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). This grant was recommended to enable Professor Liang to spend significant time engaging in our process to determine whether to provide his … with people and improve over time through interaction. Percy Liang. The Open Philanthropy Project recommended a planning grant of $25,000 to Professor Percy Liang at Stanford University. Stanford University School of Engineering 626,652 views 1:11:41 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction and Logistics - Duration: 1:07:52. In the Autumn of 2015, I was the head TA for CS221, Stanford's introductory artificial intelligence class, taught by Percy Liang. Understanding Black-box Predictions via Influence Functions. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Semantic Parsing for Natural Language Interfaces - Percy Liang, PhD . Neural Information Processing Systems (NeurIPS) 2017. SAIL is committed to advancing knowledge and fostering learning in an atmosphere of discovery and creativity. 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. Association for Computational Linguistics (ACL), 2014. Percy Liang Stanford University pliang@cs.stanford.edu Abstract A central challenge in semantic parsing is handling the myriad ways in which knowl-edge base predicates can be expressed. They discuss the challenges of conversational AI and the latest leading-edge efforts to enable people to speak naturally with computers. Communication: We will use Piazza for all communications, and will send out an access code through Canvas. https://www.youtube.com/channel/UChugFTK0KyrES9terTid8vA, https://www.linkedin.com/company/stanfordhai. Percy Liang. Percy Liang Michael Jordan Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, condi- tional, and pseudolikelihood. His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Percy Liang. Stanford University Mina Lee Stanford University {cdonahue,minalee,pliang}@cs.stanford.edu Percy Liang Stanford University Abstract We present a simple approach for text infill-ing, the task of predicting missing spans of text at any position in a document. 12/08: Homework 3 Solutions have been posted! Verified email at cs.stanford.edu - Homepage. Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. Articles Cited by. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Year; Squad: 100,000+ questions for machine comprehension of text. Cited by. Learning Dependency-Based Compositional Semantics Semantic Representations for Textual Inference Workshop – Mar. ... Percy Liang's course notes from previous offerings of this course. Given society's increasing reliance on machine learning, Jonathan Berant, Andrew Chou, Roy Frostig, Percy Liang. Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. Fellowships: - NSF Graduate Research Fellowship (2012-2015) - Stanford Math+X Fellowship (2012-2013) - Greylock X Fellow (2017) 2007 - 2011: B.S. Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. Empirical Methods in Natural Language Processing (EMNLP), 2013. Stanford News 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 … Don’t miss out. Sort. 2nd place at 2002, Music competitions (piano): Winner of KDFC Classical Star Search (2008, over-21 division), Cordura Hall A while back, I wrote a friendly introduction to natural language interfaces (XRDS magazine 2014) Search for Percy Liang's work. from MIT, 2004; Ph.D. from UC Berkeley, 2011). I was a section leader for Stanford's CS106A (Introduction to Programming) class in the Winter of 2012. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Sort by citations Sort by year Sort by title. The tension between the fuzziness of machine learning and the crispness of logic also fascinates me. (ICLR 2019). Grading from MIT, 2004; Ph.D. from UC Berkeley, 2011). I'm a 5th-year PhD student in the Stanford Linguistics Department and a member of the Stanford NLP Group.I work with Chris Manning and Dan Jurafsky. My goal is to develop trustworthy systems that can communicate effectively Title. it is critical to build tools to make machine learning more reliable in the wild. Stanford Report. Peter Bartlett's statistical learning theory course. Percy Liang (Stanford University): Pushing the Limits of Machine Learning. Search Search. Percy Liang Stanford University. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … E-mail: robinjia at stanford dot edu I am a sixth-year Ph.D. student in Computer Science at Stanford University, advised by Percy Liang.I am interested in building natural language understanding systems that are robust when given unexpected inputs. Phoenix Young Musicians Competition (2000). we showed that state-of-the-art systems, Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University Lecture 2: Machine Learning 1 – Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019) Topics: Linear classification, Loss minimization, Stochastic gradient descent Gill Bejerano. His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Description. On this note, we showed that neural networks can solve SAT problems with We are actively looking for contributors, Ph.D. Committee: Percy Liang, Wing Hung Wong, Chris Manning, and Lester Mackey. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Matthew Lamm mlamm@stanford.edu. Statistical Machine Learning Group Optional line 2 - add two-line-signature to body class to display ‎Show Behind The Tech with Kevin Scott, Ep Percy Liang: Stanford University Professor, technologist, and researcher in AI - Mar 19, 2020 ‎We talk with Stanford University Professor Percy Liang. Jeannette Bohg 210 Panama Street Autumn 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, 2017-18, 2018-19: Winter 2012-13, 2013-14, 2014-15: 2015-16: Presidential Early Career Award for Scientists and Engineers (2019), Microsoft Research Faculty Fellowship (2014), Graduate fellowships: NSF, NDSEG, GAANN, Siebel Scholar, Programming contests: We have been developing CodaLab Worksheets, of an experiment from raw data to final results. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Martin Wainwright's statistical learning theory course. Faculty & Research Scientists. Also check us … International Conference … Bio. 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. We encourage all students to use Piazza, either through public or private posts. 10, 2012 Percy Liang Google/Stanford The Stanford AI Lab is dynamic and community-oriented, providing many opportunities for research collaboration and innovation. Semantic Parsing via Paraphrasing. I'm currently visiting CoAStaL, the NLP group at University of Copenhagen.. My area of research is Natural Language Processing. so please contact me if you're interested! Articles Cited by. They discuss the challenges of conversational AI and the latest leading-edge efforts to … One can also use natural language to describe classifiers directly rather than requiring labeled data (ACL 2018). I broadly identify with the Previously, I acquired my PhD from UC San Diego where I was jointly advised by Julian McAuley (computer science) and Miller Puckette (music).. My research sits at the intersection of machine learning, music, audio, and interaction. 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. Cited by. Ph.D. in Statistics at Stanford University Advisor: Percy Liang. Boyd and Vandenberghe's Convex Optimization. Jonathan Berant, Percy Liang. Names. Our work spans the spectrum from answering deep, foundational questions in the theory of machine learning to building practical large-scale machine learning algorithms which are widely used in industry. The Stanford Statistical Machine Learning Group at Stanford is a unique blend of faculty, students, and post-docs spanning AI, systems, theory, and statistics. I'm interested in building systems that learn to translate natural language descriptions (e.g., in English or Chinese) into programs (e.g., in Python or C++). Get Stanford HAI updates delivered directly to your inbox. Recent Posts. We've worked on using influence functions to understand black-box models (ICML 2017), Share. Finally, I am a strong proponent of efficient and reproducible research. Best paper award at ICML 2017. Sham Kakade's statistical learning theory course. Despite reaching human-level performance on a wide range of benchmarks, state-of-the-art systems can be easily fooled by seemingly small perturbations that don’t affect humans. Home Percy Liang. Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. Percy Liang Computer Science Stanford University pliang@cs.stanford.edu Abstract Our goal is to learn a semantic parser that maps natural language utterances into ex-ecutable programs when only indirect su-pervision is available: examples are la-beled with the correct execution result, His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers … Megha Srivastava, Tatsunori Hashimoto, Percy Liang International Conference of Machine Learning (ICML) 2020 Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning Megha Srivastava, Hoda Heidari, Andreas Krause Knowledge Discovery and Data Mining (KDD) 2019 ... Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang International Conference of Machine Learning (ICML) 2018 I will start my PhD in Computer Science at Stanford in Fall 2020, supported by the NSF GRFP Fellowship (2018-2023). Verified email at cs.stanford.edu - Homepage. 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. Stay in Touch: Don’t miss out. The funds will be split approximately evenly across the … Sang Michael Xie's Homepage. Percy LIANG of Stanford University, CA (SU) | Read 30 publications | Contact Percy LIANG Gill Bejerano. Despite the successes of machine learning, MIT Concerto Competition (2004), in Mathematics at Duke University Minor in Biology machine learning natural language processing. Machine learning is facing a robustness crisis. Update: 2020-03-19. machine learning natural language processing. Percy Liang's 133 research works with 5,234 citations and 3,995 reads, including: Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning I am currently a postdoctoral scholar advised by Percy Liang in the Stanford department of computer science. from MIT, 2004; Ph.D. from UC Berkeley, 2011). papers. Semantic Parsing on Freebase from Question-Answer Pairs. One idea we've explored is to "naturalize" a programming language gradually into a natural language (ACL 2017). I led a team of 18 TAs for a class with 550 enrolled students. Percy Liang's 133 research works with 5,234 citations and 3,995 reads, including: Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning I am a Computer Science Ph.D. student studying Machine Learning at ... Percy Liang (Preferred) Suggest Name; Emails. 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