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. He works on methods that infer representations of meaning from sentences given limited supervision. Alexandre Bouchard-Ct, Percy Liang, Tom Griffiths, Dan Klein. Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. Certified Defenses for Data Poisoning Attacks. Liang, P. Y., Prakash, S. G., Bershader, D. Saponins and sapogenins. 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. 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. "t a","H United States, Your source for the latest from the School of Engineering, Associate Professor of Computer Science and, by courtesy, of Statistics. Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. Sep 21, 2022 All I need is the professors name and @ratemyprofessor Liang, a senior majoring in computer science and minoring in music and also a student in the Master of Engineering program, will present an Advanced Music Performance piano recital today (March 17) at 5 p.m. in Killian Hall. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices. I also consult part-time for Open Philanthropy. 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. Programming languages & software engineering. A permutation-augmented sampler for Dirichlet process mixture models. I really love his lecturing style! Liang, P., Jordan, Michael, I., Taskar, B. 475 Via Ortega /Producer (Apache FOP Version 1.0) Rajpurkar, P., Jia, R., Liang, P., Gurevych, Miyao, Y. About. His awards include the Presidential Early Career Award for Scientists and Engineers . Feature noising for log-linear 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. Hancock, B., Varma, P., Wang, S., Bringmann, M., Liang, P., Re, C., Gurevych, Miyao, Y. His research spans theoretical machine learning to practical natural language . Compared with other classical models for studying diseases, iPSCs provide considerable advantages. https://lnkd.in/g5zTPHA2 New The first half of each lecture is typically an explanation of the concepts, and the second half is done on the whiteboard and/or a live demo on screen. Professor Liang writes code faster than anyone I've ever seen. Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. Active Learning of Points-To Specifications. He likes to use intimidation and sometimes jump into conclusion recklessly when communicating with him. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. They are now the foundation of today's NLP systems. 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. Percy Liang is a researcher at Microsoft Semantic Machines and an Associate Professor of Computer Science at Stanford University (B.S. He is the judgemental, controlling, and insensitive professor I have ever seen. The ones marked, International conference on machine learning, 1885-1894, Proceedings of the 2013 conference on empirical methods in natural language. Bastani, O., Sharma, R., Aiken, A., Liang, P. A Retrieve-and-Edit Framework for Predicting Structured Outputs. Textbook: Yes. Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging. A newly emerging application of iPSCs is in vitro disease modeling, which can significantly improve the never-ending search for new pharmacological cures. A probabilistic approach to diachronic phonology. 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. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. As a graduate student, I was very fortunate to be advised by Percy Liang. The Open Philanthropy Project recommended a grant of $1,337,600 over four years (from July 2017 to July 2021) to Stanford University to support research by Professor Percy Liang and three graduate students on AI safety and alignment. 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. Percy Liang. endobj Students need to learn and advance in an open-minded and supportive environment. Lots of homework Accessible outside class Group projects. 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: Mussmann, S., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Semidefinite relaxations for certifying robustness to adversarial examples. Liang, P., Petrov, S., Jordan, Michael, I., Klein, D. An end-to-end discriminative approach to machine translation. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. On the Accuracy of Influence Functions for Measuring Group Effects. I like ultimate frisbee, power lifting, and indoor bouldering. 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. Here, we will discuss current efforts to create iPSC-dependent patient-specific disease models. arXiv . He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. from MIT, 2004; Ph.D. from UC Berkeley, 2011). INTERFEROMETRIC STUDIES OF THE JOVIAN ATMOSPHERIC PROBE FIELD. >> PhD Admissions Frequently Asked Questions, Percy Liang honored with a Presidential Early Career Award. 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. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007. Furthermore, we will review the use of iPSCs for development and testing of new therapeutic agents and the implications for high-throughput drug screening. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. The worst form of professor. Efficient geometric algorithms for parsing in two dimensions. 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 at Stanford University (B.S. A data structure for maintaining acyclicity in hypergraphs. Conversations are often depressing and toxic. 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. Np%p `a!2D4! Liang, P., Tripp, O., Naik, M., Sagiv, M. Learning programs: a hierarchical Bayesian approach. 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. << Current Ph.D. students and post-docs Textbook: Yes. 5 0 obj Lots of homework Tough grader Amazing lectures Respected 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. Ramanathan, V., Joulin, A., Liang, P., Li Fei-Fei, F. F. Zero-shot Entity Extraction from Web Pages. In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. 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. ?_l) He and his TAs are knowledgeable to answer your accounting questions. Director, Center for Research on Foundation Models, Associate Professor of Computer Science, Stanford University. 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. 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). Chaganty, A., Mussmann, S., Liang, P., Gurevych, Miyao, Y. Sharan, V., Kakade, S., Liang, P., Valiant, G., Diakonikolas, Kempe, D., Henzinger, M. Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. 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. Liang, P., Narasimhan, M., Shilman, M., Viola, P. Methods and experiments with bounded tree-width Markov networks. 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. 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. Putting Numbers in Perspective with Compositional Descriptions. He definetely is a pro! /Filter /FlateDecode Learning from measurements in exponential families. Training Classifiers with Natural Language Explanations. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. His awards include the Presidential Early Career Award for Scientists and Engineers . No personal growth of the student victim. 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. Although ongoing research is dedicated to achieving clinical translation of iPSCs, further understanding of the mechanisms that underlie complex pathogenic conditions is required. Former & Emeritus Faculty. 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. Pasupat, P., Liang, P., Toutanova, K., Wu, H. Berant, J., Liang, P., Toutanova, K., Wu, H. Altitude Training: Strong Bounds for Single-Layer Dropout. Molecular imaging has proven to be a vital tool in the characterization of stem cell behavior in vivo. A., Haque, I. S., Beery, S., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., Liang, P., Meila, M., Zhang, T. 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, Prefix-Tuning: Optimizing Continuous Prompts for Generation, Li, X., Liang, P., Assoc Computat Linguist, Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. Berant, J., Chou, A., Frostig, R., Liang, P. Dropout training as adaptive regularization. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. He is very polite, knowledgable, such a job to listen. 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. from MIT, 2004; Ph.D. from UC Berkeley, 2011). << Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 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? Chaganty, A., Liang, P., Erk, K., Smith, N. A. 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. Liang, P., Bouchard-Ct, A., Klein, D., Taskar, B. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ 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, W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec. 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. FAQs specific to the Honors Cooperative Program. Best professor in Tepper. Percy Liang Director, Center for Research on Foundation Models, Associate Professor of Computer Science, Stanford University The #AIIndex2023 launches soon, so sign up for our newsletter to make sure you see it first: https://mailchi.mp/stanford.edu/ai-index-2023 @StanfordHAI 05:05PM - Mar 22, 2023 @StanfordHAI 05:01PM - Mar 22, 2023 @StanfordHAI The fellowship is awarded by the Alfred P. Summer Research in Statistics (undergraduate Stanford students). 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. Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. Get ready to read Amazing lectures Clear grading criteria. 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. /CreationDate (D:20230418051710-07'00') Previously, I received my B.S. How Much is 131 Million Dollars? /Length 11 0 R Want to learn about meta-learning & few-shot learning? from MIT, 2004; Ph.D. from UC Berkeley, 2011). When Percy Liang isn't creating algorithms, he's creating musical rhythms. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Liang, P., Jordan, Michael, I., Klein, D. Scaling up abstraction refinement via pruning. We spoke to a Stanford prof on the tech and social impact of AI's powerful, emerging 'foundation models' 10 From single points of failure to training and policies, Percy Liang covers a wide range of topics in this Q&A Katyanna Quach Mon 23 Aug 2021 // 10:25 UTC Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Ramanathan, V., Liang, P., Li Fei-Fei, F. F. A Data Driven Approach for Algebraic Loop Invariants. He often fails to control his emotion when interacting with others. Associate Professor of Computer Science, Stanford University. rl1 >> 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. /N 3 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). 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). The price of debiasing automatic metrics in natural language evaluation. 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. Garbage. Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). 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] 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. % Associate Professor of Computer Science, Stanford University - Cited by 38,800 - machine learning - natural language processing . Let's make it official. The funds will be split approximately evenly across the four years (i.e. Simple MAP Inference via Low-Rank Relaxations. ! 390 Jane Stanford Way 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. Serafim Batzoglou. Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. Structure compilation: trading structure for features. Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. O! His research seeks to develop trustworthy systems that can c. 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). 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. {{{;}#q8?\. with departmental honors and M.S. ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. Learning semantic correspondences with less supervision. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Wager, S., Fithian, W., Wang, S., Liang, P., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. Semantic parsing on Freebase from question-answer pairs. 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 Shi, T., Steinhardt, J., Liang, P., Lebanon, G., Vishwanathan, S. V. Environment-Driven Lexicon Induction for High-Level Instructions. 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. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. A data driven approach for algebraic loop invariants. Liu, E., Raghunathan, A., Liang, P., Finn, C., Meila, M., Zhang, T. Just Train Twice: Improving Group Robustness without Training Group Information. 390Jane Stanford Way from MIT, 2004; Ph.D. from UC Berkeley, 2011). Dropout training as adaptive regularization which can significantly improve the never-ending search for new pharmacological cures the judgemental controlling. 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