Dr. Jeremias Berg obtained his Master’s degree in Mathematics in 2014 and his PhD in Computer Science in 2018 from the University of Helsinki. His doctoral studies were supervised by Professor Matti Järvisalo. Berg’s doctoral research and thesis “Solving Optimization Problems via Maximum Satisfiability: Encodings and Re-Encodings“ received both national and international recognition in the form of awards from both the University of Helsinki and the Association for Constraint Programming. After graduating Dr. Berg undertook two research visits: to the University of Melbourne in 2018 and to the University of Toronto in 2019. Both visits were fruitful, resulting in publications and ongoing research collaboration. Currently, Berg is working as an HIIT Postdoctoral Fellow in the Constraint Reasoning and Optimisation group at the University of Helsinki. Berg is actively teaching at the university, for example, supervising Bachelor’s and Master’s theses. Berg also contributes to the University’s core duty of community relations, for example via his membership in the Young Academy Finland (Nuorten Tiedeakatemia). More information regarding Berg and his research can be found on his webpage jeremiasberg.com.
Berg’s research focuses on developing effective, general-purpose, tools for solving NP-hard optimization problems arising in data analysis, machine learning and AI. He is especially interested in logic-based declarative approaches. His doctoral research focused on the Boolean optimization paradigm of Maximum Satisfiability (MaxSAT). The research developed the theoretical and algorithmic understanding of MaxSAT. It also resulted in state-of-the-art MaxSAT-based solution methods to central data-analysis problems, including data visualisation, clustering and Bayesian network structure learning. Berg’s more recent research builds on his doctoral research. His recent interests include incomplete solving, i.e. developing algorithms for quickly computing good solutions to difficult optimisation problems. He is the primary developer of the state-of-the-art incomplete MaxSAT solver Loandra – one of the best performing solvers in the recent MaxSAT Evaluations. His research has also contributed to numerous other tools for solving NP-hard problems: including the CP solver solver Geas, the MaxSAT preprocessor MaxPre, and Triangulator, an implementation of the Bouchitté-Todinca Algorithm for solving various graph optimisation problems.
Berg’s recent research highlights include the 2021 Academy of Finland award for his project entitled “Next-Generation Trustworthy Constraint Optimisation.” His project was one of forty-four post-doctoral projects that received funding for 2021. More information about Berg’s Academy of Finland project can be here A complete list of Academy of Finland projects can be found here
Sample of publications in 2021:
[1] Niskanen, A, Berg, J & Järvisalo, M 2021, Enabling Incrementality in the Implicit Hitting Set Approach to MaxSAT under Changing Weights. in LD Michel (ed.), Proceedings of the 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). pp. 44:1-44:19
[2] Ihalainen, HE, Berg, J & Järvisalo, M 2021, Refined Core Relaxation for Core-Guided MaxSAT Solving. in LD Michel (ed.), Proceedings of the 27th International Conference on Principles and Practice of Constraint Programming (CP 2021), pp. 28:1-28:19.
[3] Smirnov, P, Berg, J & Järvisalo, M 2021, Pseudo-Boolean Optimization by Implicit Hitting Sets. in LD Michel (ed.), 27th International Conference on Principles and Practice of Constraint Programming (CP 2021), pp. 51:1-51:20