AI Innovation Institute

The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.

This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.

This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event. SBU's Institute for Advanced Computational Science (IACS) is one of those satellite sites!

Location: IACS Conference Room #2

Interested applicants must first have an ACCESS ID. If you don't have the ID, please visit this page to create one: ACCESS USER REGISTRATION.

Once you have an ACCESS ID, please login (see top right here) then register here.

Language shared online through social media or messaging reflects people's thoughts and emotions. Processing this data with Natural Language Processing (NLP) and machine learning can reveal mental health and psychological traits. For example, analyzing Facebook posts enables me to predict depression before it is clinically diagnosed and highlight particular symptoms. At the population level, billions of geo-tagged Tweets can be used to monitor health risk patterns, including depression and anxiety trends across communities. Beyond assessment, I'm using Large Language Models (LLMs) to improve mental health care, including training therapists and assisting with Cognitive Behavioral Therapy. These applications of NLP and Al may lead to earlier and more effective interventions and improved access for underserved populations. Speaker: Johannes Eichstaedt, Ph.D. Assistant Professor, Psychology & Human-Centered Al, Stanford University

Abstract: Recent progress in large language and vision models demonstrates how far we can go by scaling with vast internet-scale data. In contrast, physical AI, agents that perceive and act in the real world, still lags far behind. Today, both academia and industry primarily pursue generalizable physical AI by scaling up: collecting large-scale action-video datasets or training world models that enable interaction through learned environments. However, this paradigm is inherently inefficient and will soon reach a data ceiling. In this talk, I argue for a shift from scaling up to scaling out. I introduce reality world simulators, a new paradigm that converts real-world videos into diverse, interactive simulation environments. Instead of relying on more data collection, this approach expands data through structured reconstruction and recomposition, enabling both higher data efficiency and physically grounded interaction. I will present a three-pronged approach: 1) Scaling out via Digital Twins: reconstructing controllable, interactive environments from monocular videos to support diverse agent exploration. 2) Scaling out via Digital Cousins: disentangling scene structure into compositional elements to generate large-scale variations of real-world environments. 3) Scaling out via Embodied Humans: incorporating realistic human dynamics to improve safety and social compliance in robot learning. Finally, I will outline a roadmap toward building generalizable and safe physical AI systems for open-world deployment.

Bio: Dr. Wayne Wu is a postdoctoral researcher at UCLA Computer Science, working closely with Bolei Zhou, and collaborating with Trevor Darrell (UC Berkeley EECS) and Jiaqi Ma (UCLA CEE). He received his Ph.D. in Computer Science and Technology from Tsinghua University in June 2022 and was previously a visiting Ph.D. student at Nanyang Technological University. He also spent seven years in industry, where he led the research and development of products that reached more than 10 million end users worldwide. His research lies at the intersection of computer vision, robotics, and computer graphics. He focuses on developing infrastructure and methods to scale physical AI, enabling robots to work reliably and safely in the open world. He has published over 50 papers at top-tier venues including CVPR, ICCV, ICLR, NeurIPS, and ICRA, with over 9,500 citations and 10,000 GitHub stars. His work has received a CVPR Best Paper Candidate and multiple Oral, Spotlight, and Highlight presentations. He was also honored with the 2025 UCLA Chancellor's Award for Postdoctoral Research, recognizing the best postdocs at UCLA, and he was the only awardee from the School of Engineering. He serves as an Area Chair at CVPR 2026.

Location: NCS 120

Abstract: Language is not just something we generate, but something humans use to interact with the world around them. Indeed, today's conversational AI agents speak fluently, but often treat language as prediction rather than interaction, producing responses that sound correct while failing to recognize when requests are ungrounded, impossible, or misunderstood. My research asks what it would take for multimodal agents to take the next step and use language effectively to take actions in grounded contexts and in this talk, I argue that many challenges in multimodal LLM design, including alignment, hallucination, and adaptation, are due to a lack of pragmatics: an understanding of the implicit context behind the implied actions of the words in the query. From visual understanding, to automatic speech recognition, to hallucination detection, I will demonstrate that incorporating pragmatic/contextual reasoning substantially improves agent behavior, and that pragmatic reasoning will drive a necessary shift in how we build multimodal conversational agents that can see, listen, act, and speak in context.


Bio: David M. Chan, Ph.D., is a postdoctoral scholar at the University of California, Berkeley, specializing in multimodal conversational AI. His research focuses on developing scalable AI systems that move beyond language prediction toward grounded language use, integrating vision, audio, and language to enable pragmatic interaction, improve AI-human collaboration, and reduce hallucinations in generative models. Beyond academia, he has worked with leading organizations including Amazon, Google, and NASA, to build and deploy safe, efficient, and accessible machine learning systems. He is also the developer and maintainer of TSNE-CUDA, an open-source tool for high-dimensional data visualization, used by over 40,000 researchers in fields ranging from biomedical technology to industrial manufacturing. David holds a Ph.D. and M.Sc. in Computer Science from UC Berkeley, where he was a graduate fellow with the Center for Technology, Society & Policy (CTSP), and dual B.Sc. degrees in Computer Science and Mathematics from the University of Denver.

Location: NCS 120

A great opportunity for students who will need to integrate AI as a research tool, as well as to think about how job opportunities can be impacted by the growing usage of AI.

Event information

Location: SAC Auditorium, RSVP

Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a bootcamp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, and other generative AI platforms can support you in crafting learning.

Register here.