• CEWIT's 6th annual hackathon sponsored by Major League Hacking, Hack@CEWIT2022, is taking place virtually on February 18-20, 2022. This year's theme is Hacking Into the Metaverse and will focus on NFT's, Blockchain, Crypto, and the Metaverse. To find out more about the event, mentoring, sponsoring, or to register, visit:

  • https://www.cewit.org/programs/events/hack.php

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Abstract Over the last decade, artificial neural networks have undergone a revolution, catalyzed by better
supervised learning algorithms. However, in stark contrast to young animals (including humans), training such
networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead
mainly on unsupervised learning. The reason is that most animal behavior is not the result of clever learning
algorithms--supervised or unsupervised-- but is encoded in the genome. Specifically, animals are born with
highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is
far too complex to be specified explicitly in the genome, it must be compressed through a genomic
bottleneck. I will describe results showing how the genomic bottleneck algorithm can lead to dramatic
compression of networks and better generalization, particularly for rapid transfer in supervised and
reinforcement learning.

Anthony Zador is professor of neuroscience at CSHL.

Abstract: Generative visual models like Stable Diffusion and Sora generate photorealistic images and videos that are nearly indistinguishable from real ones to a naive observer. However, their grasp of the physical world remains an open question: Do they understand 3D geometry, light, and object interactions, or are they mere pixel parrots of their training data? Through systematic probing, I will demonstrate that these models surprisingly learn fundamental scene properties--intrinsic images such as surface normals, depth, albedo, and shading (à la Barrow & Tenenbaum, 1978)--without explicit supervision, which enables applications like image relighting. But I will also show that this knowledge is insufficient. Careful analysis reveals unexpected failures: inconsistent shadows, multiple vanishing points, and scenes that defy basic physics. All these findings suggest these models excel at local texture synthesis but struggle with global reasoning: a crucial gap between imitation and true understanding. I will then conclude by outlining a path toward generative world models that emulate global and counterfactual reasoning, causality, and physics.

Bio: Anand Bhattad is a Research Assistant Professor at the Toyota Technological Institute at Chicago. He earned his PhD from the University of Illinois Urbana-Champaign in 2024 under the mentorship of David Forsyth. His research interests lie at the intersection of computer vision and computer graphics, with a current focus on understanding the knowledge encoded in generative models. Anand has received Outstanding Reviewer honors at ICCV 2023 and CVPR 2021, and his CVPR 2022 paper was nominated for a Best Paper Award. He actively contributes to the research community by leading workshops at CVPR and ECCV, including Scholars and Big Models: How Can Academics Adapt? (CVPR 2023), CV 20/20: A Retrospective Vision (CVPR 2024), Knowledge in Generative Models (ECCV 2024), and How to Stand Out in the Crowd? (CVPR 2025). For more details, visit https://anandbhattad.github.io/



Topic: AI Seminar: Stanley Bak
Time: Monday Nov 1, 2021 12:00 PM Eastern Time (US and Canada)

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Abstract: The field of formal verification has traditionally looked at proving properties about finite state machines or software programs. The surge in deep learning has been accompanied by a surge of progress in trying to apply mathematical and algorithmic techniques to prove things about the function being computed by a neural network.

This talk formalizes the neural network verification problem and describes technical methods for neural network verification based on reachability analysis. Improvements to analysis efficiency will be given, as well as research directions for further exploration. We also include an objective comparison performed this last summer trying to evaluate the best existing verification methods in terms of speed and network size. The competition was performed on common hardware and involved the participation of twelve international teams (the tool authors) on a common set of benchmarks. 

Biography: Stanley Bak is an assistant professor in the Department of Computer Science at Stony Brook University investigating the verification of autonomy, cyber-physical systems, and neural networks. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems.
Stanley Bak received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He helped run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performed teaching at Georgetown University before joining Stony Brook University as an assistant professor in Fall 2020.
AI Institute Seminar Title: A Geometric Understanding of Deep Learning Abstract: This work introduces an optimal transportation (OT) view of generative adversarial networks (GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold distribution principle: the distribution of a class of data is close to a low-dimensional manifold. GANs mainly accomplish two tasks: manifold learning and probability distribution transformation. The latter can be carried out using the classical OT method. From the OT perspective, the generator computes the OT map, while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution; both can be reduced to a convex geometric optimization process. Furthermore, OT theory discovers the intrinsic collaborative--instead of competitive--relation between the generator and the discriminator, and the fundamental reason for mode collapse. We also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT map for probability distribution transformation. This AE-OT model improves the theoretical rigor and transparency, as well as the computational stability and efficiency; in particular, it eliminates the mode collapse. The experimental results validate our hypothesis, and demonstrate the advantages of our proposed model.
Abstract: The recent expansion of online sport wagering and igaming has led to higher rates of problem gambling, particularly among emerging adults and other population subgroups. The Center for Gambling Studies (CGS) at the Rutgers University, School of Social Work, is using big data analysis, machine learning and GIS mapping to identify geographic locations with populations most at risk to guide the development of targeted interventions. This presentation will review the GIS StoryMap for the State of New Jersey, including a blueprint for the highest risk target service areas in the state. It will also present findings from a machine learning model that identifies the key risk factors for high-intensity online casino bettors. Implications for prevention, treatment and policy initiatives will be discussed.

Bio: Lia Nower, J.D., Ph.D., is a Distinguished Professor, Associate Dean for Research, and Director of the Center for Gambling Studies at Rutgers University. A clinician and attorney, her research focuses on big data analysis and machine learning models for online gambling and sports wagering; gambling and video gaming among emerging adults; policy initiatives around harm reduction and responsible gambling, and etiology and treatment of problem gambling. Dr. Nower serves as a senior editor for Addiction. She has received both the Research (2019) and the Lifetime Research Award (2022) from the National Council on Problem Gambling and the Board of Trustees Award for Research (2022) from Rutgers University.

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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
Join us at the Center for Excellence in Learning and Teaching (CELT) for an interactive Zoom workshop on Generative AI designed for faculty and staff interested in enhancing teaching and assessment practices, increasing student engagement, and navigating the rapidly evolving landscape of AI tools. Participants will be introduced to common AI tools, explore potential instructional uses, and discuss key considerations such as academic integrity, transparency, and equity.

Register now: https://stonybrook.zoom.us/meeting/register/6js1eP64T1ys8tyU57EJ7Q#/registration
Making sense of Twitter @ Bloomberg presented by Daniel Preotiuc-Pietro

ABSTRACT: The Bloomberg Terminal has provided ways for investors and journalists to sift through and understand the immense volume of tweets and discover financially-relevant content ever since the SEC approved the use of Twitter for company disclosures back in 2013.

In the first part of the talk, I will showcase how tweets impact financial markets and how Bloomberg is using Natural Language Processing methods to identify financially relevant tweets that move the markets. Our processing pipeline feeds directly to clients, journalists in the newsroom and powers several news analytic products offered by the company including trending companies and consumer sentiment for publicly traded equities.

However, understanding user pragmatic intent in individual tweets would allow us to gain deeper insights and enable new applications. I will present several recent research studies focused on understanding intent including identifying complaints and the roles with which vulgarity is used in social media and how these can help improve applications such as sentiment analysis and hate speech detection.

BIO: Daniel Preotiuc-Pietro is a Senior Research Engineer and Team Lead at Bloomberg LP, where he works on analyzing and building models for real-world large scale social media and news mining and information extraction. His research interests are focused on understanding the social and temporal aspects of text, especially from social media, with applications in domains such as Social Psychology, Law, Political Science and Journalism. Several of his research studies were featured in popular press including the Washington Post, BBC, New Scientist, Scientific American or FiveThirtyEight. He is a co-organizer of the Natural Legal Language Processing workshop series. Prior to joining Bloomberg LP, Daniel was a postdoctoral researcher at the University of Pennsylvania with the interdisciplinary World Well Being Project and obtained his PhD in Natural Language Processing and Machine Learning at the University of Sheffield, UK.
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.