Abstract: Large language models (LLMs) may exhibit unintended or undesirable behaviors. Recent works have concentrated on aligning LLMs to mitigate harmful outputs. Despite these efforts, some anomalies indicate that even a well-conducted alignment process can be easily circumvented, whether intentionally or accidentally. Does alignment fine-tuning yield have robust effects on models, or are its impacts merely superficial? In this work, we make the first exploration of this phenomenon from both theoretical and empirical perspectives. Empirically, we demonstrate the elasticity of post-alignment models, i.e., the tendency to revert to the behavior distribution formed during the pre-training phase upon further fine-tuning. Leveraging compression theory, we formally deduce that fine-tuning disproportionately undermines alignment relative to pre-training, potentially by orders of magnitude. We validate the presence of elasticity through experiments on models of varying types and scales. Specifically, we find that model performance declines rapidly before reverting to the pre-training distribution, after which the rate of decline drops significantly. Furthermore, we further reveal that elasticity positively correlates with the increased model size and the expansion of pre-training data. Our findings underscore the need to address the inherent elasticity of LLMs to mitigate their resistance to alignment.

Speaker: Huajian Zhang

Location: CS2311

https://stonybrook.zoom.us/j/94414957054?pwd=V1JMc2EwSnVGMFdaUlNobE9DSHU4dz09#success
ID: 94414957054
Password: 094758

Speaker: Heather J. Lynch


Bio:  Dr. Heather J. Lynch is an Associate Professor of Ecology & Evolution at Stony Brook University. Prior to Stony Brook, Dr. Lynch was an Adjunct Professor of Applied Math and Statistics at UC Santa Cruz and a Research Scientist in the Biology Department at the University Maryland. Dr. Lynch received her A.B. in Physics from Princeton University in 2000, an A.M. in Physics from Harvard University in 2004, and a Ph.D. in Organismal and Evolutionary Biology from Harvard University in 2006. Dr. Lynch's research is focused on spatial population dynamics of Antarctic penguins, with a particular focus on statistical and mathematical models to integrate patchy time series with remote sensing imagery. These data will allow Dr. Lynch and colleagues to develop mathematical models to explore how coloniality constrains the colonization and extinction of individual habitat patches and, ultimately, the metapopulation dynamics of colonial seabirds.   

The Future Histories Studio at Stony Brook University and Guggenheim New York are collaborating to present a day-long symposium on October 24 at the Simons Center for Geometry and Physics. This conference will explore urgent questions at the intersection of artificial intelligence, machine learning, and the human, and is co-organized by Noam Segal, LG Electronics Associate Curator at Guggenheim New York. In this role, Noam plays an important part in researching these topics, promoting a deeper understanding of the ways in which contemporary artists use new technologies, and developing and supporting the Guggenheim's engagement with technology-based art under the LG Guggenheim Art and Technology Initiative.

The event examines the profound transformations brought by automation--how AI compels us to rethink cognition, agency, and the conditions of reason itself. As these systems become ever more embedded in daily life--largely invisible yet deeply consequential--they challenge the very foundations of subjectivity and governance. We are surrounded by logics we cannot fully access, yet which shape our realities, while new forms of alterity arise--distinct modes of reasoning that propose collective unknowns beyond established frameworks of knowledge.

This emerging terrain invites us to consider cognitive plurality, where biological and technological intelligences generate new categories, concepts, and understandings. Once unique to humans--art, authorship, judgment, invention--are now co-articulated with systems of computation and planetary-scale infrastructure. The symposium brings together artists, scholars, and technologists to probe the cultural, philosophical, and ecological implications of this entanglement.

The concept of neurodiversity has shown that neurological differences such as autism, ADHD, and dyslexia are not deficits but variations that enrich collective life. Extending this to machines can be provocative: just as neurodivergence unsettles fixed definitions of intelligence, so too AI challenges anthropocentric assumptions about cognition. Yet the analogy is limited. Neurodiversity is rooted in the lived struggles of human communities, while machines neither think nor struggle. Human cognition involves perception, learning, memory, and reasoning through embodied experience. Machine cognition, by contrast, is computational pattern recognition and statistical modeling, without consciousness or lived context, and with only narrow forms of sensing.

For this reason, the symposium advances a broader framework of cognitive diversity or technodiversity--a recognition of proliferating intelligences, human, machinic, and hybrid, as part of a shared ecology. This shift calls for new models of creativity, responsibility, and collaboration that honor the irreducibility of human thought while engaging the radical alterity of machine logics.

Location: Stony Brook Simons Center for Geometry and Physics, Della Pietra Family Auditorium

This event is co organized by the Guggenheim New York

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.




Time: 04/28 Wed 3pm-4pm

Remote Access
Join Zoom Meeting https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09 
Meeting ID: 956 1719 7636 Passcode: 924293

Title: Brain imaging genetics for Alzheimer's disease: integrated analysis and machine learning

Li Shen, Ph.D.
Professor of Informatics
Department of Biostatistics, Epidemiology and Informatics 
Perelman School of Medicine
University of Pennsylvania

Bio: Li Shen, Ph.D., is a Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He is an elected fellow of the American Institute for Medical and Biological Engineering (AIMBE). He obtained his Ph.D. degree in Computer Science from Dartmouth College. The central theme of his lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput omics data, cognitive and other biomarker data, electronic health record (EHR) data, and rich biological knowledge such as pathways and networks, with applications to complex disorders. His research interests include medical image computing, biomedical informatics, machine learning, network science, imaging genomics, Alzheimer's disease, and big data science in biomedicine. He has authored over 280 peer-reviewed articles (h-index 57) in these fields. Dr. Shen's work has been continuously supported by the NIH and NSF, and he is presently the PI of multiple NIH and NSF grants on developing computational methods for various biomedical applications including brain imaging genomics, genetics of Alzheimer's disease, genetics of human connectome, mining drug effects from the EHR data, and big data mining in brain science. He is co-leading the NIA Alzheimer's Disease Sequencing Project AI4AD Consortium and oversees the imaging genomics aspect of this landmark project. Dr. Shen served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Board of Directors during 2016-2019. He has chaired and co-chaired various professional meetings in medical image computing and bioinformatics. He is an Associate Editor of BioData Mining and Frontiers in Radiology (Section of AI in Radiology), and serves on the Editorial Board of Medical Image Analysis and Brain Imaging and Behavior.

Abstract: Brain imaging genetics is an emerging data science field, where integrated analysis of brain imaging and genetics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the genetic, molecular and phenotypic characteristics of the brain as well as their impact on normal and disordered brain function and behavior. Many methodological advances in brain imaging genetics are attributed to large-scale landmark biobank projects such as the Alzheimer's Disease Sequencing Project, the Alzheimer's Disease Neuroimaging Initiative, and the UK Biobank. Using the study of Alzheimer's disease as an example, we will discuss fundamental concepts, state-of-the-art statistical and machine learning methods, and innovative applications in this rapidly evolving field. We show that the wide availability of brain imaging genetics data from various large-scale biobanks, coupled with advances in biomedical statistics, informatics and computing, provides enormous opportunities to contribute significantly to biomedical discoveries in brain science and to impact the development of new diagnostic, therapeutic and preventative approaches for complex brain disorders such as Alzheimer's disease.

More details:
https://bmi.stonybrookmedicine.edu/sites/default/files/shen_li_04_28_flyer.pdf

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks 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.

Tuesday, November 12, 2024, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Carlos Soto, CDS

Yi Huang, CDS

Kevin Yager, CFN

Abstract: Modern language agents often need to solve tasks requiring long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This leads to un-bounded memory growth, increased computational costs, and degraded reasoning performance on out-of-distribution input lengths due to LLM forgetting the context. We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant context size when solving long multi-turn tasks. At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning. Leveraging reinforcement learning (RL) and rollout trajectory truncation, we train a MEM1 agent to develop internal states that integrate prior memory with new observations from the environment while strategically discarding irrelevant or redundant information. Experiments across three domains, including internal retrieval QA, open-domain web QA, and multi-turn web shopping, show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on an augmented multi-hop QA dataset with 16 objectives in each task, and generalizes beyond the training horizon. Our results demonstrate the promise of reasoning-driven memory consolidation as a scalable alternative to existing solutions for training long-horizon task-solving agents that involve multiple interactions, where both efficiency and performance are optimized.

Speaker: Yiyang Feng

Location: CS2311
University Libraries Present: Qualitative data can be challenging to analyze and interpret effectively. In this workshop, SBU Libraries' Data Literacies Lead, Ahmad Pratama will show you how to extract meaningful insights from textual data, including understanding sentiment trends. Learn to explore qualitative data with Python using word clouds, basic natural language processing (NLP) techniques, and lexicon-based sentiment analysis with VADER.
https://stonybrook.zoom.us/meeting/register/k0r6mPYCRayk2AOGmyd0qw#/registration
West Campus - SAC- Student Activities Center - Ballrooms A & B 100 Nicolls Road Stony Brook NY 11794 Job Fair.jpg The Career Center invites Alumni Employers and Job Seekers to the IT/Computer Science Job and Internship Fair this spring. Job Seekers: A job fair is an opportunity for you to present yourself professionally in person to a potential employer, while showcasing your communication skills. Get more information Alumni Employers: Held in both the fall and spring semesters, this event is ideal for employers looking to fill internship, co-op, part-time and full-time opportunities in the field of information technology (i.e. Software Engineering, Network Administration, Web Development, etc.). Register here to recruit top SBU talent.