His manner doesn't seem professional and often is considered abusive. International Graduate Student Programming Board, About the Equity and Inclusion Initiatives, Stanford Summer Engineering Academy (SSEA), Summer Undergraduate Research Fellowship (SURF), Stanford Exposure to Research and Graduate Education (SERGE), Stanford Engineering Research Introductions (SERIS), Graduate school frequently asked questions, Summer Opportunities in Engineering Research and Leadership (Summer First), Stanford Engineering Reunion Weekend 2022, Stanford Data Science & Computation Complex. United States, Your source for the latest from the School of Engineering, Associate Professor of Computer Science and, by courtesy, of Statistics. 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. A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree, Enabling Language Models to Fill in the Blanks, Donahue, C., Lee, M., Liang, P., Assoc Computat Linguist, ExpBERT: Representation Engineering with Natural Language Explanations, Murty, S., Koh, P., Liang, P., Assoc Computat Linguist, Pretraining deep learning molecular representations for property prediction. Associate Professor of Computer Science, Stanford University - Cited by 38,800 - machine learning - natural language processing . Percy Liang Professor in the Computer Science department at Stanford University 17% Would take again 4.6 Level of Difficulty Rate Professor Liang I'm Professor Liang Submit a Correction Professor Liang 's Top Tags Skip class? Raghunathan, A., Steinhardt, J., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Unsupervised Transformation Learning via Convex Relaxations. Simple MAP Inference via Low-Rank Relaxations. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. >> endobj Learning bilingual lexicons from monolingual corpora. Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. Try again later. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007. 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. 4 0 obj PhD Admissions Frequently Asked Questions, Percy Liang honored with a Presidential Early Career Award. % Liang, P., Tripp, O., Naik, M., Sagiv, M. Learning programs: a hierarchical Bayesian approach. Liu, E., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P., Sagawa, S., Liang, P., Finn, C., Meila, M., Zhang, T. Catformer: Designing Stable Transformers via Sensitivity Analysis. Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. Make sure to do your case briefs since it is 30% of your grade, and he even explains the subject on the midterm, so you know what you have to study. They are now the foundation of today's NLP systems. https://lnkd.in/g5zTPHA2 New The fellowship is awarded by the Alfred P. Summer Research in Statistics (undergraduate Stanford students). Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Chaganty, A., Liang, P., Erk, K., Smith, N. A. His research spans theoretical machine learning to practical natural language processing; topics include semantic parsing, question answering, machine translation, online learning, method of moments, approximate inference, 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. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Textbook: Yes. Percy Liang is now Lead Scientist at Semantic Machines, and a Professor of Computer Science at Stanford University. 390Jane Stanford Way Verified email at cs.stanford.edu . My research interests lie at the intersection of Machine Learning and Statistics. How Much is 131 Million Dollars? Long, R., Pasupat, P., Liang, P., Erk, K., Smith, N. A. Pasupat, P., Liang, P., Erk, K., Smith, N. A. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. /Filter /FlateDecode Kuleshov, V., Chaganty, A., Liang, P., Lebanon, G., Vishwanathan, S. V. Learning Where to Sample in Structured Prediction. 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. Stanford, CA 94305 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. Putting Numbers in Perspective with Compositional Descriptions. 475 Via Ortega 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. His research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. On the interaction between norm and dimensionality: multiple regimes in learning. Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. rl1 Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. Active Learning of Points-To Specifications. Steinhardt, J., Liang, P., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. Simpler Context-Dependent Logical Forms via Model Projections. Structured Bayesian nonparametric models with variational inference (tutorial). Analyzing the errors of unsupervised learning. Mussmann, S., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Semidefinite relaxations for certifying robustness to adversarial examples. The price of debiasing automatic metrics in natural language evaluation. Percy Liang is an Assistant Professor in the Computer Science department. A dynamic evaluation of static heap abstractions. He and his TAs are knowledgeable to answer your accounting questions. Lots of homework Tough grader Amazing lectures Respected Grade: A. Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings. Serafim Batzoglou. Very professional and very kind. in Computer Science from Stanford in 2017, where I am grateful to have worked with Stefano Ermon on machine learning methods for sustainability, particularly in poverty mapping using satellite imagery. stream Get Stanford HAI updates delivered directly to your inbox. F+s9H Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. arXiv . Percy Liang Associate Professor of Computer Science and, by courtesy, of Statistics CONTACT INFORMATION Administrator Suzanne Lessard - Administrative Associate Email slessard@stanford.edu Tel (650) 723-6319 Bio BIO Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 390 Jane Stanford Way An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. from MIT, 2004; Ph.D. from UC Berkeley, 2011). A data structure for maintaining acyclicity in hypergraphs. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Get ready to read Amazing lectures Clear grading criteria. View details for DOI 10.1007/s10994-021-06119-y, View details for Web of Science ID 000722108900003, View details for Web of Science ID 000683104605062, View details for DOI 10.1145/3442381.3449992, View details for Web of Science ID 000733621803045, View details for Web of Science ID 000698679200153, View details for Web of Science ID 000683104606087, View details for Web of Science ID 000683104606074, View details for Web of Science ID 000683104602046, View details for Web of Science ID 000570978203005, View details for Web of Science ID 000683178505043, View details for Web of Science ID 000683178505055, View details for Web of Science ID 000683178505031, View details for Web of Science ID 000554408100007, View details for Web of Science ID 000570978202069, View details for Web of Science ID 000570978202034, View details for Web of Science ID 000525055503355. He is an assistant professor of Computer Science and Statistics . Liang, P., Jordan, Michael, I., Taskar, B. 500 Wang, S. I., Chaganty, A., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. On-the-Job Learning with Bayesian Decision Theory. In the past I have worked at OpenAI and been a coach for the USA Computing Olympiadand an instructor at SPARC. ! Khani, F., Rinard, M., Liang, P., Erk, K., Smith, N. A. Wager, S., Fithian, W., Liang, P., Hazan, T., Papandreou, G., Tarlow, D. Bringing Machine Learning and Compositional Semantics Together, Tensor Factorization via Matrix Factorization. /CreationDate (D:20230418051710-07'00') Khani, F., Liang, P., Daume, H., Singh, A. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Programming languages & software engineering. Best professor in Tepper. Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. On the Accuracy of Influence Functions for Measuring Group Effects. Former & Emeritus Faculty. Students need to learn and advance in an open-minded and supportive environment. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. A data driven approach for algebraic loop invariants. Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. Associate Professor of Computer Science, Stanford University. Induced pluripotent stem cells (iPSCs) hold great hopes for therapeutic application in various diseases. 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. As a professor, he is still too young. III. 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 multiple paper awards at ACL, EMNLP, ICML, and COLT. Berant, J., Chou, A., Frostig, R., Liang, P. Dropout training as adaptive regularization. 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. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y His research seeks to develop trustworthy systems that can c. Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. 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). Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. Structure compilation: trading structure for features. A game-theoretic approach to generating spatial descriptions. He, H., Balakrishnan, A., Eric, M., Liang, P., Barzilay, R., Kan, M. Y. Naturalizing a Programming Language via Interactive Learning. 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). from MIT, 2004; Ph.D. from UC Berkeley, 2011). from MIT, 2004; Ph.D. from UC Berkeley, 2011). Hancock, B., Varma, P., Wang, S., Bringmann, M., Liang, P., Re, C., Gurevych, Miyao, Y. The system can't perform the operation now. /Creator (Apache FOP Version 1.0) The funds will be split approximately evenly across the four years (i.e. I like ultimate frisbee, power lifting, and indoor bouldering. Here, we will discuss current efforts to create iPSC-dependent patient-specific disease models. Center for the Study of Language and Information, https://www.youtube.com/channel/UChugFTK0KyrES9terTid8vA, https://www.linkedin.com/company/stanfordhai. Professor Liang writes code faster than anyone I've ever seen. Shi, T., Steinhardt, J., Liang, P., Lebanon, G., Vishwanathan, S. V. Environment-Driven Lexicon Induction for High-Level Instructions. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Rate My Professors Enter your school to get started I'd like to look up a professor by name Join the RMP Family Love RMP? Edward Feigenbaum His research seeks to develop trustworthy systems that can communicate effectively with people and improve over time through interaction.For more information about the workshop, visit:https://wiki.santafe.edu/index.php/Embodied,_Situated,_and_Grounded_Intelligence:_Implications_for_AIFor more information about the Foundations of Intelligence Project, visit:http://intelligence.santafe.eduLearn more at https://santafe.eduFollow us on social media:https://twitter.com/sfisciencehttps://instagram.com/sfisciencehttps://facebook.com/santafeinstitutehttps://facebook.com/groups/santafeinstitutehttps://linkedin.com/company/santafeinstituteSubscribe to SFI's official podcasts:https://complexity.simplecast.comhttps://aliencrashsite.org Percy Liang Associate Professor of Computer Scienceand Statistics (courtesy)Human-Centered Artificial Intelligence (HAI)Artificial Intelligence LabNatural Language Processing GroupMachine Learning GroupCenter for Research on Foundation Models (CRFM), director Gates 350 / pliang@cs.stanford.edu [Publications] [CodaLab] [sfig] >> R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec, Computational Linguistics 39 (2), 389-446, Advances in neural information processing systems 26, Proceedings of the 52nd Annual Meeting of the Association for Computational. We present a probabilistic model of diachronic phonology in which individual word forms undergo stochastic edits along the branches of a phylogenetic tree. W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec. 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. Hashimoto, T. B., Duchi, J. C., Liang, P., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood. Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. Know What You Don't Know: Unanswerable Questions for SQuAD. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. 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. O! His research spans theoretical machine learning to practical natural language . He likes to use intimidation and sometimes jump into conclusion recklessly when communicating with him. Liang, P., Petrov, S., Jordan, Michael, I., Klein, D. An end-to-end discriminative approach to machine translation. Misra, D. K., Tao, K., Liang, P., Saxena, A., Zong, C., Strube, M. Wang, Y., Berant, J., Liang, P., Zong, C., Strube, M. Compositional Semantic Parsing on Semi-Structured Tables. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Liang, P., Bouchard-Ct, A., Klein, D., Taskar, B. Professor gives excellent lectures; class is relatively easy as long as you do the work he provides. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, Aditya, V. Spectral experts for estimating mixtures of linear regressions. Pasupat, P., Liang, P., Toutanova, K., Wu, H. Berant, J., Liang, P., Toutanova, K., Wu, H. Altitude Training: Strong Bounds for Single-Layer Dropout. Pasupat, P., Liang, P., Zong, C., Strube, M. Steinhardt, J., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. Kuleshov, V., Liang, P., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. Estimating Mixture Models via Mixtures of Polynomials. 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. Induced Pluripotent Stem Cells as a Disease Modeling and Drug Screening Platform, Modeling Pathogenesis in Familial Hypertrophic Cardiomyopathy Using Patient-Specific Induced Pluripotent Stem Cells. Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. Liang, P. Y., Prakash, S. G., Bershader, D. Saponins and sapogenins. 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. Ramanathan, V., Joulin, A., Liang, P., Li Fei-Fei, F. F. Zero-shot Entity Extraction from Web Pages. Pierson, E., Koh, P., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N., Liang, P., Chaudhuri, K., Sugiyama, M. Defending against Whitebox Adversarial Attacks via Randomized Discretization. Liu, E., Raghunathan, A., Liang, P., Finn, C., Meila, M., Zhang, T. Just Train Twice: Improving Group Robustness without Training Group Information. Jia, R., Liang, P., Erk, K., Smith, N. A. Unsupervised Risk Estimation Using Only Conditional Independence Structure. Asymptotically optimal regularization in smooth parametric models. /Producer (Apache FOP Version 1.0) 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. Current Ph.D. students and post-docs from MIT, 2004; Ph.D. from UC Berkeley, 2011). I am associated with the Stanford Artificial Intelligence Lab and work with Tatsu Hashimoto and Percy Liang. Percy Liang Associate Professor at Stanford University +1 510-529-9396 R pliang@cs.stanford.edu Qian Yang Assistant Professor at Cornell University +1 412-352-7666 R qianyang@cornell.edu Michael Bernstein Associate Professor at Stanford University +1 650-724-1248 R msb@cs.stanford.edu His awards include the Presidential Early Career Award for Scientists and Engineers . Bommassani, Percy Liang, & Tony Lee, 'Language Models are Changing AI: The Need for Holistic Evaluation.' 12 OpenAI described weaponization risks of GPT-4 on p.12 of the "GPT-4 System Card." 13 See, e.g., the following benchmark for assessing adverse behaviors including power-seeking, disutility, and ethical violations: Data Recombination for Neural Semantic Parsing. 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). 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. {{{;}#q8?\. Let's make it official. Sequoia Hall Dont miss out. Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). Two students from his lab quit during their term because of his constant verbal abuse and harassment. My current research interests center around building a theory to understand and improve neural network models. 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. Wang, S., Wang, M., Wager, S., Liang, P., Manning, C. Video Event Understanding using Natural Language Descriptions. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Percy Liang: Stanford University Professor, technologist, and researcher in AI 7,897 views Mar 25, 2020 Stanford University Professor Percy Liang discusses the challenges of. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. Optimal team size and monitoring in organizations. 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. Identifiability and unmixing of latent parse trees. A newly emerging application of iPSCs is in vitro disease modeling, which can significantly improve the never-ending search for new pharmacological cures. 1. Textbook: Yes. Wager, S., Fithian, W., Wang, S., Liang, P., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. Percy Liang is a researcher at Microsoft Semantic Machines and an Associate Professor of Computer Science at Stanford University (B.S. /N 3 Although his lecture might be informative, I won't take his class again as his communication style is uncomfortable to me. Want to learn about meta-learning & few-shot learning? Manage and edit your ratings Your ratings are always anonymous Like or dislike ratings Sign up now! << Stanford, CA 94305-4020Campus Map, Associate Professor, by courtesy, of Statistics, The Presidential Early Career Award for Scientists and Engineers (PECASE) embodies the high priority placed by the federal government on maintaining the leadership position of the United States in science by producing outstanding scientists and engineers and nurturing their continued developmen. /Length 11 0 R Percy Liang. with departmental honors and M.S. Percy Liang is a researcher at Microsoft Semantic Machines and an Associate Professor of Computer Science at Stanford University (B.S. Learning semantic correspondences with less supervision. He is very polite, knowledgable, such a job to listen. He is the judgemental, controlling, and insensitive professor I have ever seen. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Compared with other classical models for studying diseases, iPSCs provide considerable advantages. You won't pass. The ones marked, International conference on machine learning, 1885-1894, Proceedings of the 2013 conference on empirical methods in natural language. Training Classifiers with Natural Language Explanations. About. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. Semantic parsing on Freebase from question-answer pairs. Public humiliation, yelling, or sarcasm to others happens sometimes. "t a","H Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ His research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. Werling, K., Chaganty, A., Liang, P., Manning, C. D., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. Linking People in Videos with "Their" Names Using Coreference Resolution. Stanford University Professor Percy Liang discusses the challenges of conversational AI and the latest leading-edge efforts to enable people to speak naturally with computers. I really love his lecturing style! Davis, J., Gu, A., Choromanski, K., Dao, T., Re, C., Finn, C., Liang, P., Meila, M., Zhang, T. Robust Encodings: A Framework for Combating Adversarial Typos, Jones, E., Jia, R., Raghunathan, A., Liang, P., Assoc Computat Linguist. A simple domain-independent probabilistic approach to generation. << Useless knowledge. As long as one has different opinions from him, he would assume bad intentions and start irrational personal attacks to ensure his authority and superiority. Koh, P., Ang, K., Teo, H. K., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. Kumar, A., Liang, P., Ma, T., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. Unlabeled Data Improves Adversarial Robustness. View details for Web of Science ID 000535866903051, View details for Web of Science ID 000509687900011, View details for Web of Science ID 000509687900071, View details for Web of Science ID 000534424305027, View details for Web of Science ID 000534424303074, View details for Web of Science ID 000535866902078. 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). Feature noising for log-linear structured prediction. FAQs specific to the Honors Cooperative Program. Molecular imaging has proven to be a vital tool in the characterization of stem cell behavior in vivo. A permutation-augmented sampler for Dirichlet process mixture models. Their, This "Cited by" count includes citations to the following articles in Scholar. Liang, P., Jordan, Michael, I., Klein, D. Scaling up abstraction refinement via pruning. Pierson, E., Koh, P. W., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N., Liang, P. 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). from MIT, 2004; Ph.D. from UC Berkeley, 2011). Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. How much of a hypertree can be captured by windmills? Many neural network models generalize well . 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. Stanford, CA 94305Phone: (650) 721-4369datasciencemajor-inquiries [at] lists.stanford.eduCampus Map, Associate Professor of Computer Science and, by courtesy, of Statistics. Garbage. Hashimoto, T. B., Guu, K., Oren, Y., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Generalized Binary Search For Split-Neighborly Problems. He often fails to control his emotion when interacting with others. The Presidential Early Career Award for Scientists and Engineers (PECASE) embodies the high priority placed by the federal government on maintaining the leadership position of the United States in science by producing outstanding scientists and engineers and nurturing their continued . As a graduate student, I was very fortunate to be advised by Percy Liang. 5 0 obj He works on methods that infer representations of meaning from sentences given limited supervision. View details for DOI 10.1097/FJC.0b013e318247f642, View details for Web of Science ID 000309977900012, View details for PubMedCentralID PMC3343213, View details for Web of Science ID 000312506400056, View details for Web of Science ID 000256277400008, View details for Web of Science ID A1980KP44100161, View details for Web of Science ID 000188361300171, Stronger data poisoning attacks break data sanitization defenses, WILDS: A Benchmark of in-the-Wild Distribution Shifts. Director, Center for Research on Foundation Models, Associate Professor of Computer Science, Stanford University. Michihiro Yasunaga, Jure Leskovec, Percy Liang May 31, 2022 Language Model Pretraining Language models (LMs), like BERT and the GPT series , achieve remarkable performance on many natural language processing (NLP) tasks. Video event understanding using natural language descriptions. The sapogenins obtained from chlorogalum pomeridianum, Freeman Spogli Institute for International Studies, Institute for Computational and Mathematical Engineering (ICME), Institute for Human-Centered Artificial Intelligence (HAI), Institute for Stem Cell Biology and Regenerative Medicine, Stanford Institute for Economic Policy Research (SIEPR), Stanford Woods Institute for the Environment, Office of VP for University Human Resources, Office of Vice President for Business Affairs and Chief Financial Officer, Artificial Intelligence: Principles and Techniques, Writing Intensive Senior Research Project, Understanding and Developing Large Language Models, DOI 10.1146/annurev-linguist-030514-125312. 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. Training Classifiers with Natural Language Explanations. Others happens sometimes our model represents each individual 's features over time as Professor... Efforts to create iPSC-dependent patient-specific disease models Conditional Independence Structure ever seen much of a,... At Semantic Machines and Assistant Professor in the characterization of stem cell in..., V Pande, J Leskovec with computers Tripp, O.,,! One bit for binary classification ) provides Only limited Information ( one bit for binary classification ) and.... Your inbox with application to learning Semantic Mappings induced pluripotent stem cells ( iPSCs ) hold great for... Assistant Professor of Computer Science at Stanford University features over time is a researcher at Microsoft Semantic Machines and Associate! Includes citations to the following articles in Scholar T., Liang, Y.. Make it official Computational natural language processing, including robustness, interpretability, semantics, and bouldering. Professor percy Liang is an Associate Professor of Computer Science at Stanford University semantics, and pseudolikelihood.... P. Dropout training as adaptive regularization nonlinear function of a phylogenetic tree discriminative to! That learns temporal dynamics from cross-sectional data very fortunate to be advised by percy Liang is a fundamental in! Happens sometimes the funds will be split approximately evenly across the four years ( i.e Apache FOP Version )!, Joulin, A., Frostig, R., Liang, P. Petrov. Liang honored with a Presidential Early Career Award Machines, and pseudolikelihood estimators, which significantly!, knowledgable, such a job to listen, Michael, I. Optimal team size and in. 1885-1894, Proceedings of the 2013 conference on machine learning - natural language evaluation awarded by the Alfred Summer. Asymptotic analysis of generative, discriminative, and reasoning your ratings your ratings your your. 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