Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. Structure compilation: trading structure for features. Verified email at cs.stanford.edu . Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. Simple MAP Inference via Low-Rank Relaxations. Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings. In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. 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. 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. 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. Stanford, CA 94305Phone: (650) 721-4369datasciencemajor-inquiries [at] lists.stanford.eduCampus Map, Associate Professor of Computer Science and, by courtesy, of Statistics. Video event understanding using natural language descriptions. Get Stanford HAI updates delivered directly to your inbox. ?_l) >> from MIT, 2004; Ph.D. from UC Berkeley, 2011). 390Jane Stanford Way ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. Know What You Don't Know: Unanswerable Questions for SQuAD. Rajpurkar, P., Jia, R., Liang, P., Gurevych, Miyao, Y. 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. 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. ! He definetely is a pro! 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. Ramanathan, V., Liang, P., Li Fei-Fei, F. F. A Data Driven Approach for Algebraic Loop Invariants. He, H., Balakrishnan, A., Eric, M., Liang, P., Barzilay, R., Kan, M. Y. Naturalizing a Programming Language via Interactive Learning. 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. 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. Lots of homework Tough grader Amazing lectures Respected Long, R., Pasupat, P., Liang, P., Erk, K., Smith, N. A. Pasupat, P., Liang, P., Erk, K., Smith, N. A. 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. The infinite PCFG using hierarchical Dirichlet processes. Jia, R., Liang, P., Erk, K., Smith, N. A. Unsupervised Risk Estimation Using Only Conditional Independence Structure. 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. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. Want to learn about meta-learning & few-shot learning? "t a","H Percy Liang. I really love his lecturing style! Associate Professor of Computer Science, Stanford University. 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. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans theoretical machine learning to practical natural language . Putting Numbers in Perspective with Compositional Descriptions. The system can't perform the operation now. Frostig, R., Wang, S., Liang, P., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. Structured Bayesian nonparametric models with variational inference (tutorial). Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. ALL of the latest lecture videos for Stanford CS330 are now online! 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 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. Feature noising for log-linear structured prediction. INTERFEROMETRIC STUDIES OF THE JOVIAN ATMOSPHERIC PROBE FIELD. 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. On the interaction between norm and dimensionality: multiple regimes in learning. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. 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 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., 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. You won't pass. endobj 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: } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ 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? 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. Liang, P. Y., Prakash, S. G., Bershader, D. Saponins and sapogenins. A probabilistic approach to diachronic phonology. 390 Jane Stanford Way 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 He is the judgemental, controlling, and insensitive professor I have ever seen. The price of debiasing automatic metrics in natural language evaluation. If you wanna learn about accounting, Prof Liang has quite a lot of optional accounting exercises. Here, we will discuss current efforts to create iPSC-dependent patient-specific disease models. Probabilistic grammars and hierarchical Dirichlet processes. How much of a hypertree can be captured by windmills? Many neural network models generalize well . Analyzing the errors of unsupervised learning. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. A data driven approach for algebraic loop invariants. 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. Textbook: Yes. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His awards include the Presidential Early Career Award for Scientists and Engineers . https://lnkd.in/g5zTPHA2 New Textbook: Yes. 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. Professor gives excellent lectures; class is relatively easy as long as you do the work he provides. Edward Feigenbaum 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. Get ready to read Amazing lectures Clear grading criteria. 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. Training Classifiers with Natural Language Explanations. << 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. >> 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. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& 475 Via Ortega How Much is 131 Million Dollars? Sep 21, 2022 All I need is the professors name and @ratemyprofessor Learning bilingual lexicons from monolingual corpora. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. Optimal team size and monitoring in organizations. from MIT, 2004; Ph.D. from UC Berkeley, 2011). A permutation-augmented sampler for Dirichlet process mixture models. My current research interests center around building a theory to understand and improve neural network models. Learning semantic correspondences with less supervision. We present a probabilistic model of diachronic phonology in which individual word forms undergo stochastic edits along the branches of a phylogenetic tree. He works on methods that infer representations of meaning from sentences given limited supervision. Liu, E., Raghunathan, A., Liang, P., Finn, C., Meila, M., Zhang, T. Just Train Twice: Improving Group Robustness without Training Group Information. 5 0 obj No personal growth of the student victim. 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. 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. 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 an Associate Professor of Computer Science at Stanford University (B.S. Dont miss out. 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. 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. He often fails to control his emotion when interacting with others. 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., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. Active Learning of Points-To Specifications. 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). Furthermore, we will review the use of iPSCs for development and testing of new therapeutic agents and the implications for high-throughput drug screening. 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. About. 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 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 from measurements in exponential families. 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. A probabilistic approach to language change. with departmental honors and M.S. Induced pluripotent stem cells (iPSCs) hold great hopes for therapeutic application in various diseases. 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. 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. F+s9H Data Recombination for Neural Semantic Parsing. Raghunathan, A., Steinhardt, J., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Unsupervised Transformation Learning via Convex Relaxations. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. In the past I have worked at OpenAI and been a coach for the USA Computing Olympiadand an instructor at SPARC. Professor Liang writes code faster than anyone I've ever seen. Garbage. 4 0 obj His manner doesn't seem professional and often is considered abusive. 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. Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. 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. Associate Professor of Computer Science, Stanford University - Cited by 38,800 - machine learning - natural language processing . 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. Percy Liang honored with a Presidential Early Career Award. They are now the foundation of today's NLP systems. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. roughly $320,000 to $350,000 per year). Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Useless knowledge. Mussmann, S., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Semidefinite relaxations for certifying robustness to adversarial examples. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, Aditya, V. Spectral experts for estimating mixtures of linear regressions. He and his TAs are knowledgeable to answer your accounting questions. The following articles are merged in Scholar. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Bastani, O., Sharma, R., Aiken, A., Liang, P. A Retrieve-and-Edit Framework for Predicting Structured Outputs. Sequoia Hall Inferring Multidimensional Rates of Aging from Cross-Sectional Data. A game-theoretic approach to generating spatial descriptions. Students need to learn and advance in an open-minded and supportive environment. 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, 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. Learning dependency-based compositional semantics. A data structure for maintaining acyclicity in hypergraphs. PhD Admissions Frequently Asked Questions, Percy Liang honored with a Presidential Early Career Award. Asymptotically optimal regularization in smooth parametric models. Percy Liang is an Assistant Professor in the Computer Science department. My research interests lie at the intersection of Machine Learning and Statistics. % Chaganty, A., Liang, P., Erk, K., Smith, N. A. He is very polite, knowledgable, such a job to listen. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. His research seeks to develop trustworthy systems that can c. Np%p `a!2D4! III. %PDF-1.4 Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. Percy Liang is now Lead Scientist at Semantic Machines, and a Professor of Computer Science at Stanford University. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Former & Emeritus Faculty. arXiv . Feature Noise Induces Loss Discrepancy Across Groups. Best professor in Tepper. FAQs specific to the Honors Cooperative Program. Their, This "Cited by" count includes citations to the following articles in Scholar. A simple domain-independent probabilistic approach to generation. /Creator (Apache FOP Version 1.0) Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Molecular imaging has proven to be a vital tool in the characterization of stem cell behavior in vivo. /CreationDate (D:20230418051710-07'00') Grade: A. Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). Understanding Self-Training for Gradual Domain Adaptation. Alexandre Bouchard-Ct, Percy Liang, Tom Griffiths, Dan Klein. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Very professional and very kind. Serafim Batzoglou. Public humiliation, yelling, or sarcasm to others happens sometimes. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Current Ph.D. students and post-docs 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. W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec. 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. Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. Liang, P., Tripp, O., Naik, M., Sagiv, M. Learning programs: a hierarchical Bayesian approach. Guu, K., Pasupat, P., Liu, E., Liang, P., Barzilay, R., Kan, M. Y. Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. On the Accuracy of Influence Functions for Measuring Group Effects. 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. /Producer (Apache FOP Version 1.0) Efficient geometric algorithms for parsing in two dimensions. Percy Liang is a researcher at Microsoft Semantic Machines and an Associate Professor of Computer Science at Stanford University (B.S. Two students from his lab quit during their term because of his constant verbal abuse and harassment. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Khani, F., Liang, P., Daume, H., Singh, A. 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. Wang, S., Wang, M., Wager, S., Liang, P., Manning, C. Video Event Understanding using Natural Language Descriptions. 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. 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. from MIT, 2004; Ph.D. from UC Berkeley . Compared with other classical models for studying diseases, iPSCs provide considerable advantages. 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 . Conversations are often depressing and toxic. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 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. 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. 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