This site aims to give an overview of my scientific work, interests, and current and past research outputs. I am Jesper Løve Hinrich, currently a Postdoctoral researcher at the DTU Compute in the Section for Cognitive Systems and affiliated with the UCPH chemometrics group and the Pioneer Centre for AI .
I have a background in mathematics, statistics and computer science. My research interests are machine learning, intelligence and life science applications.
The main part of my research is focused on multi-way methods for decomposition and deconvolution of measurements in physical, chemical, and biological systems. This work is centered around probabilistic (Bayesian) modeling, inclusion of domain expertise, and providing interpretable models.
The core of my research is Mathematics and Statistical Science, in particular probabilistic machine learning. My work focuses on developing statistical methods for generating robust insights through Bayesian/probabilistic modelling, the incorporation of prior knowledge, and uncertainty quantification in relation to the data input and latent parameters.
I have a strong interesting in tensor modelling, with tensors being generalizations of vectors (1st order tensors) and matrices (2nd order tensor) to N-dimensional arrays (N'th order tensors), also known as multi-way or N-way arrays. A 3rd order tensor can thus be thought of as a data cube where each dimension of this cube corresponds to a mode of the tensor. Whereas conventional 2-way matrices are well understood the geometry and properties of higher order arrays are still far from fully understood. Tensors are common in modern life science data, such as;
Several tensor analysis toolboxes are now publicly available. The most prominent ones being the n-way toolbox, Tensor Toolbox, Tensorlab and Tensorly . While extremely successful, these toolboxes do not allow for probabilistic (fully Bayesian) modeling and are based solely on maximum likelihood estimation. To mitigate this, the development of the Probabilistic Tensor Toolbox was initiated as part of my PhD and is in continuous - but intermittent - development.
A probabilistic approach allows for handling parameter uncertainty in a natural way and enables incorporation of more realistic noise model assumptions such as mode heteroscedastic, auto-regressive or Markovian noise.
Brief overview of my work and educational experience, for details see the link to my CV.
Peer-reviewed scientific research articles (journal and conference) are given below. If the links do not work or the article is unaccessible to you, send me an e-mail - questions are also welcome.
Armstrong, M. D. S., Hinrich, J. L., de la Mata, A. P., & Harynuk, J. J. (2023). PARAFAC2× N: Coupled decomposition of multi-modal data with drift in N modes. Analytica Chimica Acta, 1249, 340909. (Link)
Olsen, A. S., Høegh, R. M. T., Hinrich, J. L., Madsen, K. H., & Mørup, M. (2022). Combining Electro-and Magnetoencephalography Data using Directional Archetypal Analysis. Frontiers in Neuroscience, 1256. (Link)
Andersen, B. R., Ammitzbøll, I., Hinrich, J., Lehmann, S., Ringsted, C. V., Løkkegaard, E. C. L., & Tolsgaard, M. G. (2022). Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study. BMJ open, 12(3), e049046. (Link)
Hinrich, J. L., Madsen, K. H., & Mørup, M. (2020). The probabilistic tensor decomposition toolbox. Machine Learning: Science and Technology, 1(2), 025011. (Link)
Andersen, B. R., Hinrich, J. L., Rasmussen, M. B., Lehmann, S., Ringsted, C., Løkkegaard, E., & Tolsgaard, M. G. (2019). Social ties between team members affect patient satisfaction: a data-driven approach to handling complex network analyses. Advances in Health Sciences Education, 1-26. (Link)
Krohne, L. G., Wang, Y., Hinrich, J. L., Mørup, M., Chan, R. C., & Madsen, K. H. (2019). Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task. Human brain mapping, 40(17), 4965-4981. (Link)
Hinrich JL, Bardenfleth SE, Røge RE, Churchill NW, Madsen KH, Mørup M. Archetypal Analysis for Modeling Multisubject fMRI Data. IEEE Journal of Selected Topics in Signal Processing. 2016 Oct;10(7):1160-71. (Link)
Hinrich, J. L., & Mørup, M. (2019, September). Probabilistic Tensor Train Decomposition. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5). IEEE. (Link)
Jørgensen, P. J. H., Nielsen, S. F., Hinrich, J. L., Schmidt, M. N., Madsen, K. H., & Mørup, M. (2019). Analysis of Chromatographic Data using the Probabilistic PARAFAC2. In Thirty-Third Annual Conference on Neural Information Processing Systems. (Link)
Hinrich JL, Nielsen SF, Madsen KH, Mørup M. Variational bayesian partially observed non-negative tensor factorization. In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) 2018 Sep 17 (pp. 1-6). IEEE. (Best Student Paper Award) (Link)
Hinrich JL, Mørup M. Probabilistic sparse non-negative matrix factorization. In International Conference on Latent Variable Analysis and Signal Separation 2018 Jul 2 (pp. 488-498). Springer, Cham. (Link)
Hinrich JL, Nielsen SF, Riis NA, Eriksen CT, Frøsig J, Kristensen MD, Schmidt MN, Madsen KH, Mørup M. Scalable Group Level Probabilistic Sparse Factor Analysis. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 6314-6318. (In proceedings) (Preprint on Arxiv)
Beliveau V, Papoutsakis G, Hinrich JL, Mørup M. Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI Data. In Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging 2016 Oct 21 (pp. 186-198). Springer, Cham. (In proceedings)
Hinrich JL, Nielsen SF, Madsen KH, Morup M. Variational group-PCA for intrinsic dimensionality determination in fMRI data. In Pattern Recognition in Neuroimaging (PRNI), 2016 International Workshop on 2016 Jun 22 (pp. 1-4). IEEE.(In proceedings)
My research is a combination of theoretical and practical machine learning. The practical part is implemented in either Matlab or Python and most of my projects can be found on GitHub or via the following links.
The projects are maintained and developed based on current research topics and user feedback - use Github Issues or send me an e-mail.