Biomedical Informatics Grand Rounds Talk

Educational objectives:
  1. Explain how generative AI can write data-analysis code from natural-language prompts
  2. Describe how virtual computational laboratories can be built to explore clinicalquestions
  3. Identify the key challenges of applying AI models to protected healthcare data

Speaker: Dr. Janos Hajagos, Ph.D Chief Data Analytics and Research Assistant Professor, Biomedical Informatics, Stony Brook University

Remote Access: Join Zoom meeting
Meeting ID: 95617197636 Passcode: 924293
Abstract: Neurosymbolic AI, as the combination of deep learning and knowledge representation/reasoning methods, is currently receiving significant attention due to its promise to both overcome apparent limitations of pure deep learning systems, and to address the knowledge acquisition bottleneck. This presentation will focus on some recent reseach advances made at Kansas State University related to Semantic Web / knowledge graphs and neurosymbolic AI, with particular emphasis on the use of symbolic AI methods for understanding hidden neuron activations, and on using large language models for knowledge graph and ontology engineering.

Short bio: Pascal Hitzler is University Distinguished Professor and endowed Lloyd T. Smith Creativity in Engineering Chair at the Department of Computer Science at Kansas State University, one of the Directors of the Institute for Digital Agriculture and Advanced Analytics (ID3A), and Director of the Center for Artificial Intelligence and Data Science (CAIDS). His research record lists over 400 publications in such diverse areas as neurosymbolic artificial intelligence, semantic web, knowledge graphs, knowledge representation and reasoning, denotational semantics, and set-theoretic topology. He was founding Editor-in-chief of the Semantic Web journal, the leading journal in the field, and is founding Editor-in-chief of the new Neurosymbolic Artificial Intelligence journal. He is co-author of the W3C Recommendation OWL 2 Primer, and of the book Foundations of Semantic Web Technologies by CRC Press, 2010, which was named as one out of seven Outstanding Academic Titles 2010 in Information and Computer Science by the American Library Association's Choice Magazine, and has translations into German and Chinese. He is a founding steering committee member of the Neural-Symbolic Learning and Reasoning Association that runs the annual International Conference on Neurosymbolic Learning and Reasoning (NeSy). For more information about him, see http://www.pascal-hitzler.de.

Location: NCS 120

Zoom: https://stonybrook.zoom.us/j/94657223635?pwd=FHtIvIupDAbQTCKzpDF3kTg6xRZOtj.1&jst=2


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The Department of AI and Society (AIS) at the University at Buffalo is hosting a two-day AI and Society Workshop focused on building AI systems by society, for society. This workshop brings together researchers and community organizers to explore how AI systems can be developed through meaningful collaboration across disciplines.

Topics include:

  • Labor and AI
  • Public services and AI
  • Community-centered AI systems
  • Intersections of humanities, social sciences, arts, and computing

The vision of UB's Department of AI and Society is to create a future where AI systems are built by society, for society. AIS centers community engagement at every stage of AI development through collaboration across disciplines and sectors. AIS was established with a $5 million grant from SUNY, and this workshop is made possible through that support.

Who Should Attend?

  • Researchers
  • Students
  • Community organizers
  • Practitioners interested in AI's societal impact

More about the event

Register here


Get hands-on with data cleaning techniques using Python and AI tools. Join SBU Libraries' Data Literacies Lead, Ahmad Pratama, to learn how to identify and rectify errors, handle missing data, and prepare your dataset for analysis. This workshop introduces you to powerful yet easy-to-use tools and techniques that make data cleaning efficient and effective, turning chaotic data into valuable insights.

Please register for the Data Cleaning with Python and AI here.
Abstract: In recent years, we have been developing generative AI methods to design increasingly complex objects. Our goal is to improve performance while ensuring that these objects remain controllable. This requires addressing several challenging problems, including:
  • Modeling composite objects in a way that preserves consistency under deformation.
  • Estimating the uncertainty of the surrogate models used to predict performance during optimization.
  • Co-designing objects for both high performance and ease of control.
In this talk, I will present our approach to these challenges and describe end-to-end design pipelines that have the potential to radically transform computer-assisted engineering.

Speaker: Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996, where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist.
His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 400 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of the IEEE journal Transactions For Pattern Analysis and Machine Intelligence. He often serves as the program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies.
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.
The Provost's Lecture Series features talks by SUNY Distinguished Academy faculty members at Stony Brook University, showcasing the outstanding research and scholarship that is taking place at our institution.

Joe Mitchell

SUNY Distinguished Professor, Applied Mathematics and Statistics
Chair, Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences

A Case for Algorithms: A Computational Geometer's Perspective

Algorithms are all around us in every smart device and technology that has consumed our daily lives. As a computational geometer, I study algorithms to solve problems that involve a geometric perspective on data. I have observed that practically every technology and field of study has a need for effective algorithms involving geometric data. I reflect on some favorite algorithmic problems that are easy to visualize, but challenging to solve, and argue that the formal study of algorithms remains essential in the age of AI.

Reception to follow immediately after the talks.

Register here.