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 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.
Topic: AI Seminar: Owen Rambow
Time: Mar 17, 2021 10:00 AM Eastern Time (US and Canada)
Join Zoom Meeting
https://stonybrook.zoom.us/j/93614644178?pwd=MzJtVDJYYmU5T1dtMzJiUFMxb0x4dz09
Meeting ID: 936 1464 4178. Passcode: 965936
Natural Language Understanding and Semantic Parsing
(Partly joint work with former colleagues at Elemental Cognition)
Semantic parsing refers to the task of determining the propositional content of language: who did what to whom. It is part of the larger task of natural language understanding (NLU). I will start out by discussing what full NLU means, and argue that we are still far away, as a field, from solving full NLU, or even from knowing how to evaluate it.
In the second part of the talk, I will situate semantic parsing in the context of several other NLU subtasks. Typically, the target representation of semantic parsing uses an ontology (such as PropBank or FrameNet). Semantic parsing includes the subtasks of word sense disambiguation, argument detection, and argument role labeling. I will discuss choices among possible target ontologies. I will justify why we created a new ontology, Hector, based on FrameNet and the lexical resource NOAD, and explain some of its characteristics.
In the third part of the talk, I will present experiments we performed using transformer models. We obtain best results using a two-phase model, in which we first choose the frame, and then, given the frame, choose the arguments. We encode the problem for both tasks using indices in the sentence. While we develop the parser for our new ontology Hector, this approach also beats the state of the art for FrameNet and PropBank parsing.Biography: I am a professor in the Department of Linguistics at Stony Brook University with a joint appointment in IACS.
Time: Mar 17, 2021 10:00 AM Eastern Time (US and Canada)
Join Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 936 1464 4178. Passcode: 965936
Natural Language Understanding and Semantic Parsing
(Partly joint work with former colleagues at Elemental Cognition)
Semantic parsing refers to the task of determining the propositional content of language: who did what to whom. It is part of the larger task of natural language understanding (NLU). I will start out by discussing what full NLU means, and argue that we are still far away, as a field, from solving full NLU, or even from knowing how to evaluate it.
In the second part of the talk, I will situate semantic parsing in the context of several other NLU subtasks. Typically, the target representation of semantic parsing uses an ontology (such as PropBank or FrameNet). Semantic parsing includes the subtasks of word sense disambiguation, argument detection, and argument role labeling. I will discuss choices among possible target ontologies. I will justify why we created a new ontology, Hector, based on FrameNet and the lexical resource NOAD, and explain some of its characteristics.
In the third part of the talk, I will present experiments we performed using transformer models. We obtain best results using a two-phase model, in which we first choose the frame, and then, given the frame, choose the arguments. We encode the problem for both tasks using indices in the sentence. While we develop the parser for our new ontology Hector, this approach also beats the state of the art for FrameNet and PropBank parsing.Biography: I am a professor in the Department of Linguistics at Stony Brook University with a joint appointment in IACS.
Until recently, I was a research scientist at Elemental Cognition. Elemental Cognition is working on deep natural language understanding.
I got my PhD with Aravind Joshi at the University of Pennsylvania in 1994. I have worked at CoGenTex, and at AT&T Labs -- Research, and for many years I was a research scientist at Columbia University in the Center for Computational Learning Systems.
Engineering Job & Internship Fair hosted by the Career Center
Learn more and RSVP on Handshake.
Does This Vehicle Belong to You? Processing the Language of Policing for Improving Police-Community Relations
Dan Jurasky (Stanford)
A talk by Jerome Zhengrong Liang entitled, Machine Learning from Original Images to Texture Patterns: A Paradigm Shift from Non-Medical Application to Medical Diagnosis.
Abstract: Artificial intelligence (AI) research for medical diagnosis started soon after human began to use computer, initially called artificial neural network (ANN) and now convolutional neural network (CNN). ANN has been mainly explored to classify the experts' handcrafted features from the original (or raw) images, while CNN has been mainly explored directly on the raw images for both tasks of extracting abstract features and classifying the features.
Experimental evidences have been shown that CNN can be trained by a large number of the raw images with experts' scores (or labels) to match or even surpass the experts' performance for both non-medical and medical diagnosis applications. However, the performances of the CNN models as well as the experts on medical diagnosis dropped dramatically when the labels of the raw images were replaced by the corresponding medical pathological reports.
Accumulated medical knowledge reveals that the lesion heterogeneity is a footprint of lesion evolution and ecology, and the heterogeneity is an indicator of lesion progress and response to medical intervention. The heterogeneity can be reflected by the image contrast distribution (or texture patterns) across the lesion volume. Image textures have been shown as an effective descriptor of the lesion heterogeneity for computer-aided diagnosis.
Can we map the raw images into texture patterns (or images) and train CNN to learn from the texture images? This question is the central theme of this presentation with application to CT Colonography or virtual colonoscopy, a game from AlphaGo to PolypGo.
Bio: Jerome Zhengrong Liang, PhD, IEEE Fellow
Imaging Research and Informatics Laboratory
Department of Radiology, Stony Brook University
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
Are you interested in understanding the challenges that lie ahead as Artificial Intelligence (AI) systems become increasingly autonomous, dynamically acquire information, and adapt behaviors?
Join us for an exciting afternoon of talks by visionaries and leaders from industry, government, and academia as we kickoff a three-part Trusted AI Challenge Series designed to Build the Vision - Formalize Challenges - Advance the Art of next generation of AI systems.
The Air Force Research Laboratory Information Directorate, The State University of New York, Innovare Advancement Center, NYSTEC, and Griffiss Institute invite you to join us for this half-day virtual event!
WHEN: Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT
Hosted by Innovare Advancement Center, this webinar is the first of a three-part series designed to cultivate, define and fund creative solutions to a set of challenge problems in trustworthy AI with a particular focus on dynamic, autonomous systems that learn and adapt behaviors.
Keynote speakers include Dr. David Goldstein of Space X; Dr. Scott Hubbard of Stanford University; Dr. Pramod Khargonekar of UC Irvine, and more!
This event is designed for academic and government researchers, university students, and small businesses.
Would you like to understand some of the most formidable technical challenges in future autonomous systems? Would you like to sponsor some of the brightest minds in AI to work on problems of interest to you? Would you like to learn more about AI in real systems?
If so, Save the Date! Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT.
Please see additional information on the three-part series here. Registration details to follow!
Stay tuned: https://www.innovare.org/news-events
Join us for an exciting afternoon of talks by visionaries and leaders from industry, government, and academia as we kickoff a three-part Trusted AI Challenge Series designed to Build the Vision - Formalize Challenges - Advance the Art of next generation of AI systems.
The Air Force Research Laboratory Information Directorate, The State University of New York, Innovare Advancement Center, NYSTEC, and Griffiss Institute invite you to join us for this half-day virtual event!
WHEN: Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT
Hosted by Innovare Advancement Center, this webinar is the first of a three-part series designed to cultivate, define and fund creative solutions to a set of challenge problems in trustworthy AI with a particular focus on dynamic, autonomous systems that learn and adapt behaviors.
Keynote speakers include Dr. David Goldstein of Space X; Dr. Scott Hubbard of Stanford University; Dr. Pramod Khargonekar of UC Irvine, and more!
This event is designed for academic and government researchers, university students, and small businesses.
Would you like to understand some of the most formidable technical challenges in future autonomous systems? Would you like to sponsor some of the brightest minds in AI to work on problems of interest to you? Would you like to learn more about AI in real systems?
If so, Save the Date! Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT.
Please see additional information on the three-part series here. Registration details to follow!
Stay tuned: https://www.innovare.org/news-
STEM Speaker Series: Steve Skiena - Representing Knowledge through Word and Graph Embeddings
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.
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.
#1 How to train your Scientific Chatbot by Alexandr Prozorov, Post-Doctoral Research Associate
Abstract: RHIC is closing its 25-year run with ~1 EB of data and decades of hard-won know-how that risk drifting into obscurity. The RHIC Data & Analysis Preservation Plan (DAPP) pilots an AI assistant that lets physicists talk to RHIC in natural language--searching internal notes, code, workflows, and docs, and pointing to runnable, containerized analyses. Built on Retrieval-Augmented Generation(RAG) with a Model Context Protocol orchestration layer, the system indexes heterogeneous, experiment-specific content and enforces role-aware access
for public vs. collaboration-restricted materials. Takeaway: domain-adapted AI can turn a legacy exabyte into reproducible answers, training assets, and new discovery paths.
Biography: Alexandr Prozorov is a postdoc from Czech Technical University in Prague working in STAR experiment. Fascinated by AI
#2 Quantum AI: Atoms, Cavities and Learning by Raman Kumar, Post-Doctoral Research Associate, Instrumentation Department
Abstract: The Instrumentation Department (IO) in the Discovery Technologies directorate at BNL is engaged in exploring various aspects of quantum systems research. One of the main goals of our group's effort is in developing neutral atom-cavity array platforms for remote entanglement generation and distributed quantum processing. This platform promises to herald truly scalable quantum computing systems and open new paradigms for networking and sensing. In this talk, I will explain our group's research and the role AI is playing in unlocking new insights with two examples. The first application of AI is in fabrication process prediction of micro-cavity structures. The second application revolves around role of AI in quantum error detection and correction in modern quantum computing systems.
Biography: Dr. Raman Kumar is a postdoctoral research associate in the IO department at BNL working with Dr./Prof. Sebastian Will (Columbia U.). Kumar obtained his Ph.D. degree in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign. Prior to joining BNL in Nov 2024, Kumar worked as a postdoc at the City College in New York working on topological photonic quantum sensing using NV centers in diamond. Kumar and Will combined have an extremely wide moat and expertise in a variety of different areas which include Ultra cold atoms and molecules, quantum optics, quantum condensed matter, nanofabrication, semiconductor devices and advanced electromagnetics. Their areas of research interest include scalable quantum computing, communications and sensing, all enabled by AI.
Location: CDS, Bldg. 725, Training Room
Join ZoomGov Meeting https://bnl.zoomgov.com/j/1607892208?pwd=MSjxN5btSeToZsQMwEQzCCbBo5h58V.1
Meeting ID: 160 789 2208
Passcode: 753871
Matthew Salzano (Stony Brook), AI and DEIA: Getting at the Roots
Link to the talk (no pre-registration required this time): https://stonybrook.
Abstract: Conversations about AI and DEIA (Diversity, Equity, Inclusion, and Access) often unwittingly assume that social problems can and should have technical fixes. Left unaddressed, scholars, advocates, and technologists inevitably miss important consequences in our proposed solutions, and focus on surface-level problems rather than addressing the root causes of inequity. Drawing from scholarship in communication, rhetoric, and critical digital studies, this talk explains how we are often trimming branches when we need to pull out roots -- and introduces new terms and questions that can help reorient our conversations about AI and DEIA.
Speaker Bio: Matthew Salzano, Ph.D., is a communication scholar researching new media technologies, user practices, and cultural trends that threaten to limit possibilities for diverse engagement in public argument, debate, and protest. His scholarship has appeared in journals like The Quarterly Journal of Speech, Critical Studies in Media Communication, and Women's Studies in Communication, and his research on DEIA, AI, and advocacy communications has been funded by the Waterhouse Family Institute at Villanova University. He is currently an Inclusion, Diversity, Equity, and Access fellow in Ethical AI at Stony Brook University's School of Communication and Journalism and Alan Alda Center for Communicating Science.