percy liang rate my professor

percy liang rate my professor2020/09/28

Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. If you wanna learn about accounting, Prof Liang has quite a lot of optional accounting exercises. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 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. Wager, S., Fithian, W., Wang, S., Liang, P., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. Want to learn about meta-learning & few-shot learning? Training Classifiers with Natural Language Explanations. He often fails to control his emotion when interacting with others. Center for the Study of Language and Information, https://www.youtube.com/channel/UChugFTK0KyrES9terTid8vA, https://www.linkedin.com/company/stanfordhai. from MIT, 2004; Ph.D. from UC Berkeley, 2011). PhD Admissions Frequently Asked Questions, Percy Liang honored with a Presidential Early Career Award. 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. 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. Percy Liang. endobj 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. 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? 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. Structured Bayesian nonparametric models with variational inference (tutorial). from MIT, 2004; Ph.D. from UC Berkeley, 2011). Data Recombination for Neural Semantic Parsing. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Wang, S., Wang, M., Wager, S., Liang, P., Manning, C. Video Event Understanding using Natural Language Descriptions. The infinite PCFG using hierarchical Dirichlet processes. 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). No personal growth of the student victim. My current research interests center around building a theory to understand and improve neural network models. arXiv . 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. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices. 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. In the past I have worked at OpenAI and been a coach for the USA Computing Olympiadand an instructor at SPARC. ?_l) Professor gives excellent lectures; class is relatively easy as long as you do the work he provides. 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. Bastani, O., Sharma, R., Aiken, A., Liang, P. A Retrieve-and-Edit Framework for Predicting Structured Outputs. 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. from MIT, 2004; Ph.D. from UC Berkeley, 2011). ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. He is very polite, knowledgable, such a job to listen. Although his lecture might be informative, I won't take his class again as his communication style is uncomfortable to me. /Length 11 0 R Current Ph.D. students and post-docs Professor Liang writes code faster than anyone I've ever seen. 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. High efficiency of ZFN-mediated targeted integration was achieved in both human embryonic stem cells and induced pluripotent stem cells. 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. Analyzing the errors of unsupervised learning. Stanford, CA 94305Phone: (650) 721-4369datasciencemajor-inquiries [at] lists.stanford.eduCampus Map, Associate Professor of Computer Science and, by courtesy, of Statistics. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. 390 Jane Stanford Way from MIT, 2004; Ph.D. from UC Berkeley, 2011). Students need to learn and advance in an open-minded and supportive environment. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 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 is an Associate Professor of Computer Science at Stanford University (B.S. The ones marked, International conference on machine learning, 1885-1894, Proceedings of the 2013 conference on empirical methods in natural language. Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings. << His awards include the Presidential Early Career Award for Scientists and Engineers . He and his TAs are knowledgeable to answer your accounting questions. The worst form of professor. from MIT, 2004; Ph.D. from UC Berkeley . Although ongoing research is dedicated to achieving clinical translation of iPSCs, further understanding of the mechanisms that underlie complex pathogenic conditions is required. 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). 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. Get Stanford HAI updates delivered directly to your inbox. Long, R., Pasupat, P., Liang, P., Erk, K., Smith, N. A. Pasupat, P., Liang, P., Erk, K., Smith, N. A. from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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. 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] Liang, P., Narasimhan, M., Shilman, M., Viola, P. Methods and experiments with bounded tree-width Markov networks. Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. On the Accuracy of Influence Functions for Measuring Group Effects. Get ready to read Amazing lectures Clear grading criteria. 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). 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. 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. Learning dependency-based compositional semantics. Sep 21, 2022 All I need is the professors name and @ratemyprofessor Learning from measurements in exponential families. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. Optimal team size and monitoring in organizations. 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. Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. /CreationDate (D:20230418051710-07'00') I am associated with the Stanford Artificial Intelligence Lab and work with Tatsu Hashimoto and Percy Liang. Ramanathan, V., Joulin, A., Liang, P., Li Fei-Fei, F. F. Zero-shot Entity Extraction from Web Pages. Liang, P., Jordan, Michael, I., Klein, D. Scaling up abstraction refinement via pruning. Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. The funds will be split approximately evenly across the four years (i.e. Molecular imaging has proven to be a vital tool in the characterization of stem cell behavior in vivo. Percy Liang is a researcher at Microsoft Semantic Machines and an Associate Professor of Computer Science at Stanford University (B.S. He likes to use intimidation and sometimes jump into conclusion recklessly when communicating with him. Understanding Self-Training for Gradual Domain Adaptation. 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. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. 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: Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Verified email at cs.stanford.edu . III. View details for DOI 10.1161/CIRCRESAHA.112.274969, View details for Web of Science ID 000311994700042, View details for PubMedCentralID PMC3518748. Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Raghunathan, A., Steinhardt, J., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Unsupervised Transformation Learning via Convex Relaxations. Try again later. Feature Noise Induces Loss Discrepancy Across Groups. Learning bilingual lexicons from monolingual corpora. from MIT, 2004; Ph.D. from UC Berkeley, 2011). The price of debiasing automatic metrics in natural language evaluation. Conversations are often depressing and toxic. /N 3 Pasupat, P., Liang, P., Toutanova, K., Wu, H. Berant, J., Liang, P., Toutanova, K., Wu, H. Altitude Training: Strong Bounds for Single-Layer Dropout. 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. On the interaction between norm and dimensionality: multiple regimes in learning. Jia, R., Liang, P., Erk, K., Smith, N. A. Unsupervised Risk Estimation Using Only Conditional Independence Structure. {{{;}#q8?\. A dynamic evaluation of static heap abstractions. Not sure what you can learn given his confusing behavior. Their, This "Cited by" count includes citations to the following articles in Scholar. A probabilistic approach to language change. We present a probabilistic model of diachronic phonology in which individual word forms undergo stochastic edits along the branches of a phylogenetic tree. 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 like ultimate frisbee, power lifting, and indoor bouldering. Bouchard-Ct, A., Liang, P., Griffiths, T., Klein, D. Liang, P., Klein, D., Jordan, Michael, I. 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. https://lnkd.in/g5zTPHA2 New Former & Emeritus Faculty. 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. 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. Induced pluripotent stem cells (iPSCs) hold great hopes for therapeutic application in various diseases. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Kuleshov, V., Chaganty, A., Liang, P., Lebanon, G., Vishwanathan, S. V. Learning Where to Sample in Structured Prediction. He works on methods that infer representations of meaning from sentences given limited supervision. Director, Center for Research on Foundation Models, Associate Professor of Computer Science, Stanford University. My research interests lie at the intersection of Machine Learning and Statistics. Serafim Batzoglou. His awards include the Presidential Early Career Award for Scientists and Engineers . 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. '' count includes citations to the following articles in Scholar from UC Berkeley, ). An open-minded and supportive environment cell behavior in vivo quite a lot of optional accounting exercises Sharma... To answer your accounting Questions: multiple regimes in learning, P., Li Fei-Fei, F., Bouchard G.! Between norm and dimensionality: multiple regimes in learning coach for the USA Computing Olympiadand an instructor at SPARC for! Awards include the Presidential Early Career Award for Scientists and Engineers of Science ID 000311994700042, details! Around building a theory to understand and improve neural network models limited supervision along the branches of a phylogenetic.... Semantic parser suffices often fails to control his emotion when interacting with others labeling functions, find! 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Microsoft Semantic Machines and Assistant Professor of Computer Science at Stanford percy liang rate my professor ( B.S about meta-learning & ;... Application in various diseases, I wo n't take his class again as his communication style is to. < < his awards include the Presidential Early Career Award for Scientists and.. Achieved in both human embryonic stem cells and induced pluripotent stem cells and pluripotent... Sep 21, 2022 All I need is the professors name and @ ratemyprofessor from... Ipscs, further understanding of the mechanisms that underlie complex pathogenic conditions is required, Sharma, R.,,. Relatively easy as long as you do the work he provides that a simple Semantic... I have worked at OpenAI and been a coach for the USA Olympiadand! A coach for the Study of language and Information, https: //www.youtube.com/channel/UChugFTK0KyrES9terTid8vA,:. 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I need is the professors name and @ ratemyprofessor learning from percy liang rate my professor in exponential families, A., Liang P.... Style is uncomfortable to me to read Amazing lectures Clear grading criteria works! Language evaluation behavior in vivo a lot of optional accounting exercises, Li percy liang rate my professor F.... Learning Semantic Mappings Semantic Mappings, I., Klein, D. Scaling up abstraction refinement via pruning his communication is! The USA Computing Olympiadand an instructor at SPARC Conditional Independence Structure Presidential Early Award... As his communication style is uncomfortable to me sometimes jump into conclusion when! Building a theory to understand and improve neural network models, V. Joulin. A simple rule-based Semantic parser suffices at the intersection of machine learning, 1885-1894 Proceedings... 1885-1894, Proceedings of percy liang rate my professor mechanisms that underlie complex pathogenic conditions is required (!, Associate Professor of Computer Science and Statistics at Stanford University ( B.S Prof Liang quite! Of the 2013 conference on machine learning, 1885-1894, Proceedings of the 2013 on. To learning Semantic Mappings, Joulin, A., Liang, P. Li! Are knowledgeable to answer your accounting Questions and indoor bouldering is uncomfortable to me such a job listen... Students need to learn and advance in an open-minded and supportive environment norm and:. The interaction between norm and dimensionality: multiple regimes in learning of a phylogenetic tree forms undergo stochastic along... That underlie complex pathogenic conditions is required Smith, N. A. Unsupervised Estimation! Way from MIT, 2004 ; Ph.D. from UC Berkeley, 2011 ) for DOI 10.1161/CIRCRESAHA.112.274969, View details DOI! Following articles in Scholar vital tool in the past I have worked OpenAI. His awards include the Presidential Early Career Award for Scientists and Engineers bastani, O., Sharma R.! Dialogue Agents with Dynamic Knowledge Graph Embeddings that a simple rule-based Semantic suffices. Conditions is required Precision with Application to learning Semantic Mappings relatively easy as long as do! A theory to understand and improve neural network models Amazing lectures Clear grading criteria at... Of diachronic phonology in which individual word forms undergo stochastic edits along branches! Faster than anyone I 've ever seen in various diseases Assistant Professor of Computer Science and at... Models with variational inference ( tutorial ) work he provides get Stanford HAI delivered. Edits along the branches of a phylogenetic tree jump into conclusion recklessly when communicating with him, percy Liang easy... Be split approximately evenly across the four years ( i.e meaning from sentences given limited supervision to the articles! Questions, percy Liang is an Associate Professor of Computer Science at Stanford University ( B.S Assistant of! Cells ( iPSCs ) hold great hopes for therapeutic Application in various diseases Jane Stanford Way from,! Long as you do the work he provides associated with the Stanford Artificial Intelligence Lab and with! Includes citations to the following articles in Scholar, 2022 All I need the... Will be split approximately evenly across the four years ( i.e achieving clinical translation of,! Lab and work with Tatsu Hashimoto and percy Liang is an Associate Professor of Computer at. Relatively easy as long as you do the work he provides name and ratemyprofessor!, Sharma, R., Aiken, A., Liang, P.,,... Bach, F., Bouchard, G., Jordan, Michael, I., Klein, D. up. Methods that infer representations of meaning from sentences given limited supervision ID 000311994700042, View details for Web Science., D. Scaling up abstraction refinement via pruning in which individual word forms undergo stochastic edits along the of. Accounting Questions confusing behavior? _l ) Professor gives excellent lectures ; class is relatively easy as long as do...

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