Abstract: AI is reaching children faster than the rules meant to protect them. Schools are pushed toward early AI exposure, chatbot safety rules lag behind, and families of young people with autism and intellectual and developmental disabilities (IDD) often wait longest for support. This talk asks who gets a say in the AI built for young people and their families, and when. I examine three settings, and each one brings families in earlier. The first is platform governance for young children. When YouTube implemented children's privacy law through a Made for Kids label, content creators had to classify their own videos, and parents stepped in mostly for the harms they could see. Creators and parents carried the costs after the rules were set. The second is safety by design for teenagers. We built MOSafely, a teen-centered online risk detection tool, and asked 33 online safety experts to review it before it reached teens. The experts found that to protect teens' messages, the tool must read them. The third is AI support for families with special needs. With caregivers of young adults with autism and IDD, we co-designed AI support for the workplace and found that such support can make workers too visible to employers. Across the three settings, families move from carrying the costs of technology to shaping its design. I close with a question for my next five years. Who decides when AI is ready for a family's home?

Bio: Renkai Ma is an Assistant Professor in the School of Information Technology at the University of Cincinnati. He designs, builds, and evaluates various AI technologies that support the safety and well-being of children and adolescents, parents and caregivers, and autistic people and people with intellectual and developmental disabilities (IDD). His research spans platform governance for young children, safety by design for teenagers, and AI support for families with special needs. His work appears at ACM CHI, CSCW, WWW, UIST, IDC, and DIS. In 2026, he was one of eight recipients of the University of Cincinnati's University Research Council Faculty Scholars Research Award. He received his Ph.D. in Informatics from Penn State, was a postdoctoral scholar at Vanderbilt University, and conducted user experience research at Meta and SiriusXM.

Location: Seminar Room 1310, Old Computer Science Building, Stony Brook University

2026 MIC Symposium on Multilingualism

Language, Sustainability, and AI


Friday, October 16, 9:00am - 5:00pm | Humanties 1006

Presented by: Center for Multilingual and Intercultural Communication (MIC)

Co-Sponsored by: College of Arts and Sciences (CAS), AI Innovation Institute (AI3), Office of Diversity, Inclusion, and Intercultural Initiatives (DI3), The Sir Run Run Shaw Endowment, and Department of Asian and Asian American Studies (AAAS)


Opening Remarks: David Wrobel (Dean, College of Arts and Sciences) and Lav Varshney (Director, AI Innovation Institute)

Keynote Speakers: Salikoko Mufwene, David Wei Dai, and Susan Brennan

  • 9:30 - 10:15am: Keynote #1 - Salikoko S. Mufwene
  • 10:15 - 10:45am: Faculty Response Panel #1 (chaired by S. N. Sridhar)
  • 11:00 - 11:45am: Keynote #2 - David Wei Dai
  • 11:45 - 12:15pm: Faculty Response Panel #2 (chaired by Yi Wang)
  • 2:00 - 2:45 pm: Keynote #3 - Susan E. Brennan
  • 2:45 - 3:15pm: Faculty Response Panel #3 (chaired by Jiwon Hwang)
  • 3:30 - 4:45pm: Synthesis and Open Discussion with all speakers, panelists, and participants (moderated by Shyam Sharma)

If you need special accomodation, please contact chikako.nakamura@stonybrook.edu

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.

University Libraries Presents: Do you want more control over when to use AI features in your Google apps and when browsing online? Are you tired of Gmail trying to finish your sentences for you? Drop in to this brief tutorial, which will walk through the settings you can turn on and off in various Google products to keep your agency where you want it.

Register here for the Zoom session.

Abstract: The remarkable success of large foundational models, such as LLMs and diffusion models, is built on their learning over vast amounts of static data from the Internet. However, human learning and problem-solving are fundamentally interactive processes--humans learn by engaging with their environment, tools, search engine, and feedback loops, iteratively refining their understanding and decisions. This gap between the interactivity of human learning and the static nature of model training raises a critical question: how can we imbue foundational models with the capacity for meaningful interaction?

In this talk, I will explore methods to enhance foundational models by incorporating interaction with the external environment. I will discuss strategies such as leveraging external tools, compilers, function calls to provide dynamic feedback to enhance foundation models. By drawing inspiration from human's interactive learning processes, I demonstrate how interaction-driven learning can lead to models that are not only more accurate but also more adaptable to real-world applications.

This work bridges the gap between static training paradigms and the dynamic, iterative nature of human intelligence, paving the way for a new generation of interactive AI systems.

Bio: Wenhu Chen has been an assistant professor at the Computer Science Department in University of Waterloo and Vector Institute since 2022. He obtained the Canada CIFAR AI Chair Award in 2022 and CIFAR Catalyst Award in 2024. He has worked for Google Deepmind as a part-time research scientist since 2021. Before that, he obtained his PhD from the University of California, Santa Barbara under the supervision of William Wang and Xifeng Yan. His research interest lies in natural language processing, deep learning and multimodal learning. He aims to design models to handle complex reasoning scenarios like math problem-solving, structure knowledge grounding, etc. He is also interested in building more powerful multimodal models to bridge different modalities. He received the Area Chair Award in AACL 2023, the Best Paper Honorable Mention in WACV 2021, the Best Paper Finalist in CVPR 2024, and the UCSB CS Outstanding Dissertation Award in 2021.

Abstract: This talk shows how machine learning can address challenges in Astrophysics. We specifically focus on black hole simulations and supernova observations. First, we present a super-resolution technique for black hole simulations that avoids the need for high-resolution labels by leveraging the Hamiltonian and momentum constraints from general relativity. This method reduces constraint violations by one to two orders of magnitude. Next, we introduce Maven, a multimodal foundation model for supernova science. Using contrastive learning to align photometric and spectroscopic data, Maven achieves state-of-the-art results in classification and redshift estimation by pre-training on synthetic data and fine-tuning on real observations.

Bio: Thomas Helfer is a computational physicist specializing in deep learning and physics. Currently based at the Institute for Advanced Computational Science at Stony Brook University, Thomas was previously a postdoctoral fellow at Johns Hopkins and did his PhD with Eugene Lim at King's College in London. In his work, he looks to bridge topics; in his PhD, he bridged theoretical particle physics and gravitational waves. Now, in his postdoctoral work, he aims to find novel applications of deep learning in astrophysics.

*please note: this seminar will be held in a hybrid format*


Location: IACS Seminar Room OR Join Zoom Meeting
https://stonybrook.zoom.us/j/98617630652?pwd=tb4hplPgb3bTTifPCJTCcsn3P9vX8y.1

Meeting ID: 986 1763 0652
Passcode: 882994