This workshop is the second network meeting of the SMASH project (here you can find the first edition https://indico.ung.si/event/35). SMASH is a multidisciplinary program centered on developing cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) applications for science and humanities. These include climate science, precision medicine, fundamental physics and linguistics. It is co-funded by the European Union via the Marie Skłodowska-Curie COFUND action and by the Republic of Slovenia and European Union from the European Regional Development Fund. SMASH connects scholars from five top-level institutions in Slovenia with 52 associated partners, Slovenian businesses and academic institutions globally.
The Second SMASHING workshop will gather scientists working in the SMASH research areas with the aim to create a multi-disciplinary environment that will foster knowledge exchange between different fields and between academia and industry, thereby building the SMASH community. The workshop will be structured around discussions of different classes of ML/AI techniques, followed up with examples on the successful application in SMASH research areas. More specifically the workshop will focus on:
- Agentic AI approaches to science
- Foundation models
- Generative models and computer vision
- Statistical approaches (simulation based inference, etc)
- Graphs/transformers and time series
In addition to invited talks the workshop will have discussion sessions and there will be plenty of time to exchange ideas and build the community.
Speakers include:
- Aida Alvera-Azcárate (University of Liège, Belgium), Data analysis methods for oceanographic applications
- Satardu Bag (Max Planck Institute for Astrophysics, Garching), Discovering Strongly Lensed Transients in the Era of LSST: Machine Learning for a Needle-in-a-Haystack Problem
- Maria Benito (Instituto de Astrofísica de Canarias, Spain), Representation learning for dark matter searches in the Milky Way stellar halo
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Matteo Cagiada (University of Copenhagen and Oxford University), Towards improved prediction of protein dynamics
- Imad El Haddad (Paul Scherrer, Switzerland), AI-Enabled Atmospheric Chemistry: Harmonized Measurements, Exposure Mapping, and Causal Inference
- Fabio Iocco (University of Naples, Italy), Determining dark matter distribution in Galaxies with machine learning
- Željko Ivezić (University of Washington, USA), Rubin Obs. science in the AI era, TBC
- Adriana Milic (CERN, Switzerland), The Art of Finding Needles in Haystacks: Event Selection at ATLAS
- Soebur Razzaque (University of Johannesburg, South Africa), Application of Machine Learning in Astrophysical Transient Data
- Johannes Schneider (University of Liechtenstein), How Using AI agents alters our thinking and speaking
- Gad Shaulsky (Baylor College of Medicine, Houston, USA), Agentic approach to studies of the Dictyostelium discoideum
- Dimitar Trajanov (University of Skoplje and Boston University), LLM based agentic systems and knowledge graphs
- Marta Via Gonzales (Barcelona Supercomputing Centre, Ex SMASH Fellow)
- Saman Vinke, MD (Radboud University Medical Center), AI application to the field of Deep brain stimulation
- Lili Yang (Sun Yat-sen University, China), The application of ML in astrophysics and astroparticle experiments
- Jure Zupan (University Cincinnati, USA), Simulating Particle Physics Hadronization with Machine Learning
