Seismologist & Quantitative Geophysicist
Developing machine learning, modelling, and scientific software solutions for geophysical monitoring, natural hazards, and subsurface systems.
📍 Zürich, Switzerland
I am a seismologist specialising in applied geophysics, machine learning, and scientific software development. My work bridges research and deployable tools — from real-time earthquake early warning systems to generative AI waveform synthesis, structural health monitoring, and web-based simulation platforms.
With an MSc in Geophysics from ETH Zürich and a BSc in Geology from Charles University, I bring a rigorous quantitative foundation to data-driven problems in Earth science. My operational experience spans the Swiss Seismological Service (SED), IRMOS, the Geophysical Institute of the Czech Academy of Sciences, and OpenEEW — environments that demand both scientific rigour and engineering reliability.
I specialise in translating complex geophysical methods into robust, scalable software: building processing pipelines, designing machine learning workflows, and developing interactive platforms that make scientific results accessible. My goal is to create tools and models that are not just scientifically sound, but operationally ready.
Building data-driven tools and models for understanding and monitoring Earth systems.
Scientific web platform for finite-fault earthquake simulations, machine-learning-generated waveforms, and rapid scenario analysis. Integrates interactive maps, modelling visualisations, and real-time waveform generation into a fully deployable web application.
Research integrating diffusion model–generated broadband envelope synthetics into real-time earthquake source characterisation workflows. Demonstrated quantitative improvement over conventional EEW algorithms using synthetic training data generated at scale on HPC infrastructure.
Development and testing feasibility of anEEW system for Nepal at the Geophysical Institute of the Czech Academy of Sciences. Involved hardware integration, real-time data acquisition, field deployment, and end-to-end validation workflows in one of the world's most seismically active regions.
Automation and analysis of vibration-based structural health monitoring workflows for civil infrastructure. Led field measurement campaigns including sensor deployment and quality control, and developed automated processing pipelines using operational modal analysis techniques.
Swiss Seismological Service (SED) — ETH Zürich
IRMOS AG
Swiss Seismological Service (SED) — ETH Zürich
Geophysical Institute — Czech Academy of Sciences (CAS)
Grillo / OpenEEW
ETH Zürich
2023 – 2025
Specialised in quantitative geophysics, machine learning for Earth sciences, and earthquake early warning. Master's thesis: generative deep learning models for synthetic waveform generation to optimise real-time EEW algorithms.
Charles University in Prague
2019 – 2022
Awarded Best Bachelor Thesis by the EAGE local chapter Prague for research on real-time magnitude estimation using MEMS-based accelerometers. Received Merit Scholarship from Faculty of Sciences for academic excellence.
Combining geophysics, machine learning, modelling, and software engineering to solve complex Earth science problems.
Areas where I am actively developing expertise and seeking new collaborations.
Applying generative and discriminative AI to geophysical data — from waveform synthesis to subsurface characterisation.
Designing and deploying real-time geophysical and structural monitoring networks with automated processing pipelines.
Quantitative seismic and multi-hazard risk assessment, with focus on early warning and rapid response systems.
Applying non-destructive testing, structural health monitoring, modal analysis, vibration analytics, and machine learning to assess the condition and performance of critical infrastructure.
Building deployable, production-ready scientific software tools that make research results operationally accessible.
Numerical and probabilistic modelling of Earth processes including finite-fault simulations and ground-motion prediction.
Manuscript integrating diffusion model–generated synthetic waveforms into real-time earthquake source characterisation workflows, demonstrating improved EEW performance over conventional approaches.
European Geosciences Union (EGU), Vienna, Austria — 2026
Poster Presentation
doi:10.5194/egusphere-egu26-20840 →Institute for Rock Magnetism (IRM) — 2021
Charles University in Prague — 2022
View thesis →ETH Zürich (MSc Thesis) — 2025
View thesis →BGC Engineering Canada | Jun. 2026 – Aug. 2026
Selected participant in an international research program focused on applied geoscience and natural hazard modelling.
GeoInquire Italy | 2026
Worked on integrated hazard modelling approaches, combining seismic and tsunami processes in a collaborative research environment.
UCL Warning Research Centre | 2023
Participation in interdisciplinary conference focused on inclusive early warning system design and implementation.
Prague, Czechia | 2025 - ongoing
Organizing and coordinating scientific outreach initiatives and career networking events that bridge the gap between students, academic institutions, and private sector partners. These events facilitate knowledge exchange, promote career opportunities in geosciences, and strengthen collaboration between university departments and industry stakeholders.
Sicily, Italy | 2022
Developed innovative virtual field excursions using drone-based data acquisition and 3D modeling techniques. This interdisciplinary bottom-up project combined geoscience fieldwork, remote sensing technology, and educational methodology to create immersive online learning experiences, making geological field sites accessible to students regardless of physical location or mobility constraints.
Faculty of Sciences, Charles University in Prague | 2021 - 2022
Honorary award by the EAGE local chapter Prague | 2022
I'm always open to discussing new opportunities, research collaborations, or connecting with fellow geoscientists and engineers.