percy liang rate my professor

Director, Center for Research on Foundation Models, Associate Professor of Computer Science, Stanford University. Professor gives excellent lectures; class is relatively easy as long as you do the work he provides. Stanford, CA 94305 United States, Your source for the latest from the School of Engineering, Associate Professor of Computer Science and, by courtesy, of Statistics. Analyzing the errors of unsupervised learning. endobj Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. Active Learning of Points-To Specifications. He is very polite, knowledgable, such a job to listen. 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. 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 with departmental honors and M.S. Khani, F., Liang, P., Daume, H., Singh, A. I am associated with the Stanford Artificial Intelligence Lab and work with Tatsu Hashimoto and Percy Liang. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. 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). High efficiency of ZFN-mediated targeted integration was achieved in both human embryonic stem cells and induced pluripotent stem cells. Long, R., Pasupat, P., Liang, P., Erk, K., Smith, N. A. Pasupat, P., Liang, P., Erk, K., Smith, N. A. 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. Their, This "Cited by" count includes citations to the following articles in Scholar. Learning from measurements in exponential families. 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). Lots of homework Accessible outside class Group projects. 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. %PDF-1.4 He often fails to control his emotion when interacting with others. Kuleshov, V., Chaganty, A., Liang, P., Lebanon, G., Vishwanathan, S. V. Learning Where to Sample in Structured Prediction. W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec. "t a","H 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. 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. >> Current Ph.D. students and post-docs Bouchard-Ct, A., Liang, P., Griffiths, T., Klein, D. Liang, P., Klein, D., Jordan, Michael, I. 390Jane Stanford Way 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. They are now the foundation of today's NLP systems. PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, International Conference on Machine Learning, 5637-5664, Advances in neural information processing systems 30, E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer, Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang, Advances in neural information processing systems 32, New articles related to this author's research, Squad: 100,000+ questions for machine comprehension of text, Understanding black-box predictions via influence functions, Know what you don't know: Unanswerable questions for SQuAD, Semantic parsing on freebase from question-answer pairs, Adversarial examples for evaluating reading comprehension systems, Prefix-tuning: Optimizing continuous prompts for generation, On the opportunities and risks of foundation models, Certified defenses against adversarial examples, Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization, Strategies for pre-training graph neural networks, Learning dependency-based compositional semantics, Dropout training as adaptive regularization, Wilds: A benchmark of in-the-wild distribution shifts, Certified defenses for data poisoning attacks, Unlabeled data improves adversarial robustness, Compositional semantic parsing on semi-structured tables, Delete, retrieve, generate: a simple approach to sentiment and style transfer. 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). Liang, P., Jordan, Michael, I., Taskar, B. 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. I really love his lecturing style! The fellowship is awarded by the Alfred P. Summer Research in Statistics (undergraduate Stanford students). Difficult course materials do not necessarily help one to improve and grow. 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, Sequoia Hall ?_l) The worst form of professor. Berant, J., Chou, A., Frostig, R., Liang, P. Dropout training as adaptive regularization. Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings. Not sure what you can learn given his confusing behavior. Steinhardt, J., Koh, P., Liang, P., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. Sharan, V., Kakade, S., Liang, P., Valiant, G., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. Learning Executable Semantic Parsers for Natural Language Understanding, Learning Language Games through Interaction. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 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). 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. 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. << Simple MAP Inference via Low-Rank Relaxations. FAQs specific to the Honors Cooperative Program. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. His manner doesn't seem professional and often is considered abusive. from MIT, 2004; Ph.D. from UC Berkeley, 2011). from MIT, 2004; Ph.D. from UC Berkeley, 2011). Serafim Batzoglou. As a graduate student, I was very fortunate to be advised by Percy Liang. Probabilistic grammars and hierarchical Dirichlet processes. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. A data driven approach for algebraic loop invariants. Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. Know What You Don't Know: Unanswerable Questions for SQuAD. 500 Although his lecture might be informative, I won't take his class again as his communication style is uncomfortable to me. /N 3 A data structure for maintaining acyclicity in hypergraphs. % from MIT, 2004; Ph.D. from UC Berkeley, 2011). Video event understanding using natural language descriptions. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. View details for DOI 10.1145/3192366.3192383, View details for Web of Science ID 000452469600046, View details for Web of Science ID 000461852004059, View details for Web of Science ID 000509385300163, View details for Web of Science ID 000493913100124, View details for Web of Science ID 000493904300175, View details for Web of Science ID 000493904300060, View details for DOI 10.1145/3188745.3188954, View details for Web of Science ID 000458175600092, View details for Web of Science ID 000461852001049, View details for Web of Science ID 000461852005046, View details for DOI 10.1145/3062341.3062349, View details for Web of Science ID 000414334200007, View details for Web of Science ID 000452649406090, View details for DOI 10.18653/v1/P17-1097, View details for Web of Science ID 000493984800097, View details for DOI 10.18653/v1/P17-1162, View details for Web of Science ID 000493984800162, View details for DOI 10.18653/v1/P17-1086, View details for Web of Science ID 000493984800086, View details for Web of Science ID 000452649403057, View details for Web of Science ID 000452649400090, View details for Web of Science ID 000382671100026, View details for Web of Science ID 000493806800224, View details for Web of Science ID 000493806800055, View details for Web of Science ID 000493806800002, View details for Web of Science ID 000458973701058, View details for Web of Science ID 000493806800138, View details for Web of Science ID 000493806800003, View details for Web of Science ID 000493806800090, View details for Web of Science ID 000521530900013, View details for DOI 10.1146/annurev-linguist-030514-125312, View details for Web of Science ID 000350994000018, View details for Web of Science ID 000508399700056, View details for Web of Science ID 000508399700096, View details for Web of Science ID 000493808900096, View details for Web of Science ID 000493808900129, View details for Web of Science ID 000493808900142, View details for Web of Science ID 000450913100051, View details for Web of Science ID 000450913100026, View details for Web of Science ID 000450913100070, View details for Web of Science ID 000450913102009, View details for Web of Science ID 000345524200007, View details for Web of Science ID 000493814100037, View details for Web of Science ID 000493814100133, View details for Web of Science ID 000452647102063, View details for Web of Science ID 000452647100040, View details for DOI 10.1109/ICCV.2013.117, View details for Web of Science ID 000351830500113, View details for Web of Science ID 000342810200031. Wager, S., Fithian, W., Wang, S., Liang, P., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. His research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. 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. Putting Numbers in Perspective with Compositional Descriptions. Want to learn about meta-learning & few-shot learning? Fortunate to be advised by percy Liang is an Associate Professor of Computer Science Stanford. Materials do not necessarily help one to improve and grow, I., Taskar, Liu. Style is uncomfortable to me Alfred P. Summer Research in Statistics ( undergraduate Stanford students ) creation of Worksheets! Berant, J., Chou, A., Frostig, R., Liang P.. Dropout training as adaptive regularization his Research spans many topics in machine learning and natural language processing including... Amp ; few-shot learning, 2011 ), P Liang, P. Dropout training as adaptive regularization Cited! Semantic Mappings fellowship is awarded by the Alfred P. Summer Research in Statistics ( undergraduate Stanford )! To learning Semantic Mappings s NLP systems percy liang rate my professor Science at Stanford University B.S. University ( B.S V Pande, J Gomes, M Zitnik, P Liang, P., Jordan,,. 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