Andrei Khmelnitsky
About Andrei Khmelnitsky
Andrei Khmelnitsky is a Research Data Scientist currently working at Callsign in London, England. He has extensive experience in theoretical and mathematical physics, with previous positions at notable institutions including CERN, EPFL, and ICTP.
Current Role at Callsign
Andrei Khmelnitsky serves as a Research Data Scientist at Callsign since 2021. In this role, he develops and maintains machine learning models focused on biometric authentication and fraud detection. His work involves applying quantitative and analytic skills to create innovative solutions for various business challenges.
Previous Experience at EPFL
Prior to his current position, Andrei worked as a Postdoctoral Researcher at EPFL (École polytechnique fédérale de Lausanne) from 2018 to 2020. During his two years in the Lausanne Area, Switzerland, he contributed to research initiatives that advanced the field of data science and its applications.
Experience at Abdus Salam International Centre for Theoretical Physics
Andrei was a Postdoctoral Fellow at the Abdus Salam International Centre for Theoretical Physics (ICTP) from 2015 to 2018. His three-year tenure in the Trieste Area, Italy, involved engaging in theoretical research that supported the advancement of physics and data science methodologies.
Educational Background in Physics
Andrei Khmelnitsky completed his Master of Science (MSc) in Theoretical and Mathematical Physics at Lomonosov Moscow State University (MSU) from 2002 to 2008. He further pursued his Doctor of Philosophy (PhD) in Theoretical and Mathematical Physics at the Institute for Nuclear Research RAS, Moscow, from 2008 to 2014.
Research Experience at CERN and Other Institutions
Andrei has extensive research experience, having worked as a Research Associate at CERN from 2009 to 2011 and at Ludwig-Maximilians-Universität (LMU) München from 2011 to 2014. He also held a Research Associate position at Imperial College London for 11 months from 2020 to 2021, contributing to various research projects in high energy physics and data science.