InterNLP 2021

Yoav Artzi is an Associate Professor in the Department of Computer Science and Cornell Tech at Cornell University. His research focuses on developing learning methods for natural language understanding and generation in automated interactive systems. He received an NSF CAREER award, and his work was acknowledged by awards and honorable mentions at ACL, EMNLP, NAACL, and IROS. Yoav holds a B.Sc. from Tel Aviv University and a Ph.D. from the University of Washington.

Ido Dagan is a Professor at the Department of Computer Science at Bar-Ilan University, Israel. His interests are in applied semantic processing, focusing on textual inference, natural semantic representation, open representation and consolidation of multi-text information, and interactive text summarization. He received his B.A. and Ph.D. in Computer Science at the Technion, Israel. He holds a Fellowship of the Association for Computational Linguistics (ACL) of which he was President in 2010 and served on its Executive Committee during 2008-2011.

Dan Goldwasser is an Associate Professor at the Department of Computer Science at Purdue University. He is broadly interested in connecting natural language with real world scenarios and using them to guide natural language understanding. His current interests focus on grounding political discourse to support understanding real-world scenarios by using neuro-symbolic representations. Dan Completed his PhD in Computer Science at the University of Illinois at Urbana-Champaign and was a postdoctoral researcher at the University of Maryland. He has received research support from the NSF, including a recent CAREER award, DARPA and Google.

Seung-Won Hwang is a Professor at the Department of Computer Science and Engineering, Seoul National University, South Korea. Her research revolves around data(-driven) intelligence and knowledge graphs and focuses on search engines, query optimization, and natural language understanding. She has received her B.S. at the Korea Advanced Institute of Science and Technology, and her M.Sc. and Ph.D. degree at the University of Illinois at Urbana-Champaign. Her work is recognized by the Microsoft Research Outstanding Collaborator Award and best paper awards and runner-ups at SSTD, WSDM, and ACML.

Julia Kreutzer is a research scientist at Google Research, Montreal. Her research revolves around improving machine translation with the help of Deep and Reinforcement Learning and Human-in-the-loop approaches. She's also part of the Masakhane community, building translation models for low-resource African languages. She received her Ph.D. degree in Computational Linguistics at the Heidelberg University.

Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. 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.

Alison Renner is a Senior HCI/AI Research Scientist at Dataminr. Her experience covers 13+ years of designing, building, and evaluating intelligent systems and interactive visualizations for data exploration, analysis, and augmented decision making. Her research lies at the intersection of AI and human-computer interaction, building explainable and interactive AI systems to engender trust, improve performance, and support human-machine collaboration. She received her B.S. in Mathematics at The College of William and Mary and her M.S. and Ph.D. in Computer Science at the University of Maryland, College Park.

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the Department of Computer and Information Science, University of Pennsylvania and the NLP Science Leader at Amazon AWS. His research focuses on the computational foundations of intelligent behavior; developing theories and systems pertaining to intelligent behavior using a unified methodology, and focuses on machine learning and inference methods to facilitate natural language understanding. He received various awards such as the John McCarthy Award and is a Fellow of the AAAS, ACM, AAAI, and ACL. Dan received his B.A. in Mathematics from the Technion, Israel and his Ph.D. in Computer Science from Harvard University.

Dorsa Sadigh is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the intersection of robotics, learning, and control theory. Specifically, she is interested in developing algorithms for safe and adaptive human-robot and multi-agent interaction. Dorsa received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2017, and received her bachelor's degree in EECS from UC Berkeley in 2012. She is recognized by awards such as the NSF CAREER award, the AFOSR Young Investigator award, the IEEE TCCPS early career award, MIT TR35, as well as industry awards such as the JP Morgan, Google, and Amazon faculty research awards.

Sherry Tongshuang Wu is a rising final year Ph.D. candidate at the University of Washington, co-advised by Jeffrey Heer and Daniel S. Weld. Her research sits in the intersection of Human-Computer Interaction and Natural Language Processing, and aims to help humans more effectively and systematically interact, evaluate, and improve their models.