AI is everywhere and so are the privacy concerns that come with it. At its core, the most common forms of AI we use today are online digital services and thus inherit the usual privacy risks. We'll take a look at indirect prompt injection- a technique that can trick AI tools into revealing or extracting private information as well as techniques being used in academic contexts to manipulate systems and even mislead researchers.

Register here for the online session.
Educational objectives:

1. Explain how AI represents a Cognitive Revolution in academic medicine, redefining thefundamental limits of human cognition and knowledge work.
2. Differentiate between superficial AI adoption (innovation theatre) and truetransformationthrough AI-native institutional design.
3. Analyze how AI fundamentally reshapes clinical, research, and educational work-from dataentry to verification, recall to recognition, and hypothesis generation to evaluation-andidentify implications for redesigning academic health systems.

Speaker: Jiajie Zhang, Ph.D.,Dean, Professor, and Glassell Family FoundationDistinguished Chair in Informatics Excellence,D. Bradley McWilliams School of BiomedicalInformatics, UTHealth Houston

Location: MART Building, Room: 7M-0602 (7th Floor)

Please join us this Friday, February 13th for the CSE 600 seminar given by Associate Professor Debswapna Bhattacharya, from the Department of Computer Science at Virginia Tech.

Abstract: Building a model of a biological system that can provide actionable hypotheses to form a solid foundation for experimental and theoretical analyses is one of the key challenges in biology and medicine. In this talk, I will present my group's ongoing work in developing, evaluating, and disseminating a new generation of computational methods for biomolecular modeling powered by artificial intelligence (AI) and machine learning (ML). First, I will introduce a new generation of AI/ML methods for improved modeling and characterization of protein-nucleic acid assemblies by deep graph learning using embeddings from biological large language models (LLMs) as well as geometric attention-enabled pairing of heterogeneous biological LLMs, a previously unexplored avenue. Then, I will present a novel generative deep learning model based on equivariant flow matching for end-to-end generation of all-atom RNA 3D structural ensemble. Finally, I will outline my future research directions on attaining atomic-level accuracy in computational modeling of biomolecules and their assemblies at scale.

Speaker: Debswapna Bhattacharya is an Associate Professor in the Department of Computer Science at Virginia Tech. He received his Ph.D. in Computer Science from the University of Missouri-Columbia in 2016. Before joining Virginia Tech in 2022, he was an Assistant Professor at Auburn University from 2017 to 2021. His research interests lie at the intersection of computational biology and machine learning, with a particular focus on artificial intelligence for computational structural biology, specifically in modeling and characterization of biomolecular structures and interactions. His research group has been developing novel computational and data-driven methods, software, and information systems for diverse biomolecular modeling problems, ranking among the best methods in community-wide blind assessments and serving the worldwide community of biomedical users. He received various research awards (NSF CAREER Award, NIH Maximizing Investigators' Research Award, NSF National AI Research Resource Award) and numerous institutional honors (National Distinction and Outstanding Contributor at Virginia Tech, Ginn Faculty Fellowship at Auburn University, Outstanding Engineering Faculty Award at Auburn University).
Location: NCS 120

We invite faculty to deliver a 10-minute presentation during our afternoon session at the CELT Symposium on April 11, 2025. Showcase how you use emerging technology (i.e. AI, VR, etc.) to support diverse student populations and enhance learning experiences. Share your innovative strategies and inspire others!

CELT Symposium Theme: A New Era of Inclusivity and Innovation in Higher Education

https://t.e2ma.net/click/5w0gph/5wwlu4oe/9v63j6

We will demonstrate how to make a personalized AI partner that can do certain tasks for you. This step by step process can be used to develop your own companion.

Understand what is a Gemini Gems and how it can be personalized for your custom needs. We will show you how to capture the tasks that you would like to complete and give you higher quality responses that will ensure your communication and tasks are completed with ease.

Register here.

The International Neuroethics Society (INS) Speaker Series on AI & Consciousness

AI has existed as a tool for a long time, performing simple tasks such as sorting documents, suggesting music, and so on. But with the development of new generations of AI, the perception of its value to society has been increasing, as it can bring potential and promising benefits in many areas of human life. AI is known to have errors or biases that result in strange or even dangerous responses, but what happens when in AI-human interaction, the latter have errors or biases? cultural errors or biases? And what could be the implications for human relationships?

Speaker Bio

Dr. Karen Herrera-Ferrá is an independent and global consultant on ethical, medical, psychological, legal, social, cultural, policy-making, human rights and political issues and concerns on the development and use of neuroscience, neurotechnology and AI. She is a former member of the Board of Directors of the International Neuroethics Society.

Register here

https://umaryland.zoom.us/meeting/register/tJMvfuqsqDspG9BKMLfUU49UbuUyP_IEvXRh

The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.

Title: J-Space

Abstract: The rise of the term mechanistic interpretability has accompanied increasing interest in understanding neural models -- particularly language models. However, this jargon has also led to a fair amount of confusion. So, what does it mean to be mechanistic? We describe four uses of the term in interpretability research. The most narrow technical definition requires a claim of causality, while a broader technical definition allows for any exploration of a model's internals. However, the term also has a narrow cultural definition describing a cultural movement. To understand this semantic drift, we present a history of the NLP interpretability community and the formation of the separate, parallel mechanistic interpretability community. Finally, we discuss the broad cultural definition -- encompassing the entire field of interpretability -- and why the traditional NLP interpretability community has come to embrace it. We argue that the polysemy of mechanistic is the product of a critical divide within the interpretability community.

Location: NCS 220

Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=ysKMaQx7Hp0lkq5nOb3zJ9ivNZVvLv.1&jst=2