Abstract: Large Language Models (LLMs) have transitioned from standalone prediction interfaces into integrated systems that incorporate content protection, external knowledge retrieval, and multi-step reasoning. While these functional layers expand model capabilities, they also introduce complex, inter-component dependencies that create novel and systemic security risks. This research provides a systematic deconstruction of the structural vulnerabilities emerging across these functional layers.

In this proposal, we evaluate the security boundaries of LLM systems through three pivotal dimensions:
The Content Layer: We present Watermark under Fire, revealing the inherent fragility of content-based tracing mechanisms under adaptive perturbations and highlighting the limitations of surface-level safety measures.
The Retrieval Layer: We introduce GraphRAG under Fire to examine the security of topology-aware knowledge integration. We reveal how graph-based indexing can be exploited as a structural lever for high-success poisoning attacks.
The Reasoning Layer: We detail AutoRAN, the first framework demonstrating the hijacking of internal safety reasoning in Large Reasoning Models (LRMs). This work proves that the transparency of the reasoning process itself creates a critical and exploitable attack surface.

Collectively, these studies demonstrate a systemic failure of add-on safety mechanisms in securing the broader LLM ecosystem. By identifying recurring patterns of exploitation across different system layers, this research provides the necessary foundation for transitioning from reactive patching to a more unified and architecturally-grounded approach to AI trustworthiness.

Speaker: Jiacheng Liang

Zoom: https://stonybrook.zoom.us/j/6669990420?pwd=dkY0eEw5YXpPSWo3RUE4OE1oVW90UT09&omn=97367037382
Meeting ID: 666 999 0420
Passcode: 075299
The SUNY Office of Research, Innovation & Economic Development (ORIED) is hosting a webinar, Pathways to Innovation: Exclusive STEM Opportunities for Students at Premier Labs, with the Air Force Research Laboratory (AFRL), the Griffiss Institute and Brookhaven National Laboratory (BNL).

Please join us on October 30 from 12:30 - 2:00 pm to learn more about the labs and the wide variety of research, education, and workforce development programs they offer.

Register here: https://rfsuny.zoom.us/webinar/register/WN_fjWNU9l8Sr6WO_M3AoZ-Rw?mc_cid=50c2045945&mc_eid=357e15f9df#/registration

The Program in Writing and Rhetoric
Invites you to
A Rhetorical/Deliberative Framework for AI Language Model Alignment
featuring
Prof Zoltan Majdik Professor
North Dakota State University
In this talk, Prof. Majdik proposes a framework for aligning LLMs with values grounded in the norms of rhetorical culture and deliberative democracy. Alongside long-standing AI alignment value targets like safety and transparency, this AI alignment framework assesses to what extent a language model exhibits human and humane values that foster communicative engagement, and it codifies approaches to tuning existing models to better align with such values.

Location: Humanities 1008
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

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.


New York Scientific Data Summit (NYSDS) is a premier annual conference that brings together researchers and thought leaders from academia, national labs and industry to exchange ideas and foster collaboration focused on data-driven science and technology. Co-hosted by Brookhaven National Laboratory and the Institute for Advanced Computational Science (IACS) at Stony Brook University, NYSDS 2025 will take place on September 11-12, 2025, in the SUNY Global Center in New York City.

NYSDS 2025 will spotlight artificial intelligence (AI), machine learning (ML) and robotics - fields currently at a pivotal point with transformative impacts on science and technology. From accelerating computationally demanding simulations to discerning signals from noisy data, AI/ML has become an integral part of the scientific workflows. Despite many advances, challenges remain to ensure that AI/ML applications are reliable, explainable and trustworthy.

Robotics, a growing field that couples AI with physically actuated mechanical bodies, has seen increased interest in areas spanning science, technology and manufacturing. The need for real-time decision-making and control, along with the intricate morphology of robots, makes robotics an intriguing application of AI, advanced computing and optimization.


This NYSDS 2025 is open to the public. To be eligible to attend, all participants must register online by August 30, 2025. For questions or assistance with registering, please contact the Summit Coordinator.

Register here.


Abstract: Generating high-fidelity EEG data at scale is essential for overcoming dataset scale limitation and satisfying privacy requirements in computational neuroscience. However, the predominant reliance on discrete denoising formulations fails to adequately capture the continuous temporal evolution and frequency-domain characteristics intrinsic to EEG recordings. Consequently, such approaches often compromise long-range temporal coherence and introduce structural discrepancies in both spectral and temporal domains.
In this work, We argue that faithful EEG synthesis demands generative models that directly characterize the continuous dynamics underlying neural signals. To this end, we propose a conditional flow matching framework that treats EEG as raw waveform sequences traversing continuous-time trajectories. Rather than relying on discretized denoising steps or handcrafted signal representations, our method learns a smooth velocity field mapping noise distributions to the target EEG manifold, thereby naturally preserving temporal continuity and transient neural phenomena. To enforce fidelity to fundamental EEG characteristics, we incorporate principled constraint terms that maintain spectral consistency, temporal stationarity, and signal-level statistical properties. Evaluated on large-scale benchmarks, our approach establishes new state-of-the-art results compared to competitive baselines. Comprehensive analyses further confirm that the proposed framework faithfully recovers core structural attributes of neural dynamics, offering a scalable and theoretically grounded solution for high-fidelity EEG generation.

Speaker: Yifan Wang

Location: https://stonybrook.zoom.us/j/95300279172?pwd=maxkSIma0Go2O8Wzre3CLYWHflnUi3.1&jst=2
Meeting ID: 953 0027 9172
Passcode: 418725