Maciej Zdanowicz
About Maciej Zdanowicz
Maciej Zdanowicz is a Machine Learning Researcher with a strong academic background in Mathematics and Computer Science. He has worked at various prestigious institutions, including the University of Amsterdam and Huawei Ireland Research Center, and currently contributes to RTB House in Warsaw, Poland.
Work at RTB House
Maciej Zdanowicz has been employed at RTB House as a Machine Learning Researcher since 2022. He is based in Warsaw, Mazowieckie, Poland. In this role, he focuses on developing machine learning algorithms and software solutions that can enhance operational efficiency and support development processes within the organization.
Education and Expertise
Maciej Zdanowicz earned a Doctor of Philosophy (PhD) in Mathematics from the University of Warsaw, where he studied from 2012 to 2017. Prior to that, he completed a Master of Science (MS) in Mathematics and Computer Science at the same institution from 2006 to 2012. His academic background provides a strong foundation in mathematical principles, which he applies in his research and work in machine learning.
Background
Before transitioning to machine learning and data analysis, Maciej Zdanowicz specialized in algebraic geometry research. His academic journey includes significant postdoctoral roles, including a position at the Ecole Polytechnique Fédérale de Lausanne from 2017 to 2020 and at the University of Amsterdam from 2020 to 2021. These experiences contributed to his expertise in data-driven research.
Previous Work Experience
Maciej Zdanowicz has held various positions in the field of data science and research. He worked as a Data Scientist at Huawei Ireland Research Center from 2021 to 2022. His role involved applying machine learning techniques to real-world problems. His previous postdoctoral positions further enriched his experience in research and development.
Research Interests
Maciej Zdanowicz aims to create software solutions that can positively impact operations and development processes in both business and research settings. He enjoys simplifying complex data into clear, actionable insights, demonstrating his commitment to making data comprehensible and useful.