Self-Supervised Language-and-Vision Reasoning: 15 JUNE @ 6PM
IEEE CC EVENT-15 JUNE @ 6PM – PROFESSOR WILLIAM WANG PRESENTS
“Self-Supervised Language-and-Vision Reasoning”
Register for Event Now @ https://events.vtools.ieee.org/event/register/315372
Location – Rusty’s Pizza 5934 Calle Real, Goleta, CA 93117
6:00 PM – Complimentary Pizza, Salad, Beverage
6:25 PM – Central Coast Status
6:30 PM – Professor Wang Presents
Dr. Wang will introduce his team’s recent work on visually-grounded language reasoning via the studies of vision-and-language navigation. In particular, he will emphasize three benefits of self-supervised learning:
1) improves generalization in unseen environments;
2) creates counterfactuals to augment observational data;
3) enables transfer learning for challenging settings.
Best regards, Ruth Franklin, IEEE Central, Coast Chair
Self-Supervised Language-and-Vision Reasoning
A key challenge for Artificial Intelligence research is to go beyond static observational data, and consider more challenging settings that involve dynamic actions and incremental decision-making. In this talk, Dr. Wang will introduce his team’s recent work on visually-grounded language reasoning via the studies of vision-and-language navigation. In particular, he will emphasize three benefits of self-supervised learning:
1) improves generalization in unseen environments;
2) creates counterfactuals to augment observational data;
3) enables transfer learning for challenging settings.
I will conclude by briefly introducing other reasoning problems that my groups are working on recently.
Biography: William Wang (PhD, CMU) is the Duncan and Suzanne Mellichamp Chair in Artificial Intelligence and Designs, and an Associate Professor in the Department of Computer Science at UCSB. He is the Director of UCSB’s Natural Language Processing group, and Center for Responsible Machine Learning. 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 100 papers at leading NLP/AI/ML conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, EMNLP 2015, and CVPR 2019, a DARPA Young Faculty Award (Class of 2018), IEEE AI’s 10 to Watch (2020), NSF CAREER Award (2021), two Google Faculty Research Awards (2018, 2019), three IBM Faculty Awards (2017-2019), two Facebook Research Awards (2018, 2019), an Amazon AWS Machine Learning Research Award, a JP Morgan Chase Faculty Research Award, an Adobe Research Award in 2018, and the Richard King Mellon Presidential Fellowship in 2011. His work and opinions appear in major tech media outlets such as Wired, VICE, Scientific American, Fortune, Fast Company, NPR, NASDAQ, The Next Web, Law.com, and Mental Floss. He is an elected member of IEEE Speech and Language Processing Technical Committee (2021-2023) and a member of ACM Future of Computing Academy
