About
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Enthusiastic professional with over 5 years of experience in developing software, performing statistical analyses, and implementing Machine Learning algorithms to deliver high-impact results. Currently working as the Head of Analytics for QA-UK Ltd. performing cutting-edge analysis and research into novel and ground-breaking green energy technologies with the potential of revolutionising the entire energy market and, hopefully, the world.
Previously, Statistical Production Analyst and Senior Executive Officer co-team leader for the Office for National Statistics towards the collection and analysis of International Trade in Services data for the UK; Research Fellow at the University of Sussex on the development and implementation of tracking algorithm in software and accelerated hardware; Data Scientist in collaboration with Public Health England with focus on Machine Learning synthetic data generation and statistical data analysis.
A PhD in Particle Physics from the University of Sussex, a former member of the ATLAS experiment at CERN, and a DISCnet bursary scholarship awardee, with extensive training in data science, problem-solving, time management, big data analysis, and machine learning techniques and models. Has been invited to national and international conferences and workshops to present findings and analyses. Results have been published in several distinguished journals.
Skills
OSs
Coding
Analysis Tools/Libraries
DevOps/Versioning/Cloud
Soft skills
Languages
Resume
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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
Professional Experience
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.
Conferences, Seminars, and Workshops
Xilinx adaptive compute PhD School
CERN openlab - Programming and environments for parallelism
40th International Conference of High Energy Physics (ICHEP)
European School of High-Energy Physics
ATLAS UK Workshop
Large Hadron Collider Physics (LHCP) Conference
21st IEEE Real Time Conference
Awards
UKRI SWIFT HEP Grant Award
2021 - Post-doctoral Fellowship
DISCnet
2017 - Full Scholarship Grant
Institute of High Energy Physics - Beijing, China
2019 - Visiting Scientist
University of Sussex
2017 - PhD scholarship in collaboration with CERN
European School of High Energy Physics
2019 - Outreach Activity Project Winner
MPS Sussex Plus Careers Award
2015 - For the completion of studies in communication, personal skills, and employment cometencies.
Publications
International peer-reviewed conferences/proceedings
-
M. Grandi for the ATLAS Collaboration.
“The ATLAS Inner Detector Trigger performance in pp collisions at 13 TeV during LHC Run 2”.
Proceedings, 40th International Conference on High Energy Physics (ICHEP 2020): Prague, Checz Republic, 29 Jul, 2020
PhD Thesis
-
Mario Grandi and Prof. Fabrizio Salvatore.
“Search for supersymmetry with the ATLAS detector at the Large Hadron Collider in final states with two hadronically decaying τ-leptons.”.
Presented 04 Aug 2021
http://cds.cern.ch/record/2815398
Articles in peer-reviewed journals
-
The ATLAS Collaboration.
“Search for direct stau production in events with two hadronic τ-leptons in √s=13 TeV pp collisions with the ATLAS detector”.
Physical Review D 101, 032009 – Published 28 February 2020
DOI: 10.1103/physrevd.101.032009
https://journals.aps.org/prd/abstract/10.1103/PhysRevD.101.032009 -
The ATLAS Collaboration.
“The ATLAS inner detector trigger performance in pp collisions at 13 TeV during LHC Run 2”.
European Physics Journal C - Published 08 March 2022
DOI: 10.1140/epjc/s10052-021-09920-0
https://link.springer.com/article/10.1140/epjc/s10052-021-09920-0
Contact
Location:
45a Borough Street, Brighton, BN1 3BG
Email:
dr.mario.grandi@gmail.com
Call:
+44 79 992 063 47