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.

AI for Neutrino Oscillation Fits

Abstract: Neutrino oscillation experiments face the problem of performing likelihood fits in a very highdimensional space to extract the oscillation parameters from measured spectra. The current strategy for this is to fix all but a few parameters, reducing the dimensionality of the fit to a manageable number, but this risks missing correlations between the parameters, which can impact the systematics of the measurement. This is an area where artificial intelligence and machine learning could make great improvements. I will discuss the problem, explain how it is currently dealt with, and sketch one possible way of implementing AI to solve it, using a sampling method combining Smolyak's algorithm, for efficient sampling using sparse grids, with an adaptive grid refinement to increase sampling in regions that are more likely to contain the global minimum.

Speaker: Steven Linden is a physicist in the Instrumentation Department at BNL working on neutrino and dark matter experiments. He got his PhD from Yale in 2010 doing analysis on the MiniBooNE experiment and then worked on various dark matter detectors (MiniCLEAN, Pico, SENSEI) at SNOLAB in Canada for nearly ten years before moving to BNL.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1614473319?pwd=e4QSSgFHqDzHx870ixJpwuG3yqBere.1

Meeting ID: 161 447 3319
Passcode: 733283

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks 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.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room


Speakers

Sanket Jantre
Tao Zhang
Xi Yu


Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

Hosted by the College of Business faculty and staff, this virtual session will explore how our MS in Business Analytics & Intelligence program can enable you to become a future leader with AI expertise. This session will also cover the program's benefits, curriculum, cost, and admission requirements.

Register here.
Zoom Link: https://github.com/giorgianb/spdhackspring2021/blob/main/bit.ly/spdhack2021

ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
 
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md

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

University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting! Location: Melville Library, Scholarly Communication Seminar Room

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. 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.

We meet once a month at noon in CDSD's Training Room (building 725, room 2-124) 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.

Multilayer machine-learning framework for screening catalytic activity and selectivity

Abstract: Machine learning (ML) studies based on quantum chemical datasets have emerged as a powerful tool for accelerating catalyst discovery. However, their potential applications remain limited by high costs, low data quality, and restricted reliability. Here, we report a multilayer binary classification framework (MLBC) for screening catalytic performance in multistep processes with ML models. Key features include low-cost synthetic data generation via kinetic Monte Carlo simulations, high-quality data that capture catalytic behavior under reaction conditions, robust treatment of imbalanced data distributions, and descriptor selection that enhances reliability and interpretability. Using carbon dioxide (CO 2 ) hydrogenation to methanol (CH 3 OH) on copper (Cu)-based catalysts as a case study, the MLBC framework outperforms conventional ML models by demonstrating high reliability and strong generalization in classifying systems with activity and methanol selectivity exceeding those of Cu. In addition, feature analysis reveals that control over the critical transition steps between competing pathways governs activity and selectivity in CO 2 hydrogenation.

In addition to our speaker, we will have a number of CDS staff in attendance with expertise in AI methods and applications including image analysis, foundation models development, and inverse problem solving.

Biography: Dr. Wenjie Liao is a research associate in the Chemistry Division at Brookhaven National Laboratory, where he develops computational and machine-learning approaches to understand and improve heterogeneous catalysts. He earned his Ph.D. from Stony Brook University. His research focuses on identifying active sites, mapping reaction pathways, and predicting catalyst performance, with the goal of accelerating the discovery of more efficient catalysts for energy and chemical transformations.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

Please Note: Due to a funding shortfall, we are for the time being no longer able to provide pizza and sodas for these events. We will have coffee though, and all are of course welcome to bring their lunch.


Abstract: The faster AI automation spreads through the economy, the more profound its potential impacts, both positive (improved productivity) and negative (worker displacement). The previous literature on AI Exposure cannot predict this pace of automation since it attempts to measure an overall potential for AI to affect an area, not the technical feasibility and economic attractiveness of building such systems. In this work, we present a new type of AI task automation model that is end-to-end, estimating: the level of technical performance needed to do a task, the characteristics of an AI system capable of that performance, and the economic choice of whether to build and deploy such a system. The result is a first estimate of which tasks are technically feasible and economically attractive to automate - and which are not. We focus on computer vision, where cost modeling is more developed. We find that at today's costs U.S. businesses would choose not to automate most vision tasks that have AI Exposure, and that only 23% of worker wages being paid for vision tasks would be attractive to automate. This slower roll-out of AI can be accelerated if costs fall rapidly or if it is deployed via AI-as-a-service platforms that have greater scale than individual firms, both of which we quantify. Overall, our findings suggest that AI job displacement will be substantial, but also gradual - and therefore there is room for policy and retraining to mitigate unemployment impacts.

Details of this work can be found here.

Speaker Bio: Neil Thompson is the Director of the FutureTech research project at MIT's Computer Science and Artificial Intelligence Lab and a Principal Investigator at MIT's Initiative on the Digital Economy.

Previously, he was an Assistant Professor of Innovation and Strategy at the MIT Sloan School of Management, where he co-directed the Experimental Innovation Lab (X-Lab), and a Visiting Professor at the Laboratory for Innovation Science at Harvard. He has advised businesses and government on the future of Moore's Law, has been on National Academies panels on transformational technologies and scientific reliability, and is part of the Council on Competitiveness' National Commission on Innovation & Competitiveness Frontiers.

He has a PhD in Business and Public Policy from Berkeley, where he also did Masters degrees in Computer Science and Statistics. He also has a masters in Economics from the London School of Economics, and undergraduate degrees in Physics and International Development. Prior to academia, He worked at organizations such as Lawrence Livermore National Laboratory, Bain and Company, the United Nations, the World Bank, and the Canadian Parliament.

Location: IACS Seminar Room

The AI Community will be hosting our very first Datathon💡📊

Ready to turn data into groundbreaking insights? 🧠

Compete in our Datathon, where you'll analyze real-world data 📈 and share innovate solutions in these tracks:

🏫 Student Life

🌱 Environment & Sustainability

💉 Health & Wellness

💰 Finance & Economics

Whether you're a data pro or just starting out, this is your chance to network, learn, and win exciting prizes! 🏆🎉 Bring your creativity 🧩 collaborate with fellow students 🧑‍🤝‍🧑 and gain hands-on experience showcasing your analytical skills 💻

Submissions will be judged by professors 🧑‍🏫 so take this chance to impress them!

There will be free food ☕ and games 🎲 to fuel your brain and imagination! Don't miss out--register now and unleash the power of data! 🔥✨

Registration Form: https://forms.gle/6XYMfmhyAByzFpxz5

Time: Friday (4/4) 10:30am - 5pm ⏰

Location: Bauman Center 📍