Simone Amoroso
Head of Technology & CISO
Experience
Head of Technology & CISO
AI Quality and Testing Hub
- Lead developer of Prof. Valmed, the first LLM-powered medical device (utilising RAG on a medical corpus of 2.5M+ documents) to receive a CE certification.
- Designed and implemented cloud-native MLOps infrastructure for ENBW’s energy trading analytics division, enabling scalable deployment and monitoring of predictive models.
- Architected end-to-end testing and validation frameworks for AI/ML systems, ensuring quality, compliance, and robustness in critical and regulated applications.
- Conducted professional training on AI testing, EU regulatory frameworks, and quality assurance for production AI systems.
Staff Scientist in the CMS experiment and Lecturer at Uni. Wuppertal
DESY (Deutsches Elektronen-Synchrotron)
- Led a 50 people group in the development of ten python data analyses for peer-reviewed publications using a variety of statistical (classification, regression, hypothesis testing) and machine learning (BDT, Random Forest, DNN) techniques.
- Introduced and optimised Deep Neural Network machine learning algorithms in tensorflow to reweight Monte Carlo simulation parameters, reducing by up to a factor of five the need for (expensive) simulated events in the collaboration.
- Trained generative AI models in pytorch to accurately reproduce expensive Monte Carlo simulations.
- Spearheaded the development of the xFitter open-source C++ code for non-linear regression analyses.
- Lecturer in particle physics and data analysis at university courses and physics schools.
- Mentored and supervised over 20 students (from B.Sc. to PostDoc) in data analysis projects.
Senior Research Fellow in the ATLAS experiment
DESY (Deutsches Elektronen-Synchrotron)
- Lead a statistical combination of W-boson mass measurements for which I developed a python tool to study uncertainty models and correlations.
- Head of the Physics Modeling Group, liaised between data analysis teams and upper management for the optimal and efficient distribution of computing cloud and HPC resources (∼300K CPU cores).
- Defined the long-term computing strategy of the experiment (CPU and GPU needs).
- Steered the 150-member strong team to commission, validate, and bring to production novel theoretical models and Monte Carlo simulation.
Senior Research Fellow in the ATLAS experiment
CERN (European Organization for Nuclear Research)
- Head of a 4 people strong team which I steered to timely complete two data analysis publications using python and C++.
- Improved the efficiency of the real-time C++ data selection algorithms of the ATLAS experiment leading to a 30% performance enhancement.
- Optimised theoretical model parameters through a linear regression analyses optimising 100 parameters using over 50 input datasets (over 10000 measurements) and documented the results in several peer-reviewed publications.
Dr.Rer.Nat. (Ph.D.) in Physics working in the ATLAS experiment
Albert-Ludwigs Universität Freiburg
- Developed a novel model-independent global search, an unsupervised approach to classify and simultaneously analyse all high-energy events produced by the Large Hadron Collider (105 regions in over 700 distributions) to test for possible deviations from the theoretical models.
- Developed Professor, a python program for hyperparameter tuning of problems with expensive to evaluate objective functions, extended the code to deal with systematic uncertainties and correlations, and implemented a Support Vector Regressor for parameter optimisation.
Summary
Physics Ph.D. with a decade of hands-on experience in data science, AI, and scientific computing. I’ve worked on everything from statistical modeling and simulation to machine learning and large-scale data processing across high-stakes, large-scale settings; from the big particle detectors at CERN to critical applications in healthcare and the energy sector. I’ve taken on both technical leadership and deeply hands-on roles, with end-to-end experience from early design and prototyping to implementation and large-scale production deployment. I thrive working on hard problems in fast-paced environments, especially when the stakes are high and the work demands both focus and creativity.
Skills
- Programming: Python (Numpy, Scipy, Pandas, Matplotlib), C++, Sql, Fortran, Bash
- Machine Learning: Pytorch, Tensorflow, Keras, Pymc, Scikit-learn, Langchain
- Applications: Ms Office, Ms Excel/vba, Ms Power Bi
- Tools: Aws/azure, Github/gitlab, Cmake, Jupyter, Jira, Docker
- Soft Skills: Problem Solving, Project Management, Strategic Thinking, Communication
Languages
Education
Albert-Ludwigs Universität Freiburg
Dr.rer.nat. in Physics, working in the ATLAS experiment · Physics · Freiburg im Breisgau, Germany
University of Rome Tor Vergata
M.Sc. in Physics · Physics · Rome, Italy · 110/110 cum laude
University of Rome Tor Vergata
B.Sc. in Physics · Physics · Rome, Italy · 110/110 cum laude
Certifications & licenses
CE certification
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