As generative AI tools become increasingly prevalent in education, their impact on collegiate writing raises important questions about creativity, academic integrity, and effective teaching practices. This panel brings together faculty and students to share perspectives on the opportunities and challenges that AI presents in an academic setting. Through an open dialogue, participants will engage in meaningful conversations, allowing for a deeper understanding of each other's viewpoints and fostering collaboration. Students and faculty will explore diverse ways AI can be used in teaching and learning and seek solutions to utilize AI writing tools ethically. This exchange aims to build a community of trust and shared knowledge, ensuring that AI's role in education is both innovative and responsible.

Register here: https://stonybrook.zoom.us/meeting/register/tJAqdOitpjIpHtDGAsGBfEb3ah0YIzhIJolN
Abstract:

What is the nature of linguistic knowledge, and how is it acquired from limited data? In recent years, the program of subregular linguistics has identified formal language classes expressive enough to account for most phenomena in natural language but also sufficiently limited to be efficiently learned from positive data. An advantage to these formal learning algorithms is that they come with mathematically proven guarantees about their performance, and it is easy to reason about how and why they behave the way they do.

In this talk, I discuss the Multi Tier-based 2-Strictly Local Inference Algorithm (MT2SLIA), which probably learns the syntactically relevant class of 2-Factor Muti Tier-based Strictly Local (2FMSTL) tree languages. This algorithm efficiently learns from a polynomially-sized sample of positive data by identifying missing substructures and generalizing these as constraints over tiers in a principled manner.

I will introduce a working prototype implementation of this algorithm and demonstrate its behavior on a curated sample of natural language data to show how it can learn relevant syntactic patterns.

Bio:

Logan Swanson is a third year PhD student in the Department of Linguistics at Stony Brook University. He is advised by Dr. Jefferey Heinz and Dr. Thomas Graf. His interests include learning theory, computational syntax, and language change. His current research focuses on understanding the learning-theoretic elements of natural language by designing, implementing, and testing learning algorithms for linguistically relevant formal language classes.

*Please note: this seminar will be held in person (IACS Seminar Room w/ food provided) and online.

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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.

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.

AI and Edge Processing Co-Design for Radiation Detectors

Abstract: Artificial Intelligence (AI) offers exciting new opportunities for enhancing the performance of radiation detectors, ultimately leading to improved physics outcomes. Furthermore, with the explosive growth in data rates being seen by next-generation radiation detectors, deployment of AI algorithms at the edge by embedding intelligence within or near the detector front-end can be transformative. Such integration enables real-time data filtering, noise suppression, feature extraction, and adaptive control, while reducing downstream bandwidth and power consumption. This talk will cover three efforts that bring AI to the forefront of detector technology. First, we demonstrate how AI-based algorithms can be used for position reconstruction in virtual Frisch-grid (VFG) detectors by compensating for charge transport distortions and detector non- uniformities, leading to significantly enhanced fidelity in imaging of gamma-ray interactions. Second, we present a smart readout application specific integrated circuit (ASIC) that combines digital signal processing with co-designed artificial neural networks to enable on-chip regression and classification of detector signals, while meeting stringent constraints on accuracy, speed, and area. Finally, we introduce our recent efforts related to the development of electro-photonic processing architectures that integrate CMOS electronics and silicon photonics for near-sensor AI acceleration. These architectures aim to leverage cross-disciplinary co-design from algorithms to hardware, to achieve low latency and energy-efficient processing of detector data.

Biography: Dr. Prashansa Mukim is an early-career researcher in the Instrumentation Department at BNL, where she works on the design of front-end electronics for extreme environments and the development of co-design methodologies for novel processing modalities and beyond-CMOS technologies. Prior to joining BNL, she was a post-doctoral researcher at the National Institute of Standards and Technology (NIST) in Maryland, where she focused on characterizing the properties of CMOS circuits at cryogenic temperatures and applications of spintronic devices for neuromorphic computing. She received her Ph.D. in Electrical and Computer Engineering from the University of California, Santa Barbara, in 2021.

Location: CDS, Bldg. 725, Training Room

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To truly understand human language, we must look at words in the context of the human generating the language. Factors such as demographics, personality, modes of communication, and emotional states have shown to play a crucial role in NLP models pre-LLMs era. Steps of mathematically defining the inclusion of human context in language modeling and more will be discussed with Nikita Soni, a PhD student at Stony Brook University co-advised by H. Andrew Schwartz and Niranjan Balasubramanian. She is the lead organizer of the workshop on human-centered large language modeling.

Please register for the STEM Speaker Series Zoom event here

Please RSVP for the STEM Speaker Series in-person event here
The talk will be exclusively on zoom https://stonybrook.zoom.us/j/7851507944 Speaker: Sooyeon Lee, Rochester Institute of Technology Title: Design and Evaluation of Accessible AI Technologies for Users with Disabilities Abstract: Over one billion people in the world live with some type of disability. Many of them experience barriers in accessing information or using technologies, which can limit social interactions in both physical and digital spaces. In my research, I focus on investigating and designing nonvisual interaction for the community of blind users and non-audio and non-speech interaction for the community of deaf and hard of hearing users. In this talk, I will first present my research investigating nonvisual interaction prototypes for supporting shopping activities for blind users, with an exploration of one-way instructional and two-way conversational interactions and with a variety of form factors and communication modalities through the use of human-computer interaction research methodologies. I will also discuss incorporation of AI technology and its impact on the nonvisual guidance experiences, and further meanings of independence and new ways for designing independence for people with visual impairments. This collaborative work included AI researchers, the community of the blind, and an industry research partner. Additionally, I will discuss my findings and further exciting research opportunities. Secondly, I will overview research projects investigating AI-based applications and tools that support deaf and hard of hearing people's equitable information access and societal participation. This work addresses engagement in online social media spaces, workplace communication, participation in gig work, and interaction with mainstream technology through American Sign Language (ASL) interaction. I will focus on a recent project on users' experiences with AI deep-fake face-transformation technologies to support anonymous participation of deaf and hard of hearing signers in online social media. Lastly, I will discuss my future research directions informed and inspired by this prior and current research. Bio: Sooyeon Lee is a postdoctoral research associate in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology. She received her Ph.D., advised by Dr. John M. Carroll, in Information Sciences and Technology from the College of Information Sciences and Technology at The Pennsylvania State University, and she also conducted design research at Google and Uber. Her research is in the fields of Human-Computer Interaction and Human-AI Interaction with focus on accessibility. She designs, builds, and evaluates new systems and applications that address accessibility barriers. Her work investigates the diversity of users, explores and leverages emerging technologies, and adopts human-centered design and inclusive design approaches in an interdisciplinary research framework. She has multiple publications in top-tier human-computer interaction and computing accessibility journals and conferences, including ACM CHI, CSCW, ASSETS, and TACCESS, and she has received a Best Paper Award Nomination at ASSETS 2021. She has served on Associate Chair for the ACM CHI conference and will serve on Program Committee for ASSETS 2022.
Abstract: Autonomous systems, whether on Earth or in space, rely on 3D perception to understand and interact with the world around them. Yet traditional techniques for 3D understanding often depend on human designed features, fixed sensors, and conventional imaging modalities. This constrained approach can limit every stage of perception, from sensing to interpretation to decision making.
In this talk, we'll explore an alternative paradigm for imaging: physically based neural representations for 3D scenes and 3D sensing systems. We will discuss how recent advances in large scale learned representations can be used to jointly optimize both 3D scene models and the design of sensing systems for 3D capture, with the goal of enabling task specific perception systems.
Unlike modern AI models trained on internet scale datasets, these specialized 3D representations typically operate in data sparse regimes and therefore require a different kind of prior. We'll examine how grounding these learned representations in the physics of light transport can improve our understanding of scene structure, and inform imaging system design even with limited data. By connecting physical insights with learned representations, we'll highlight new possibilities for robust, efficient, and adaptive perception in challenging environments.

Speaker: Nikhil Behari is a graduate student in the Camera Culture group at the MIT Media Lab, advised by Professor Ramesh Raskar. His research interests include computational imaging, 3D scene understanding, and multi-agent decision-making under uncertainty, with a focus on automating imaging system design for 3D perception in human and planetary health. His research is supported by the NASA Space Technology Graduate Research Fellowship. He received his bachelor's in Computer Science and Statistics from Harvard University in 2022.
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