Bio

Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011) and the director of the Center for Research on Foundation Models (CRFM). He is currently focused on making foundation models (in particular, language models) more accessible through open-source and understandable through rigorous benchmarking. In the past, he has worked on many topics centered on machine learning and natural language processing, including robustness, interpretability, human interaction, learning theory, grounding, semantics, and reasoning. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. 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), a Microsoft Research Faculty Fellowship (2014), and paper awards at ACL, EMNLP, ICML, COLT, ISMIR, CHI, UIST, and RSS.

Academic Appointments

Professional Education

  • BS, MIT (2004)
  • MEng, MIT (2005)
  • PhD, UC Berkeley (2011)

Contact

  • Academic
    University - Faculty Department: Computer Science Position: Assoc Professor
    • 353 JANE STANFORD WAY
    • STANFORD,  California  94305 

Additional Info

  • Mail Code: 9010

2024-25 Courses

Stanford Advisees

All Publications

  • Stronger data poisoning attacks break data sanitization defenses MACHINE LEARNING Koh, P., Steinhardt, J., Liang, P. 2021

    View details for DOI 10.1007/s10994-021-06119-y

    View details for Web of Science ID 000722108900003

  • WILDS: A Benchmark of in-the-Wild Distribution Shifts Koh, P., Sagawa, S., Marklund, H., Xie, S., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B. A., Haque, I. S., Beery, S., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., Liang, P., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104605062

  • Beyond IID: Three Levels of Generalization for Question Answering on Knowledge Bases Gu, Y., Kase, S., Vanni, M. T., Sadler, B. M., Liang, P., Yan, X., Su, Y., ACM ASSOC COMPUTING MACHINERY. 2021: 3477-3488

    View details for DOI 10.1145/3442381.3449992

    View details for Web of Science ID 000733621803045

  • Prefix-Tuning: Optimizing Continuous Prompts for Generation Li, X., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2021: 4582-4597

    View details for Web of Science ID 000698679200153

  • Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices Liu, E., Raghunathan, A., Liang, P., Finn, C., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104606087

  • Just Train Twice: Improving Group Robustness without Training Group Information Liu, E., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P., Sagawa, S., Liang, P., Finn, C., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104606074

  • Catformer: Designing Stable Transformers via Sensitivity Analysis Davis, J., Gu, A., Choromanski, K., Dao, T., Re, C., Finn, C., Liang, P., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104602046

  • Robust Encodings: A Framework for Combating Adversarial Typos Jones, E., Jia, R., Raghunathan, A., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 2752–65

    View details for Web of Science ID 000570978203005

  • Concept Bottleneck Models Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020

    View details for Web of Science ID 000683178505043

  • Understanding Self-Training for Gradual Domain Adaptation Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020

    View details for Web of Science ID 000683178505055

  • Feature Noise Induces Loss Discrepancy Across Groups Khani, F., Liang, P., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020

    View details for Web of Science ID 000683178505031

  • A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree Li, R., Liang, P., Mussmann, S., ACM ASSOC COMPUTING MACHINERY. 2020: 102–21

    View details for Web of Science ID 000554408100007

  • Enabling Language Models to Fill in the Blanks Donahue, C., Lee, M., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 2492–2501

    View details for Web of Science ID 000570978202069

  • ExpBERT: Representation Engineering with Natural Language Explanations Murty, S., Koh, P., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 2106–13

    View details for Web of Science ID 000570978202034

  • Pretraining deep learning molecular representations for property prediction Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. AMER CHEMICAL SOC. 2019

    View details for Web of Science ID 000525055503355

  • Inferring Multidimensional Rates of Aging from Cross-Sectional Data. Proceedings of machine learning research Pierson, E., Koh, P. W., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N., Liang, P. 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

  • SPoC: Search-based Pseudocode to Code Kulal, S., Pasupat, P., Chandra, K., Lee, M., Padon, O., Aiken, A., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000535866903051

  • Inferring Multidimensional Rates of Aging from Cross-Sectional Data Pierson, E., Koh, P., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N., Liang, P., Chaudhuri, K., Sugiyama, M. MICROTOME PUBLISHING. 2019: 97–107

    View details for Web of Science ID 000509687900011

  • Defending against Whitebox Adversarial Attacks via Randomized Discretization Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. MICROTOME PUBLISHING. 2019: 684–93

    View details for Web of Science ID 000509687900071

  • On the Accuracy of Influence Functions for Measuring Group Effects Koh, P., Ang, K., Teo, H. K., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000534424305027

  • Verified Uncertainty Calibration Kumar, A., Liang, P., Ma, T., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000534424303074

  • Unlabeled Data Improves Adversarial Robustness Carmon, Y., Raghunathan, A., Schmidt, L., Liang, P., Duchi, J. C., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000535866902078

  • Training Classifiers with Natural Language Explanations. Proceedings of the conference. Association for Computational Linguistics. Meeting Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. 2018; 2018: 1884–95

    Abstract

    Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100* faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.

    View details for PubMedID 31130772

  • Active Learning of Points-To Specifications Bastani, O., Sharma, R., Aiken, A., Liang, P. ASSOC COMPUTING MACHINERY. 2018: 678–92

    View details for DOI 10.1145/3192366.3192383

    View details for Web of Science ID 000452469600046

  • A Retrieve-and-Edit Framework for Predicting Structured Outputs Hashimoto, T. B., Guu, K., Oren, Y., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

    View details for Web of Science ID 000461852004059

  • Generalized Binary Search For Split-Neighborly Problems Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. MICROTOME PUBLISHING. 2018

    View details for Web of Science ID 000509385300163

  • Know What You Don't Know: Unanswerable Questions for SQuAD Rajpurkar, P., Jia, R., Liang, P., Gurevych, Miyao, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 784–89

    View details for Web of Science ID 000493913100124

  • Training Classifiers with Natural Language Explanations Hancock, B., Varma, P., Wang, S., Bringmann, M., Liang, P., Re, C., Gurevych, Miyao, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 1884–95

    View details for Web of Science ID 000493904300175

  • The price of debiasing automatic metrics in natural language evaluation Chaganty, A., Mussmann, S., Liang, P., Gurevych, Miyao, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 643–53

    View details for Web of Science ID 000493904300060

  • Prediction with a Short Memory Sharan, V., Kakade, S., Liang, P., Valiant, G., Diakonikolas, Kempe, D., Henzinger, M. ASSOC COMPUTING MACHINERY. 2018: 1074–87

    View details for DOI 10.1145/3188745.3188954

    View details for Web of Science ID 000458175600092

  • Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss Mussmann, S., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

    View details for Web of Science ID 000461852001049

  • Semidefinite relaxations for certifying robustness to adversarial examples Raghunathan, A., Steinhardt, J., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

    View details for Web of Science ID 000461852005046

  • Synthesizing Program Input Grammars Bastani, O., Sharma, R., Aiken, A., Liang, P. ASSOC COMPUTING MACHINERY. 2017: 95–110

    View details for DOI 10.1145/3062341.3062349

    View details for Web of Science ID 000414334200007

  • Unsupervised Transformation Learning via Convex Relaxations Hashimoto, T. B., Duchi, J. C., Liang, P., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

    View details for Web of Science ID 000452649406090

  • From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood Guu, K., Pasupat, P., Liu, E., Liang, P., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 1051-1062

    View details for DOI 10.18653/v1/P17-1097

    View details for Web of Science ID 000493984800097

  • Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings He, H., Balakrishnan, A., Eric, M., Liang, P., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 1766-1776

    View details for DOI 10.18653/v1/P17-1162

    View details for Web of Science ID 000493984800162

  • Naturalizing a Programming Language via Interactive Learning Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 929–38

    View details for DOI 10.18653/v1/P17-1086

    View details for Web of Science ID 000493984800086

  • Certified Defenses for Data Poisoning Attacks Steinhardt, J., Koh, P., Liang, P., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

    View details for Web of Science ID 000452649403057

  • Learning Overcomplete HMMs Sharan, V., Kakade, S., Liang, P., Valiant, G., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

    View details for Web of Science ID 000452649400090

  • Learning Executable Semantic Parsers for Natural Language Understanding COMMUNICATIONS OF THE ACM Liang, P. 2016; 59 (9): 68-76

    View details for DOI 10.1145/2866568

    View details for Web of Science ID 000382671100026

  • Learning Language Games through Interaction Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 2368–78

    View details for Web of Science ID 000493806800224

  • How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions Chaganty, A., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 578-587

    View details for Web of Science ID 000493806800055

  • Data Recombination for Neural Semantic Parsing Jia, R., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 12-22

    View details for Web of Science ID 000493806800002

  • Unsupervised Risk Estimation Using Only Conditional Independence Structure Steinhardt, J., Liang, P., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2016

    View details for Web of Science ID 000458973701058

  • Simpler Context-Dependent Logical Forms via Model Projections Long, R., Pasupat, P., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 1456-1465

    View details for Web of Science ID 000493806800138

  • Inferring Logical Forms From Denotations Pasupat, P., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 23-32

    View details for Web of Science ID 000493806800003

  • Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings Khani, F., Rinard, M., Liang, P., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 952-962

    View details for Web of Science ID 000493806800090

  • Data Augmentation via Levy Processes PERTURBATIONS, OPTIMIZATION, AND STATISTICS Wager, S., Fithian, W., Liang, P., Hazan, T., Papandreou, G., Tarlow, D. 2016: 343-373

    View details for Web of Science ID 000521530900013

  • Tensor Factorization via Matrix Factorization Kuleshov, V., Chaganty, A., Liang, P., Lebanon, G., Vishwanathan, S. V. MICROTOME PUBLISHING. 2015: 507-516

    View details for Web of Science ID 000508399700056

  • Learning Where to Sample in Structured Prediction Shi, T., Steinhardt, J., Liang, P., Lebanon, G., Vishwanathan, S. V. MICROTOME PUBLISHING. 2015: 875-884

    View details for Web of Science ID 000508399700096

  • Environment-Driven Lexicon Induction for High-Level Instructions Misra, D. K., Tao, K., Liang, P., Saxena, A., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 992-1002

    View details for Web of Science ID 000493808900096

  • Building a Semantic Parser Overnight Wang, Y., Berant, J., Liang, P., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 1332-1342

    View details for Web of Science ID 000493808900129

  • Compositional Semantic Parsing on Semi-Structured Tables Pasupat, P., Liang, P., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 1470-1480

    View details for Web of Science ID 000493808900142

  • Learning with Relaxed Supervision Steinhardt, J., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015

    View details for Web of Science ID 000450913100051

  • Calibrated Structured Prediction Kuleshov, V., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015

    View details for Web of Science ID 000450913100026

  • Estimating Mixture Models via Mixtures of Polynomials Wang, S. I., Chaganty, A., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015

    View details for Web of Science ID 000450913100070

  • On-the-Job Learning with Bayesian Decision Theory Werling, K., Chaganty, A., Liang, P., Manning, C. D., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015

    View details for Web of Science ID 000450913102009

  • Bringing Machine Learning and Compositional Semantics Together ANNUAL REVIEW OF LINGUISTICS, VOL 1 Liang, P., Potts, C. 2015; 1: 355-376

    View details for DOI 10.1146/annurev-linguist-030514-125312

    View details for Web of Science ID 000350994000018

  • Linking People in Videos with "Their" Names Using Coreference Resolution 13th European Conference on Computer Vision (ECCV) Ramanathan, V., Joulin, A., Liang, P., Li Fei-Fei, F. F. SPRINGER INT PUBLISHING AG. 2014: 95–110

    View details for Web of Science ID 000345524200007

  • Zero-shot Entity Extraction from Web Pages Pasupat, P., Liang, P., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 391-401

    View details for Web of Science ID 000493814100037

  • Semantic Parsing via Paraphrasing Berant, J., Liang, P., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 1415-1425

    View details for DOI 10.3115/v1/p14-1133

    View details for Web of Science ID 000493814100133

  • Altitude Training: Strong Bounds for Single-Layer Dropout Wager, S., Fithian, W., Wang, S., Liang, P., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014

    View details for Web of Science ID 000452647102063

  • Simple MAP Inference via Low-Rank Relaxations Frostig, R., Wang, S., Liang, P., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014

    View details for Web of Science ID 000452647100040

  • Video Event Understanding using Natural Language Descriptions IEEE International Conference on Computer Vision (ICCV) Ramanathan, V., Liang, P., Li Fei-Fei, F. F. IEEE. 2013: 905–912

    View details for DOI 10.1109/ICCV.2013.117

    View details for Web of Science ID 000351830500113

  • A Data Driven Approach for Algebraic Loop Invariants 22nd European Symposium on Programming (ESOP) Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. SPRINGER-VERLAG BERLIN. 2013: 574–592

    View details for Web of Science ID 000342810200031

  • A data driven approach for algebraic loop invariants. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, Aditya, V. 2013
  • Spectral experts for estimating mixtures of linear regressions. Chaganty, A., Liang, P. 2013
  • Semantic parsing on Freebase from question-answer pairs. Berant, J., Chou, A., Frostig, R., Liang, P. 2013
  • Dropout training as adaptive regularization. Wager, S., Wang, S., Liang, P. 2013
  • Video event understanding using natural language descriptions. Ramanathan, V., Liang, P., Fei-Fei, L. 2013
  • Feature noising for log-linear structured prediction. Wang, S., Wang, M., Wager, S., Liang, P., Manning, C. 2013
  • Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging CIRCULATION RESEARCH Wang, Y., Zhang, W. Y., Hu, S., Lan, F., Lee, A. S., Huber, B., Lisowski, L., Liang, P., Huang, M., de Almeida, P. E., Won, J. H., Sun, N., Robbins, R. C., Kay, M. A., Urnov, F. D., Wu, J. C. 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

  • Induced Pluripotent Stem Cells as a Disease Modeling and Drug Screening Platform JOURNAL OF CARDIOVASCULAR PHARMACOLOGY Ebert, A. D., Liang, P., Wu, J. C. 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

  • Modeling Pathogenesis in Familial Hypertrophic Cardiomyopathy Using Patient-Specific Induced Pluripotent Stem Cells Basic Cardiovascular Sciences Scientific Session Lan, F., Lee, A., Liang, P., Navarrete, E., Wang, L., Leng, H., Sanchez, V., Yen, M., Wang, Y., Nguyen, P., Sun, N., Abilez, O., Lewis, R., Yamaguchi, Y., Ashley, E., Bers, D., Robbins, R., Longaker, M., Wu, J. LIPPINCOTT WILLIAMS & WILKINS. 2012

    View details for Web of Science ID 000312506400056

  • Identifiability and unmixing of latent parse trees. Hsu, D., Kakade, Sham, M., Liang, P. 2012
  • Learning dependency-based compositional semantics. Liang, P., Jordan, Michael, I., Klein, D. 2011
  • Learning minimal abstractions. Liang, P., Tripp, O., Naik, M. 2011
  • Scaling up abstraction refinement via pruning. Liang, P., Naik, M. 2011
  • A dynamic evaluation of static heap abstractions. Liang, P., Tripp, O., Naik, M., Sagiv, M. 2010
  • Learning programs: a hierarchical Bayesian approach. Liang, P., Jordan, Michael, I., Klein, D. 2010
  • A game-theoretic approach to generating spatial descriptions. Golland, D., Liang, P., Klein, D. 2010
  • Type-based MCMC. Liang, P., Jordan, Michael, I., Klein, D. 2010
  • A simple domain-independent probabilistic approach to generation. Angeli, G., Liang, P., Klein, D. 2010
  • On the interaction between norm and dimensionality: multiple regimes in learning. Liang, P., Srebro, N. 2010
  • Learning from measurements in exponential families. Liang, P., Jordan, Michael, I., Klein, D. 2009
  • Learning semantic correspondences with less supervision. Liang, P., Jordan, Michael, I., Klein, D. 2009
  • Probabilistic grammars and hierarchical Dirichlet processes. The Oxford Handbook of Applied Bayesian Analysis Liang, P., Jordan, Michael, I., Klein, D. 2009
  • Online EM for unsupervised models. Liang, P., Klein, D. 2009
  • Asymptotically optimal regularization in smooth parametric models. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. 2009
  • Optimal team size and monitoring in organizations ACCOUNTING REVIEW Liang, P. J., Rajan, M. V., Ray, K. 2008; 83 (3): 789-822

    View details for Web of Science ID 000256277400008

  • A probabilistic approach to language change. Bouchard-Côté, A., Liang, P., Griffiths, T., Klein, D. 2008
  • Agreement-based learning. Liang, P., Klein, D., Jordan, Michael, I. 2008
  • Learning bilingual lexicons from monolingual corpora. Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. 2008
  • Structure compilation: trading structure for features. Liang, P., Daume, H., Klein, D. 2008
  • Analyzing the errors of unsupervised learning. Liang, P., Klein, D. 2008
  • An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. Liang, P., Jordan, Michael, I. 2008
  • A probabilistic approach to diachronic phonology. Bouchard-Côté, A., Liang, P., Griffiths, T., Klein, D. 2007
  • A permutation-augmented sampler for Dirichlet process mixture models. Liang, P., Jordan, Michael, I., Taskar, B. 2007
  • Structured Bayesian nonparametric models with variational inference (tutorial). Liang, P., Klein, D. 2007
  • The infinite PCFG using hierarchical Dirichlet processes. Liang, P., Petrov, S., Jordan, Michael, I., Klein, D. 2007
  • An end-to-end discriminative approach to machine translation. Liang, P., Bouchard-Côté, A., Klein, D., Taskar, B. 2006
  • Alignment by agreement. Liang, P., Taskar, B., Klein, D. 2006
  • Linear programming in bounded tree-width Markov networks. Liang, P., Srebro, N. 2005
  • A data structure for maintaining acyclicity in hypergraphs. Massachusetts Institute of Technology Technical Report Liang, P., Srebro, N. 2005
  • Efficient geometric algorithms for parsing in two dimensions. Liang, P., Narasimhan, M., Shilman, M., Viola, P. 2005
  • Methods and experiments with bounded tree-width Markov networks. Massachusetts Institute of Technology Technical Report Liang, P., Srebro, N. 2004
  • How much of a hypertree can be captured by windmills? Massachusetts Institute of Technology Technical Report Liang, P., Srebro, N. 2003
  • INTERFEROMETRIC STUDIES OF THE JOVIAN ATMOSPHERIC PROBE FIELD Liang, P. Y., Prakash, S. G., Bershader, D. AMER INST PHYSICS. 1980: 1093–94

    View details for Web of Science ID A1980KP44100161

  • Saponins and sapogenins. III. The sapogenins obtained from chlorogalum pomeridianum JOURNAL OF THE AMERICAN CHEMICAL SOCIETY Liang, P., Noller, C. R. 1935; 57 (1): 525-527

    View details for Web of Science ID 000188361300171