The AI Phenomenon is here to stay. Will it help or hurt your career path? Industry leaders will share their insights and help us understand how AI will affect your ability to manage your career path. Technology will always evolve and add layers to the process. Don't run from it - Embrace it.

Presented by: SBU Career Center and Co-hosted with Alumni Relations

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Abstract:

Recent advances in deep learning have significantly enhanced the capabilities of Natural Language Processing (NLP) and Vision-Language Models (VLMs). However, these advancements come with increased vulnerabilities, notably through backdoor attacks that pose severe security threats. This thesis addresses two critical dimensions of Trustworthy AI and Efficient Multimodal Representation Learning: (1) security through analyzing, detecting, and designing backdoor attacks in NLP and VLMs, and (2) efficiency through advanced multimodal representation methods tailored for clinical and medical imaging applications.

In the first dimension, we explore the internal mechanisms exploited by backdoor attacks, identifying the distinctive phenomenon of attention focus drifting in compromised transformer models, where trigger tokens consistently hijack attention. Leveraging these insights, we propose robust detection frameworks, including the attention-based Trojan detector (AttenTD) and a task-agnostic logit-based detection method (TABDet), achieving effective identification of backdoored NLP models across diverse tasks. We further introduce novel backdoor attack methodologies: the Trojan Attention Loss (TAL), enhancing attack efficiency and stealth through direct attention manipulation, and BadCLM, demonstrating critical vulnerabilities in clinical decision-support systems by effectively compromising clinical language models.

Extending our security exploration to multimodal settings, we investigate backdoor attacks on Vision-Language Models (VLMs), particularly in complex image-to-text generation tasks, proposing innovative techniques (TrojVLM, VLOOD) capable of embedding backdoors without direct access to original training data, thus showcasing practical risks in real-world scenarios.

In the second dimension, we address efficiency and interpretability challenges in clinical and pathology applications. We introduce TCP-LLaVA, the first multimodal large language model (MLLM) designed explicitly for Whole Slide Image (WSI) Visual Question Answering (VQA). Utilizing a novel token compression mechanism inspired by transformer-based models, TCP-LLaVA substantially reduces computational resource consumption while maintaining superior VQA performance across multiple tumor subtypes. Additionally, we present a multimodal transformer model integrating structured Electronic Health Records (EHR) with clinical notes, demonstrating enhanced predictive accuracy and interpretability for in-hospital mortality prediction through integrated gradient-based interpretability methods.

Together, these contributions present a comprehensive approach to ensuring AI models are not only secure against malicious manipulation but also efficient and interpretable for critical clinical applications, underscoring the essential need for trustworthy and effective AI systems.

Speaker: Weimin Lyu

Zoom: https://stonybrook.zoom.us/j/2392326575?pwd=SVQ2VkFXTnZZYmJUMXgvTXBuZWM3UT09

Meeting ID: 239 232 6575
Passcode: 436192
CSE 600 Talk: Securing Software-Defined Networking Infrastructure by Dr. Guofei Gu

ABSTRACT: Today's network and computing infrastructure rests on inadequate  foundations. An emerging, promising new foundation for computing is software-defined infrastructure (SDI), which offers a range of  
technologies including: compute, storage and network virtualization;  novel separation of concerns at the systems level; and new approaches to system and device management. As a representative example of SDI,  
software-defined networking (SDN) is a new networking paradigm that decouples the control logic from the closed and proprietary implementations of traditional network data plane infrastructure. SDN is now becoming the networking foundation for data-center/cloud, future Internet and 5G infrastructures.  

We believe that SDN is an impactful technology to drive a variety of innovations in network management and security. It is now clear that security will be a top concern, as well as a new killer app, for SDN. In this talk, I will discuss some new opportunities, as well as challenges, in this new direction and demonstrate with our recent  
research results. I will discuss how SDN can enhance network security. And I will also discuss some unique new security problems inside SDN and introduce some of our work to enhance the security of SDN. Finally, I will share my vision on programmable system security in a software-defined world.  

BIO: Dr. Guofei Gu is a professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU). Before coming to Texas A&M, he received his PhD degree in Computer Science from the College  
of Computing, Georgia Institute of Technology. His research interests are in network and systems security.  
Dr. Gu is a recipient of 2010 NSF CAREER Award, 2013 AFOSR Young  Investigator Award, 2010 IEEE S&P Best Student Paper Award, 2015 ICDCS Best Paper Award, Texas A&M Dean of Engineering Excellence Award,  
Presidential Impact Fellow, Charles H. Barclay Jr. '45 Faculty Fellow and the Google Faculty Research Award. He is an active member of the security research community and has pioneered several new research directions such as botnet detection/defense and SDN security. Dr. Gu has served on the program committees of top-tier security conferences such as IEEE S&P, ACM CCS, USENIX Security and NDSS. He is an ACM Distinguished Member, an Associate Editor for IEEE Transactions on Information Forensics and Security (T-IFS), and the Steering Committee co-chair for SecureComm. He is currently directing the SUCCESS Lab at TAMU.
Abstract: Humans perceive the world around them by recognizing global patterns and structures such as object parts, branches, their spatial arrangement, and so on. Most deep learning models, however, take a fundamentally local approach. They process images pixel-by-pixel rather than focusing on structures as a whole. While these models indeed perform well on many tasks, the local (pixel-level) versus global (structure-level) disconnect makes them harder to interpret and control.

Topology, in a general sense, is a mathematical language for describing structure. It delineates how different parts of an image relate to one another, capturing both individual structures and their overall layout. Preserving topology enforces structural correctness and, by extension, semantic validity.

In this thesis, we investigate how topological constraints can be used to bridge the gap between local and global understanding. We use topology to inform the design of deep learning models that are explicitly structure-aware. Our thesis focuses on dense prediction tasks, which include image segmentation, uncertainty estimation, and generative modeling. First, we introduce a topological interaction module for semantic segmentation that encodes containment and exclusion constraints directly into the learning process. This preserves anatomical hierarchies and improves multi-class consistency. Next, since segmentation models can never be truly perfect, we address the need for reliable uncertainty estimation to identify error-prone regions. Unlike conventional pixel-wise uncertainty maps, which tend to be noisy and difficult to interpret, we propose reasoning at the level of structural units--branches and connections--which are more visually discernible and actionable. Finally, we leverage topology for generative modeling. We propose a topology-guided diffusion framework that can be controlled using structural attributes like object count and connectivity.

Together, these contributions establish a unified approach to topology-informed, structure-preserving dense prediction models. By integrating topological reasoning with deep networks, this thesis advances models that are not only accurate, but also structurally consistent, interpretable, and controllable. The results from this thesis have been published in ECCV, NeurIPS, and ICLR.

Speaker: Saumya Gupta

Location: New Computer Science (NCS) 120


Zoom: https://stonybrook.zoom.us/j/93643318604?pwd=kv8DagpbayzizivU29UCYItnlzlYRM.1&jst=2
Abstract: Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes per-pixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23-26% on synthetic data and 6-16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining.

Speaker: Irene Kim

Location: NCS 115
Join Zoom Meeting https://stonybrook.zoom.us/j/511954566?pwd=d3hGdHlGZVJISnV3d2pRcnBRbFBJZz09 (ID: 511954566, password: fxnmj4) Join by phone (US) +1 646-876-9923 Join using SIP 511954566@zoomcrc.com (passcode: 542040) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DDBgsLRhCTQCGwD9DAxf7mw%253D%253D%26signature%3D02e2f3393481f8bc18cbb5520e174a6b8ceee7e9e58c9dca9d0aa4db9a63279a&sa=D&usg=AOvVaw1e3Mi7F_8TdXl5XstjsrIx Password: fxnmj4

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Abstract: Two-dimensional (2D) materials such as graphene, hBN, and TMDs offer atomically sharp interfaces and unprecedented tunability when vertically assembled into van der Waals heterostructures. These stacks have enabled discoveries ranging from moiré superconductivity and correlated insulators to quantum emitters and next-generation nanoelectronic devices. Yet constructing high-quality heterostructures remains largely artisanal: researchers manually identify exfoliated flakes, align a polymer stamp by eye, and finely adjust temperature and contact geometry through tacit skill. This manual workflow is difficult to reproduce, scales poorly, and prevents systematic exploration of the enormous combinatorial space of materials, twist angles, and interfacial conditions. AutoLab is an autonomous platform that translates this tacit human expertise into programmable, feedback-driven control. Instead of pressing flakes with predefined trajectories, AutoLab uses machine vision to detect polymer-wafer contact, dynamically regulates contact evolution through closed-loop actuation and temperature control, and captures high-quality flakes with the cleanliness and precision of expert manual fabrication. The system integrates perception, decision making, and motion planning into a single robotic framework, enabling reproducible stacking, wafer-level coverage, and accelerated discovery. Beyond 2D materials, AutoLab illustrates a broader paradigm for AI-native scientific automation: codifying human experimental reasoning into algorithms that interrogate data in real time, adaptively adjust instrumentation, and generate scalable, high-fidelity datasets. Such platforms could generalize to diverse research domains--quantum device fabrication, optical alignment, surface science, autonomous microscopy, and other workflows where expert intuition currently limits throughput and reproducibility. By bridging artisanal manipulation and robotic autonomy, AutoLab points toward a future where scientific discovery is accelerated by machines that not only execute instructions, but learn, respond, and collaborate with human scientists.

Biography: Dr. Yutao Li is a research associate from Department of Condensed Matter Physics and Material Science, Brookhaven National Laboratory. He has 8 years of experience in 2D material sample fabrication, and investigation in their electronic transport, optical and mechanical properties.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449