Professor, DTU Compute,
Section for Cognitive Systems
Richard Petersens Plads building 321 r. 118
DTU Compute
Technical University of Denmark
e-mail: mmor@dtu.dk
About
I am professor of machine learning for the life sciences at the Section for Cognitive Systems, DTU Compute. My field of research is machine learning and data science where I research methods for unsupervised learning and pattern recognition in particular with focus on multi-way/tensor decomposition approaches, statistical complex network modeling approaches and Bayesian inference with applications to life-science data.
Short Biography
Copenhagen University
Bio-physics and Mathematics: (Fall 1999 – Summer 2001)
Washington State University
Exchange student following courses within Computational Neuroscience (Spring 2004)
Technical University of Denmark
Cand. Polyt. Applied Mathematics (February 2005)
PhD Intelligent Signal Processing Group at DTU Informatics (29. September 2008)
PostDoc Intelligent Signal Processing Group at DTU Informatics (2008-2009)
Assistant Professor, Section for Cognitive Systems, DTU Informatics (2010-2012)
Associate Professor, Section for Cognitive Systems, DTU Compute (2012-2019)
Professor of Machine Learning for the Life-Sciences (current position), Section for Cognitive Systems, DTU Compute (2020-)
Stanford University
Visiting Ph.D. Student at Department for Scientific Computing (Summer 2006 – Fall 2006)
Host: Professor Gene H. Golub
UC Berkeley
Visiting Ph.D. Student at Department of Mathematics (Fall 2007)
Host: Morrey Assistant Professor Lek-Heng Lim
Publications
Key Research Domains
Tensor Decomposition
Statistical Modeling of (Dynamic) Complex Networks
(Non-)Parametric Bayesian Inference
Polytope Extraction and Archetypal Analysis
Functional Neuroimaging Data Modeling
Life-Science Data Analysis and Data Fusion
Recent popular communications
Vi er ved at miste internettet til spam (P3 Essensen)
Detecting AI-manipulated content is a challenging arms race (DTU)
Kunstig intelligens har lavet et af disse billeder, men hvilket? (Politiken)
Deep Fake Voice Conversion (radio 24syv)
Hvad er Kunstig Intelligens (InterMat 2022)
Vigtigheden af at universiteterne udbyder data science uddannelser… (ING/DataTech 2022)
Machine Learning og Kunstig Intelligens (IDA Future Talks 2020)
Machine Learning (podcast ved Kompetencer i Tech)
Current PhD students and PostDocs
Alexander Neergaard Zahid (PostDoc, Deep Learning and EEG)
Jesper Løve Hinrich (PostDoc, Probabilistic Tensor Decomposition)
Niccolò Ciolli (PhD – project description)
Kenny Falkær Olsen (PhD)
Javier Garcia Ciudad (PhD, co-supervisor)
Anna Emilie Jennow Wedenborg (PhD – project description)
Laura Rose (PhD, co-supervisor)
Anders Stevnhoved Olesen (PhD – project description)
Louis Boucherie (PhD –project description, co-supervisor)
Former PhD students and PostDocs
Abdulkadir Çelikkanat (PostDoc, Graph Representation Learning)
Nathan Churchill (PostDoc, tensor and mixture modeling of fMRI)
Matthew Liptrot (PostDoc, diffusion MRI processing and modeling)
Nikolaos Nakis (PhD- project description)
Rasmus Malik Thaarup Høegh Lindrup (PhD – project description)
Ali Mohebbi (PhD – project description)
Jesper Løve Hinrich (PhD – project description)
Rasmus Bonnevie (PhD – project description, co-supervisor)
Maciej Jan Korzepa (PhD – project description, co-supervisor)
Laura Frølich (PhD – project description, co-supervisor)
Søren Føns Vind Nielsen (PhD – project description)
Kristoffer Jon Albers (PhD, PostDoc – project description, co-supervisor)
Karen Marie Sandø Ambrosen (PhD, PostDoc – project description)
Rasmus Røge (PhD – project description)
Kit Melissa Larsen (PhD – project description)
Tue Herlau (PhD – project description)