Alexander Schliep

Head

BTU Cottbus-Senftenberg
Faculty of Health Sciences
Lipezker Straße 47
03048 Cottbus
Germany

Office: 9.323
Phone: ‭+49-355-5818-721‬
E-mail: alexander@schlieplab.org

Since October 2022 Alexander Schliep is the chair for Medical Bioinformatics with a focus on patient data acquisition at the Faculty of Health Sciences Brandenburg at Brandenburg Technical University Cottbus-Senftenberg, with a secondary appointment at the University of Gothenburg.

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 the Department of Computer Science and the BioMaPS Institute for Quantitative Biology. In 2016 he joined the University of Gothenburg in the department of Computer Science and Engineering (CSE), which is jointly between Chalmers Technical University and University of Gothenburg.

He is a former NIH-funded PI, member of the WASP (Wallenberg AI, Autonomous Systems and Software Program) research collegium and serves as an associate editor for BMC Bioinformatics and PeerJ as well as a regular PC member of top-level bioinformatics conferences.

Further information can be found at his Google Scholar https://scholar.google.com/citations?user=djKBKswAAAAJ or the Orcid Profile 0000-0002-3555-3188.

Publications

Bello et al.. Compressed computations using wavelets for hidden Markov models with continuous observations. PLOS One 2023, 6:18, e0286074.

Johansson et al.. Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes. In 2023 IEEE International Conference on Big Data (BigData), IEEE Computer Society, 5260–5265, Dec 2023.

Johansson et al.. de Novo Generated Combinatorial Library Design. Digital Discovery 2023, issue 1, 2024, 122–135. First published 27 Nov 2023.

Tavara et al.. Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II, Springer, 459–467, 2022.

Viet Johansson et al.. Using active learning to develop machine learning models for reaction yield prediction. Molecular Informatics 2022, 41, 2200043.

Presentations

Sept. 24, 2024. Molecular simulation and machine learning to delineate target binding of therapeutic oligonucleotides. Invited Talk at WASP Gothenburg, Sweden

Feb. 22, 2024. Bioinformatics for Pathogen Genomics. Invited Talk at Zentrum für Künstliche Intelligenz in der Public Health-Forschung (ZKI-PH), Robert-Koch-Institut

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

Project or Software Lead

ASODrugDesignAI: AI-Accelerated Oligonucleotide Drug Design.

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.