Manuel Cáceres

Manuel Cáceres

Postdoctoral Researcher

Aalto University

manuel.caceres AT


I am an HIIT Postdoctoral Fellow at Aalto University working in the group Combinatorics of Efficient Computations led by Parinya Chalermsook. Before that I was a Postdoctoral Researcher at University of Helsinki working in the Graph Algorithms team led by Alexandru I. Tomescu. Previously, I was a Doctoral Student in the same group. My Doctoral Thesis is titled “Parameterized and Safe & Complete Graph Algorithms for Bioinformatics”. Before that, I was a Master Student from the Department of Computer Science at University of Chile. I worked under the supervision of Gonzalo Navarro. My thesis is titled “Compressed Suffix Trees for Repetitive Collections based on Block Trees”.


  • Algorithmic Bioinformatics
  • Graph Algorithms
  • String Algorithms
  • Compressed Data Structures
  • Safe & Complete Algorithms
  • Parameterized Algorithms


  • PhD. in Computer Science, 2023

    University of Helsinki

  • MSc. in Computer Science, 2019

    University of Chile

  • Computer Science Engineering, Minor in Algorithms and Combinatorial Optimization, 2019

    University of Chile

  • BSc. in Computer Science, 2016

    University of Chile

Recent Publications

Practical Minimum Path Cover

We present the first publicly available high-performance implementation of state-of-the-art MPC algorithms, including the parameterized approaches. Our experiments on random DAGs show that parameterized algorithms are orders-of-magnitude faster on dense graphs. Additionally, we present new pre-processing heuristics based on transitive edge sparsification. We show that our heuristics improve MPC-solvers by orders-of-magnitude.

Finding Maximal Exact Matches in Graphs

We show an $O(n\cdot L \cdot d^{L-1} + m + M_{\kappa,L})$-time algorithm finding all $\kappa$-MEMs between $Q$ and $G$ spanning exactly $L$ nodes in $G$, where $n$ is the total length of node labels, $d$ is the maximum degree of a node in $G$, $m = |Q|$, and $M_{\kappa,L}$ is the number of output MEMs.

Width Helps and Hinders Splitting Flows

We show that, for acyclic graphs, considering the width of the graph yields advances in our understanding of MFD approximability. For the version of the problem that uses only non-negative weights, we identify and characterise a new class of width-stable graphs, for which a popular heuristic is a $O(\log Val(X))$-approximation ($Val(X)$ being the total flow of $X$), and strengthen its worst-case approximation ratio from $\Omega(\sqrt{m})$ to $\Omega(m/\log{m})$ for sparse graphs. We also study a new problem on graphs with cycles, Minimum Cost Circulation Decomposition (MCCD), and show that it generalises MFD through a simple reduction. For the version allowing also negative weights, we give a $(\lceil \log ||X|| \rceil + 1)$-approximation ($||X||$ being the maximum absolute value of $X$ on any edge) using a power-of-two approach, combined with parity fixing arguments and a decomposition of unitary circulations ($||X||\le 1$), using a generalised notion of width for this problem.

Sorting Finite Automata via Partition Refinement

We provide a $O(|\delta| \log |Q|)$-time algorithm computing a co-lex total preorder when the input is a Wheeler NFA, and an algorithm with the same time complexity computing the smallest-width co-lex partial order of any DFA.

Minimum Chain Cover in Almost Linear Time

A minimum chain cover (MCC) of a $k$-width directed acyclic graph (DAG) $G = (V, E)$ is a set of $k$ chains (paths in the transitive closure) of $G$ such that every vertex appears in at least one chain in the cover. We present an algorithm running in time $O(T_{MF}(|E|) + (|V|+|E|)\log{k})$.