Machine Learning Engineer. Physics-Informed and Structure-Aware Modeling

About

Physics-trained ML engineer working on time-series modeling, anomaly detection, and generative models with physical constraints. Experienced in leading research teams, building widely used scientific software, and translating domain knowledge into robust ML systems. Interested in data-efficient learning for dynamical systems and hybrid physics–ML approaches.

Experience

ML and Physics Research Leadership (2011–Present) Austria, France, Germany

• Led physics-driven ML projects and large scale physics projects on real-life data
• Managed €1.3M+ in competitive funding and supervised interdisciplinary teams
• Lead developer of widely used Python scientific software (SMoldeS)
• Regular invited speaker at conferences, workshops, and applied ML meetups

Selected Projects

Physics-Aware Latency Prediction and Network Anomaly Detection

• Demo and code
• Physics-aware anomaly detection for networked systems
• Improved sensitivity to rare, physically meaningful anomalies while stabilizing false-positive rates
• Emphasis on interpretability and operational robustness

Physics-Informed Variational Autoencoder for Signal Generation

• VAE with physics-inspired latent structure for dynamical signals
• Enabled generation of physically consistent synthetic data beyond observed regimes
• Explored trade-offs between expressivity and physical consistency

Physics-Aware LSTM for Anomaly Detection

• Demo and code
• LSTM architecture guided by physical intuition about temporal dynamics
• More stable detection of rare anomalies compared to unconstrained baselines
• Bridge between black-box deep learning and physics-informed modeling

Remaining Useful Life Prediction (NASA Turbofan Data)

• Demo and code
• Structured time-series modeling preserving unit-level dependencies
• Uncertainty-aware RUL estimates using gradient boosting and automated tuning
• Focus on evaluation aligned with maintenance decision-making

Agentic Framework for Music Analysis and Recommendation

• Modular agent-based pipeline for exploratory analysis and recommendation
• Focus on decision logic and interpretability over end-to-end optimization
• Demonstrates interest in agentic ML system design

Talks and Community

• Invited and meetup talks on physics-informed ML
• Recent online talk on PIML for dynamical systems. Slides
• Member of Styrian vision group of the Women in AI, Austria
• Created AI-generated TedXGraz theme song; documented process in a LinkedIn article demonstrating creative application of ML in collaborative, real-world settings.

Selected Publications

Long-Lived Particle Searches at the LHC

Publication

• Python-based simulation and visualization for search optimization
• Influenced parameter choices and experimental search strategies

Snowmass 2021 Dark Showers Report

Publication

• Coordinated 50+ researchers across theory and experiment
• Shaped field-level synthesis and research directions

Constraining new physics with SModelS version 2

Publication

• Lead architect of Python codebase covering 100+ new-physics searches
• Accelerated interpretation of collider constraints on dark matter models