KEYNOTE SPEAKERS

(in alphabetical order)

 

Louis Gomez

University of California, Los Angeles, USA

Title:  America's Singular Education Challenge is Equity: Does CS have a Role in the Way Forward?

Bio:  Louis Gomez is professor of education and of information studies at UCLA and a senior fellow at the Carnegie Foundation for the Advancement of Teaching. As a social scientist dedicated to educational improvement, his research and design efforts are aimed at helping to support community formation in schools and other organizations, so that they can collaboratively create new approaches to teaching, learning and assessment. With colleagues, he has worked to bring “Networked-based improvement Science” to the field of education. This work is aimed at helping the field take a new perspective on design, educational engineering and development efforts that catalyze long-term, cooperative initiatives. The work gains much of its power because it is carried out in highly focused collaborations that Gomez and colleagues call Networked Improvement Communities.

 

Gomez received a bachelor’s degree in psychology from the State University of New York at Stony Brook in 1974 and a doctorate in cognitive psychology in 1979 from UC Berkeley. He previously served as the MacArthur Foundation Chair in Digital Media & Learning from 2011 to 2016. Before joining the UCLA faculty he was the Helen S. Faison Professor of Urban Education and senior scientist at the Learning Research and Development Center (LRDC) at the University of Pittsburgh. Gomez became a member of the National Academy of Education in 2014. 

C.-C. Jay Kuo

University of Southern California, USA

Title:  Resolving the Gap between Image Pixels and Semantics: Yesterday, Today and Tomorrow

 

Abstract: How to resolve the gap between low-level signals and high-level semantics has been a long-standing problem for several decades. For example, earlier image retrieval work focused on similarities of low-level features such as color, shape and texture, which can be derived from image pixels easily, with limited success since they do not capture semantic meaning of images well. We have witnessed breakthrough in narrowing down the gap between signals and semantics in recent years due to the rapid development of deep learning technologies. Deep neural networks provide a mapping from the image pixel domain to the semantic domain (e.g. object recognition, semantic segmentation, etc.) through a large number of training samples with human labels. It is proper to say that it is human labeling effort that narrows down the semantic gap. Most deep learning systems are heavily supervised. Yet, a powerful AI system should be able to learn under the weak supervision setting with much fewer training samples. The capability of a pure data-driven solution is limited. It is time to revisit the idea of integrating the traditional knowledge-driven (or rule-based) and the modern data-driven approaches so as to benefit from both. New research directions along this line will be presented.

 

Bio: Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Distinguished Professor of Electrical Engineering and Computer Science. His research interests are in the areas of visual computing and communication. He is a Fellow of AAAS, IEEE and SPIE. Dr. Kuo’s research interests are in the areas of multimedia computing and data science and engineering. He has received numerous awards for his outstanding research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award and the 2020 IEEE TCMC Impact Award. Dr. Kuo has guided 155 students to their PhD degrees and supervised 30 postdoctoral research fellows. His educational achievements have won a wide array of recognitions such as the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award, the 2017 IEEE Signal Processing Society Carl Friedrich Gauss Education Award, and the 2018 USC Provost’s Mentoring Award.

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Michael Neff
University of California, Davis, USA

Title:  Computational Approaches to the Semantics of Nonverbal Communication

 

Abstract: Research has thoroughly established that nonverbal communication in general, and gestures in particular, conveys meaning.  The semantics of gesture, however, are not well codified and can take varied form.  A gesture might provide information on a person's personality or emotions, provide the reference necessary to decode spoken language, replace words, or illustrate an idea.  It may even perform all of these functions at once.  This talk will review the various semantic manifestations of gesture.  I will then discuss how synthetic animated agents can be used to study the semantics of gesture, with a particular focus on personality.  From there, I will discuss how people employ nonverbal semantics when embodied in virtual reality.  I will conclude with some thoughts on the challenges of applying machine learning approaches to modeling nonverbal semantics.

Bio: Michael Neff is a Professor in Computer Science and Cinema & Digital Media at the University of California, Davis where he leads the Motion Lab, an interdisciplinary research effort in character animation and embodied interaction. He holds a Ph.D. from the University of Toronto and is also a Certified Laban Movement Analyst. His research focus has been on character animation, especially modeling expressive movement, nonverbal communication, gesture and applying performing arts knowledge to animation.  Additional interests include human computer interaction related to embodiment, motion perception, character based applications, motor control and VR/XR.   Select distinctions include an NSF CAREER Award, the Alain Fournier Award and several paper awards.  He is the former Chair of the Department of Cinema and Digital Media at UC Davis. 

Stefan Schlobach
Vrije Universiteit Amsterdam, The Netherlands

Title:  The Spirits that we called: will Semantics survive its own success?

 

Abstract: Semantic technology has proven to be a powerful enabler for publishing and consuming data, with mature tools around, such as robust and scalable semantic databases, as well as established standardised languages and protocols. Our group’s work on Semantics helped enabling linking and publishing of hundreds of thousands of online datasets seamlessly in an integrated and unified way, making huge numbers of integrated knowledge graphs accessible in this way.

 

Not surprisingly, such successes of the semantic methods for integrating data come at a cost, though. As Semantic Data turns into Big Semantic Data, I will argue that the foundations on which our methods were initially built start to crumble. For a logician like me, this is a painful observation, and my mission for  this talk is to convince the audience that formal Semantics is worth being rescued, and how we can help it survive.

Bio: Stefan Schlobach is an Associate Professor at the Knowledge Representation and Reasoning group of the Department of Computer Science of the Vrije Universiteit Amsterdam. He holds a PhD from King’s College, London, on the combination of Learning and Reasoning in Description Logics. A formal logician at heart, he spends his working life balancing the beauty and simplicity of formal systems and their well-understood semantics, with the messiness of real life data, e.g. of large-scale Web data. This has led to work on explanation of reasoning and ontology integration, as well as alternative, more robust and context aware, methods for reasoning and querying, often based on some kind of emerging semantics.