Join us for the New York State Innovation Summit on October 28-29, 2024 in Syracuse, NY. This multi-day is event for NYS organizations that want to showcase and discover new and emerging technologies that support innovation and drive business growth. The event serves as an opportunity to foster collaboration; introduce industry to experts that can assist growth, strengthen our statewide innovation ecosystem and showcase promising early stage companies. Whether you're a startup, an economic developer, or an established manufacturer, the NYS Innovation Summit is for you. The 2024 New York State Innovation Summit will showcase companies and researchers at the forefront of emerging technologies and new advancements in production capabilities. This event celebrates New York State leadership in technology-led economic growth with experts in biotechnology, new materials, energy innovation, and artificial intelligence that will explore current technology convergence opportunities, ways to accelerate commercialization, and issues of manufacturing sustainability.
University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting! Location: Melville Library, Scholarly Communication Seminar Room
Virtual Talk: Metadata Matters: Robust Document Classification via Adaptation Methods for Text-driven Public Health by Xiaolei Huang

Zoom link to follow.

Abstract: Document classifiers have been widely applied in solving health-related issues, such as suicide prevention, flu vaccination surveillance and disease diagnosis. However, document metadata including time, gender, age and location has an enormous impact on robustness of 
document classifiers. Language varies across the metadata bringing both challenges and opportunities to build reliable document classifiers. For example, online written language changes over time, and males and females express opinions differently. This talk describes how to use domain adaptation to integrate temporal and user demographic factors into document classifiers. By adapting knowledge of how language varies across the metadata, models can learn generalized representations of language through the metadata-invariant embeddings. 
This approach will lead to metadata-adapted document classifiers and can also extend to personalize classification models by user embedding. 

Bio: Xiaolei Huang is a 4th-year PhD candidate in Information Science at the University of Colorado, Boulder. He is currently a visiting scholar at the Johns Hopkins University. His research interests are in Natural Language Processing, Machine Learning and Public Health. Particularly, he focuses on domain adaptation, cross-lingual transfer learning, user modeling and fairness.

Abstract: Large Language Models (LLMs) have revolutionized how people interact with knowledge, offering unprecedented opportunities to accelerate the pace of scientific discovery. In this talk, I will discuss my research on the synergy between LLMs and scientific knowledge--specifically how these models extract, induce, and verify knowledge to automate the research lifecycle. First, I will cover our work on improving knowledge extraction from vast scientific literature, focusing on enabling models to comprehend long documents in a cost-efficient and comprehensive manner. I will describe a novel paradigm for representing document-level structured information as question-answer pairs and how we address the challenges of long-context understanding by leveraging global context through retrieval-augmented modeling. Next, I present our pioneering work on using LLMs for new scientific hypothesis generation. We introduce a framework employing reinforcement learning with fine-grained reward modeling and adaptive controllers.
This approach balances novelty, feasibility, and effectiveness to generate inspiring and actionable research hypotheses. Finally, I will discuss work on the first LLM Scientist for machine learning research. I will demonstrate how LLMs can move beyond hypothesis generation to participate in the execution and validation of scientific hypotheses, ensuring that the discovered knowledge is not only innovative but also grounded and verified.

Bio: Xinya Du is a tenure-track assistant professor at UT Dallas Computer Science Department. He earned a Ph.D. degree from Cornell University and was a Postdoctoral Research Associate at the University of Illinois (UIUC). He has also worked at Microsoft Research, Google Research, and Allen Institute AI. His research is on large language models, deep learning, and their applications in science.His work has been published in leading NLP and ML conferences (ACL, ICLR, NeurIPS). His research has received multiple recognitions, including a Best Paper Award at AAAI AI for Research and a Best Poster Award at ICML AI for Science workshop. His work was included in the list of Most Influential ACL Papers and has been covered by major media like New Scientist. He was named a Spotlight Rising Star in Data Science by the University of Chicago and is the recipient of several prestigious awards, including the Amazon Research Award, Cisco Research Award, Open Philanthropy Award, and the NSF CAREER Award.

Location: NCS 120
Defending Software Systems from Cyber Attack Campaigns Presented by R. Sekar The DNC hack of 2016, the Equifax breach of 2017, and the spate of ransomware campaigns in 2019 demonstrate the formidable challenges we face in securing our network and software systems against highly stealthy and sophisticated adversaries. In this talk, I will describe two avenues of research we have been pursuing to help tilt the table against such powerful adversaries. The first is software hardening techniques that make software vulnerabilities harder to exploit. To maximize their applicability and ease of use, our techniques are implemented into compilers, or they directly transform binary code. I will outline some of the exciting new developments we have had in this area over the years, including randomization, memory safety, information-flow tracking, control-flow integrity, and code-pointer integrity. We complement this first line of defense with techniques for analyzing and understanding attack campaigns that manage to slip past all deployed defenses. Our techniques can sift through logs consisting of hundreds of millions of events to zoom in on attack activity that may span just a few hundred events. I will describe our experience in mapping out several DARPA-sponsored red team attack campaigns.
The North East AI Agents Day Organizing Committee invites you to '2026 AI Agents Day.'

The goal of this workshop is to offer a comprehensive overview of AI agents, bring ML, Systems, and HCI research communities together to share progress, discuss common problems and evaluation setups, and identify opportunities for collaboration. We aim to bring together attendees from diverse disciplines to foster interdisciplinary collaboration and discuss open research questions.

Location: Jane Street Offices, New York

Register here.
Join us as we celebrate this year's Brook & Beyond Challenge finalists.
The Office for Research and Innovation invites you to hear about the two-month journey in which the Brook & Beyond team supported eight cohorts in bringing their bold ideas from the lab to the marketplace. It's an energizing evening that highlights the collaboration, creativity, and entrepreneurial spirit driving discovery across the University.
Meet this year's award recipients, hear pitches from the emerging founders, and applaud their achievements.
Connect, celebrate, and be part of the momentum shaping the future of innovation at
Stony Brook University.
Refreshments will be served. Registration is required.
Register Here.

As AI drives rapid change across professional fields, how do you bring these developments into your classroom? The CELT AI Panel Discussion will gather academic thought leaders to explore how generative AI is reshaping teaching, learning, and the knowledge students need for today's world. Our panelists will share practical strategies for integrating AI-related advancements into course content, highlight both opportunities and challenges, and discuss how educators can help students build critical thinking, ethical awareness, and hands-on experience with emerging AI technologies. Join us to examine how teaching can evolve alongside an AI-transformed society.

Register here.