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
AI for Conservation: AI and Humans Combating Extinction Together by Daniel I. Rubenstein of Princeton University

ABSTRACT: The state of our planet is not good. We have lost more than 60% of the world's wildlife. Stopping the decline remains a challenge, especially since acquiring appropriate knowledge is expensive, time consuming and risky. Visual observations following the fates of a few individuals was the currency of the realm. But GPS technology and now machine learning provide a non-invasive scalable alternative. Photographs, taken by field scientists, tourists, automated cameras and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation coordinated by a non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high-resolution information databases, enabling scientific inquiry, conservation and citizen science.

BIO: Dan Rubenstein is the Class of 1877 Professor of Zoology. He is currently Director of Princeton's Environmental Studies Program and is former Chair of Princeton University's Department of Ecology and Evolutionary Biology and Director of Princeton's Program in African Studies. He is a behavioral ecologist who studies how environmental variation and individual differences shape social behavior, social structure, sex
roles and the dynamics of populations. He has special interests in all species of wild horses, zebras and asses, and has done field work on them throughout the world identifying rules governing decision-making, the emergence of complex behavioral patterns and how these understandings influence their management
and conservation. In Kenya he also works with pastoral communities to develop and assess impacts of various grazing strategies on rangeland quality, wildlife use and livelihoods. He has also developed a scout program for gathering data on Grevy's zebras and created curricular modules for local schools to raise awareness about the plight of this endangered species. He engages people as 'Citizen Scientists' and has recently extended his work to measuring the effects of environmental change, including issues pertaining to the global commons
and changes wrought by management and by global warming, on behavior.



Abstract: The current approach to materials design, driven by strategic experimentation and supported by physics-based simulation across relevant scales, has been the standard for decades. While the theoretical component in this workflow provides valuable understanding of material behavior, it often fails to deliver actionable guidance for implementation. Advances in artificial intelligence and machine learning (AI/ML), together with high-performance computing (HPC), now offer a viable pathway to close this gap and accelerate both discovery and process optimization. This presentation will outline practical approaches for integrating AI/ML with HPC-enabled, high-throughput computation to explore high-dimensional search spaces. Examples will include the development of engineering alloys for extreme environments, the use of neural networks to rapidly improve computational thermodynamic models, and vapor processing optimization for the manufacturing of ultra-high-temperature ceramics. I will highlight how scientific insight and domain expertise remain essential for translating surrogate model predictions into impactful outcomes. Finally, I will conclude with current challenges and future opportunities for AI/HPC-driven materials research.

Speaker: Dongwon Shin
This seminar will be held in person and online

Join Zoom Meeting: https://stonybrook.zoom.us/j/93730374357?pwd=YDLJ7ELqOQnTZEQhlN8Pa4TuhaiFK8.1
Abstract:
Artificial intelligence (AI)-based methods and computational materials science continue to make inroads into accelerated materials design and development. I will review Al-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. I will describe exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing, and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. Al- powered workflows help efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve forward and inverse materials design problems. A practical informatics-based design protocol involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol will be demonstrated for several energy and sustainability-related applications. Finally, I will offer an outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.

Speaker Bio:
Prof. Ramprasad is the Regents' Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. He is also the CEO and co-founder of Matmerize, Inc. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.

Location: Room 301, Engineering Building

AI3 Seminar

Meir Feder

Professor, School of Electrical Engineering
Jokel Chair in Information Theory, School of Electrical and Computer Engineering
Tel-Aviv University

Information-Theoretic Framework for Understanding Modern Machine-Learning

Abstract:

Information Theory views learning as universal prediction under log loss, characterized through regret bounds. Unlike the classical results that considered ``small'' model classes and provided uniform regret, the proposed framework provides non-uniform, model dependent bounds utilizing an effective notion of architecture-based model complexity. This complexity is defined by the probability mass or volume of the set of all models in the vicinity of the target model \theta_0, in an informational distance. This volume might be hard to evaluate, yet by local analysis it is related to spectral properties of the expected Hessian or the Fisher Information Matrix at \theta_0, leading to tractable approximations. We argue that successful architectures possess a broad complexity range, enabling learning in highly over-parameterized model classes. The framework sheds light on the role of inductive biases, the effectiveness of stochastic gradient descent (SGD) algorithm, and phenomena such as flat minima. It unifies online, batch, supervised, and generative settings, and applies across the stochastic-realizable and agnostic regimes. Moreover, it provides insights into the success of modern machine-learning architectures, such as deep neural networks and transformers, suggesting that their broad complexity range naturally arises from their layered structure. These insights open the door to the design of alternative architectures with potentially comparable or even superior performance.

Biography:

Meir Feder received the Sc.D. degree in Electrical Engineering and Ocean Engineering in 1987 from the Massachusetts Institute of Technology (MIT) and the Woods Hole Oceanographic Institution (WHOI). After being a Research Associate and a Lecturer at MIT, he joined the School of Electrical Engineering, Tel-Aviv University in 1990, where he is the Jokel Chaired Professor and the former founding head of Tel-Aviv university center for Artificial intelligence and Data science (TAD). Parallel to his academic career, he is closely involved with the high-tech industry: he founded 5 companies, among them Peach Networks (Acq: MSFT) and Amimon (Acq:LON.VTC). Recently, with his renewed interest in machine learning and AI, he co-founded Run:ai (Acq:NVDA), a virtualization, orchestration, and acceleration platform for AI infrastructure, acquired by Nvidia to support its GPU cloud operation.

Prof. Feder received several academic and professional awards including the IEEE Information Theory Society best paper award, the Padovani lectureship, the creative thinking award of the Israeli Defense Forces, and the Research Prize of the Israeli Electronic Industry, awarded by the President of Israel. For the technology he developed in Amimon, he received the 2020 Scientific and Engineering Award of the Academy of Motion Picture Arts and Sciences (OSCAR) and was announced the principal inventor of the technology that attained the 73rd Engineering Emmy Award of the Television Academy.

Location: NCS120

Zoom Like a Pro! Unlock Whiteboard, Polls, AI Companion, and more to supercharge student participation. This hands-on workshop explores innovative ways to use Zoom's built-in tools to enhance active learning activities in your classes. Learn how to utilize the Whiteboard feature to make collaborative work more engaging, use Polling and Quizzes for instant feedback, AI Companion for summary, and Breakout Sessions for group activities. Register here: https://stonybrook.zoom.us/meeting/register/tJckf--rpj4pGdRV0ItgTW8Lk7gn_RuykByO#/registration




https://stonybrook.zoom.us/j/91775729097pwd=Qlc5Nks0NmlyKzJwMjR0S0hrdVZ3QT09

Meeting ID: 917 7572 9097
Passcode: 555459


Abstract: As the saying goes, there are many ways to skin a cat.
While we don't want to go around skinning cats, the world of
optimization is rich with different problems, problem formulations,
and methods and approaches, each with different guarantees and
computational benefits. In this talk we will take a tour down the
problem of structured sparsity in sensing to see how one simple
problem can inspire a wide range of analysis and tools. First, I will
present the optimality conditions for a generalized structured sparse
problem, which can be geometrically visualized as alignment of vectors
and matrices. Then I will introduce three approximation methods for
the problem of phase retrieval, which are a twist on stochastic
gradient and coordinate descent methods. These methods leverage
fundamental numerical linear algebra concepts to give fast approximate
solutions to large-scale problems, which then after postprocessing can
produce more reliable sensing results.

Bio: Yifan Sun received her PhD in Electrical Engineering from the
University of California Los Angeles in 2015, with research focusing
on convex optimization and semidefinite programming. She was then
Technicolor Research and Innovation, focusing on machine learning and
data science applications. More recently, she completed two postdocs,
at the University of British Columbia in Vancouver, Canada and
L'Institut National de Recherche en Informatique et Automatique
(INRIA) in Paris, France.

Abstract: The advent of ChatGPT has redrawn the boundary of pedagogical discourse, where the dyadic configuration of teacher-student has, for many, become triadic -- one that includes AI as an relevant third party, not to be missed or dismissed. Within applied linguistics, AI-focused research has predominantly targeted the teaching and learning of writing (Fang & Han, 2025). The work on AI and speaking, on the other hand, has largely involved perception studies documenting its positive impact on learners' willingness to communicate (Goh & Aryadoust, 2025). In this talk, I explore the role of AI in the teaching and learning of speaking, and in particular, the development of interactional competence. Based on a corpus of learner-AI interactions, I demonstrate the ways in which ChatGPT excels and fails at acting as a useful conversation partner, with a view towards furthering our ongoing deliberation on the affordances and constraints of AI in language education.

Speaker: Hansun Zhang Waring (Teachers College, Columbia University)

Hansun Zhang Waring is Professor of Linguistics and Education at Columbia University and founder The Language and Social Interaction Working Group (LANSI). As an applied linguist and a conversation analyst, Hansun is interested in all things interaction -- (second language) pedagogical interaction, communication with the public, parent-child interaction, and human-AI interaction (HAI). Her work has appeared in leading journals in applied linguistics and discourse analysis as well as numerous book volumes, some of which she (co-)authored or co-edited. She is on the editorial boards of Chinese Language and Discourse (CLD), Classroom Discourse (CD), and International Review of Applied Linguistics (IRAL).

Location: Wang Center, Lecture Hall #1

If you need special accommodation, please contact chikako.nakamura@stonybrook.edu.