Learn how these two AI tools will help you this year. AI has been all over, but figuring out the tools that we may use is critical. Background remover of images and a replacement for Google Search may disrupt the industry this year. Learn and refresh your knowledge about these tools.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2025 will be held from June 11th to June 15th, 2025, at the Music City Center, Nashville, TN. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Register here.
Abstract: Datalog is a powerful language for expressing recursive computations through rules: Horn clauses in first order logic. Although effective at expressing queries over existential properties, Datalog and many of its popular implementations struggle with queries that involve more complex aggregates, requiring users to apply verbose, non-composable, and/or inefficient workarounds. Recent work on lattice-based datalogs addresses many of these concerns for aggregates that can be encoded as lattices (e.g., min or max), but more general aggregates like count remain problematic. In this talk, I will argue that this is not a fundamental limitation of Datalog, but rather from its model of truth: Both datalog semantics and evaluation rules make heavy use of the fact that insertion is both monotone and idempotent. Once a fact is known to be true, it can not be retracted, nor can further discoveries of the same fact alter its truth. Monotonicity is critical for forward progress under Datalog's ``open world'' model, as it allows us to safely assert the truth of a body. Meanwhile, idempotence makes it easier to reason about evaluation, as we need only guarantee that each head atom will be derived at-least-once. Unfortunately, more general aggregates like sum() are neither idempotent, nor monotone. I will introduce Hedgelog, a strict generalization of Datalog that uses general monoids as a basis for truth. I will show that this generalization remains compatible with Datalog's open world model, how it enables cleaner and more composable datalog programs, and how the underlying monoid relations open the door to interesting datastructure-level optimizations.

Bio: Oliver Kennedy is an associate professor at the University at Buffalo. He earned his PhD from Cornell University in 2011 and now leads the Online Data Interactions (ODIn) lab, which operates at the intersection of databases and programming languages. Oliver is the recipient of an NSF CAREER award, an IEEE Region 1 Technological Innovation Award, UB's Exceptional Scholar Award, and several UB SEAS teaching awards. Oliver is also one of the founding board members of Breadcrumb Analytics. Several of Oliver's papers have been invited to Best of compilations from SIGMOD and VLDB. The ODIn lab is currently exploring (i) how we can leverage database techniques like incremental view maintenance to make compilers faster, (ii) how to make it easier for data scientists to track how sources of uncertainty, ambiguity, and/or bias affect analyses, and (iii) how to streamline the interfaces --- both human and software --- between different tools for data science, like python, sql, and spreadsheets.

Location: NCS 120
AI/ML Working Group Seminar

Time/Date: 12:00 PM ET, Tuesday, March 1st, 2022

Seminar Speaker: Yen-Chi (Sam) Chen, CSI, Brookhaven National Laboratory

Title: When reinforcement learning meets quantum computing

Abstract: Recently, reinforcement learning (RL) has demonstrated
various applications with superhuman performance such as mastering the
game of Go.  Meanwhile, the development of quantum computing hardware
shed light on building practical quantum applications to tackle
previously unsolved problems. What will happen if we combine these two
fascinating techniques? In this talk, I will present the recent
progress in quantum RL as well as using classical RL to help certain
tasks in quantum computing.



Host: Meifeng Lin, Computational Science Initiative

_______________________________________________

Nicole Medaglia is inviting you to a scheduled ZoomGov meeting.

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The University at Albany will host a national gathering of professionals and academics that will focus on the transformative potential of AI while addressing the ethical, technical and institutional challenges posed by AI in education.

The Symposium will feature dynamic keynotes, hands-on workshops and engaging conversations with other participants and subject matter experts.

Topics

  • Teaching, Learning and Workforce Development
  • Research, Creative Arts, and Practice
  • Ethics, Governance and Academic Administration

For event information and registration, visit Events@Albany.

Abstract: This talk shows how machine learning can address challenges in Astrophysics. We specifically focus on black hole simulations and supernova observations. First, we present a super-resolution technique for black hole simulations that avoids the need for high-resolution labels by leveraging the Hamiltonian and momentum constraints from general relativity. This method reduces constraint violations by one to two orders of magnitude. Next, we introduce Maven, a multimodal foundation model for supernova science. Using contrastive learning to align photometric and spectroscopic data, Maven achieves state-of-the-art results in classification and redshift estimation by pre-training on synthetic data and fine-tuning on real observations.

Bio: Thomas Helfer is a computational physicist specializing in deep learning and physics. Currently based at the Institute for Advanced Computational Science at Stony Brook University, Thomas was previously a postdoctoral fellow at Johns Hopkins and did his PhD with Eugene Lim at King's College in London. In his work, he looks to bridge topics; in his PhD, he bridged theoretical particle physics and gravitational waves. Now, in his postdoctoral work, he aims to find novel applications of deep learning in astrophysics.

*please note: this seminar will be held in a hybrid format*


Location: IACS Seminar Room OR Join Zoom Meeting
https://stonybrook.zoom.us/j/98617630652?pwd=tb4hplPgb3bTTifPCJTCcsn3P9vX8y.1

Meeting ID: 986 1763 0652
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Professor Petar M. Djuric, SUNY Distinguished Professor and Savitri Devi Bangaru Professor in Artificial Intelligence at Stony Brook University, has been selected as a plenary speaker at the upcoming 23rd IEEE Statistical Signal Processing Workshop (SSP 2025). The event will be held from June 8-11, 2025, in Edinburgh, Scotland, and is one of the premier international forums for the latest advances in statistical signal processing.

Professor Djuric's plenary talk, titled Quantifying causal relationships: Dynamic strengths, attributions, and confounders, will take place on June 10 from 9:00 AM to 10:00 AM EST. His presentation addresses foundational challenges in data-driven causality, proposing novel methodologies for quantifying causal strength in both static and dynamic systems, with special attention to latent confounders and attribution analysis.

This work has broad implications across disciplines including healthcare, economics, and climate science--areas where causal understanding drives critical decisions and innovations.

Professor Djuric has been a long-standing leader in the fields of machine learning and signal and information processing. After receiving his Ph.D. from the University of Rhode Island, he joined the faculty at Stony Brook University, where he served as Chair of the Department of Electrical and Computer Engineering from 2016 to 2023. He is also the founding Editor-in-Chief of the IEEE Transactions on Signal and Information Processing Over Networks and a Fellow of IEEE, EURASIP, AAIA, and AIIA.

Early bird registration for the workshop is open until April 30, 2025. For more information, visit the official SSP 2025 website.

Abstract:

In recent years, the landscape of artificial intelligence (AI) has been reshaped by the rapid emergence of Foundation Models (FMs). These versatile models have garnered widespread attention for their remarkable ability to transcend the boundaries of traditional, bespoke AI solutions and to generalize to a large set of downstream tasks. In this presentation we will describe the development of geospatial FMs with earth observation and weather data and discuss initial results of such models. We will also show how such foundation models can be a new and exciting tool for assisting with and accelerating scientific discovery.

Speaker:

Hendrik Hamann
Distinguished Researcher
IBM T.J. Watson Research Center

CG Group member (and SBU faculty) Chao Chen will speak on Fri, March 12, about the use of topological data analysis in machine learning for image analysis.
Chao has shared some of his research with the CG Group previously, and this will be a great opportunity to learn more about this exciting research area related to computational geometry/topology!

Time: Friday, March 12, 2pm-3pm
Place: Zoom
https://stonybrook.zoom.us/my/profweizhu?pwd=RjVIVXg3YUhudzZZQ3pheHUydTJBUT09



Title: Learning with Topological Information - Image Analysis and Label Noise
Speaker: Prof. Chao Chen (SBU)

Abstract: Modern machine learning faces new challenges. We are
analyzing highly complex data with unknown noise. Topology provides
novel structural information to model such data and noise. In this
talk, we discuss two directions in which we are using topological
information in the learning context. In image analysis, we propose a
topological loss to segment and to generate images with not only
per-pixel accuracy, but also topological accuracy. This is necessary
in analysis of images of fine-scale biomedical structures such as
neurons, vessels, etc.  Extracting these structures with correct
topology is essential for the success of downstream
analysis. Meanwhile, we discuss how to use topological information to
train classifiers robust to label noise. This is important in practice
especially when we are using deep neural networks which tend to
overfit noise. These results have been published in NeurIPS, ECCV,
ICML and ICLR.

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning.

ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics.

ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.


For more information and registration, visit the official website.