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2nd SMASHING Workshop

19–23 Oct 2026
Lanthieri Mansion, Vipava
Europe/Ljubljana timezone

Contribution List

51 out of 51 displayed
  1. 31. Data analysis methods for oceanographic applications
    Dr Aida Alvera-Azcárate (University of Liège, Belgium)
    19/10/2026, 09:00
  2. 32. Discovering Strongly Lensed Transients in the Era of LSST: Machine Learning for a Needle-in-a-Haystack Problem
    Dr Satardu Bag ((Max Planck Institute for Astrophysics, Garching)
    19/10/2026, 09:45
  3. 27. Machine Learning classification of Tidal Disruption Events from partial optical light curves
    Oleksandra Razim (University of Nova Gorica)
    19/10/2026, 11:00

    LSST is expected to find about several thousands of Tidal Disruption Events (TDEs) in its 10 years of operation. The physical mechanisms governing TDE emission, as well as the diversity of TDE subclasses, are only partially understood, and in order to clarify them, we require spectroscopic follow-up observations during the pre-peak rise phase and around peak brightness. This promotes attempts...

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  4. 18. Anomaly Detection for Faint Galactic Substructure with EagleEye
    Sven Põder (Scuola Internazionale Superiore di Studi Avanzati (SISSA))
    19/10/2026, 11:15

    Detecting faint stellar substructure in the Milky Way halo and its surroundings favours methods that remain sensitive to weak signals without restrictive assumptions about either the signal morphology or the Galactic background. We present recent applications of EagleEye, a model-independent anomaly detection framework that compares multidimensional data distributions to identify localized...

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  5. 7. Two-Phase Rapid Simulation-Based Inference with Differentiable Simulators
    Andre Scaffidi (SISSA)
    19/10/2026, 11:30

    I will introduce introduce Rapid Simulation Based Inference (RSBI), a diffusionbased variational approach to likelihood-free Bayesian inference that achieves high sampling efficiency under large prior-to-posterior volumes with multi-modal posterior structure.
    RSBI builds on advances in Schrödinger Bridge diffusion sampling to handle multimodal posteriors, with the likelihood initially...

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  6. 2. Transfer learning for regional data-driven weather prediction
    Matjaž Puh (UNG, ARSO)
    19/10/2026, 11:45

    Training high-resolution data-driven weather prediction models from scratch is computationally expensive and requires long regional datasets. We explore whether a pretrained regional machine-learning model can be transferred to a new geographic domain.
    A graph neural network-based limited-area weather model trained over Switzerland is transferred to the Eastern Alps and Northern Adriatic in...

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  7. 13. RiverTiles: An Open Web-Based Platform For Hosting And Visualizing Uhr River Imagery And Annotations
    Tulio Soto Parra (Faculty of Computer and Information Science, University of Ljubljana)
    19/10/2026, 12:00

    River monitoring increasingly relies on hyperspatial imagery capable of resolving geomorphological and ecological features at centimeter scales. Although UAVs and other low-altitude airborne platforms have made such data more accessible, resulting datasets often remain dispersed across individual projects, inconsistently documented, and rarely accompanied by reusable spatial annotations. This...

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  8. 16. Learning Multi-Hazard Early Warning from Atmospheric State: Graph and State-Space Models for Alpine Catchments
    Aman Arora (University of Nova Gorica, Vipavska cesta 13, Rožna dolina, 5000 Nova Gorica)
    19/10/2026, 12:15

    Forecasting flash floods, landslides and debris flows in Alpine terrain is hard for a structural reason: the conventional warning chain runs from quantitative precipitation forecast (QPF) through hydrological model to alert, so its skill is capped by its least predictable link. Convective, orographically forced rainfall is routinely misplaced by numerical weather prediction (NWP). During the...

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  9. 34. Representation learning for dark matter searches in the Milky Way stellar halo
    Dr Maria Benito (Instituto de Astrofísica de Canarias, Spain)
    19/10/2026, 16:00
  10. 35. Machine-learning methods for aerosol source apportionment and harmonisation
    Dr Marta Via Gonzales (Barcelona Supercomputing Centre, ex SMASH Fellow)
    19/10/2026, 16:30
  11. 36. Simulating Particle Physics Hadronization with Machine Learning
    Dr Jure Zupan (University Cincinnati, USA)
    20/10/2026, 09:00
  12. 37. AI-Enabled Atmospheric Chemistry: Harmonized Measurements, Exposure Mapping, and Causal Inference
    Dr Imad El Haddad (Paul Scherrer Institute, Switzerland)
    20/10/2026, 09:45
  13. 29. Clustering for large-scale reinterpretation of new physics searches at the LHC
    Judita Mamuzic (IJS, Ljubljana)
    20/10/2026, 11:00

    Beyond the Standard Model (BSM) searches show no statistically significant sign of new physics to date. However, several analyses reported small excesses, higher than 2σ SD beyond the SM expectation. In this work, clustering algorithms are used to extract additional insights from existing searches and to motivate a next round of BSM analyses. The flexible framework of the phenomenological...

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  14. 11. Anomaly detection with normalizing flows for model-agnostic new physics searches
    Rafał Masełek (Jozef Stefan Institute)
    20/10/2026, 11:15

    Discovering new particles from beyond the Standard Model remains one of the main goals of present-day particle physics. Traditional searches for new physics at the Large Hadron Collider rely on specific theoretical scenarios and simulation-based background estimates, limiting their reach and introducing modeling uncertainties. We present an anomaly detection method that uses normalizing flows...

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  15. 24. Integrating Photo-Thermal Aerosol Absorption Monitor and Single-Particle Spectroscopy with Machine Learning to Improve Mass Absorption Cross-Section Measurements of Black Carbon and Mineral Dust Aerosols
    Ankur Bhardwaj (Centre for Atmospheric Research, University of Nova Gorica)
    20/10/2026, 11:30

    Light-absorbing aerosols, particularly black carbon (BC) and mineral dust (MD), remain among the most uncertain light-absorbing components in our understanding of Earth's radiative balance. The mass absorption cross-section (MAC) serves as the critical link between aerosol mass and light absorption, yet literature reported values vary dramatically –by a factor of four for BC alone (Wang et...

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  16. 12. Trustworthy species distribution modelling: reliability audits for inference from imperfect observations
    Kristian Miok (FRI UL)
    20/10/2026, 11:45

    Machine-learned models of species distributions are built, more often than not, on observations the modeller did not collect. Aggregated archives pool records from many contributors, instruments and eras, and with them a wide range of positional and observational quality. Our work asks one question across this pipeline: how far can the resulting predictions be trusted, and where exactly do...

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  17. 23. Representation Learning for Coupled Earth-System State Estimation
    Lukas Kugler (Faculty of Mathematics and Physics, University of Ljubljana)
    20/10/2026, 12:00

    Modern Earth-system models simulate interacting atmospheric, oceanic, land-surface, cryospheric, and chemical processes. Yet estimating the current state of these systems from incomplete and noisy observations remains a fundamental challenge. Current approaches rely on separate estimation procedures for data assimilation in different Earth-system components, leading to inconsistencies,...

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  18. 6. AI for Security & Security for AI
    Areeb Ahmed (University of Ljubljana)
    20/10/2026, 12:15

    Artificial Intelligence (AI) is reshaping cybersecurity by enhancing threat detection, automating incident response, and supporting intelligent security operations. At the same time, AI systems have become attractive targets for attacks such as adversarial manipulation, data poisoning, model extraction, and prompt injection. This dual role positions AI as both a powerful cybersecurity tool and...

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  19. 25. Diffusion-Generated Synthetic Ear Images for Cross-Dataset Recognition
    iyyakutti ganapathi
    20/10/2026, 15:45

    Synthetic biometric data is attractive when privacy constraints, annotation cost, and limited subject availability restrict the collection of large real datasets. This paper presents a diffusion-first synthetic ear generation and benchmarking framework. The proposed pipeline uses Stable Diffusion 2.1 image-to-image synthesis: real ear crops define the source identity structure, multiple...

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  20. 4. Heart failure prediction using non-invasive voice biomarkers using machine learning and SHapley Additive exPlanations
    Muniba Ashfaq (University of Ljubljana, Faculty of Computer and Information Science)
    20/10/2026, 16:00

    Heart failure (HF) is a chronic disease all around the world, affecting millions of people. Despite recent advances in the diagnosis of heart failure, the traditional diagnosis is highly invasive and time-consuming. The disease results in physiological changes in patients, including increased vocal edema in the vocal tract and accumulation of fluid in the lungs, thereby affecting the laryngeal...

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  21. 38. AI application to the field of Deep brain stimulation
    Saman Vinke, MD (Radboud University Medical Center, Netherlands)
    20/10/2026, 16:15
  22. 39. How Using AI agents alters our thinking and speaking
    Dr Johannes Schneider (University of Liechtenstein)
    21/10/2026, 09:00
  23. 40. Towards improved prediction of protein dynamics
    Dr Matteo Cagiada (University of Copenhagen and Oxford University)
    21/10/2026, 09:45
  24. 14. Toward the first syntactically annotated corpus of Slovenian dialects: an LLM-assisted pipeline from raw transcripts to Universal Dependencies
    Varvara Magomedova (UNG)
    21/10/2026, 11:00

    Slovenian dialects — among the most diverse in the Slavic world — remain almost invisible to language technology: no dialect corpus with morphosyntactic annotation exists, and standard NLP tools fail on dialect input. I present work toward the first Universal Dependencies (UD) annotated corpus of Slovenian dialects, built with a modular, LLM-assisted pipeline.
    The pipeline has four stages....

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  25. 26. Structural plausibility without binding specificity: what confidence scores do and don't tell us about antibody–antigen prediction
    Klara Kropivšek Brumat (University of Nova Gorica)
    21/10/2026, 11:15

    Antibody discovery increasingly uses AI structure prediction to select candidate binders before experimental testing. Such selection assumes that a model's confidence score reports on binding specificity, and not merely on the plausibility of the predicted complex.
    We benchmarked AlphaFold3, Boltz-2 and Chai-1 on 106 nanobody–antigen and 46 antibody–antigen complexes. Each binder was paired...

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  26. 20. A pipeline for shortlisting de novo designed protein binders towards CDKL5
    Marco Orlando (University of Nova Gorica)
    21/10/2026, 11:30

    Artificial Intelligence (AI) is now considered a mature technology for the de novo design of protein binders, offering a potentially more reliable option when working with poorly immunogenic or highly conserved target antigens, or when a specific conformational epitope must be targeted. Current benchmarks indicate that more than 10 designs typically must be characterized to yield binders with...

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  27. 9. Mito-Syn: Mitosis Synthesis via Chromatin Modelling and Language-Grounded Inpainting
    Dominik Hirling (University of Ljubljana, Faculty of Computer and Information Science)
    21/10/2026, 11:45

    Detecting and subtyping mitotic figures in histopathology is central to tumor grading as well as patient survival prognosis, yet training data for rare phases and atypical mitoses remains scarce and costly to annotate, thus, generating synthetic data for these cases is desirable. Generative models have been introduced in the past that copy and paste mitotic chromatin structure from one context...

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  28. 5. From Retrieval to Trustworthy Answers: Practical Lessons from Building RAG Systems for Scientific Evidence
    Yun Wang (University of Ljubljana)
    21/10/2026, 12:00

    Retrieval-augmented generation (RAG) is becoming an important tool for scientific literature search and evidence synthesis. Here I introduce the core ideas behind RAG through practical examples from the BioASQ and TREC-RAG benchmarks. The presentation covers evidence retrieval, reranking, grounded answer generation, and evaluation, together with common pitfalls and practical lessons learned...

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  29. 22. Fermipylot: An AI Assistant for Automated Fermi-LAT Data Analysis
    Ermes Aviano (University & INFN Trieste)
    21/10/2026, 12:15

    The Large Area Telescope (LAT) onboard NASA’s Fermi Gamma-ray Space Telescope continuously surveys the high-energy gamma-ray sky, providing observations of astrophysical phenomena ranging from variable sources and transient events to large-scale diffuse emission. Its extensive public data archive can be analysed using Fermipy, an open-source Python package that provides a high-level interface...

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  30. 15. Resource-Efficient LLMs for Depression Symptoms Screening: Performance and Limitations in Zero Shot Setting
    Muhammad Rizwan (University of Ljubljana)
    21/10/2026, 12:30

    Depression is the leading cause of global disability and early detection is crucial for effective intervention. Recent advances in large language models (LLMs) offer potential for analyzing text to identify depression symptoms. This work investigates the zero-shot capability of LLMs to recognize nine DSM5 depression symptoms from short-text
    inputs. We evaluated eight open LLMs with model...

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  31. 41. From Forecasting Human Behavior to Forecasting Extreme Weather and Climate Events
    Dr Juergen Gall (Lamarr Institute for Machine Learning and Artificial Intelligence, Germany)
    21/10/2026, 14:15
  32. 42. Application of Machine Learning in Astrophysical Transient Data
    Dr Soebur Razzaque (University of Johannesburg, South Africa)
    21/10/2026, 15:00
  33. 43. Determining dark matter distribution in Galaxies with machine learning
    Dr Fabio Iocco (University of Naples, Italy)
    21/10/2026, 15:30
  34. 44. Application of ML in astrophysics and astroparticle experiments
    Dr Lili Yang (Sun Yat-sen University, China)
    22/10/2026, 09:00
  35. 45. Deciphering Dictyostelium Biology: From Genetic Pathways to AI Systems for Knowledge and Discovery
    Dr Gad Shaulsky (Baylor College of Medicine, Houston, USA)
    22/10/2026, 09:45
  36. 46. Application of Simulation Based Inference to astrophysics
    Dr Christopher Eckner (Instituto de Astrofísica de Canarias, Spain, ex SMASH Fellow)
    22/10/2026, 10:30
  37. 8. Luminosity function of millisecond pulsars in globular clusters from their gamma-ray spectra
    Francesco Xotta (University of Nova Gorica)
    22/10/2026, 11:30

    Millisecond pulsars (MSPs) are widely believed to be responsible for the gamma-ray emission of globular clusters (GCs), yet the underlying MSP gamma-ray luminosity function remains uncertain. Existing GC-based determinations of the latter often rely on external prescriptions for the number of MSPs in each cluster.

    In this work, we constrain the MSP gamma-ray luminosity function in Milky-Way...

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  38. 28. Simulation-based cosmological inference from 3D maps of multiple tracers
    Giulio Scelfo (SISSA, INFN)
    22/10/2026, 11:45

    Extracting maximum cosmological information from current and upcoming large-scale structure data requires going beyond summary statistics as currently used in likelihood-based inference. Simulation-Based Inference (SBI) promises to enable the exploitation of field-level information and the rich physics of modern hydrodynamical simulations. We develop a proof-of-concept SBI pipeline to explore...

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  39. 30. Simulation-Based inference for massive black hole binary from mock LISA data
    Gianmarco Puleo (Scuola Internazionale Superiore di Studi Avanzati (SISSA))
    22/10/2026, 12:00

    The Laser Interferometer Space Antenna (LISA) will observe gravitational waves produced by several massive black hole binary (MBHB) mergers per year. While the likelihood can be written in closed form under idealised stationary, Gaussian noise assumptions, including realistic effects — instrumental glitches, gaps in the data, and non-stationary noise — make it intractable or computationally...

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  40. 50. Rapid Detection and Inference of Extreme-Mass-Ratio Inspirals in LISA: Divide and Conquer
    Rahul Srinivasan (SISSA, Italy)
    22/10/2026, 12:15

    Extreme-mass-ratio inspirals (EMRIs) are key gravitational-wave sources for the Laser Interferometer Space Antenna (LISA), but their detection and parameter inference are computationally challenging due to the extreme concentration of posterior distributions within vast prior volumes. In this work, we introduce a novel divide-and-conquer strategy that reformulates global inference as a...

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  41. 52. DeepExtractor: Model-Agnostic Signal and Glitch Reconstruction for Gravitational-Wave Astronomy
    Tom Dooney (NIKEF)
    22/10/2026, 12:30
  42. 47. Rubin Obs. science in the AI era
    Dr Željko Ivezić (University of Washington, USA)
    23/10/2026, 09:00
  43. 48. LLM based agentic systems and knowledge graphs
    Dr Dimitar Trajanov (University of Skoplje and Boston University)
    23/10/2026, 09:45
  44. 49. The Art of Finding Needles in Haystacks: Event Selection at ATLAS
    Dr Adriana Milic (CERN, Switzerland)
    23/10/2026, 10:30
  45. 17. MicroSight: Low-Cost, Image-Based Identification and Classification of Microplastics in Freshwater Environments
    Nerea Portillo De Arbeloa
    23/10/2026, 11:30

    Microplastic (MP) pollution in freshwater systems remains poorly monitored due to the cost and complexity of existing identification methods, which typically rely on high-magnification imaging or laboratory-based spectroscopy. MicroSight addresses this gap by developing a machine learning pipeline for automated MP classification from images captured with consumer-grade devices, enabling...

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  46. 21. Statistical Approaches for Dark Matter Signatures in Astrophysical Neutrino Data
    Khushboo Dixit (Centre for Astro-Particle Physics, University of Johannesburg)
    23/10/2026, 11:45

    High-energy astrophysical neutrinos offer a unique opportunity to investigate possible interactions between dark matter and Standard Model particles. In particular, neutrinos produced near active galactic nuclei may traverse the enhanced dark matter densities expected around supermassive black holes, where dark matter--neutrino scattering could produce observable attenuation and spectral...

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  47. 10. Probing the Parameter Space of Axion-Like Particles Using Simulation-Based Inference
    Pooja Bhattacharjee (University of Nova Gorica)
    23/10/2026, 12:00

    Axion-like particles (ALPs) appear in various extensions of the Standard Model and can interact with photons, leading to ALP-photon conversions in external magnetic fields. This phenomenon can introduce characteristic energy-dependent “wiggles” in gamma-ray spectra. The Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based gamma-ray observatory, designed to provide...

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  48. 3. High-energy Multi-messenger Emission from Galaxy Clusters in the Local Universe
    Saqib Hussain (Center for Astrophysics and Cosmology, University of Nova Gorica, Slovenia)
    23/10/2026, 12:15

    In this work, we investigate the diffuse $\gamma-$ray and neutrino emission from the Virgo, Perseus, and Coma clusters using a detailed numerical method, combining MHD simulations with Monte Carlo methods. The MHD simulation provides the distributions of temperature, gas, and magnetic field in clusters. The Monte Carlo simulations are used to investigate the cosmic-ray (CR) propagation in ICM...

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  49. 33. Coffee break
  50. 51. DeepExtractor: Model-Agnostic Signal and Glitch Reconstruction for Gravitational-Wave Astronomy
    Tom Dooney (Nikhef)

    Gravitational-wave detectors like LIGO, Virgo, and KAGRA are extremely sensitive instruments that pick up faint ripples in spacetime from distant cosmic events. However, that same sensitivity means they also pick up "glitches": brief bursts of instrumental or environmental noise that can mimic genuine astrophysical signals or mask their true characteristics.

    We present DeepExtractor, a deep...

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  51. 19. Determining Dark Matter distribution in galaxies, using ML
    Fabio Iocco (Università di Napoli "Federico II")

    I will illustrate the development of new methods, and their results, to determine the dark matter distribution in field galaxies.
    Machine learning algorithms trained within the synthetic environment of numerical cosmological simulations -such as the IllustrisTNG- offer the potential to validate and test the reliability of the Dark Matter distribution in such controlled environment, before...

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