Andreas Grigorjew

About me

Welcome! I am a postdoctoral researcher at the University of Helsinki in the Department of Computer Science, working with Alexandru I. Tomescu. I am part of the Graph Algorithms Team.

My research interests broadly lie in theoretical computer science and include graph theory, algorithms and combinatorics.
In spring 2024, I visited the Applied Algorithms Group at the Montana State University.

In my free time, I enjoy climbing/bouldering and spending time in nature.

You are welcome to contact me via email: contact [at] andreasgrigorjew [dot] de (PGP key).

Research

Publications and preprints

• Manuel Cáceres, Andreas Grigorjew, Wanchote Po Jiamjitrak, Alexandru I. Tomescu
  Maximum Coverage k-Antichains and Chains: A Greedy Approach
  arXiv, 2025 (view preprint)
• Andreas Grigorjew, Wanchote Po Jiamjitrak, Brendan Mumey, Alexandru I. Tomescu
  Parameterised Approximation and Complexity of Minimum Flow Decompositions
  arXiv, 2024 (view preprint)
• Andreas Grigorjew, Fernando H. C. Dias, Andrea Cracco, Romeo Rizzi, Alexandru I. Tomescu
  Accelerating ILP solvers for Minimum Flow Decompositions through search space and dimensionality reductions
  SEA 2024 - International Symposium on Experimental Algorithms, 14:1-14:19, 2024 (view)
  Best paper award runner-up
• Manuel Cáceres, Massimo Cairo, Andreas Grigorjew, Shahbaz Khan, Brendan Mumey, Romeo Rizzi, Alexandru I. Tomescu, Lucia Williams
  Width Helps and Hinders Splitting Flows
  ACM Transactions on Algorithms, 20(2), 1--20, 2024 (view extended version)
  ESA 2022 - European Symposium on Algorithms (Track A), 31:1--31:14, 2022 (view)
• Andreas Grigorjew, Artur Gynter, Fernando Dias, Benjamin Buchfink, Hajk-Georg Drost, Alexandru I. Tomescu
  Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD
  Genome Biology 24, 168, 2023 (view)

PhD thesis: Algorithms and Graph Structures for Splitting Network Flows, in Theory and Practice

Software

• Optimized FD: An optimized flow decomposition solver using Integer Linear Programming, for exact optimum and heuristic solutions (GitHub).
  Flow decompositions are used e.g. for RNA transcriptict assembly or viral strain reconstruction.
• EMERALD: a protein sequence aligner that finds protein regions preserved in the suboptimal alignment space (GitHub).

Teaching

I was a teaching assistant in the following courses holding exercise classes:

• Models of Computation, spring 2024 (Helsinki)
• Models of Computation, spring 2023 (Helsinki)
• Elements of Bioinformatics, autumn 2022 (Helsinki)
• Design and analysis of algorithms, autumn 2021 (Helsinki)
• Mathematik für Data Science und Physik Studierende I, winter 2021 (Erlangen)
• Analysis III, winter 2019 (Erlangen)
• Theorie der Programmierung, summer 2019 (Erlangen)
• Theorie der Programmierung, summer 2018 (Erlangen)