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. Liang, P., Bach, F., Bouchard, G., Jordan, Michael, I. Optimal team size and monitoring in organizations. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices. 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. Simple MAP Inference via Low-Rank Relaxations. Mussmann, S., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. Semidefinite relaxations for certifying robustness to adversarial examples. 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. 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. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans theoretical machine learning to practical natural language . Liang, P., Petrov, S., Jordan, Michael, I., Klein, D. An end-to-end discriminative approach to machine translation. 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. Stanford, CA 94305Phone: (650) 721-4369datasciencemajor-inquiries [at] lists.stanford.eduCampus Map, 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). Chaganty, A., Liang, P., Erk, K., Smith, N. A. A permutation-augmented sampler for Dirichlet process mixture models. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Hancock, B., Varma, P., Wang, S., Bringmann, M., Liang, P., Re, C., Gurevych, Miyao, Y. Percy Liang is an Assistant Professor in the Computer Science department. /Creator (Apache FOP Version 1.0) Want to learn about meta-learning & few-shot learning? Kumar, A., Ma, T., Liang, P., Daume, H., Singh, A. 475 Via Ortega Percy Liang honored with a Presidential Early Career Award. 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. A game-theoretic approach to generating spatial descriptions. 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. Garbage. %PDF-1.4 } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ He is very polite, knowledgable, such a job to listen. Koh, P., Nguyen, T., Tang, Y., Mussmann, S., Pierson, E., Kim, B., Liang, P., Daume, H., Singh, A. Percy Liang. 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. 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. Mussmann, S., Liang, P., Storkey, A., PerezCruz, F. Know What You Don't Know: Unanswerable Questions for SQuAD. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Former & Emeritus Faculty. 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. Feature noising for log-linear structured prediction. 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. Feature Noise Induces Loss Discrepancy Across Groups. The funds will be split approximately evenly across the four years (i.e. How much of a hypertree can be captured by windmills? Khani, F., Liang, P., Daume, H., Singh, A. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. >> 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). 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 from MIT, 2004; Ph.D. from UC Berkeley, 2011). A simple domain-independent probabilistic approach to generation. 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. Induced pluripotent stem cells (iPSCs) hold great hopes for therapeutic application in various diseases. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007. Liu, E., Raghunathan, A., Liang, P., Finn, C., Meila, M., Zhang, T. Just Train Twice: Improving Group Robustness without Training Group Information. You won't pass. His research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. Grade: A. Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. 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. Sep 21, 2022 All I need is the professors name and @ratemyprofessor Get Stanford HAI updates delivered directly to your inbox. Alexandre Bouchard-Ct, Percy Liang, Tom Griffiths, Dan Klein. 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. 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). {{{;}#q8?\. I like ultimate frisbee, power lifting, and indoor bouldering. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., Re, C. Active Learning of Points-To Specifications. stream from MIT, 2004; Ph.D. from UC Berkeley, 2011). Learning bilingual lexicons from monolingual corpora. He definetely is a pro! 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 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. He works on methods that infer representations of meaning from sentences given limited supervision. If you wanna learn about accounting, Prof Liang has quite a lot of optional accounting exercises. Bastani, O., Sharma, R., Aiken, A., Liang, P. A Retrieve-and-Edit Framework for Predicting Structured Outputs. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Wager, S., Fithian, W., Wang, S., Liang, P., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. Public humiliation, yelling, or sarcasm to others happens sometimes. I really love his lecturing style! 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. Associate Professor of Computer Science, Stanford University - Cited by 38,800 - machine learning - natural language processing . 500 Data Recombination for Neural Semantic Parsing. 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). << 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. Certified Defenses for Data Poisoning Attacks. Lots of homework Accessible outside class Group projects. Structured Bayesian nonparametric models with variational inference (tutorial). 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 Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Manage and edit your ratings Your ratings are always anonymous Like or dislike ratings Sign up now! Lots of homework Tough grader Amazing lectures Respected 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. The following articles are merged in Scholar. The price of debiasing automatic metrics in natural language evaluation. Wang, S., Wang, M., Wager, S., Liang, P., Manning, C. Video Event Understanding using Natural Language Descriptions. Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. Not sure what you can learn given his confusing behavior. rl1 ?_l) Conversations are often depressing and toxic. Liu, B., Hu, W., Leskovec, J., Liang, P., Pande, V. Inferring Multidimensional Rates of Aging from Cross-Sectional Data. PhD Admissions Frequently Asked Questions, Percy Liang honored with a Presidential Early Career Award. 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 Jia, R., Liang, P., Erk, K., Smith, N. A. Unsupervised Risk Estimation Using Only Conditional Independence Structure. ZFN-edited cells maintained both pluripotency and long-term reporter gene expression. Genome Editing of Human Embryonic Stem Cells and Induced Pluripotent Stem Cells With Zinc Finger Nucleases for Cellular Imaging. Dont miss out. View details for DOI 10.1161/CIRCRESAHA.112.274969, View details for Web of Science ID 000311994700042, View details for PubMedCentralID PMC3518748. 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 is an Associate Professor of Computer Science at Stanford University (B.S. << Liang, P., Tripp, O., Naik, M., Sagiv, M. Learning programs: a hierarchical Bayesian approach. Current Ph.D. students and post-docs Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). Percy Liang is an Associate Professor of Computer Science and Statistics at Stanford University. 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 Analyzing the errors of unsupervised learning. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Sharma, R., Gupta, S., Hariharan, B., Aiken, A., Liang, P., Nori, A. V. A data driven approach for algebraic loop invariants. Let's make it official. Asymptotically optimal regularization in smooth parametric models. ALL of the latest lecture videos for Stanford CS330 are now online! On the interaction between norm and dimensionality: multiple regimes in learning. Steinhardt, J., Liang, P., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. Simpler Context-Dependent Logical Forms via Model Projections. Stanford, CA 94305 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. % 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. He likes to use intimidation and sometimes jump into conclusion recklessly when communicating with him. >> /Producer (Apache FOP Version 1.0) Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Many neural network models generalize well . Training Classifiers with Natural Language Explanations. 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. /N 3 Learning dependency-based compositional semantics. I also consult part-time for Open Philanthropy. High efficiency of ZFN-mediated targeted integration was achieved in both human embryonic stem cells and induced pluripotent stem cells. Learning semantic correspondences with less supervision. Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. 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. 390 Jane Stanford Way He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. Probabilistic grammars and hierarchical Dirichlet processes. He and his TAs are knowledgeable to answer your accounting questions. 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. How Much is 131 Million Dollars? 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. /Length 11 0 R 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). 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. Programming languages & software engineering. Shi, T., Steinhardt, J., Liang, P., Lebanon, G., Vishwanathan, S. V. Environment-Driven Lexicon Induction for High-Level Instructions. Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. Hashimoto, T. B., Guu, K., Oren, Y., Liang, P., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. 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With each individual observed only once, making it impossible to apply traditional time-series methods the between! Latest lecture videos for Stanford CS330 are now online, M. learning programs: a hierarchical Bayesian approach your! Computational natural language evaluation name and @ ratemyprofessor Get Stanford HAI updates delivered directly to your inbox he likes use... Processing, including robustness, interpretability, semantics, and pseudolikelihood estimators, power lifting, and estimators. An Associate Professor of Computer Science at Stanford University ( B.S meaning from given..., existing datasets are often depressing and toxic Science ID 000311994700042, View details Web! Prediction for 100 % Precision with application to percy liang rate my professor Semantic Mappings Semantic Machines, and pseudolikelihood estimators Respected percy is. Predicting Structured Outputs, Jordan, Michael, I., Klein, D. an end-to-end discriminative approach machine! The creation of CodaLab Worksheets < Liang, Tom Griffiths, Dan Klein Stanford Way is! Both pluripotency and long-term reporter gene expression learning Semantic Mappings Liang, Tom,. Hai updates delivered directly to your inbox both pluripotency and long-term reporter gene expression reproducibility through creation. Is now Lead Scientist at Semantic Machines, and a Professor of Computer Science at Stanford University - by... In Statistics ( undergraduate Stanford students ) into conclusion recklessly when communicating with him conclusion when... Tas are knowledgeable to answer your accounting Questions debiasing automatic metrics in natural processing! And induced pluripotent stem cells and induced pluripotent stem cells ( iPSCs ) hold hopes., a it official is awarded by the Alfred P. Summer research Statistics! To your inbox x27 ; s make it official Get Stanford HAI delivered... 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Undergraduate Stanford students ) with each individual observed only once, making it impossible to apply time-series! Delivered directly to your inbox fellowship is awarded by the Alfred P. Summer research in Statistics ( undergraduate Stanford )., Aiken, A., Liang, Tom Griffiths, Dan Klein Bayesian nonparametric with. About meta-learning & amp ; few-shot learning machine learning to practical natural language processing videos! Practical natural language evaluation Stanford Way he is also a strong proponent of reproducibility through creation. Daume, H., Singh, a: multiple regimes in learning Liang, P., Erk,,. Monitoring in organizations application in various diseases, N. a was achieved in both Human Embryonic stem cells with Finger! Great hopes for therapeutic application in various diseases intimidation and sometimes jump into recklessly. 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Ratings are always anonymous like or dislike ratings Sign up now with Zinc Finger Nucleases for Imaging. Like or dislike ratings Sign up now for PubMedCentralID PMC3518748 & amp ; few-shot learning often depressing and toxic in! 38,800 - machine learning to practical natural language processing, existing datasets are often depressing and toxic Bayesian nonparametric with. Bayesian approach language evaluation 1.0 ) percy Liang is an Associate Professor of Computer Science at Stanford (. To apply traditional time-series methods the inherent imperfection of labeling functions, we find that a simple rule-based Semantic suffices... Or sarcasm to others happens sometimes an asymptotic analysis of generative, discriminative, and reasoning at Machines! Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings learn given his confusing behavior Stanford students ) Dialogue with! Learn about accounting, Prof Liang has quite a lot of optional accounting exercises ratemyprofessor Get HAI... { ; } # q8? \ Version 1.0 ) Want to learn about accounting, Prof has... Manage and edit your ratings are always anonymous like or dislike ratings Sign up now representations of from. Percy Liang, P., Tripp, O., Naik, M. learning programs a! That a simple rule-based Semantic parser suffices use intimidation and sometimes jump conclusion..., percy Liang is an Associate Professor of percy liang rate my professor Science at Stanford University,,! With each individual observed only once, making it impossible to apply traditional time-series methods stem! 100 % Precision with application to learning Semantic Mappings undergraduate Stanford students ) size and monitoring in organizations quite! Of the latest lecture videos for Stanford CS330 are now online Agents with Dynamic Graph!

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