efficient Distributed Dual Coordinate Ascent
Neural Information Processing Systems · 2014
357
cited
ActiveClean: Interactive Data Cleaning For Statistical Modeling
Proceedings of the VLDB Endowment · 2016
262
cited
Learning to Optimize Join Queries With Deep Reinforcement Learning
arXiv.org · 2018
239
cited
Deep Unsupervised Cardinality Estimation
Proceedings of the VLDB Endowment · 2019
231
cited
Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning
International Symposium of Robotics Research · 2017
150
cited
Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning
IEEE International Conference on Robotics and Automation · 2017
144
cited
Multi-Level Discovery of Deep Options
arXiv.org · 2017
131
cited
A sample-and-clean framework for fast and accurate query processing on dirty data
SIGMOD Conference · 2014
126
cited
Fine-grained partitioning for aggressive data skipping
SIGMOD Conference · 2014
118
cited
BoostClean: Automated Error Detection and Repair for Machine Learning
arXiv.org · 2017
111
cited
How Large Language Models Will Disrupt Data Management
Proceedings of the VLDB Endowment · 2023
104
cited
A methodology for learning, analyzing, and mitigating social influence bias in recommender systems
ACM Conference on Recommender Systems · 2014
93
cited
DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations
Conference on Robot Learning · 2017
88
cited
PALM: Machine Learning Explanations For Iterative Debugging
HILDA@SIGMOD · 2017
86
cited
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression
2014 IEEE International Conference on Automation Science and Engineering (CASE) · 2014
83
cited
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations
IEEE International Conference on Robotics and Automation · 2016
78
cited
TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning
IEEE International Conference on Robotics and Automation · 2016
76
cited
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
IEEE International Conference on Robotics and Automation · 2017
74
cited
SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards
Int. J. Robotics Res. · 2018
74
cited
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Sanjay Krishnan | Researcher Profile | Sotabase | Sotabase