Dr Bjorn Jensen
- Lecturer in Computing Science (Computing Science)
I (Bjørn Sand Jensen) am a Lecturer in the Information, Data and Analysis Section in the School of Computing Science, University of Glasgow, working in the interdisciplinary field of probabilistic machine learning, mathematical modeling, interaction and data analysis.
In 2006, I received my Master’s degree in electronic engineering from the Technical University of Denmark, specialising in probabilistic machine learning and signal processing. From 2006-2009 I joined the R&D department of Bang & Olufsen ICEpower.
In 2009, I returned to the Technical University of Denmark to pursue a PhD degree on a fully-funded university stipend under the supervision of Prof Jan Larsen and Prof Lars Kai Hansen. I was awarded the PhD degree in Marts 2013 for my work on preference elicitation and machine learning for integrating subjective and objective observations using Bayesian and non-parametric methods. I continued my research as a Postdoctoral researcher in the Danish strategic research project CoSound, working with several partners from industry, public service and universities (the Royal Library, Denmark's Radio, Bang & Olufsen, Copenhagen University Humanities and Information Science, University of Glasgow, Aalborg University) on large-scale analysis and enrichment of multimedia and cultural heritage archives. I was appointed Lecturer in the School of Computing Science, University of Glasgow in May 2016.
Full CV [pdf]
- Probabilistic (/Bayesian) machine learning
- Planning/sequential decision-making under uncertainty (using principles from reinforcement learning, including Bayesian optimisation)
- Audio processing and modelling
- Eliciting and modelling human decision-making
- Bayesian non-parametrics (especially Gaussian processes)
- Uncertainty quantification in scientific inquiry (e.g. biological image analysis and digital humanities)
- KTP (Innovate UK) project, £178k with industry partner Qumodo, Frank Pollick and Paul Siebert.
- CRUK-funded project: A high-content platform for cellular mechanobiology in cancer research (PI: Nikolaj Gadegaard, ~£200k, my ownership 30%).
- EPSRC-funded project: Closed-loop data science (PI: Roderick Murray-Smith, total ~£3M, my ownership: 4%)) https://www.gla.ac.uk/schools/computing/research/researchsections/ida-section/closedloop/
Prospective PhD Students: Feel free to contact me via e-mail or take a look at the School's PGR website .
Current postgraduate research students (PhD and MSc by research):
(Anders: Primary, Yanni: Primary (with Ke Yuan); Martin: Primary (with Jeremy Singer), Antanas: Academic supervisor, Andrew: 2nd, Nikolas: 2nd)
- Anders Kirk Uhrenholt, 2017- (primary supervisor), funded by the College of Science and Engineering.
- Yanni Ji 2019- (primary supervisor), funded by the CSC (with Dr Ke Yuan).
- Antanas Kascenas, Sep 2018- (EngD with Canon medical research and the EPSRC Centre for Doctoral Training in Applied Photonics)
- Charvet, Valentin
Reinforcement Learning in Closed-loop data science
- Fyfe, Andrew
Adaptive Tools in Immersive Digital Music Performance
- Pitsillos, Nikos
An Introspection Approachto Self-Awareness in Robotics
- Sablotny, Martin
The Application of Machine Learning to Cyber Defence In Critical Applications
- Uhrenholt, Anders
Efficient target vector identification and Bayesian complexity reduction in Gaussian processes (tentative)
Completed postgraduate research projects (PhD and MSc by research):
- Marie F. Cutiongco, 2019, PhD (co-supervisor, Biomedical engineering, main supervisor: Nikolaj Gadegaard), Correlating single cell form and function under the influence of nanotopography.
- Jasper Kirton-Wingate, October 2018-2019 (MSc by Research with Widex a/s)
- Fatma A.I. Elsafoury, 2019, joint supervisor (with Simon Rogers and Chris Claassen), Measuring and Accounting for Spatial and Temporal Trends in Electoral Violence.
- 2019-2020: Introduction to Data Science and System COMPSCI 5087 (M) (Semester 1), with Dr Jeff Dalton and Dr Nikos Ntarmos.
- 2019-2020: Artificial Intelligence COMPSCI4004 / COMPCI5987 M/H (Semester 1)
- 2018-2019: Artificial Intelligence COMPSCI4004 (Semester 2)
- 2018-2019: Music Curation and Analysis (H) ARTMED4038 (Semester 1, three lectures), with Dr Tim Duguid
- 2018-2019: Software Engineering (M) COMPSCI5059 (Semester 2), with Dr Ke Yuan
- 2017-2018: Artificial Intelligence COMPSCI4004 (Semester 1)
- 2017-2018: Software Engineering (M) COMPSCI5059 (Semester 2), with Dr Ke Yuan
- 2016-2017: Artificial Intelligence COMPSCI4004 (Semester 1)
MSci projects (representative examples):
- Variational Inference in Bayesian Inverse Reinforcement Learning, P.D., 2018-2019.
- Machine learning for understanding human decision-making, P.C., 2017-2018.
- Data-efficient reinforcement learning, G.R., 2017-2018.
MSc projects (representative examples):
- Representation Learning with Sparse VAE for Single-Cell Images, A.R. 2020
- Tangent propagation for environmental sound classification, B.H., 2020
- Accelerating Image-Based Cell Profiling with Machine Learning, L.L, 2018 [best project award]
- Perceptual Embeddings of Music Objects using Machine Learning, H. W., 2017
- A toolbox for performance modelling and selection of dense neural network architectures, F. B., 2017
Honors projects (i.e. final year projects - representative examples)
- Preference Elicitation for Music using Spoken Dialogue Systems, M.B, 2017-2018.
- Hybrid Music Recommendation, A.G., 2017-2018
- Interactive Machine Learning for Time Series Prediction, B.A., 2017-2018
- Neural Networks for Dialogue Systems, Z.I, 2017-2018
Summer internships (representative examples):
- Generative modelling and representation learning for audio, S.M., Summer 2019 (funded by SoCS)
- Deep Learning for Image Analysis of Biological Cells, M.D., 2017
Local activities (UofG)
- 2016- : Member of the Research Student Committee in the School of Computing Science.
Reviewer / programme committee:
- I am a recurrent reviewer for NeurIPS (formerly NIPS), AISTATS, ICML, ICLR, UAI, MLSP, ICASSP, ICMI, IEEE Affective Computing.