
Percy Liang
Associate Professor of Computer Science and, by courtesy, of Statistics
Bio
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).
Academic Appointments
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Associate Professor, Computer Science
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Associate Professor (By courtesy), Statistics
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Professional Education
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BS, MIT (2004)
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MEng, MIT (2005)
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PhD, UC Berkeley (2011)
2020-21 Courses
- Artificial Intelligence: Principles and Techniques
CS 221 (Aut) -
Independent Studies (13)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390C (Aut, Win, Spr, Sum) - Independent Project
CS 399 (Aut, Win, Spr, Sum) - Independent Project
CS 399P (Aut, Win, Spr) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199P (Aut, Win, Spr) - Part-time Curricular Practical Training
CS 390D (Aut, Win, Spr) - Research
STATS 399 (Spr, Sum) - Senior Project
CS 191 (Aut, Win, Spr) - Writing Intensive Senior Project (WIM)
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2019-20 Courses
- Artificial Intelligence: Principles and Techniques
CS 221 (Aut) - Artificial Intelligence: Principles and Techniques
OSPKYOTO 221K (Aut)
2018-19 Courses
2017-18 Courses
- Artificial Intelligence: Principles and Techniques
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Maxime Cauchois, Urvashi Khandelwal, Adam Lavertu, Xinkun Nie, Reid Pryzant, Florian Tramer -
Postdoctoral Faculty Sponsor
Chris Donahue -
Doctoral Dissertation Advisor (AC)
Steve Mussmann -
Orals Evaluator
Pranav Rajpurkar -
Master's Program Advisor
Prabhat Agarwal, Jonathan Li, Lucas Lin, Suvir Mirchandani, Claire Pajot, Chetanya Rastogi, Christopher Wolff, Edward Xu, Sifan Ye, David Yin -
Doctoral Dissertation Co-Advisor (AC)
John Hewitt, Siddharth Karamcheti, Ananya Kumar, Daniel Levy, Pranav Rajpurkar -
Doctoral (Program)
Fereshte Khani, Pang Wei Koh, Mina Lee, Nelson Liu, Steve Mussmann, Aditi Raghunathan, Shiori Sagawa, Michael Xie, Michihiro Yasunaga
All Publications
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Inferring Multidimensional Rates of Aging from Cross-Sectional Data.
Proceedings of machine learning research
2019; 89: 97–107
Abstract
Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.
View details for PubMedID 31538144
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Inferring Multidimensional Rates of Aging from Cross-Sectional Data
MICROTOME PUBLISHING. 2019: 97–107
View details for Web of Science ID 000509687900011
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Defending against Whitebox Adversarial Attacks via Randomized Discretization
MICROTOME PUBLISHING. 2019: 684–93
View details for Web of Science ID 000509687900071
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A Retrieve-and-Edit Framework for Predicting Structured Outputs
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461852004059
- Feature noising for log-linear structured prediction. 2013
- A data driven approach for algebraic loop invariants. 2013
- Spectral experts for estimating mixtures of linear regressions. 2013
- Semantic parsing on Freebase from question-answer pairs. 2013
- Dropout training as adaptive regularization. 2013
- Video event understanding using natural language descriptions. 2013
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Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging
CIRCULATION RESEARCH
2012; 111 (12): 1494-?
Abstract
Molecular imaging has proven to be a vital tool in the characterization of stem cell behavior in vivo. However, the integration of reporter genes has typically relied on random integration, a method that is associated with unwanted insertional mutagenesis and positional effects on transgene expression.To address this barrier, we used genome editing with zinc finger nuclease (ZFN) technology to integrate reporter genes into a safe harbor gene locus (PPP1R12C, also known as AAVS1) in the genome of human embryonic stem cells and human induced pluripotent stem cells for molecular imaging.We used ZFN technology to integrate a construct containing monomeric red fluorescent protein, firefly luciferase, and herpes simplex virus thymidine kinase reporter genes driven by a constitutive ubiquitin promoter into a safe harbor locus for fluorescence imaging, bioluminescence imaging, and positron emission tomography imaging, respectively. High efficiency of ZFN-mediated targeted integration was achieved in both human embryonic stem cells and induced pluripotent stem cells. ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. Functionally, we successfully tracked the survival of ZFN-edited human embryonic stem cells and their differentiated cardiomyocytes and endothelial cells in murine models, demonstrating the use of ZFN-edited cells for preclinical studies in regenerative medicine.Our study demonstrates a novel application of ZFN technology to the targeted genetic engineering of human pluripotent stem cells and their progeny for molecular imaging in vitro and in vivo.
View details for DOI 10.1161/CIRCRESAHA.112.274969
View details for Web of Science ID 000311994700042
View details for PubMedID 22967807
View details for PubMedCentralID PMC3518748
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Induced Pluripotent Stem Cells as a Disease Modeling and Drug Screening Platform
JOURNAL OF CARDIOVASCULAR PHARMACOLOGY
2012; 60 (4): 408-416
Abstract
Induced pluripotent stem cells (iPSCs) hold great hopes for therapeutic application in various diseases. Although ongoing research is dedicated to achieving clinical translation of iPSCs, further understanding of the mechanisms that underlie complex pathogenic conditions is required. Compared with other classical models for studying diseases, iPSCs provide considerable advantages. A newly emerging application of iPSCs is in vitro disease modeling, which can significantly improve the never-ending search for new pharmacological cures. Here, we will discuss current efforts to create iPSC-dependent patient-specific disease models. Furthermore, we will review the use of iPSCs for development and testing of new therapeutic agents and the implications for high-throughput drug screening.
View details for DOI 10.1097/FJC.0b013e318247f642
View details for Web of Science ID 000309977900012
View details for PubMedID 22240913
View details for PubMedCentralID PMC3343213
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Modeling Pathogenesis in Familial Hypertrophic Cardiomyopathy Using Patient-Specific Induced Pluripotent Stem Cells
Basic Cardiovascular Sciences Scientific Session
LIPPINCOTT WILLIAMS & WILKINS. 2012
View details for Web of Science ID 000312506400056
- Identifiability and unmixing of latent parse trees. 2012
- Learning dependency-based compositional semantics. 2011
- Learning minimal abstractions. 2011
- Scaling up abstraction refinement via pruning. 2011
- A dynamic evaluation of static heap abstractions. 2010
- Learning programs: a hierarchical Bayesian approach. 2010
- A game-theoretic approach to generating spatial descriptions. 2010
- Type-based MCMC. 2010
- A simple domain-independent probabilistic approach to generation. 2010
- On the interaction between norm and dimensionality: multiple regimes in learning. 2010
- Learning from measurements in exponential families. 2009
- Learning semantic correspondences with less supervision. 2009
- Probabilistic grammars and hierarchical Dirichlet processes. The Oxford Handbook of Applied Bayesian Analysis 2009
- Online EM for unsupervised models. 2009
- Asymptotically optimal regularization in smooth parametric models. 2009
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Optimal team size and monitoring in organizations
ACCOUNTING REVIEW
2008; 83 (3): 789-822
View details for Web of Science ID 000256277400008
- A probabilistic approach to language change. 2008
- Agreement-based learning. 2008
- Learning bilingual lexicons from monolingual corpora. 2008
- Structure compilation: trading structure for features. 2008
- Analyzing the errors of unsupervised learning. 2008
- An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. 2008
- A probabilistic approach to diachronic phonology. 2007
- A permutation-augmented sampler for Dirichlet process mixture models. 2007
- Structured Bayesian nonparametric models with variational inference (tutorial). 2007
- The infinite PCFG using hierarchical Dirichlet processes. 2007
- An end-to-end discriminative approach to machine translation. 2006
- Alignment by agreement. 2006
- Linear programming in bounded tree-width Markov networks. 2005
- A data structure for maintaining acyclicity in hypergraphs. Massachusetts Institute of Technology Technical Report 2005
- Efficient geometric algorithms for parsing in two dimensions. 2005
- Methods and experiments with bounded tree-width Markov networks. Massachusetts Institute of Technology Technical Report 2004
- How much of a hypertree can be captured by windmills? Massachusetts Institute of Technology Technical Report 2003
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INTERFEROMETRIC STUDIES OF THE JOVIAN ATMOSPHERIC PROBE FIELD
AMER INST PHYSICS. 1980: 1093–94
View details for Web of Science ID A1980KP44100161
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Saponins and sapogenins. III. The sapogenins obtained from chlorogalum pomeridianum
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
1935; 57 (1): 525-527
View details for Web of Science ID 000188361300171