How Language Makes us Smart (without Big Data) presented by Charles Yang

Abstract: Language provides the glue that combines simpler concepts into complex ones. To study how language guides conceptual development, we need precise accounts of how rules are learned from the child's linguistic experience, which is extremely limited in comparison to the amount of data available to current machine learning methods. In this talk, I discuss a mathematical model of inductive generalization, which enables language learning with very small amount of data. Such a view of learning has strong implications for the cross-cultural/linguistic variation of development. As a case study, I show that Hong Kong children learning Cantonese, which has a relatively simpler formal counting system, develop understanding of symbolic numbers a full year ahead of English-learning children in the United States, which is precisely predictable from the learning model. The new conception of learning adds another wrinkle to the eternal question of how language and thought are related to each other.

Bio: Charles Yang studied at the MIT AI lab and now teaches linguistics, computer science and psychology and directs the Program in Cognitive Science at the University of Pennsylvania. He is the author of several books: The Price of Linguistic Productivity (2016 MIT Press) won the Leonard Bloomfield Award from the Linguistic Society of America. His honors include a Guggenheim fellowship.

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: Designing custom proteins could revolutionize medicine and materials, but it remains an immense scientific challenge. Our work uses large-scale AI foundation models to generate novel proteins tailored to bind specific small molecules. Each AI-generated design is passed through a rigorous, multi-stage validation pipeline to ensure it is biophysically realistic. A key innovation is fine-tuning our model with data from molecular dynamics (MD) simulations, exposing it to the conformational dynamics and energetics of protein-ligand binding. This physics-aware training results in novel protein designs with enhanced stability and more effective binding capabilities.

Bio: Xin Dai is an Assistant Computational Scientist in the Artificial Intelligence Department of the CDS. His work centers on AI for Science with a strong focus on computational biology. He earned his PhD in Physics from Tsinghua University.

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

Meeting ID: 160 438 3624
Passcode: 558449

CSE 600 Seminar Series | Fall 2025


Abstract: Large reasoning models have demonstrated capabilities to solve competition-level math problems, answer deep research questions, and address complex coding needs. Much of this progress has been enabled by scaling of data: pre-training data to learn vast knowledge, fine-tuning data to learn natural language reasoning, and RL environments to refine that reasoning. In this talk, I will describe the current LLM reasoning paradigm, its boundaries, and the future of LLM reasoning beyond scaling. First, I will describe the state of reasoning models and where I think scaling can lead to some additional (though perhaps limited) successes. I will then shift to discussing more fundamental issues with models that scale will not resolve in the next few years. I will touch on four current limitations: outdated knowledge, generator-validator gaps, limited creativity, and poor compositional generalization. In all cases, fundamental limitations of LLMs or of supervised learning in general make these problems challenging, inviting future study and novel solutions beyond scaling.

Bio: Greg Durrett is an associate professor in the Department of Computer Science and the Center for Data Science at New York University. His research is broadly in the areas of natural language processing and machine learning. Currently, his group's focus is on reasoning about knowledge in text, verifying correctness of generation methods, and studying how to make progress on problems that defy LLM scaling. He is a 2023 Sloan Research Fellow and a recipient of a 2022 NSF CAREER award. He has served in numerous roles for ACL conferences, recently as a member of the NAACL Board since 2024 and as Senior Area Chair for ACL 2025 and EMNLP 2025. He received his BS in Computer Science and Mathematics from MIT and his PhD in Computer Science from UC Berkeley, where he was advised by Dan Klein.

Submit an abstract celebrating research, new discoveries and achievements in medicine and science!

We encourage faculty, nurse practitioners, post-doctoral fellows, fellows, residents, medical students, graduate students and undergraduate students to submit an abstract. Original research, case reports and case series are welcome.

Abstract submission deadline: FEBRUARY 7, 2025

For more details, visit here.

Abstract: Datalog is a powerful language for expressing recursive computations through rules: Horn clauses in first order logic. Although effective at expressing queries over existential properties, Datalog and many of its popular implementations struggle with queries that involve more complex aggregates, requiring users to apply verbose, non-composable, and/or inefficient workarounds. Recent work on lattice-based datalogs addresses many of these concerns for aggregates that can be encoded as lattices (e.g., min or max), but more general aggregates like count remain problematic. In this talk, I will argue that this is not a fundamental limitation of Datalog, but rather from its model of truth: Both datalog semantics and evaluation rules make heavy use of the fact that insertion is both monotone and idempotent. Once a fact is known to be true, it can not be retracted, nor can further discoveries of the same fact alter its truth. Monotonicity is critical for forward progress under Datalog's ``open world'' model, as it allows us to safely assert the truth of a body. Meanwhile, idempotence makes it easier to reason about evaluation, as we need only guarantee that each head atom will be derived at-least-once. Unfortunately, more general aggregates like sum() are neither idempotent, nor monotone. I will introduce Hedgelog, a strict generalization of Datalog that uses general monoids as a basis for truth. I will show that this generalization remains compatible with Datalog's open world model, how it enables cleaner and more composable datalog programs, and how the underlying monoid relations open the door to interesting datastructure-level optimizations.

Bio: Oliver Kennedy is an associate professor at the University at Buffalo. He earned his PhD from Cornell University in 2011 and now leads the Online Data Interactions (ODIn) lab, which operates at the intersection of databases and programming languages. Oliver is the recipient of an NSF CAREER award, an IEEE Region 1 Technological Innovation Award, UB's Exceptional Scholar Award, and several UB SEAS teaching awards. Oliver is also one of the founding board members of Breadcrumb Analytics. Several of Oliver's papers have been invited to Best of compilations from SIGMOD and VLDB. The ODIn lab is currently exploring (i) how we can leverage database techniques like incremental view maintenance to make compilers faster, (ii) how to make it easier for data scientists to track how sources of uncertainty, ambiguity, and/or bias affect analyses, and (iii) how to streamline the interfaces --- both human and software --- between different tools for data science, like python, sql, and spreadsheets.

Location: NCS 120
https://stonybrook.zoom.us/j/99820812332?pwd=c05BSTVLNmw3L04yZjdEcG5pem1OZz09 Speaker: Alexei Koulakov of Cold Spring Harbor Laboratory Brain evolution as a machine learning problem We have entered a golden age of artificial intelligence research, driven mainly by the advances in ANNs over the last decade or so. Applications of these techniques--to machine vision, speech recognition, autonomous vehicles, machine translation and many other domains--are coming so quickly that many observers predict that the long-elusive goal of Artificial General Intelligence (AGI) is within our grasp. However, we still cannot build a machine capable of building a nest, stalking prey, or loading a dishwasher. I will describe several projects, ranging from theories of evolution of neural development to the perception of smells, in which we are attempting to understand the algorithms that the nervous system is using to solve some of these challenging problems.
Abstract: Self-supervised representation learning (SRL) has emerged as a pivotal advancement in machine learning, offering high-quality data representations without the need for labeled datasets. While SRL has demonstrated enhanced adversarial robustness compared to supervised learning, its resilience against other attack types, particularly backdoor attacks, remains an open question. Recent studies have revealed potential vulnerabilities in SRL, underscoring the necessity for a comprehensive security analysis. However, existing research often extrapolates attacks from supervised learning paradigms, neglecting the unique challenges and opportunities inherent to self-supervised mechanisms.

This thesis proposal aims to address three critical objectives in the realm of self-supervised learning: (1) exploring novel attack vectors, (2) implementing and evaluating practical attacks, and (3) developing robust countermeasures. We focus on two key SRL paradigms: Contrastive Learning and Diffusion Models. For Contrastive Learning, we synthesize existing security vulnerabilities and introduce innovative attack vectors, such as CTRL, to uncover distinctive risks. We conduct a comparative analysis of contrastive and supervised learning approaches in their defense against these threats, exploring potential safeguards and highlighting the limitations of current protective measures in self-supervised contexts. Regarding Diffusion Models, we demonstrate inherent vulnerabilities in their application to adversarial purification.

Our research aims to illuminate the unique challenges posed by emerging attack vectors in self-supervised learning, fostering technical advancements to address underlying security risks in real-world applications. By contributing to the development of more resilient and secure self-supervised representation learning systems, we seek to enhance their reliability and trustworthiness in practical scenarios. This comprehensive examination of SRL's security landscape will provide valuable insights for the broader machine-learning community and pave the way for more robust AI systems.

Join here.

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

Abstract: Theory-internal work on opacity in phonology has been focused on the challenges these interactions present for one theory (rules, constraints) versus another. But there has also been interest in studying the formal, invariant properties of opaque and other process interactions (Chandlee et al. 2018; Bakovic and Blumenfeld 2024), though these works crucially differ in their underlying assumptions. In this talk I will recontextualize Chandlee et al. (2018)'s result that opaque maps are ISL in light of Bakovic and Blumenfeld (2024)'s recent formal typology of process interactions, and this recontextualization will provide an answer to an open question about the k-value of an interaction map. I will then discuss the implications of this collective formal understanding of opacity for a recent model of lexicon and phonological grammar learning (i.e., Hua and Jardine 2021, Chandlee and Jardine to appear).


Speaker: Prof. Jane Chandlee, Associate Professor in the Department of Linguistics at Haverford College

Location: IACS Seminar room.