Related Events

Upcoming External Events

  • AstroAI Summer Program - Unveiling the Universe with AI/ML, AstroAI
    • Monday, June 2, 2025 – Friday, August 29, 2025
    • Center for Astrophysics - Harvard & Smithsonian (CfA), 60 Garden St., Cambridge, MA
    • Join AstroAI for a transformative summer research experience in 2025, where undergraduate and graduate students will collaborate with AstroAI scientists at the exciting intersection of astronomy and artificial intelligence. Students will gain hands-on experience analyzing astronomical data, developing new algorithms, and exploring groundbreaking discoveries in astrophysics. As part of this immersive program, you will work alongside a multidisciplinary team, expand your professional network, and build valuable research and technical skills that will set you apart in today’s competitive scientific landscape. Whether you are looking to build a solid foundation for graduate school or further your career in astrophysics, data science, or AI, the summer program at AstroAI is the perfect launchpad for your journey.
  • Duke Machine Learning Summer School: Generative AI, The Duke AI Health Community of Practice
    • Monday, June 2, 2025 – Friday, June 6, 2025
    • Duke University, Schiciano Auditorium
    • The Duke AI Health Community of Practice is pleased to announce the Duke Machine Learning Summer School 2025: Generative AI (MLSS-GenAI), offered in June as a (presential) five-day class that provides lectures on the fundamentals of generative artificial intelligence methods and applications. The curriculum in the MLSS-GenAI is targeted to individuals interested in learning about generative AI, with a focus on recent deep learning methodologies. The MLSS-GenAI will introduce the mathematics and statistics at the foundation of current generative and representation learning models and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLSS-GenAI will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework. This is the 12th iteration of the Duke Machine Learning School presented since 2017. This series has reached hundreds of participants from academia and industry, including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. The last machine summer school held in 2022 attracted 130 participants from around the world, representing 41 universities, institutes, and corporations.
  • Center for Decoding the Universe Annual Conference: Data-Driven Discovery in the Rubin Era, Stanford Data Science and the Kavli Institute for Particle Astrophysics and Cosmology
    • Thursday, June 5, 2025 – Friday, June 6, 2025
    • Simonyi Conference Center, Stanford, CA
    • The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced computational methods. From anomaly detection in massive data sets and foundation models that compactly describe multi-modal data, to simulation-based inference that bridges the gap between the observed and simulated Universe, new methodologies will enable new insights from large and complex datasets. These new approaches are poised to impact the next big thing in astrophysics: data from the Vera C Rubin Observatory’s Legacy Survey of Space and Time (LSST). This meeting will gather leading researchers in astrophysics, AI/ML, data science, and statistics, and will identify new opportunities for inference and data-driven discovery with the imminent LSST data. The Conference is inviting abstracts for short talks and posters by May 12, 2025. Talks should be accessible to researchers spanning astrophysics, AI/ML, data science, and statistics. Work in progress is welcome, and talks are especially encouraged by early-career researchers.
  • Advanced Summer School in Quantum Field Theory and Quantum Gravity, CERN
    • Sunday, June 8, 2025 – Saturday, June 21, 2025
    • International Center for Interdisciplinary Science and Education, Quy Nhon, Vietnam
    • This Summer School is aimed at advanced doctoral students and postdoctoral fellows. It is the 2025 edition in a biennial series of Advanced Summer Schools in High Energy Theoretical Physics, which were traditionally held either in Cargèse or Les Houches. This session will take place at ICISE, the International Center for Interdisciplinary Science and Education in Quy Nhon, Vietnam. The center is surrounded by a lush garden and just a few steps away from a beautiful beach. The School will include introductory and advanced lectures on the following topics: Quantum gravity, string theory, black holes, amplitudes, holography, cosmology, quantum information theory, theoretical condensed matter and artificial intelligence. The schedule will leave ample room for discussions. This School is part of the network of CERN Summer Schools of Excellence and is supported by CERN, ICISE and other funds.
  • Session during EAS Annual Meeting: The changing macrocosm of astroinformatics: big data, artificial intelligence, statistical inference, challenges, and more (Abstracts due Monday, March 3, 2025), AstroAI
    • Thursday, June 26, 2025 – Friday, June 27, 2025
    • University College Cork, Ireland
    • Astronomical advances in data science, computation, machine learning, and statistics are all captured within the growing field of astroinformatics. As astronomical datasets continue to grow in size and complexity, associated challenges continue to emerge, such as infrastructure for storage and access to datasets, evolution of data format through survey lifespans, preserving discovery potential in the era of overwhelming data sizes, and ensuring the continuing development of astronomical knowledge while minimising the environmental impact of computations.
      In recognition of these challenges we invite abstracts on advances in distributed data processing, effective data access, rapid data analysis, computational infrastructure, accessible toolkits and pipelines for data analysis using automated approaches. We will also explore cutting-edge statistical inference techniques, particularly focusing on simulation-based and deep learning approaches to inference in our 2-day scientific session at the European Week of Astronomy in Cork, Ireland.
  • Advanced Artificial Intelligence for Precision High Energy Physics, The Università degli Studi di Milano (Italy), Nikhef and Vrije Universiteit Amsterdam (The Netherlands), and the Higgs Centre for Theoretical Physics, Edinburgh University (UK)
    • Monday, July 14, 2025 – Friday, July 25, 2025
    • Lake Como School of Advanced Studies (Como, Italy)
    • Artificial intelligence techniques are becoming increasingly important in high energy physics (HEP), with a range of applications that goes from analytic computations and symbolic regression to modeling, optimization and pattern recognition. Many of these applications tackle cutting-edge problems in machine learning (ML), and in fact address issues, such as the need to accurate uncertainty estimations, that are often disregarded in the most common ML applications. The goal of this school is to provide hands-on training on cutting edge machine learning methods in HEP by combining extensive advanced courses taught with extended tutorial sessions. The tutorials will exploit as a playing ground the NNPDF open-source code, which, originally aimed at the determination of parton distribution, makes use of a variety of current ML, statistical and analysis tools and techniques of wide applicability.
  • CNTR Tech & Policy Summer School, Brown University Center for Technological Responsibility, Reimagination and Redesign (CNTR)
    • Tuesday, July 22, 2025 – Thursday, July 31, 2025
    • Providence, RI and Washington, DC
    • Redesign (CNTR) is launching an AI Policy Summer School from Tuesday, July 22nd, through Thursday, July 31st, 2025. This summer school will take place in person in both Providence, RI and Washington, DC. The goals of this summer school are to: (1) Enable graduate students to conduct high-quality policy-informed AI research, (2) Empower students to advocate for new AI policies or changes to existing policy, and (3) Build a pipeline of qualified technologists to fill emerging needs in government.
  • TPC25, The Trillion Parameter Consortium
    • Monday, July 28, 2025 – Thursday, July 31, 2025
    • San Jose, California
    • TPC25 will convene AI leaders globally from industry, academia, and national laboratories to explore collaboration across these sectors and internationally, with the goal of accelerating advances in AI to responsibly and safely address today’s scientific and engineering grand challenges. The theme for TPC25 is to explore and pursue collaborations between industry and public sector AI research — national laboratories, universities, and institutes — including those building and utilizing AI capabilities, infrastructure, and applications.
  • Princeton Machine Learning Theory Summer School, Boris Hanin (Assistant Professor, Princeton ORFE)
    • Tuesday, August 12, 2025 – Thursday, August 21, 2025
    • Princeton University
    • This summer school is aimed at PhD students interested in machine learning theory. The primary goal is to showcase, through four main courses, a range of exciting recent developments in the subject. The primary focus this year is on theoretical advances in deep learning. An important secondary goal is to connect young researchers and foster community within theoretical machine learning.

Upcoming Workshops

  • Data Parallelism Workshop: How to Train Deep Learning Models on Multiple GPUs, The Kempner Institute
    • Wednesday, June 4, 2025, 9:00am– 5:00pm
    • Harvard Science and Engineering Complex (SEC)
    • This workshop teaches you techniques for data-parallel deep learning training on multiple GPUs to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, you’ll learn how to decrease model training time by distributing data to multiple GPUs, while retaining the accuracy of training on a single GPU.
  • Frontiers in NeuroAI - Kempner Institute Symposium, The Kempner Institute
    • Thursday, June 5, 2025 – Friday, June 6, 2025
    • Allston, MA (with option for virtual attendance)
    • Frontiers in NeuroAI is a two-day symposium, open to the public, exploring key topics in the rapidly-advancing field of NeuroAI, including: How natural and artificial systems learn, and how these processes are similar or different across systems; The implications of rapid progress in machine learning on understanding neural processes, the nature of intelligence, and human cognitive abilities; and Innovations that are pushing forward the reasoning and generative capabilities of AI systems. The symposium will feature presentations from cutting-edge experts from neuroscience and AI as they discuss advances in learning across both fields.
  • Accelerating statistical inference and experimental design with machine learning, The Isaac Newton Institute for Mathematical Sciences
    • Monday, June 23, 2025 – Friday, June 27, 2025
    • Isaac Newton Institute in Cambridge, UK
    • This workshop focuses on leveraging modern machine learning to accelerate statistical inference, experimental design, and scientific discovery. It features research talks, tutorials, and a poster session, with ample time reserved for informal discussions. The tutorials cover important background, e.g. on experimental design and diffusion models.

      The overall aim is that, by the end of the workshop, attendees will be well-acquainted with both the foundations and the latest trends and advances in the field.
  • AstroAI Workshop - Unveiling the Universe with AI/ML, AstroAI
    • Monday, July 7, 2025 – Friday, July 11, 2025
    • Center for Astrophysics - Harvard & Smithsonian (CfA), 60 Garden St., Cambridge, MA 02138
    • The AstroAI Workshop is designed to foster an environment of learning, collaboration, and innovation. Over the course of five days, participants will engage in a series of lectures and discussions led by experts in the fields of AI/ML and astrophysics, hands on sessions, talks by early career scientists in the field, and networking events. This workshop is an opportunity for early career scientists to deepen their understanding of foundational AI/ML models for astrophysical research, and their potential in unraveling the mysteries of the universe.
  • AI for Astrophysics Workshop co-located at ICML 2025, Carol Cuesta-Lazaro (IAIFI Fellow), Alex Gagliano (IAIFI Fellow), Ashley Villar (IAIFI Senior Investigator), and collaborators
    • Sunday, July 20, 2025, 9:00am– 5:00pm
    • ICML 2025, Vancouver, Canada
    • Submissions are open for the AI for Astrophysics Workshop co-located at ICML 2025. Submissions are due by June 9, 2025.

      Contemporary and upcoming surveys are producing petabytes of multimodal, heterogeneous data — imaging billions of galaxies, mapping large-scale structures through spectroscopy, and discovering diverse transient phenomena in real time. In parallel, simulations are modeling complex physical systems at increasing resolution and fidelity. These settings demand models that are robust, interpretable, sample-efficient, and physically grounded. This workshop aims to connect the ML and astrophysics communities to drive methodological innovations toward real-world scientific impact.
  • BOOST 2025 – 17th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements, and Searches at Colliders, BOOST 2025 Local Organizing Committee
    • Monday, July 28, 2025 – Friday, August 1, 2025
    • Brown University, Providence, Rhode Island
    • BOOST 2025 is the 17th conference of a series of successful joint theory/experiment workshops that bring together the world’s leading experts in theoretical and experimental collider physics to discuss the latest progress and develop new approaches on the reconstruction of and use of jet substructure to study Quantum Chromodynamics (QCD) and search for physics beyond the Standard Model.
  • Build Big or Build Smart: Examining Scale and Domain Knowledge in Machine Learning for Fundamental Physics, MIAPbP (Munich Institute for Astro-, Particle- and Biophysics)
    • Monday, August 25, 2025 – Friday, September 19, 2025
    • Munich, Germany
    • MIAPbP will be hosting a workshop on the topic of ‘Build Big or Build Smart: Examining Scale and Domain Knowledge in Machine Learning for Fundamental Physics’ from August 25 - September 19 at the Munich Institute for Astro-, Particle- and Biophysics (MIAPbP) in Munich, Germany. Applications for the workshop are open until January 19th 2025. If you are interested in large-scale foundation models for science, physics-inspired methods in artificial intelligence, this workshop may be a great fit for you. The MIAPbP team hopes to discuss over the course of four weeks, what the interplay is between these ideas for scientific applications.
  • Northeast Quantum Forum 2025: AI in Quantum, NEQT Organizing Committee, including Liang Fu (IAIFI Affiliate)
    • Tuesday, October 14, 2025 – Friday, October 17, 2025
    • The University of New Hampshire
    • Northeast or Greater New England area has a strong presence of quantum materials and quantum technology research. Unfortunately, for many years, there is no recurring regional workshops to bring scientists on these fields together. The Northeast Quantum Forum (NEQT) aims to fill this gap and create a collaborative platform for leading experts and motivative students in and out of New England area to exchange of ideas, advancements, and insights in the ever-evolving field of quantum science and quantum materials. We expect that this new platform becomes a hub for exciting physics and fruitful collaborations. The 2025 workshop, taking place on October 14th to 17th, 2025 at the University of New Hampshire, will feature applications of artificial intelligence methods in quantum science broadly defined.

Past Workshops

Other organizations that hold public events relevant to the IAIFI community:

This project is supported by National Science Foundation under Cooperative Agreement PHY-2019786. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.