Mario Grandi

PhD in Particle Physics

Sept 2017 - Jul 2021

University of Sussex, Brighton, UK

"Search for supersymmetry with the ATLAS detector at the Large Hadron Collider in final states with two hadronically decaying τ-leptons."

MPhys in Physics and Astrophysics

2010 - 2014

University of Sussex, Brighton, UK

Grade: 1st class degree with honours.

"Simulation studies for e⁺e^-→VH final states at a linear collider in the standard effective field theory."

International Baccalaureate

2010 - 2014

International School of Geneva, Geneva, Switzerland

Grade: 34

Higher Level subject: Physics, Mathematics, Economics

Standard Level subjects: English, Spanish, Chemistry

Head of Analytics

May 2023 - Present

QA-UK Ltd., London, UK

• Lead researcher responsible for driving all project pipelines from the planning stage to providing deliverables, investigating and prototyping new technologies, and analysing collected data for the full stack of green technologies portfolio.
• Implemented and analysed complex fluid, thermal, and optical simulation using parallelisation techniques on AWS and large High-Performance Computing systems.
• Aided in the negotiation and implementation of software license extension, delivering 25% additional simulation time at no extra cost.
• Led the testing and analysis of a prototype for a multi-million dollar green energy project and presented results to investors and stakeholders.

Statistical Production Analyst

Sept 2022 – Mar 2023

Office for National Statistics, Titchfield, UK

• Was the co-lead of an 8-member team in charge of the collection and analysis of international trade data and provide effective insight, used by over 5 external clients and 2 internal branches to make government-wide decisions.
• Improved code quality through automated testing, reviewing, and validation.
• Produced clean and error-free Python code using Git for version control, to streamline data analysis and improve internal quality coding standards.
• Collaborated with 3 cross-platform professionals in an Agile environment and with a strict schedule, to oversee the successful delivery of requirements needed for a new software analysis platform.
• Identified several areas of improvement in the used statistical analysis methods and implemented cost-effective solutions.
• Streamlined pipeline processes using the Pandas Python library to reduce overall production and preparation time by over two weeks.

Post-Doctoral Research Fellow

Aug 2021 – Aug 2022

University of Sussex, Brighton, UK

• Created a complex tracking algorithm in Python and C++, accelerated on FPGA hardware using HLS and on software using OpenCL and OpenMP.
• Achieved a decrease in the processing latency by a factor of 15 compared to software and occupy less than 50% of available hardware resources.
• Maintained Linux-based server system hosting accelerator cards interfaced with a distributed computing system to streamline computation.
• Developed large-scale statically accurate Monte Carlo physics simulations using C++ and Python to test the performance of the developed algorithms.
• Successfully delivered the final product and presented final results to the funding agents and collaboration stakeholders, ensuring the programme’s continued funding.
• Provided support and training for master and final year physics students.

Data Scientist

Jun 2019 – Sept 2019

Public Health England, Cambridge, UK

• Developed and trained a Python-based Machine Learning algorithm using TensorFlow and Keras to simulate statistically significant and anonymised synthetic patient data from sensitive patient health data.
• Used data science analysis techniques in Python and Jupyter to define data integrity, data leakage and statistical resemblance and delivered the framework to the production team to enable them to draw meaningful conclusions and provide actionable recommendations to stakeholders and research partners.
• Monitored and optimise ML training and development using cometML
• Analysed and synthesised patient data with SQL tables, views, and Jupyter Notebooks in Python to provide insightful reports on its statical properties to team and stockholders.
• Documented full system analysis, testing, and implementation for production team handover.

High Energy Physics PhD Researcher

Sept 2017 – Jul 2021

University of Sussex, Brighton, UK

• Performed large-scale data analysis using data science methods and skills on petabytes of data collected by the ATLAS experiment at CERN to perform feature extraction, data cleaning and transformation, and maximise experimental sensitivity.
• Used CI/CD with GitLab to help develop and implement C++, Python, and Bash C++ code to perform cut-based statistical analyses and machine learning based identification methods to discriminate events with very rare theoretically predicted particles from large background of standard model physics.
• Received extensive training in big data analysis, machine learning, and high- performance computing techniques through the DISCnet bursary programme.
• Maintained and improved on large analysis framework using coding best practices and large-scale project management with JIRA and GitLab to resolve bugs and issues quickly and effectively.
• Presented the work at several workshops, group meetings, and international conferences.
• The results of my analyses have been published in several respected physics journals.