18th Annual Engineering Ball
Flowerfield, St. James, NY
Thursday April, 2nd, 7:00 to 10:00 pm
Pick up your tickets in 231 Engineering (Monday - Friday, 10:00 am to 4 pm)
Presenting Partner: L3Harris
LIN 627: Pragmatics Seminar
Th 3.30-6.20, Old Computer Science Building - CS2311, Zoom option
Instructor: Owen Rambow
Pragmatics is the study of how context (linguistic and non-linguistic) affects language use: the utterer (speaker or writer) chooses among many linguistic means licensed by the grammar of the language. Core explanatory notions in pragmatics are the common ground between utterer and addressee, and the use of speech acts to change the common ground.
This seminar will investigate the proposal of Grice, which has been called intentionalism. According to Grice, an utterer forms a communicative intention, performs a speech act to achieve the intention, and the speech act succeeds when the addressee recognizes the intention. The result is a change in common ground. With intentionalism, the notion of cognitive state becomes central to pragmatic theory. We will investigate Gricean intentionalism, including formalizations such as Stalnaker's, empirical evidence from cognitive science, and alternate pragmatic theories. In the second part of the course, we will then examine specific issues in pragmatics in light of the foundational theories we have discussed, including information structure, specific linguistic issues such as discourse particles, intonation, and politeness, and neuro-divergent communication. We will pay attention to empirical evidence throughout the course.
You can enroll for 0-3 credits, and further topics of interest may be included.
List of papers: https://protect.checkpoint.com
Th 3.30-6.20, Old Computer Science Building - CS2311, Zoom option
Instructor: Owen Rambow
Pragmatics is the study of how context (linguistic and non-linguistic) affects language use: the utterer (speaker or writer) chooses among many linguistic means licensed by the grammar of the language. Core explanatory notions in pragmatics are the common ground between utterer and addressee, and the use of speech acts to change the common ground.
This seminar will investigate the proposal of Grice, which has been called intentionalism. According to Grice, an utterer forms a communicative intention, performs a speech act to achieve the intention, and the speech act succeeds when the addressee recognizes the intention. The result is a change in common ground. With intentionalism, the notion of cognitive state becomes central to pragmatic theory. We will investigate Gricean intentionalism, including formalizations such as Stalnaker's, empirical evidence from cognitive science, and alternate pragmatic theories. In the second part of the course, we will then examine specific issues in pragmatics in light of the foundational theories we have discussed, including information structure, specific linguistic issues such as discourse particles, intonation, and politeness, and neuro-divergent communication. We will pay attention to empirical evidence throughout the course.
You can enroll for 0-3 credits, and further topics of interest may be included.
List of papers: https://protect.checkpoint.com
Learn how these two AI tools will help you this year.
AI has been all over, but figuring out the tools that we may use is critical. Background remover of images and a replacement for Google Search may disrupt the industry this year. Learn and refresh your knowledge about these tools.
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.
Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
Title: Building foundation models for scientific data Seminar
Speaker: Ruben Ohana, Ph.D. and Michael McCabe, Ph.D - Flatiron Institute, New York
Abstract: Foundation models are very large architectures trained on large-scale datasets and can be used to transfer knowledge from a domain to another. Scientific data, particularly numerical simulations of partial differential equations (PDEs), presents unique challenges due to its complexity and the need for domain expertise to assess prediction quality, complicating the building of the first foundation models in this field. In this talk, we will develop our approach of building foundation models for scientific data, highlighting the requirements and expectations for achieving meaningful results. We will also introduce The Well, a comprehensive collection of datasets encompassing multi-scale simulations of fluid dynamics, astrophysics, and biological systems. The Well serves as a foundation for developing models that generalize across diverse physical phenomena, aiming to accelerate scientific discovery through large-scale learning.
Join Zoom Meeting: https://bnl.zoomgov.com/j/1606898802?pwd=GbbPiLGHlEokDskxjeFheMFWfuboxO.1
Meeting ID: 160 689 8802
Passcode: 281575
Speaker: Ruben Ohana, Ph.D. and Michael McCabe, Ph.D - Flatiron Institute, New York
Abstract: Foundation models are very large architectures trained on large-scale datasets and can be used to transfer knowledge from a domain to another. Scientific data, particularly numerical simulations of partial differential equations (PDEs), presents unique challenges due to its complexity and the need for domain expertise to assess prediction quality, complicating the building of the first foundation models in this field. In this talk, we will develop our approach of building foundation models for scientific data, highlighting the requirements and expectations for achieving meaningful results. We will also introduce The Well, a comprehensive collection of datasets encompassing multi-scale simulations of fluid dynamics, astrophysics, and biological systems. The Well serves as a foundation for developing models that generalize across diverse physical phenomena, aiming to accelerate scientific discovery through large-scale learning.
Join Zoom Meeting: https://bnl.zoomgov.com/j/1606898802?pwd=GbbPiLGHlEokDskxjeFheMFWfuboxO.1
Meeting ID: 160 689 8802
Passcode: 281575
Abstract: The landscape of machine learning evolves rapidly and the complexity of the networks and their architectures defies easy comprehension. AI is touted as the next scientific revolution by allowing the processing and pattern-finding in increasingly massive data sets. One potential end results could be AI enhanced measurement technologies, but what does that mean? This talk will give examples of how classical tools indicate the technical obstacles to this vision in terms of understanding training processes, model comparisons, and feature embeddings. While the results in this talk are largely empirical, they point to interesting directions for (infomation?) theoretical investigation.
Bio: Anand D. Sarwate is an Associate Professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in math and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security.
Location: Light Engineering 250
ICB&DD 19th Annual Symposium
Iwao Ojima, Director, ICB&DD
Ivet Bahar Chair, Organizing Committee
Dima KozakovCo-Chair, OrganizingCommittee
There will be poster sessions on projects conducted in the ICB&DD member's laboratories aswell as other laboratories in the area. Awards will be given to the best three posters.
Please see the link for the registration and poster sessions in:
https://www.stonybrook.edu/commcms/icbdd/https://forms.gle/
Iwao Ojima, Director, ICB&DD
Ivet Bahar Chair, Organizing Committee
Dima KozakovCo-Chair, OrganizingCommittee
There will be poster sessions on projects conducted in the ICB&DD member's laboratories aswell as other laboratories in the area. Awards will be given to the best three posters.
Please see the link for the registration and poster sessions in:
https://www.stonybrook.edu/commcms/icbdd/https://forms.gle/
The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery
Guest speaker Doctor Ozanan Meireles, the Director of the Surgical AI and Innovation Lab at Massachusetts General Hospital and a faculty member at Harvard Medical School, presents The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery.
Objectives:
* Become familiar with the subfields of AI used in surgery
* Understand the importance of a potential paradigm shift in surgical practice, training, and continue medical development
* The importance of data acquisition, sharing and ownership, and development of machine learning algorithms
Abstract: The remarkable success of large foundational models, such as LLMs and diffusion models, is built on their learning over vast amounts of static data from the Internet. However, human learning and problem-solving are fundamentally interactive processes--humans learn by engaging with their environment, tools, search engine, and feedback loops, iteratively refining their understanding and decisions. This gap between the interactivity of human learning and the static nature of model training raises a critical question: how can we imbue foundational models with the capacity for meaningful interaction?
In this talk, I will explore methods to enhance foundational models by incorporating interaction with the external environment. I will discuss strategies such as leveraging external tools, compilers, function calls to provide dynamic feedback to enhance foundation models. By drawing inspiration from human's interactive learning processes, I demonstrate how interaction-driven learning can lead to models that are not only more accurate but also more adaptable to real-world applications.
This work bridges the gap between static training paradigms and the dynamic, iterative nature of human intelligence, paving the way for a new generation of interactive AI systems.
Bio: Wenhu Chen has been an assistant professor at the Computer Science Department in University of Waterloo and Vector Institute since 2022. He obtained the Canada CIFAR AI Chair Award in 2022 and CIFAR Catalyst Award in 2024. He has worked for Google Deepmind as a part-time research scientist since 2021. Before that, he obtained his PhD from the University of California, Santa Barbara under the supervision of William Wang and Xifeng Yan. His research interest lies in natural language processing, deep learning and multimodal learning. He aims to design models to handle complex reasoning scenarios like math problem-solving, structure knowledge grounding, etc. He is also interested in building more powerful multimodal models to bridge different modalities. He received the Area Chair Award in AACL 2023, the Best Paper Honorable Mention in WACV 2021, the Best Paper Finalist in CVPR 2024, and the UCSB CS Outstanding Dissertation Award in 2021.