Uploaded June 2023 | Updated September 2026, 2 weeks ago
Informatics Seminar Series Presents:
Kristen Shinohara
Assistant Professor, School of Information at Rochester Institute of Technology
Abstract: Computing education is charged with teaching future tech professionals skills and knowledge to innovate next generation technologies that will shape societies and futures to come. However, few computing students and fewer technology professionals identify as having a disability; meanwhile institutional, curricular, and academic infrastructure are ill-equipped to support disabled students and to educate future technologists about accessible technologies. Disabled innovators and accessible solutions spawn brilliant technical advances, and can only serve to benefit the future of tech. In this talk, I argue why it is important to include students with disabilities in computing, what can be done to improve support for disabled students, and how we can enhance computing education by including accessibility and accessible design.
Bio: Kristen Shinohara (Google Scholar) is an Assistant Professor in the School of Information at the Rochester Institute of Technology where she co-directs the Center for Accessibility and Inclusion Research (CAIR) Lab. Kristen’s research is at the intersection of human-computer interaction, accessibility, and design, with a focus on accessible design, research, and computing education. She developed the Design for Social Accessibility (DSA) perspective and method cards, which supports how designers engage with disabled user needs and preferences, particularly for social situations. Her NSF funded research projects focus on how to empower disabled graduate students and designers, and on the prevalence of accessibility practice in the tech industry and how to improve teaching accessibility in computing education. Her work has received Best Paper and Honorable Mention awards from the CHI Conference in Human Factors in Computing and has appeared as the cover story in the Communications of the ACM. She is the recipient of a 2022 Google Scholar Award to improve user centered design methods for deaf and hard of hearing designers, and she is a faculty member of RIT’s AWARE-AI NSF Research Traineeship Program. Kristen received her PhD from the University of Washington in Seattle in 2017.
Informatics Seminar Series Presents:
Kristen Shinohara
Assistant Professor, School of Information at Rochester Institute of Technology
Abstract: Computing education is charged with teaching future tech professionals skills and knowledge to innovate next generation technologies that will shape societies and futures to come. However, few computing students and fewer technology professionals identify as having a disability; meanwhile institutional, curricular, and academic infrastructure are ill-equipped to support disabled students and to educate future technologists about accessible technologies. Disabled innovators and accessible solutions spawn brilliant technical advances, and can only serve to benefit the future of tech. In this talk, I argue why it is important to include students with disabilities in computing, what can be done to improve support for disabled students, and how we can enhance computing education by including accessibility and accessible design.
Bio: Kristen Shinohara (Google Scholar) is an Assistant Professor in the School of Information at the Rochester Institute of Technology where she co-directs the Center for Accessibility and Inclusion Research (CAIR) Lab. Kristen’s research is at the intersection of human-computer interaction, accessibility, and design, with a focus on accessible design, research, and computing education. She developed the Design for Social Accessibility (DSA) perspective and method cards, which supports how designers engage with disabled user needs and preferences, particularly for social situations. Her NSF funded research projects focus on how to empower disabled graduate students and designers, and on the prevalence of accessibility practice in the tech industry and how to improve teaching accessibility in computing education. Her work has received Best Paper and Honorable Mention awards from the CHI Conference in Human Factors in Computing and has appeared as the cover story in the Communications of the ACM. She is the recipient of a 2022 Google Scholar Award to improve user centered design methods for deaf and hard of hearing designers, and she is a faculty member of RIT’s AWARE-AI NSF Research Traineeship Program. Kristen received her PhD from the University of Washington in Seattle in 2017.





![AI/ML Seminar Series: Joe Marino (2/1/2021)
UCI AI/ML Seminar Series
https://cml.ics.uci.edu/aiml/
Joe Marino
PhD Student
Computation and Neural Systems
California Institute of Technology
Connecting Variational Autoencoders Back to the Brain
Unsupervised machine learning has recently dramatically improved our ability to model and extract structure from data. One such approach is deep latent variable models, which includes variational autoencoders (VAEs) [Kingma & Welling, 2014; Rezende et al., 2014]. These models can be traced back to the Helmholtz machine [Dayan et al., 1995], which, in turn, was inspired by ideas from theoretical neuroscience [Mumford, 1992]. In the intervening years, neuroscientists have further developed these ideas into a popular theory: predictive coding [Rao & Ballard, 1999; Friston, 2005]. Yet, the machine learning community remains largely unaware of these connections. In this talk, I discuss the links between modern deep latent variable models and predictive coding, yielding several striking implications for the correspondences between machine learning and neuroscience. This motivates a more nuanced view in connecting these fields, including the search for backpropagation in the brain.
Bio:
Joe Marino is a PhD candidate in the Computation & Neural Systems program at Caltech, advised by Yisong Yue. His work focuses on improving probabilistic models and inference techniques, using neuroscience-inspired ideas, within the areas of generative modeling and reinforcement learning. AI/ML Seminar Series: Joe Marino (2/1/2021)](https://i.ytimg.com/vi/iVz6uwD7i6A/mqdefault.jpg)

![Interaction-Centric AI: Designing Useful and Usable AI Applications
Juho Kim
Associate Professor, KAIST; Chief Scientist, Ringle
Abstract:
AI-powered services and applications are introduced at a rapid pace and massive scale across various domains. Remarkable model performance, however, does not necessarily translate to an improved user experience. I argue that human-AI interaction should be considered a first-class object in designing AI-powered systems. In this talk, I will present a few novel interactive systems that use AI to support complex real-life tasks. I will discuss how we considered human-AI interaction in designing these systems, what tensions we encountered and how we addressed them, and how to design better AI-powered systems for real-world users. My ultimate proposal is that we need a fundamental shift to “interaction-centric AI”—an approach to systematically designing and engineering human-AI interaction that overcomes the limitations of the model- and data-centric views.
Bio:
Juho Kim [juhokim.com] is an Associate Professor in the School of Computing at KAIST, affiliate faculty in the Kim Jaechul Graduate School of AI at KAIST, and a director of KIXLAB (the KAIST Interaction Lab) [kixlab.org]. His research in human-computer interaction and human-AI interaction focuses on building interactive and intelligent systems that support interaction at scale, with the goal of improving the ways people learn, collaborate, discuss, make decisions, and take action online. He earned his Ph.D. from MIT in 2015, M.S. from Stanford University in 2010, and B.S. from Seoul National University in 2008. In 2015-2016, he was a Visiting Assistant Professor and a Brown Fellow at Stanford University. He is a recipient of KAIST’s Songam Distinguished Research Award, Grand Prize in Creative Teaching, and Excellence in Teaching Award, as well as 14 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP. He is currently spending his sabbatical year at Ringle Inc., a startup building an online language tutoring platform, to transfer his research on automatically analyzing and diagnosing learners’ English proficiency into a real product. Interaction-Centric AI: Designing Useful and Usable AI Applications](https://i.ytimg.com/vi/j0v1Cr74kN8/mqdefault.jpg)


