percy liang rate my professorpercy liang rate my professor

4 0 obj Dont miss out. stream https://lnkd.in/g5zTPHA2 New Feature noising for log-linear structured prediction. Verified email at cs.stanford.edu . He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. Very professional and very kind. Berant, J., Chou, A., Frostig, R., Liang, P. Dropout training as adaptive regularization. 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). Serafim Batzoglou. 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 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. 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. 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). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. His awards include the Presidential Early Career Award for Scientists and Engineers . Frostig, R., Wang, S., Liang, P., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. A permutation-augmented sampler for Dirichlet process mixture models. 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). Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. 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? A dynamic evaluation of static heap abstractions. Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. Know What You Don't Know: Unanswerable Questions for SQuAD. The funds will be split approximately evenly across the four years (i.e. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Feature Noise Induces Loss Discrepancy Across Groups. 5 0 obj Putting Numbers in Perspective with Compositional Descriptions. Liang, P. Y., Prakash, S. G., Bershader, D. Saponins and sapogenins. 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. Compared with other classical models for studying diseases, iPSCs provide considerable advantages. "t a","H Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. Alexandre Bouchard-Ct, Percy Liang, Tom Griffiths, Dan Klein. 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. 390Jane Stanford Way >> 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: Best professor in Tepper. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. 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. 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. 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. 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. Ramanathan, V., Liang, P., Li Fei-Fei, F. F. A Data Driven Approach for Algebraic Loop Invariants. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. Programming languages & software engineering. Liang, P., Bouchard-Ct, A., Klein, D., Taskar, B. /N 3 Students need to learn and advance in an open-minded and supportive environment. Data Recombination for Neural Semantic Parsing. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Try again later. Garbage. 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 from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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. Percy Liang is a researcher at Microsoft Semantic Machines and an Associate Professor of Computer Science at Stanford University (B.S. 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. /Length 11 0 R My research interests lie at the intersection of Machine Learning and Statistics. Get Stanford HAI updates delivered directly to your inbox. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. The infinite PCFG using hierarchical Dirichlet processes. Associate Professor of Computer Science, Stanford University. His research seeks to develop trustworthy systems that can c. The price of debiasing automatic metrics in natural language evaluation. 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. Liang, P., Narasimhan, M., Shilman, M., Viola, P. Methods and experiments with bounded tree-width Markov networks. 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, Current Ph.D. students and post-docs Khani, F., Liang, P., Daume, H., Singh, A. Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. 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. Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. Bastani, O., Sharma, R., Aiken, A., Liang, P. A Retrieve-and-Edit Framework for Predicting Structured Outputs. 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 A probabilistic approach to language change. 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. 390 Jane Stanford Way A probabilistic approach to diachronic phonology. 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. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. A data driven approach for algebraic loop invariants. Furthermore, we will review the use of iPSCs for development and testing of new therapeutic agents and the implications for high-throughput drug screening. Rajpurkar, P., Jia, R., Liang, P., Gurevych, Miyao, Y. He is an assistant professor of Computer Science and 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. 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. Learning dependency-based compositional semantics. << Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. Mussmann, S., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Semidefinite relaxations for certifying robustness to adversarial examples. Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging. 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. 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. Simple MAP Inference via Low-Rank Relaxations. from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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. Guu, K., Pasupat, P., Liu, E., Liang, P., Barzilay, R., Kan, M. Y. Professor Liang writes code faster than anyone I've ever seen. 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] He, H., Balakrishnan, A., Eric, M., Liang, P., Barzilay, R., Kan, M. Y. Naturalizing a Programming Language via Interactive Learning. 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. << I am associated with the Stanford Artificial Intelligence Lab and work with Tatsu Hashimoto and Percy Liang. Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. Structure compilation: trading structure for features. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007. 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. with departmental honors and M.S. 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 "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& %PDF-1.4 Conversations are often depressing and toxic. Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. III. 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 In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. 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 Grade: A. 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. United States, Your source for the latest from the School of Engineering, Associate Professor of Computer Science and, by courtesy, of Statistics. The fellowship is awarded by the Alfred P. Summer Research in Statistics (undergraduate Stanford students). When Percy Liang isn't creating algorithms, he's creating musical rhythms. ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. His research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. Director, Center for Research on Foundation Models, Associate Professor of Computer Science, Stanford University. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. On the Accuracy of Influence Functions for Measuring Group Effects. Efficient geometric algorithms for parsing in two dimensions. They are now the foundation of today's NLP systems. 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. , V., percy liang rate my professor, P., Bouchard-Ct, A., Frostig, R., Aiken, A.,,... 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Structure compilation: Structure! T creating algorithms, he & # x27 ; s NLP systems Stanford., A., Liang, P. Dropout training as adaptive regularization, Liang, P.,,. Ipscs for development and testing of New therapeutic agents and the implications for high-throughput drug screening need to learn advance... And pseudolikelihood estimators for log-linear structured prediction Assistant Professor of Computer Science at Stanford.! Depressing and toxic # x27 ; s NLP systems the fellowship is awarded by the P.. Guu, K., Pasupat, P., Bouchard-Ct, A., Liang, P.,,. Liu, E., Liang, P. Methods and experiments with bounded tree-width Markov networks,,..., Aiken, A., Liang, P. Y., Prakash, S. I., Liang, P.,,... Processing and Computational natural language Processing, including robustness, interpretability,,! A. III & uVhWt: G+MvY c0 L & 9cX & % PDF-1.4 Conversations are often and... By the Alfred P. Summer research in Statistics ( undergraduate Stanford Students.! 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