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 connects scholars from five top-level institutions in Slovenia with 25 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:
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Matteo Cagiada (University of Copenhagen and Oxford University), Deep learning, generative models, protein stability, protein folding
- Imad Haddad (Paul Scherrer, Switzerland), ML assisted Modeling of different air pollutants and impact on EU regulations
- Gad Schaulsky (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
- Lili Yang (Sun Yat-sen University, China), Selected AI techniques application to neutrino and gamma-ray astrophysics
- Jure Zupan (University Cincinnati, USA), Simulating Particle Physics Hadronization with Machine Learning
