Alexander Schliep

Head

Department of Computer Science and Engineering
Chalmers | Gothenburg University
Chalmers D&IT 5481
Rännvägen 6 B
41258 Göteborg, Sweden

Phone: +46-76-608 69 63
E-mail: alexander@schlieplab.org

Alexander Schliep received a MS in Mathematics while a Fulbright scholar at the University of Delaware working with Felix Lazebnik in extremal graph theory and a PhD degree in computer science from the Center for Applied Computer Science (ZAIK/ZPR) at the Universität zu Köln, Germany (2001; advisor Rainer Schrader), in collaboration with David C. Torney in the Theoretical Biology and Biophysics Group (T-10) at Los Alamos National Laboratory. From 2002-2009 he was the group leader of the Bioinformatics Algorithms Group in the Department for Computational Molecular Biology at the Max Planck Institute for Molecular Genetics in Berlin.

From 2009-2016 he was an associate professor Rutgers University with a joint position in theDepartment of Computer Science and the BioMaPS Institute for Quantitative Biology. In 2016 he joined the department for Computer Science and Engineering (CSE) which is jointly between Chalmers and Gothenburg University.

He is member of the WASP (Wallenberg AI, Autonomous Systems and Software Program) research collegium and serves as an associate editor for BMC Bioinformatics and http://peerj.com. Previously, he served as an associate editor for Discrete Mathematics, Algorithms and Applications.

Further information can be found at his Google Scholar profile and Orcid Profile 0000-0002-3555-3188.

Publications

Tavara et al.. Effects of Network Topology On the Performance of Consensus and Distributed Learning of SVMs Using ADMM. PeerJ Computer Science 2021, 7, e397.

Ryazanov et al.. Deep Learning for Deep Waters: An Expert-in-the-loop Machine Learning Framework for Marine Sciences. Journal of Marine Science and Engineering 2021, 9:2, 169.

Gustafsson et al.. Fast parallel construction of variable-length Markov chains. BMC Bioinformatics 2021, 22:1, 1–23.

Tavara et al.. Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-based SVM. In Sep 2021. PharML 2021 workshop at ECML/KDD 2021.

Johansson et al.. Reinforcement Learning as an Alternative to Reachability Analysis for Falsification of AD Functions. In Dec 2021. To appear at ML4AD (NeurIPS workshop on Autonomous Driving).

Presentations

March 6, 2020. Compression and computation in Hidden Markov Models. Invited Talk at Department of Computer Science, University of Helsinki, Helsiniki, Finland

Nov. 29, 2018. Machine learning in biology and health. Invited Talk at Keynote at the 10th Swedish Meeting on Mathematics in Biology, KTH and Stockholm University, Stockholm, Sweden

Jan. 24, 2017. Statistical Bioinformatics at Genome-Scale. Invited Talk at SciLifeLab, Stockholm University, Stockholm, Sweden

Nov. 11, 2016. Compressive Omics: Data science for biomedical applications. Invited Talk at 4th Swedish Workshop on Data Science (SweDS 2016), Skövde, Sweden

Oct. 17, 2016. Statistical Bioinformatics for Genome-Scale Data. Invited Talk at Lifescience Area of Advance Seminar Series, Chalmers, Gothenburg, Sweden

Project or Software Lead

GenExpTimecourses: Analysis of gene expression time-courses.

ArrayCGH: Analyzing comparative genomic hybridization data.

SCG: Bioinformatics for Single-Cell Genomics.

Tiling: Design of Tiling Arrays.

MicrorarrayDetection: Detecting biological agents with DNA Micorarray.

HomologyClassification: Detecting remote homologs as a classification problem.

AlgorithmAnimations: Displaying how algorithms compute.

RemoteHomologues: Identifying clusters of remote homologues.

Tuberculosis: Image processing and systems biology of macrophage infection.

AlgoEngineering: Reduced representations and cache-efficient algorithms and data structures for bioinformatics.

MVQueries: Classifying short gene expression time-courses.

Tileomatic: Design of oligonucleotide arrays.

GHMM: General Hidden Markov Model library.

Gato: Graph Animation Toolbox.

MCPD: Markov Chain Pooling Decoder.

Proclust: Protein clustering by transitive homology.

PBQ: The Python Batch Queue.