Percy Liang
Associate Professor of Computer Science and Statistics at Stanford Graduate School of Business
Schools
- Stanford Graduate School of Business
- Stanford University (ONLINE)
Expertise
Links
Biography
Stanford Graduate School of Business
Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
Education/Experience
- 2012: post-doc at Google
- 2011: Ph.D. from Berkeley (advisors: Michael Jordan and Dan Klein)
- 2005: MEng from MIT (advisor: Michael Collins)
- 2004: B.S. from MIT
Honors
- Presidential Early Career Award for Scientists and Engineers (2019)
- IJCAI Computers and Thought Award (2016)
- NSF CAREER Award (2016)
- Sloan Research Fellowship (2015)
- Microsoft Research Faculty Fellowship (2014)
- Graduate fellowships: NSF, NDSEG, GAANN, Siebel Scholar
- Programming contests: 2nd place at 2002 ACM ICPC World Finals, silver medalist at IOI 2000
- Music competitions (piano): Winner of KDFC Classical Star Search (2008, over-21 division), MIT Concerto Competition (2004), Phoenix Young Musicians Competition (2000)
Videos
Percy Liang at AI Frontiers 2018: Pushing the Limits of Machine Learning
Episode 19 – Percy Liang: Stanford University Professor, technologist, and researcher in AI
Lecture 3: Machine Learning 2 - Features, Neural Networks | Stanford CS221: AI (Autumn 2019)
Stanford HAI 2019 - Percy Liang
Lecture 15: Bayesian Networks 3 - Maximum Likelihood | Stanford CS221: AI (Autumn 2019)
Lecture 14: Bayesian Networks 2 - Forward-Backward | Stanford CS221: AI (Autumn 2019)
Lecture 8: Markov Decision Processes - Reinforcement Learning | Stanford CS221: AI (Autumn 2019)
Lecture 17: Logic 2 - First-order Logic | Stanford CS221: AI (Autumn 2019)
Lecture 1: Overview | Stanford CS221: AI (Autumn 2019)
TrustML Seminar: Percy Liang on Surprises in the Quest for Robust Machine Learning
Lecture 4: Machine Learning 3 - Generalization, K-means | Stanford CS221: AI (Autumn 2019)
Lecture 13: Bayesian Networks 1 - Inference | Stanford CS221: AI (Autumn 2019)
Percy Liang: Semantic Parsing for Natural Language Interfaces
Lecture 16: Logic 1 - Propositional Logic | Stanford CS221: AI (Autumn 2019)
Lecture 2: Machine Learning 1 - Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019)
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