Docent Antti Hyttinen obtained his Master’s degree in Information Technology in Tampere University (of Technology) in 2004 and his PhD in Computer Science in 2013 from the University of Helsinki. His doctoral studies were supervised by Docent Patrik Hoyer. Dr. Hyttinen did research at California Institute of Technology during 2014. Dr. Hyttinen has also obtained the competitive personal post-doc funding from the Academy of Finland (2016). Dr. Hyttinen received the title of Docent in February 2020. Currently, Dr. Hyttinen works as a HIIT Research Fellow in the Sums of Products research group, collaborating with several research groups at the department.
Hyttinen’s research focuses on causal inference and probabilistic graphical models. Dr. Hyttinen has developed theory and structure learning algorithms for several different types of probabilistic graphical models. Many of these are causal models that allow also for latent confounding and feedback. These methods build on many different kinds of techniques such as Branch and Bound, MIP, MaxSAT, ASP, and dynamic programming. In addition, Dr. Hyttinen has developed methods for causal effect identification, experiment selection and combining different types of data sources for the aforementioned tasks.
During 2021, Dr. Hyttinen published papers on particularly in causal effect identification and structure estimation. Dr. Hyttinen lectured the probabilistic graphical models course in the data science master program. He also participated in research and thesis supervision at Ph.D. and M.Sc. levels.
Sample of publications in 2021:
[1] K. Rantanen, A. Hyttinen, M. Järvisalo. Maximal Ancestral Graph Structure Learning via Exact Search. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021.
[2] S. Tikka, A. Hyttinen, J. Karvanen. Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach. Journal of Statistical Software, Articles, Volume 99, Issue 5, 2021.
[3] J. Karvanen, S. Tikka, A. Hyttinen. Do-search – a tool for causal inference and study design with multiple data sources. Epidemiology (journal), Volume 32, Issue 1, p. 111-119, 2021.