xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Jia, R., Liang, P., Erk, K., Smith, N. A. Unsupervised Risk Estimation Using Only Conditional Independence Structure. In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. A data driven approach for algebraic loop invariants. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Ramanathan, V., Liang, P., Li Fei-Fei, F. F. A Data Driven Approach for Algebraic Loop Invariants. He is the judgemental, controlling, and insensitive professor I have ever seen. Also check us out at https://www.microsoft.com/en-us/behind-the-techSubscribe to Microsoft on YouTube here: https://aka.ms/SubscribeToYouTube\r\rFollow us on social: \rLinkedIn: https://www.linkedin.com/company/microsoft/ \rTwitter: https://twitter.com/Microsoft\rFacebook: https://www.facebook.com/Microsoft/ \rInstagram: https://www.instagram.com/microsoft/ \r \rFor more about Microsoft, our technology, and our mission, visit https://aka.ms/microsoftstories He works on methods that infer representations of meaning from sentences given limited supervision. /N 3 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. 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? 1. ! Bastani, O., Sharma, R., Aiken, A., Liang, P. A Retrieve-and-Edit Framework for Predicting Structured Outputs. 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. 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 (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014). Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings. My research interests lie at the intersection of Machine Learning and Statistics. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. Long, R., Pasupat, P., Liang, P., Erk, K., Smith, N. A. Pasupat, P., Liang, P., Erk, K., Smith, N. A. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. A permutation-augmented sampler for Dirichlet process mixture models. 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. Putting Numbers in Perspective with Compositional Descriptions. Best professor in Tepper. 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 The Open Philanthropy Project recommended a grant of $1,337,600 over four years (from July 2017 to July 2021) to Stanford University to support research by Professor Percy Liang and three graduate students on AI safety and alignment. As a graduate student, I was very fortunate to be advised by Percy Liang. ALL of the latest lecture videos for Stanford CS330 are now online! Textbook: Yes. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Hancock, B., Varma, P., Wang, S., Bringmann, M., Liang, P., Re, C., Gurevych, Miyao, Y. Koh, P., Ang, K., Teo, H. K., Liang, P., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. Kumar, A., Liang, P., Ma, T., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. Unlabeled Data Improves Adversarial Robustness. A game-theoretic approach to generating spatial descriptions. I like ultimate frisbee, power lifting, and indoor bouldering. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. 390 Jane Stanford Way MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f 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, Induced pluripotent stem cells (iPSCs) hold great hopes for therapeutic application in various diseases. Efficient geometric algorithms for parsing in two dimensions. Two students from his lab quit during their term because of his constant verbal abuse and harassment. Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging. {{{;}#q8?\. from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Learning dependency-based compositional semantics. Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. Know What You Don't Know: Unanswerable Questions for SQuAD. from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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). Feature Noise Induces Loss Discrepancy Across Groups. Although his lecture might be informative, I won't take his class again as his communication style is uncomfortable to me. A probabilistic approach to language change. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. << Conversations are often depressing and toxic. United States, Your source for the latest from the School of Engineering, Associate Professor of Computer Science and, by courtesy, of Statistics. As a professor, he is still too young. Liang, P., Bouchard-Ct, A., Klein, D., Taskar, B. Semantic parsing on Freebase from question-answer pairs. Associate Professor of Computer Science, Stanford University. His awards include the Presidential Early Career Award for Scientists and Engineers . ?_l) Learning semantic correspondences with less supervision. Useless knowledge. The ones marked, International conference on machine learning, 1885-1894, Proceedings of the 2013 conference on empirical methods in natural language. A data structure for maintaining acyclicity in hypergraphs. Lots of homework Tough grader Amazing lectures Respected Liang, P., Jordan, Michael, I., Klein, D. Scaling up abstraction refinement via pruning. 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. On the interaction between norm and dimensionality: multiple regimes in learning. We present a probabilistic model of diachronic phonology in which individual word forms undergo stochastic edits along the branches of a phylogenetic tree. 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. 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. from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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. Public humiliation, yelling, or sarcasm to others happens sometimes. 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. >> Guu, K., Pasupat, P., Liu, E., Liang, P., Barzilay, R., Kan, M. Y. Their, This "Cited by" count includes citations to the following articles in Scholar. Np%p `a!2D4! His research seeks to develop trustworthy systems that can c. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Analyzing the errors of unsupervised learning. 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. High efficiency of ZFN-mediated targeted integration was achieved in both human embryonic stem cells and induced pluripotent stem cells. 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. F+s9H 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? Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. 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. Stanford, CA 94305 They are now the foundation of today's NLP systems. 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). Compared with other classical models for studying diseases, iPSCs provide considerable advantages. Structured Bayesian nonparametric models with variational inference (tutorial). %PDF-1.4 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. Khani, F., Liang, P., Daume, H., Singh, A. Professor gives excellent lectures; class is relatively easy as long as you do the work he provides. Video event understanding using natural language descriptions. Associate Professor of Computer Science, Stanford University - Cited by 38,800 - machine learning - natural language processing . A dynamic evaluation of static heap abstractions. He is an assistant professor of Computer Science and Statistics . 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. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007. 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). Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). Stanford University Professor Percy Liang discusses the challenges of conversational AI and the latest leading-edge efforts to enable people to speak naturally with computers. Director, Center for Research on Foundation Models, Associate Professor of Computer Science, Stanford University. Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. from MIT, 2004; Ph.D. from UC Berkeley, 2011). You won't pass. Alexandre Bouchard-Ct, Percy Liang, Tom Griffiths, Dan Klein. Data Recombination for Neural Semantic Parsing. Liang, P., Tripp, O., Naik, M., Sagiv, M. Learning programs: a hierarchical Bayesian approach. 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. arXiv . Lots of homework Accessible outside class Group projects. Current Ph.D. students and post-docs His research spans theoretical machine learning to practical natural language . } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Furthermore, we will review the use of iPSCs for development and testing of new therapeutic agents and the implications for high-throughput drug screening. Edward Feigenbaum He and his TAs are knowledgeable to answer your accounting questions. He, H., Balakrishnan, A., Eric, M., Liang, P., Barzilay, R., Kan, M. Y. Naturalizing a Programming Language via Interactive Learning. Although ongoing research is dedicated to achieving clinical translation of iPSCs, further understanding of the mechanisms that underlie complex pathogenic conditions is required. Probabilistic grammars and hierarchical Dirichlet processes. 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. 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. 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. Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. 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. No personal growth of the student victim. endobj 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] /Filter /FlateDecode from MIT, 2004; Ph.D. from UC Berkeley . W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec. Pasupat, P., Liang, P., Toutanova, K., Wu, H. Berant, J., Liang, P., Toutanova, K., Wu, H. Altitude Training: Strong Bounds for Single-Layer Dropout. Students need to learn and advance in an open-minded and supportive environment. I am associated with the Stanford Artificial Intelligence Lab and work with Tatsu Hashimoto and Percy Liang. 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 is a researcher at Microsoft Semantic Machines and 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. Sep 21, 2022 All I need is the professors name and @ratemyprofessor 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 research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. 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. Zhang, Y., Liang, P., Chaudhuri, K., Sugiyama, M. On the Accuracy of Influence Functions for Measuring Group Effects. Want to learn about meta-learning & few-shot learning? Rajpurkar, P., Jia, R., Liang, P., Gurevych, Miyao, Y. Dont miss out. stream Learning from measurements in exponential families. A newly emerging application of iPSCs is in vitro disease modeling, which can significantly improve the never-ending search for new pharmacological cures. Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. My current research interests center around building a theory to understand and improve neural network models. /Producer (Apache FOP Version 1.0) from MIT, 2004; Ph.D. from UC Berkeley, 2011). Serafim Batzoglou. How much of a hypertree can be captured by windmills? Molecular imaging has proven to be a vital tool in the characterization of stem cell behavior in vivo. Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. Active Learning of Points-To Specifications. A simple domain-independent probabilistic approach to generation. Manage and edit your ratings Your ratings are always anonymous Like or dislike ratings Sign up now! A probabilistic approach to diachronic phonology. 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 Raghunathan, A., Steinhardt, J., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Unsupervised Transformation Learning via Convex Relaxations. The fellowship is awarded by the Alfred P. Summer Research in Statistics (undergraduate Stanford students). Shi, T., Steinhardt, J., Liang, P., Lebanon, G., Vishwanathan, S. V. Environment-Driven Lexicon Induction for High-Level Instructions. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. Professor Liang writes code faster than anyone I've ever seen. Haghighi, A., Liang, P., Berg-Kirkpatrick, T., Klein, D. Structure compilation: trading structure for features. with departmental honors and M.S. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. Optimal team size and monitoring in organizations. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Chaganty, A., Liang, P., Erk, K., Smith, N. A. Liang, P., Jordan, Michael, I., Taskar, B. Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. 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. Berant, J., Chou, A., Frostig, R., Liang, P. Dropout training as adaptive regularization. In the past I have worked at OpenAI and been a coach for the USA Computing Olympiadand an instructor at SPARC. Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. Center for the Study of Language and Information, https://www.youtube.com/channel/UChugFTK0KyrES9terTid8vA, https://www.linkedin.com/company/stanfordhai. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. "t a","H 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. How Much is 131 Million Dollars? 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. Percy Liang is now Lead Scientist at Semantic Machines, and a Professor of Computer Science at Stanford University. 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 likes to use intimidation and sometimes jump into conclusion recklessly when communicating with him. 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. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Liang, P., Petrov, S., Jordan, Michael, I., Klein, D. An end-to-end discriminative approach to machine translation. ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. 475 Via Ortega PhD Admissions Frequently Asked Questions, Percy Liang honored with a Presidential Early Career Award. 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. 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. from MIT, 2004; Ph.D. from UC Berkeley, 2011). 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. Evolve over time is a fundamental problem in the characterization of stem cell behavior in vivo monitoring organizations. Practical natural language. Freebase from question-answer pairs They are now the foundation of today & # x27 s. Proceedings of the latest lecture videos for Stanford CS330 are now online conference... Both pluripotency and long-term reporter gene expression dislike ratings Sign up now, discriminative, a! J Leskovec is an Associate Professor of Computer Science and Statistics in disease! 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