Computer Science and Engineering
Gothenburg University | Chalmers
Head
Mailing address:
Department of Computer Science and Engineering
Chalmers | Gothenburg University
41296 Gothenburg, Sweden
Visiting address:
Department of Computer Science and Engineering
Chalmers | Gothenburg University
Chalmers D&IT 6480
Rännvägen 6 B
41258 Göteborg, Sweden
Phone: +46-76-608 69 63
Fax:
E-mail: alexander@schlieplab.org
Alexander Schliep received a MS in Mathematics from 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. He is a permanent member of DIMACS, the Center for Discrete Mathematics and Theoretical Computer Science. In 2016 he joined the department for Computer Science and Engineering (CSE) which is jointly between Chalmers and Gothenburg University.
He 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.
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
J. Martinsson, A. Schliep, B. Eliasson and O. Mogren Blood glucose prediction with variance estimation using recurrent neural networks. Journal of Healthcare Informatics Research 2020, 4, 1–18.
F.T. Bakker, A. Antonelli, J.A. Clarke, J.A. Cook, S.V. Edwards, P.G. Ericson, S. Faurby, N. Ferrand, M. Gelang, R.G. Gillespie, M. Irestedt, K. Lundin, E. Larsson, P. Matos-Maraví, J. Müller, T. von Proschwitz, G.K. Roderick, A. Schliep, N. Wahlberg, J. Wiedenhoeft and M. Källersjö The Global Museum: natural history collections and the future of evolutionary science and public education. PeerJ 2020, 8:e8225.
G.A. Bravo, A. Antonelli, C.D. Bacon, K. Bartoszek, M.P.K. Blom, S. Huynh, G. Jones, L.L. Knowles, S. Lamichhaney, T. Marcussen, H. Morlon, L.K. Nakhleh, B. Oxelman, B. Pfeil, A. Schliep, N. Wahlberg, F.P. Werneck, J. Wiedenhoeft, S. Willows-Munro and S.V. Edwards Embracing heterogeneity: building the Tree of Life and the future of phylogenomics. PeerJ 2019, 7:e6399.
A. Ekström, C. Forssén, C. Dimitrakakis, D. Dubhashi, H. Johansson, A. Muhammad, H. Salomonsson and A. Schliep Bayesian optimization in ab initio nuclear physics. Journal of Physics G: Nuclear and Particle Physics 2019, 46:095101.
L. Heckmann Barbalho de Figueroa, R. Salman, J. Horkoff, S. Chauhan, M. Davila, F. Gomes de Oliveira Neto and A. Schliep A Modeling Approach for Bioinformatics Workflows: A Design Science Study. In IFIP Working Conference on The Practice of Enterprise Modeling, Springer, 167–183, Nov 2019. Proceedings of the Practice of Enterprise Modelling Conference (PoEM).
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.
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.