Uploaded June 2022 | Updated September 2026, 2 weeks ago
Boats bobbing atop soft ocean waves on a warm spring evening set the perfect Southern California backdrop to celebrate exceptional Anteater engineering and information and computer sciences alumni – in person for the first time since the COVID-19 pandemic hit.
More than 225 alumni, faculty and community members of the UC Irvine Samueli School of Engineering and the Donald Bren School of Information and Computer Sciences gathered to induct six alumni at the seventh annual Hall of Fame event at the Balboa Yacht Club in Corona Del Mar on May 13.
The three alumni from each school were selected for making a significant impact on their profession or bringing distinction to their alma mater. Fifty-eight engineering alumni and 46 ICS alumni have now been named Hall of Famers since it was established in 2015 to coincide with UC Irvine’s 50th anniversary.
ICS Inductees:
* Rohit Khare
Ph.D. 2003, M.S. 2000
* Srinivas Mantripragada
Ph.D. 2000
* Peyman Oreizy
Ph.D. 1999, M.S. 1995, B.S. 1993
Engineering Inductees:
* John Olivier
B.S. 1985 – Civil Engineering
* Cecilia Richards
Ph.D. 1990 – Mechanical Engineering
* Elizabeth San Miguel
B.S. 2002 – Computer Engineering
Boats bobbing atop soft ocean waves on a warm spring evening set the perfect Southern California backdrop to celebrate exceptional Anteater engineering and information and computer sciences alumni – in person for the first time since the COVID-19 pandemic hit.
More than 225 alumni, faculty and community members of the UC Irvine Samueli School of Engineering and the Donald Bren School of Information and Computer Sciences gathered to induct six alumni at the seventh annual Hall of Fame event at the Balboa Yacht Club in Corona Del Mar on May 13.
The three alumni from each school were selected for making a significant impact on their profession or bringing distinction to their alma mater. Fifty-eight engineering alumni and 46 ICS alumni have now been named Hall of Famers since it was established in 2015 to coincide with UC Irvine’s 50th anniversary.
ICS Inductees:
* Rohit Khare
Ph.D. 2003, M.S. 2000
* Srinivas Mantripragada
Ph.D. 2000
* Peyman Oreizy
Ph.D. 1999, M.S. 1995, B.S. 1993
Engineering Inductees:
* John Olivier
B.S. 1985 – Civil Engineering
* Cecilia Richards
Ph.D. 1990 – Mechanical Engineering
* Elizabeth San Miguel
B.S. 2002 – Computer Engineering






![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)

