AI3, SBU Libraries and IACS present
at International Love Data Week
sponsored by The Office of the Provost and
Educational and Institutional Effectiveness (EIE)

Special Talk and Panel Discussion

How I Learned to Stop Worrying and Love AI (For Now)

with Paul Fain from The Job and Work Shift

A reporter's take on what we know--and what we don't know--about AI's emerging impacts on the labor market. The discussion will include the latest research from economists and the AI labs themselves about how workers are using AI, and current thinking among experts on how the tech's rapid deployment will play out across job roles, industries, and regions.

Panel discussion to follow with:

  • Lav Varshney, Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute
  • Nicholas Johnson, Director of AI, SBU Libraries
  • Marianna Savoca, Associate Vice President for Career Readiness and Experiential Education
Paul Fain is co-founder of Work Shift, editor of the must-read newsletter, The Job, and host of The Cusp podcast. A veteran higher education reporter, Paul is perhaps the nation's top journalist focused on connections between education and work. He started Work Shift after a decade as a senior reporter and then news editor at Inside Higher Ed, where he led the outlet's coverage of low-income and first-generation students, college completion, community colleges, federal policy, and emerging models of higher education. He also was the founding host of the successful podcast, The Key with Inside Higher Ed, and has contributed chapters for books on innovation in higher education, published by the Harvard University Press and the Stanford University Press. Earlier in his career, Paul was a senior reporter at The Chronicle of Higher Education.

Limited Seats!

Registration is required.
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.
Title: Cyberinfrastructure for forward prediction and inversion estimation with uncertainty quantification

Seminar Speaker: Dr. Mengyang Gu, Assistant Professor, Department of Statistics and Applied Probability, University of California, Santa Barbara

Abstract: In this talk, we introduce four useful tools for forward prediction and inversion estimation. The first tool is the parallel partial Gaussian process surrogate model for emulating expensive computer simulations with massive coordinates. The tool is implemented in the RobustGaSP package available in R, MATLAB, and Python, for predicting both scalar- and vector-valued outputs with uncertainty assessment. The second tool is implemented in the RobustCalibration package, which handles Bayesian data inversion or model calibration by one or multiple types of experimental observations. A unique feature of the package is the inclusion of fast surrogate models of both scalar- and vector-valued computer simulations that bypass the expensive simulation in one line of code. The third tool is implemented in the AIUQ package, available in both R and MATLAB. In this approach, we show that differential dynamic microscopy, a scattering-based analysis tool that extracts dynamical information from microscopy videos, is equivalent to fitting the temporal auto-covariance in Fourier space, based on a latent factor model we construct. We develop a more efficient estimator and reduce the computational cost to pseudolinear order with respect to the number of observations without approximation, by utilizing the generalized Schur algorithm for the Toeplitz covariance. In the last tool, we developed a new method called the inverse Kalman filter, which enables fast matrix-vector multiplication between a covariance matrix from a dynamic linear model and any real-valued vector with a linear computational cost. These new approaches outline a wide range of applications that include emulating expensive simulation at molecular-, meso- and macro-scales, active learning with error control, nonparametric estimation of particle interaction functions, and data inversion from microscopy and velocity fields.

Join Zoom Meeting: https://bnl.zoomgov.com/j/1606285496?pwd=2yJYSG6lx8gMPiibzgAIBQtKHIjuHV.1
Meeting ID: 160 628 5496
Passcode: 472506
What Does Learning Mean? presented by Jeffrey Heinz

ABSTRACT
When we develop learning algorithms, what computational problems are we solving? In this talk, I discuss different answers that have been proposed for this question, and discuss some of the consequences for machine learning and artificial intelligence. The main lessons I offer are that (1) feasible solutions to learning problems require careful consideration of a target class C of functions, (2) that such a class C cannot include all functions, or even all computable functions, and so many logically possible functions must be outside of C and (3) class C must have significant structure which the solutions take advantage of. These main ideas are motivated and illustrated from modeling language acquisition and the related problem of grammatical inference from example sequences belonging to formal languages.

The University's Main Commencement Ceremony will take place on Friday, May 23, 2025 at 11 am at Kenneth P. LaValle Stadium. Gates open at 10 am.

All guests need a valid ticket to enter LaValle Stadium - no exceptions. Children age 1 and older require a ticket. Seating is first-come, first-served.

Register here.



Time:
Sep 7, Tue, 11:00am EDT

Place:
NCS 220 or on Zoom (info below)

Title: Data-Driven Document Unwarping


Abstract:
Capturing document images is a common way to digitize and record physical documents due to the ubiquitousness of mobile cameras. To make text recognition easier, it is often desirable to digitally flatten a document image when the physical document sheet is folded or curved. However, unwarping a document from a single image in natural scenes is very challenging due to the complexity of document sheet deformation, document texture, and environmental conditions. Previous model-driven approaches struggle with inefficiency and limited generalizability. In this thesis, I investigate several data-driven approaches to tackle the document unwarping problem.

Data acquisition is the central challenge in data-driven methods. I first design an efficient data synthesis pipeline based on 2D image warping and train DocUNet, the pioneering data-driven document unwarping model, on the synthetic data. A benchmark dataset is also created to facilitate comprehensive evaluation and comparison. To improve the unwarping performance by training on more realistic data, I introduce the Doc3D dataset and DewarpNet. Supervised by 3D shape ground truth in Doc3D, DewarpNet is significantly better than DocUNet. DocUNet and DewarpNet depend on the synthetic data for the ground truth deformation annotation. To exploit the real-world images, I propose PaperEdge, a weakly supervised model trained with in-the-wild document images with easy-to-obtain boundary information. PaperEdge surpasses DewarpNet by utilizing both the synthetic data and weakly annotated real data in the Document In the Wild (DIW) dataset. Finally, I propose directly predicting the $uv$ parameterized 3D mesh of the document with 3D constraints and using the accessible 3D presentations like depth maps as training targets. Predicting the 3D mesh of the document solves the unwarping task and also benefits VR/AR applications.

Join Zoom Meeting
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Meeting ID: 964 4059 2912
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Abstract: Foundation models brought a paradigm shift on representation learning and the deep learning community. In my talk, I will examine the role of foundation models in medical imaging, focusing on their potential to unify diverse tasks through large-scale, generalist architectures. While these models achieve strong performance, their deployment in healthcare raises challenges related to data limitations, privacy, validation, and trust. We will also discuss domain-specific models for imaging, along with efficient adaptation techniques to adapt such models on domains that they have not been trained on. The presentation will also address key issues of reliability and interpretability, highlighting approaches like conformal prediction and counterfactual intervention to improve uncertainty estimation and model transparency. Overall, the talk will emphasize that despite their promise, foundation models require robust evaluation and trustworthy design to ensure safe and effective use in clinical settings.

Speaker: Maria Vakalopoulou is an assistant professor (MCF) in applied mathematics at CentraleSupelec, University Paris Saclay in France and the group leader of the biomathematics group of MICS Laboratory focusing on mathematical modeling in Life Sciences. She is affliated with Inria Saclay in France and Archimedes Unit in Greece. Her main research interest include the development of computational methods for image perception focusing on earth observation and medical applications. Before that, she was a postdoctoral student at CentraleSupelec, where she worked with Nikos Paragios. She completed her PhD at the Remote Sensing Laboratory at the School of Rural, Surveying and Geo-Informatics Engineering of the National Technical University of Athens under the supervision of Konstantinos Karantzalos.

Location: NCS 220

Join us to share your thoughts about teaching, learning, and AI!

The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.

This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.

We'll be exploring critical questions like:

  • In the age of AI, what are the opportunities you see for enriching the classroom and curriculum? How can it enhance student learning or your professional practice?

  • What are the most significant challenges and concerns that AI raises for you regarding academics, student integrity, or your workload?

  • What resources (tools, training, technical support, policy guidance, etc.) do you need to feel confident and successful in the age of AI?

Dates/Times:

  • Tuesday, 2/3 at 2pm

  • Friday, 2/6 at 9:30am

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

We understand schedules are tight. If you cannot attend the live discussion, you can still share your thoughts! Join our AI Zoom Room to share your thoughts via video recording or email rose.tirotta-esposito@stonybrook.edu with your comments and ideas.

Videos will not be shared publicly and comments will only be shared in aggregate.

Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!

  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)

  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)

  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)

  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)

  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)