The Empirical Methods in Natural Language Processing (EMNLP) conference is a premier international academic conference in the field of artificial intelligence and natural language processing (NLP). Organized annually by the Association for Computational Linguistics (ACL) special interest group on linguistic data (SIGDAT), it focuses on research that uses empirical methods to solve language processing problems.

For more information, and registration, visit the official website.

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Experiencing Machine Learning in Collider-Accelerator Control System

Abstract: The Relativistic Heavy Ion Collider (RHIC) at Collider-Accelerator Department (C-AD) of BNL provides the world's only high-energy polarized proton beam. It is in the unique position to study where nuclei obtain their spin. During 25 years of operation at RHIC, the C-AD controls group has developed its own control system to tune the accelerator performance, which contains millions of control points. The successful operation of this system will highly affect the machine performance. RHIC's successor, the Electron-Ion Collider (EIC), will be one of the most complex scientific instruments ever built, with the capability of colliding polarized proton and electron beams. The increasing complexity of instruments will require new, sophisticated control methods/tools to tune and optimize the accelerator performance. In this talk, I will summarize some projects developed in recent years that utilize machine learning in the C-AD controls group.

Biography: Dr. Yuan Gao is an assistant scientist at the Collider-Accelerator Department (C-AD) at Brookhaven, primarily working on developing new machine learning schemes in the control group to enhance system performance. His research interests include game theory, algorithm design, anomaly detection, and simulation modeling.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604302440?pwd=0x2I95PIvbkkzIi6rA0MNnon5k2sux.1

Meeting ID: 160 430 2440
Passcode: 478223

AI Seminar: Computational Pathology: Deep Learning, Classification and
Predicting the Future  - Joel Saltz

Abstract:  Pathologists have been looking at tissue through microscopes since the 1800s.  During each pathologist's career,  he or she views slides having  roughly 1,000,000,000,000 cells. Deep learning methods are rapidly being developed to assimilate the huge amount of information walked inside of tissue images and to use this information to predict outcomes and responses to treatments.

Stony Brook is a leader in this type of multi-disciplinary work. I will provide an overview of Stony Brook computational Pathology efforts and articulate how these have the potential to create biomedical advances as well as to drive development of new computer science. 


Bio: Dr. Joel Saltz is a leader in research on advanced information technologies for large scale data science and biomedical/scientific research. He has developed innovative pathology informatics methods, including: the first published whole slide virtual microscope system; pioneering pathology computer-aided diagnosis techniques; and methods for decomposing pathology images into features and linking those features to cancer omics, response to treatment and outcome. He has broken new ground in big data through development of the filter-stream based DataCutter system, the map-reduce style Active Data Repository and the inspector-executor runtime compiler framework. He has also been an active contributor in clinical informatics, having developed
predictive models for hospital readmissions, point of care laboratory testing quality assurance systems, decision support systems for electrophoresis interpretation and graphical user interfaces to support clinical data warehouse queries. Dr. Saltz has been a pioneer in establishing the field of biomedical informatics; he founded and built two highly successful departments of biomedical informatics, one at Ohio State University and one at Emory University. In 2013, he came to Stony Brook as Vice President for Clinical Informatics and Founding Department Chair of Biomedical Informatics - to create a living laboratory for biomedical informatics and to create a third unique biomedical informatics department dually housed in the School of Medicine and the College of Engineering. Dr. Saltz is trained both as a computer scientist and as a physician through the MSTP program at Duke University. He has deep experience in computer science, having served on the computer science faculties at Yale University and the University of Maryland. He completed his residency in clinical
pathology at Johns Hopkins University and he is a practicing, board-certified clinical pathologist. 

The event will take place on Zoom and will feature two distinguished guest speakers: SBU alumnus, Velchamy Sankarlingam, president of Product and Engineering at Zoom, and Simeon Ananou, vice president for Information Technology and CIO at Stony Brook University. The discussion will be moderated by Haresh Gurnani, dean of the College of Business at Stony Brook University.

Exploring AI's Impact on Communication and Connection

Artificial Intelligence (AI) has rapidly evolved, becoming an integral part of various industries, including education and business. This event aims to delve into how AI is reshaping the way we learn and work, particularly in enhancing communication and fostering human connections. Velchamy Sankarlingam, an SBU alumnus and a key figure at Zoom, will share his insights on how AI-driven tools are revolutionizing virtual communication platforms, making interactions more seamless and effective.

Simeon Ananou, with his extensive experience in information technology, will provide a perspective on how AI is being integrated into educational institutions to improve learning outcomes and administrative efficiency. His role at Stony Brook University places him at the forefront of implementing innovative technologies that benefit both students and staff.

A Conversation Led by Expertise

Dean Haresh Gurnani, known for his leadership and expertise in business education, will guide the conversation, ensuring that the discussion remains focused on the practical implications of AI. He will explore how AI is not only boosting productivity but also enriching overall experiences in the workplace and educational settings. The event will include an interactive Q&A session, allowing attendees to engage directly with the speakers and gain deeper insights into the topics discussed.

As AI continues to develop, events like this are crucial for understanding its impact and potential. Stony Brook University's College of Business is committed to providing platforms for such important discussions, fostering an environment where innovation and education intersect.

This event is open to all. Please visit https://www.givecampus.com/schools/StonyBrookUniversity/events/artificial-intelligence-reshaping-learning-and-work to register.

Fantastic Futures is an international conference series organized by AI4LAM as one of the most important global events at the intersection of AI and cultural heritage, shaping how memory institutions adapt to rapidly evolving technologies while maintaining public trust.

The conference brings together professionals, researchers, technologists, and cultural‑heritage institutions to explore how AI can support organizational workflows, users services and data preservation, access, discovery, as well as innovation in the cultural‑heritage sector:

  • Librarians, archivists, museum professionals and all other GLAM enthusiasts
  • Technologists working on cultural‑heritage applications, AI researchers and developers
  • Digital humanists, researchers of cultural heritage
  • University professors, students, and librarians
  • Policy and ethics experts, governmental employees
  • Strategic leaders and innovation managers

To register, visit AI4LAM's official website.

The Office of Proposal Development is pleased to announce the next installation of the series, Translating the Funding Landscape for Stony Brook Researchers. This virtual presentation series is designed to inform the Stony Brook University research community about funding trends across key topic areas. Our Strategic Research Initiatives team will provide insight into the current funding environment using policy briefs, budget priorities, and relevant legislation. We will highlight federal and state priorities for the current and upcoming years to help Stony Brook researchers develop strategies for pursuing funding in a competitive environment. This series is moderated by Mónica Bugallo, Interim Vice President for Research & Innovation.

Join us for the third in the series, focused on artificial intelligence:

Translating the Funding Landscape for Stony Brook Researchers: Artificial Intelligence

Presented by Catherine Chen, PhD, Research Development Associate

Faculty Respondent: Nav Nidhi Rajput, Assistant Professor, Department of Materials Science and Chemical Engineering


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
Passcode: 882994

Learn how to prompt AI to help clean datasets and write formulas in Google Sheets.

When you have a messy dataset, it can take a lot of time to clean it up before you can start analyzing. Can AI help? In this workshop, we'll collect live data and then use Gemini AI (the stand alone tool) to help clean up the data. Then, we'll use it to help do some analysis. Because we'll be working with live data live in Gemini, we don't know exactly what will happen, but that's the reality of data and data cleaning!

In this session, you will

  1. Craft effective AI prompts to generate Google Sheets formulas for data analysis and manipulation
  2. Utilize Gemini to develop regular expression formulas to extract, reformat, clean text-based data
  3. Develop formulas for numerical analysis using Gemini AI

https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_dht1o3rNzlZhHka?source=event+manager&session=0815250900sheets