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, November 26, 2024, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Hanfei Yan, NSLS-II

David Park, CDS, AI Dept

Xihaier Luo, CDS, AI Dept

Join Zoom Meeting

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Chat with Sociology faculty as they share their paths to StonyBrook-what inspired their careers, what led them to teaching,and the experiences that shaped their academic journey.

Dr. Yongjun Zhang

Assistant Professor of Sociology, Departments of Sociology and AAAS

Join this opportunity to talk to Yongjun Zhang about his new interest in the following responsible usage of AI in addressing climate and health issues. Lunch will be served.

Location: SBS Level 4- Sociology Reading Room

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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
Abstract:
Artificial intelligence (AI)-based methods and computational materials science continue to make inroads into accelerated materials design and development. I will review Al-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. I will describe exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing, and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. Al- powered workflows help efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve forward and inverse materials design problems. A practical informatics-based design protocol involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol will be demonstrated for several energy and sustainability-related applications. Finally, I will offer an outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.

Speaker Bio:
Prof. Ramprasad is the Regents' Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. He is also the CEO and co-founder of Matmerize, Inc. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.

Location: Room 301, Engineering Building

Join the Office of Educational Effectiveness' upcoming workshop on the transformative potential of AI tools to enhance program assessment. Learn how to leverage AI to create targeted learning objectives, detailed rubrics, and precise benchmarks that will elevate the quality and effectiveness of your program assessment process. Join in-person on Oct. 17 at 10:30 am or virtually on Oct. 21 at 12 pm.

Register in advance: https://calendar.stonybrook.edu/site/office-educational-effectiveness/event/leveraging-ai-in-assessment-zoom/

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



The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.



ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

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

The rapidly developing field of deep learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. ICLR takes a broad view of the field and includes topics such as feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization.

A non-exhaustive list of relevant topics explored at the conference include:



  • Unsupervised, Semi-supervised, and Supervised Representation Learning
  • Representation Learning for Planning and Reinforcement Learning
  • Metric Learning and Kernel Learning
  • Sparse Coding and Dimensionality Expansion
  • Hierarchical Models

  • Optimization for Representation Learning
  • Learning Representations of Outputs or States
  • Implementation Issues, Parallelization, Software Platforms, Hardware
  • Applications in Vision, Audio, Speech, Natural Language Processing, Robotics, Neuroscience, or Any Other Field


For more information or registration, please visit the official website.
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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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)

Abstract: Sub-grid turbulence is challenging to resolve in climate models; therefore, it is parameterized. Traditionally, turbulent parameterizations have relied on physics-based and equation-based approaches. However, ad hoc and uncertain components in these parameterizations introduce uncertainty in future climate predictions. Recently, data-driven techniques have emerged as an alternative for modeling sub-grid fluxes. I will demonstrate the use of machine learning to model vertical turbulent fluxes in the ocean surface boundary layer and its impact on reducing biases in NOAA's Geophysical Fluid Dynamics Laboratory ocean climate model.

I will show how neural networks, trained to predict the eddy diffusivity profile from high-fidelity yet computationally expensive turbulence schemes, enhance the vertical mixing scheme in the climate model. These networks replace ad hoc components while maintaining the conservation principles of the standard ocean model equations. The enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and modestly improving tropical upper-ocean stratification in ocean-only global simulations. Furthermore, simplified equations that can replace the neural networks show similar improvements but with lower computational cost and better interpretability. They point to structural deficiencies in the baseline parameterization. This work is one of the first successful applications of machine learning to improve a sub-grid parameterization of turbulent mixing in ocean climate models.

IACS Seminar Speaker: Aakash Sane, Princeton University

Location: IACS Seminar Room or Zoom

Join Zoom Meeting: https://stonybrook.zoom.us/j/97764942108?pwd=MzCWupCe3L9mKdrgfO2bJg3GBbvXuf.1
Meeting ID: 977 6494 2108
Passcode: 519324