Base
| Name | |
| Professional Classification | |
| Job Title | Data Scientist |
| Company or Institution | EPAM Enterprise Analytics |
| Location (City, Country) | Budapest |
Summary
| Summary Bio | I am a data scientist, ex-particle physicist and ex-time metrologist with over a decade’s worth of experience working at the forefront of scientific research within large international collaborations. I have a PhD in particle physics and spent several years based at the European Organization for Nuclear Research (CERN) in Geneva and was a member of the team that discovered the Higgs boson, the observation of which led to the award of the 2013 Nobel Prize in Physics. I played a major role in the development of the core software framework and algorithms for a real-time multi-stage cascade classifier that processes data at an input rate of 60 TB per second and I worked on using advanced machine learning techniques to develop classification algorithms for recognising particles based on their decay properties. I have over ten years’ experience of software development and testing, scientific computing, Monte Carlo simulation, mathematical modelling, and statistical analysis of large datasets and interpretation of results. I am co-author of more than 400 peer-reviewed scientific publications and have spoken in numerous international workshops and conferences. |
| Most Recent Experience | Processed large volumes of customer data using advanced analytical solutions. Designed and prepared customer reports, dashboards and analyses. Developed statistical and machine learning models that deliver better decisions and support customer goals. Turned analysed data into actionable insights and business value. Created high-quality data visualisations in cooperation with business analysts. Acted as a consultant, working directly and collaboratively with clients and other key stakeholders to ensure technical compatibility and user satisfaction. |
| Previous Work Experience | Performed statistical data analysis for the ALICE experiment at CERN, which recreates conditions that are believed to have existed a fraction of a second after the Big Bang. Used state-of-the-art machine learning to develop predictive classification algorithms for recognising particles based on their decay properties. The goal of this work is to find new variables and algorithms that can be used to extract more information from physics data than would have been possible otherwise, thereby improving discovery reach in searches for new physics and enhancing precision tests of particle physics theory at the energy frontier. |
