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SUMMARY:2nd SMASHING Workshop
DTSTART:20261019T070000Z
DTEND:20261023T121500Z
DTSTAMP:20261006T213400Z
UID:indico-event-55@indico.ung.si
DESCRIPTION:This workshop is the second network meeting of the SMASH proje
 ct (here you can find the first edition https://indico.ung.si/event/35). S
 MASH is a multidisciplinary program centered on developing cutting-edge Ma
 chine Learning (ML) and Artificial Intelligence (AI) applications for scie
 nce and humanities. These include climate science\, precision medicine\, f
 undamental physics and linguistics.  It is co-funded by the European Unio
 n via the Marie Skłodowska-Curie COFUND action and by the Republic of Slo
 venia and European Union from the European Regional Development Fund.  SM
 ASH connects scholars from five top-level institutions in Slovenia with 52
  associated partners\, Slovenian businesses and academic institutions glob
 ally.\nThe Second SMASHING workshop will gather scientists working in the 
 SMASH research areas with the aim to create a multi-disciplinary environme
 nt that will foster knowledge exchange between different fields and betwee
 n academia and industry\, thereby building the SMASH community. The worksh
 op will be structured around discussions of different classes of ML/AI tec
 hniques\, followed up with examples on the successful application in SMASH
  research areas. More specifically the workshop will focus on:\n\nAgentic 
 AI approaches to science\nFoundation models\nGenerative models and compute
 r vision\nStatistical approaches (simulation based inference\, etc) \nGra
 phs/transformers and time series\n\nIn addition to invited talks the works
 hop will have discussion sessions and there will be plenty of time to exch
 ange ideas and build the community. \nSpeakers include:\n\nAida Alvera-Az
 cárate (University of Liège\, Belgium)\, Data analysis methods for ocean
 ographic applications\nSatardu Bag (Max Planck Institute for Astrophysics\
 , Garching)\, Discovering Strongly Lensed Transients in the Era of LSST: M
 achine Learning for a Needle-in-a-Haystack Problem\nMaria Benito (Institut
 o de Astrofísica de Canarias\, Spain)\, Representation learning for dark 
 matter searches in the Milky Way stellar halo\n\nMatteo Cagiada (Universit
 y of Copenhagen and Oxford University)\, Towards improved prediction of pr
 otein dynamics\n\nChristopher Eckner (Instituto de Astrofísica de Canaria
 s\, Spain\, ex SMASH Fellow)\, Application of Simulation Based Inference t
 o astrophsyics\nJuergen Gall (Lamarr Institute for Machine Learning and Ar
 tificial Intelligence)\, From Forecasting Human Behavior to Forecasting Ex
 treme Weather and Climate Events\nImad El Haddad (Paul Scherrer\, Switzerl
 and)\, AI-Enabled Atmospheric Chemistry: Harmonized Measurements\, Exposur
 e Mapping\, and Causal Inference \nFabio Iocco (University of Naples\, Ita
 ly)\, Determining dark matter distribution in Galaxies with machine learni
 ng \nŽeljko Ivezić (University of Washington\, USA)\, Rubin Obs. science
  in the AI era\, TBC\nAdriana Milic (CERN\, Switzerland)\, The Art of Find
 ing Needles in Haystacks: Event Selection at ATLAS\nSoebur Razzaque (Unive
 rsity of Johannesburg\, South Africa)\, Application of Machine Learning in
  Astrophysical Transient Data\nJohannes Schneider (University of Liechtens
 tein)\, How Using AI agents alters our thinking and speaking\nGad Shaulsky
  (Baylor College of Medicine\, Houston\, USA)\, Deciphering Dictyostelium 
 Biology: From Genetic Pathways to AI Systems for Knowledge and Discovery\n
 Dimitar Trajanov (University of Skoplje and Boston University)\, LLM based
  agentic systems and knowledge graphs\nMarta Via Gonzales (Barcelona Super
 computing Centre\, ex SMASH Fellow)\, Machine-learning methods for aerosol
  source apportionment and harmonisation\nSaman Vinke\, MD (Radboud Univers
 ity Medical Center)\, AI application to the field of Deep brain stimulatio
 n\nLili Yang (Sun Yat-sen University\, China)\, The application of ML in a
 strophysics and astroparticle experiments\nJure Zupan (University Cincinna
 ti\, USA)\, Simulating Particle Physics Hadronization with Machine Learnin
 g\n\n \n\n\nhttps://indico.ung.si/event/55/
IMAGE;VALUE=URI:https://indico.ung.si/event/55/logo-538041608.png
LOCATION:Lanthieri Mansion\, Vipava
URL:https://indico.ung.si/event/55/
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