Kirill Bykov is currently pursuing his doctoral research in Machine Learning at the Technical University of Berlin (TU Berlin). He is a member of the Understandable Machine Intelligence lab, under the guidance of Prof. Dr. Marina Höhne. Prior to embarking on his doctoral journey, Kirill earned a Cum Laude Master’s degree in Computer Science Engineering from TU Berlin and TU Eindhoven, as part of EIT Digital double-degree program. His academic foundation was laid with a Bachelor’s degree in Applied Mathematics and Computer Science from Saint-Petersburg State University. Kirill’s research primarily focuses on Explainable AI. He has a particular interest in global explanation methods, mechanistic interpretability, and Bayesian Neural Networks.
- 2020 - Cum Laude MSc, Computer Science and Engineering (double degree TU Berlin and TU Eindhoven)
- 2018 - EIT Digital Excellence scholarship recipient
- 2018 - Prize winner of Skoltech statistical Learning Theory Olympiad
- 2018 - Prize winner of HSE applied Mathematics and Computer Science Olympiad
- 2018 - International data science olympiad (IDAO) finalist
- Machine Learning
- Explainable AI
- Mechanistic interpretability
- Bayesian Neural Networks
Dilyara Bareeva, Marina M.-C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov
Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina MC Höhne
Kirill Bykov, Laura Kopf, Marina M.-C. Höhne
Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne
Two research groups associated with BIFOLD take part in the organization of the 2nd World Conference on Explainable Artificial Intelligence. Each group is hosting a special track and has already published a Call for Papers. Researchers are encouraged to submit their papers by March 5th, 2024.