Join us for presentations, a keynote speaker, a Q&A session, and a chance to network with faculty and industry professionals

Hosted by College of Engineering and Applied Sciences, Long Island Manufacturing Extension Partnership (LIMEP), and Center of Excellence Wireless and Information Technology (CEWIT)

Register here

Location: Stony Brook University, CEWIT Room 152

University Libraries Presents: Do you want more control over when to use AI features in your Google apps and when browsing online? Are you tired of Gmail trying to finish your sentences for you? Drop in to this brief tutorial, which will walk through the settings you can turn on and off in various Google products to keep your agency where you want it.

Register here for the Zoom session.

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.
University Libraries Presents:
Join librarian Christine Fena for an interactive workshop that invites you to explore AI tools first hand, not just as users, but as critical investigators.
Through playful experimentation and collaborative discovery, you'll uncover inherent biases, probe algorithmic flaws, and gain a deeper understanding of AI's limitations and societal impacts.

RSVP on SBEngaged

Location: Melville Library, Central Reading Room, Lab B
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.


Date of Event

Joel H. Saltz, MD, PhD
SUNY Distinguished Professor Cherith Professor and Founding Chair
Department of Biomedical Informatics
Stony Brook University

Apostolos K. Tassiopoulos, MD, FACS
Professor of surgery and vice chair for quality and outcomes Chief of the Division of Vascular and Endovascular Surgery
Director of the Stony Brook Vascular Center Stony Brook Medicine

Title: Clinical applications of artificial intelligence to improve diagnosis and risk stratification for patients with aortic aneurysms

Time: Wednesday, Feb 17, 2021 3 pm - 4 pm

Join Zoom Meeting
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISj...
Meeting ID: 956 1719 7636 Passcode: 924293

Abstract:

It is known that models like large language models (LLMs) can often suggest colloquial plans given verbal descriptions of tasks, yet they are unable to reliably provide executable and verifiable plans given formally specified environments. In this talk, I will discuss a strand of efforts to have LLMs generate accurate and explainable plans in textual simulations. Instead of directly generating the plan or actions, LLMs are prompted to generate Planning Domain Definition Language (PDDL) that specifies the environment (domain file) and the task (problem file), which can then be deterministically solved with an off-the-shelf planner. In a 3-phase study, my collaborators and I first observed that it is possible but very challenging for LLMs to generate long-form code such as PDDL domain and problem files given textual specifications. Next, we devise methodologies for LLMs to iteratively generate and refine problem files while exploring a partially-observed, simulated, textual environment. Finally, we show that domain files are even more difficult to generate correctly, even on well-established planning tasks such as BlocksWorld. Finally, I will discuss ongoing efforts to improve said ability of structured generation and promising frontiers to explore.

Bio:
Li Harry Zhang is an assistant professor at Drexel University, focusing on Natural Language Processing (NLP) and artificial intelligence (AI). He obtained his PhD degree from the University of Pennsylvania advised by Prof. Chris Callison-Burch. Prior, he obtained his Bachelor's degree at the University of Michigan mentored by Prof. Rada Mihalcea and Prof. Dragomir Radev. His current research uses large language models (LLMs) to reason and plan via symbolic and structured representations. He has published more than 20 peer-reviewed papers in NLP and AI conferences, such as ACL, EMNLP, and AACL, that have been cited more than 1,000 times. He also consistently serves as Area Chair, Session Chair, and reviewer in those venues. Being a musician, producer, and content creator having over 50,000 subscribers, he is also passionate in the research of AI music and creativity.

Visual Analytics and Machine Learning for Biomedical Imaging Diagnosis

 

Arie Kaufman

 

We present an integrated approach using visual analytics and machine learning (ML) to diagnose abnormalities in 3D radiological imaging and biological microscopes. The primary example will involve 3D virtual pancreatography (VP), a novel visualization-ML procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes an ML-based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, an ML-based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists. Other applications include virtual colonoscopy, COVID-19, pathology, brain neurites, etc.


Biography: Arie Kaufman is Distinguished Professor and formerChair of the Department of Computer Science at Stony Brook University, where he is also Director of the Center for Visual Computing (CVC), and Chief Scientist at the Center of Excellence in Wireless and Information Technology (CEWIT). 

He received his PhD in Computer Science at Ben-Gurion University of the Negev in 1977.   He is known for his work in visualization, graphics, virtual reality, user interfaces, multimedia, and their applications, especially in bio-medicine. He is especially well known for his work on the 3-dimensional virtual colonoscopy, a revolutionary low-risk technique for colon cancer screening, and for pioneering the use of Graphics Processing Units (GPUs) and GPU-clusters. In 2012, he presided over the development and opening of the Reality Deck, the largest virtual reality display in the world, at Stony Brook University.

Kaufman was the founding Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG), co-founded the IEEE Visualization Conference and Volume Graphics series, and is currently the director of IEEE Computer Society Technical Committee on Visualization and Graphics. He is an IEEE Fellow, ACM Fellow, winner of many awards, including the IEEE Visualization Career Award, and member of the European Academy of Sciences.



Steven Skiena is inviting you to a scheduled Zoom meeting.

Topic: AI Seminar: Arie Kaufman
Time: Apr 21, 2021 10:00 AM Eastern Time (US and Canada)

Join Zoom Meeting
https://stonybrook.zoom.us/j/96017498640?pwd=SE0rdHB6ZVlCM2ZpY2RnRUxyVnR3Zz09