Martin Gajek
About Martin Gajek
Martin Gajek is the Director of Machine Learning and Applied Research at Thomson Reuters, where he has worked since 2023. He has a strong background in machine learning and applied research, with previous roles at Casetext, Spin Memory, and IBM T. J. Watson Research Center.
Current Role at Thomson Reuters
Martin Gajek serves as the Director of Machine Learning and Applied Research at Thomson Reuters. He has held this position since 2023, contributing to the company's focus on advancing machine learning technologies. His role involves overseeing research initiatives and applying machine learning techniques to enhance product offerings.
Previous Experience at Casetext
Prior to joining Thomson Reuters, Martin Gajek worked at Casetext, where he held the position of Head of Machine Learning from 2022 to 2023. During his tenure, he led efforts in developing machine learning solutions until the company was acquired by Thomson Reuters. He also served as a Machine Learning Engineer at Casetext from 2020 to 2022.
Educational Background
Martin Gajek has an extensive educational background in physics and applied physics. He earned a Bachelor of Science (BS) and a Master of Science (MS) in Physics from Sorbonne University. He also completed a Ph.D. in Applied Physics at the same institution. Additionally, he was a Postdoctoral Research Associate at the University of California, Berkeley, where he studied Physical Sciences from 2006 to 2010.
Research and Development Experience
Martin Gajek has a solid foundation in research and development, with roles spanning various organizations. He worked as a Graduate Research Assistant at ICMAB - UAB in Barcelona from 2002 to 2003. He also held positions as a Postdoctoral Researcher at IBM T. J. Watson Research Center from 2010 to 2013 and as a Research Scientist at Spin Memory from 2013 to 2018, focusing on magnetic materials.
Technical Skills and Expertise
Martin Gajek possesses strong skills in statistical modeling and analysis, complemented by extensive programming experience. His expertise spans both computer hardware and software industries, particularly in machine learning and deep learning. His work has contributed to advancements in these fields, showcasing his technical capabilities.