Luke Zettlemoyer is a Research Scientist at Facebook and an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. At Facebook, he is leading the efforts to build a new FAIR lab in Seattle. His research focuses on empirical computational semantics, and involves designing machine learning algorithms and building large datasets. Honors include multiple paper awards, a PECASE award, and an Allen Distinguished Investigator Award. Luke received his PhD from MIT and was a postdoc at the University of Edinburgh.

William is the Director of UC Santa Barbara's Natural Language Processing group and an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara. He received his PhD from School of Computer Science, Carnegie Mellon University. He has broad interests in machine learning approaches to data science, including statistical relational learning, information extraction, computational social science, speech, and vision. He has published more than 50 papers at leading NLP/AI/ML conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, and EMNLP 2015, a best reviewer award at NAACL 2015, a DARPA Young Faculty Award in 2018, a Facebook Research Award in 2018, two IBM Faculty Awards in 2017 and 2018, an Adobe Research Award in 2018, and the Richard King Mellon Presidential Fellowship in 2011. He served as an Area Chair for NAACL, ACL, EMNLP, and AAAI. He is an alumnus of Columbia University, Yahoo! Labs, Microsoft Research Redmond, and University of Southern California. His work and opinions appear at major tech media outlets such as Wired, VICE, Fast Company, and Mental Floss.

He He is a postdoctoral researcher at Stanford University. She received her Ph.D. in Computer Science at the University of Maryland, College Park. She is broadly interested in natural language processing and machine learning. Her current research focuses on dialogue systems and text generation.

Since September 2017, I’ve worked on dialogue systems research and natural language generation on the Messenger team. Before that, I was a master’s student at Stanford, where I worked on deep learning for goal-oriented dialog, as well as other projects in NLP and computer vision. Before my master’s, I spent some time at Siri, where I productionized semantic parsing models for Siri and Spotlight Search.

Yulia Tsvetkov is an assistant professor in the Language Technologies Institute at Carnegie Mellon University. Her research interests are at or near the intersection of natural language processing, machine learning, linguistics, and computational social science. Her current research projects focus on multilinguality, controllable text generation, automated negotiation, and NLP for social good. Prior to joining LTI, Yulia was a postdoc in the department of Computer Science at Stanford; she received her PhD from Carnegie Mellon University.

Hoifung Poon is the Director of Precision Health NLP at Microsoft Research. He leads Project Hanover, with the overarching goal of advancing machine reading for precision health, by combining probabilistic logic with deep learning. He has given tutorials on this topic at top AI conferences such as the Association for Computational Linguistics (ACL) and the Association for the Advancement of Artificial Intelligence (AAAI). His research spans a wide range of problems in machine learning and natural language processing (NLP), and his prior work has been recognized with Best Paper Awards from premier venues such as the North American Chapter of the Association for Computational Linguistics (NAACL), Empirical Methods in Natural Language Processing (EMNLP), and Uncertainty in AI (UAI). He received his PhD in Computer Science and Engineering from University of Washington, specializing in machine learning and NLP.

I am a research scientist working on conversational AI systems. I completed my PhD at Stanford University in 2015 where I worked with Chris Manning on weakly supervised and interpretable information extraction. Prior to that, I finished my Master’s at University of Texas at Austin where I worked with Raymond Mooney and Kristen Grauman on combining language and vision modes for information extraction. Before joining FB, I developed deep learning models for conversational AI systems at Viv, a startup later acquired by Samsung.

Liang Huang is Principal Scientist at Baidu Research in Silicon Valley and Assistant Professor (on leave) at Oregon State University. He received his PhD from the University of Pennsylvania in 2008 and BS from Shanghai Jiao Tong University in 2003. He has also been a research scientist at Google, a research assistant professor at USC/ISI, an assistant professor at CUNY, and a part-time research scientist at IBM. He is a leading expert in natural language processing (NLP), where he is known for his work on fast algorithms and provable theory in parsing, machine translation, and structured prediction. He also works on applying the same linear-time algorithms he developed for parsing to computational structural biology. He received a Best Paper Award at ACL 2008, a Best Paper Honorable Mention at EMNLP 2016, several best paper finalists (ACL 2007, EMNLP 2008, ACL 2010, SIGMOD 2018), two Google Faculty Research Awards (2010 and 2013), a Yahoo! Faculty Research Award (2015), and a University Teaching Prize at Penn (2005). The NLP group he led at Oregon State University ranks 15th on csrankings.org. He enjoys teaching algorithms and co-authored a best-selling textbook in China on algorithms for programming contests.

Alona Fyshe is an Assistant Professor in the Computing Science and Psychology Departments at the University of Alberta. Alona received her BSc and MSc in Computing Science from the University of Alberta, and a PhD in Machine Learning from Carnegie Mellon University. Alona uses machine learning to leverage large amounts of text and neuroimaging data to find connections between how people and computer models represent meaning.

Kevin Knight is Chief Scientist for Natural Language Processing (NLP) at DiDi Chuxing. He leads a DiDi lab in Los Angeles devoted to NLP research. He was previously Dean's Professor of Computer Science at the University of Southern California (USC) and a Research Director and Fellow at USC's Information Sciences Institute (ISI). He received a PhD in computer science from Carnegie Mellon University and a bachelor's degree from Harvard University. Dr. Knight's research interests include human-machine communication, machine translation, language generation, automata theory, and decipherment. He has authored over 150 research papers on natural language processing and received several best paper awards. Dr. Knight also co-authored the widely-adopted textbook "Artificial Intelligence" (McGraw-Hill). In 2001, he co-founded Language Weaver, Inc., a machine translation company acquired by SDL plc in 2010. Dr. Knight was a key researcher in programs run by the Defense Advanced Research Projects Agency (DARPA). He served as President of the Association for Computational Linguistics (ACL) in 2011, as General Chair for the Annual Conference of the ACL in 2005, and as General Chair for the North American ACL conference in 2016. He is a Fellow of the ACL, a Fellow of ISI, and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).

Gill Bejerano is a tenured Stanford professor whose primary appointments span Computer Science, Medical Genetics and Developmental Biology. Gill did his PhD in machine learning, added human genomics in 2003, discovered the most mysterious regions in our genome, published first/last authorships in Science and Nature, and has been recognized by multiple academic awards. The Bejerano lab likes to skate to where the puck is going to be. Our current obsession is prototyping the future of medical diagnosis, by mixing NLP, ML, Genomics into good old fashioned patient care. See http://bejerano.stanford.edu/ for more information.

Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles. His research interests include designing robust machine learning methods for large and complex data and building language processing models for social good applications. Kai-Wei has published broadly in machine learning,natural language processing, and artificial intelligence and has been involved in developing machine learning libraries (e.g., LIBLINEAR, Vowpal Wabbit, and Illinois-SL) that are being used widely by the research community. His awards include the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award (2010), the Yahoo! Key Scientific Challenges Award (2011), and the Okawa Research Grant Award (2018). Additional information is available here. (http://web.cs.ucla.edu/~kwchang/)

Dilek Hakkani-Tür is a senior principal scientist at Amazon Alexa AI focusing on enabling natural dialogues with machines. Prior to joining Amazon, she was leading the dialogue research group at Google (2016-2018), a principal researcher at Microsoft Research (2010-2016), International Computer Science Institute (ICSI, 2006-2010) and AT&T Labs-Research (2001-2005).
Her research interests include conversational AI, natural language and speech processing, spoken dialogue systems, and machine learning for language processing. She has over 50 patents that were granted and co-authored more than 200 papers in natural language and speech processing. She is the recipient of three best paper awards for her work on active learning for dialogue systems, from IEEE Signal Processing Society, ISCA and EURASIP. She was a member of the IEEE Speech and Language Technical Committee (2009-2014), area editor for speech and language processing for Elsevier's Digital Signal Processing Journal and IEEE Signal Processing Letters (2011-2013), and currently serves on ISCA Advisory Council (2015-2019). She is the Editor-in-Chief of the IEEE/ACM Transactions on Audio, Speech and Language Processing, and a fellow of the IEEE and ISCA.

Noam Shazeer is a software engineer at Google Brain, and is focused on building bigger, better neural language models. His recent projects include: sparsely-gated mixtures-of-experts, attention-based language models (Transformer), and most recently Mesh-TensorFlow, a language for parallelizing tensor computations across supercomputers.