Max Planck Institute for Informatics
Department 1: Algorithms and Complexity
Campus E1 4, Room 324
66123, Saarbrücken
Germany
Email:
wellnitzmpi-inf.mpg.de
Currently, I'm a PhD student at the Max Planck Institute for Informatics, my advisor is Karl Bringmann. I am interested in designing (faster) algorithms and matching conditional lower bounds for problems that are solvable in polynomial time, and those that are (probably) not. In particular, I work on problems in the area of combinatorial pattern matching and on Knapsack-type problems. Further, I am interested in the (parametrized) counting complexity of problems such as counting how often a graph appears in another graph as an (induced) subgraph.
"Counting and Finding Homomorphisms is Universal for Parameterized Complexity Theory" [arXiv] We show that any problem P in #W[1] (or W[1]) is equivalent to the problem of counting homomorphisms between graphs of graph classes H(P) and G(P). SODA'20 |
"Counting Induced Subgraphs: An Algebraic Approach to #W[1]-hardness" [arXiv] [slides] We show a complexity dichotomy for the problem of counting induced subgraphs of a graph satisfying a property Φ for properties Φ that are non-trivial on edge-transitive graphs with a prime-power number of edges. MFCS'19 |
"Counting Answers to Existential Questions" [arXiv] We study the problem of counting conjunctive queries in (relational) databases and identify 5 (complexity) classes to which an instance may belong; thus refining existing classification results. ICALP'19 |
"Few Matches or Almost Periodicity: Faster Pattern Matching with Mismatches in Compressed Texts" [slides] We provide both new structural insight for pattern matching with mismatches in general, as well as a new, faster algorithm for pattern matching with mismatches in SLP-compressed texts. SODA'19 |
"Clique-Based Lower Bounds for Parsing Tree-Adjoining Grammars" [arXiv] [slides] We provide a conditional lower bound based of the k-clique conjecture for the parsing problem of tree-adjoining grammers, thereby showing that the current algorithms are likely optimal. CPM'17 |