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Research profile
Iris Huijben is a Doctoral Candidate in the Signal Processing Systems group of the Electrical Engineering department at Eindhoven University of Technology (TU/e). She received TU/e’s MSc Thesis Award 2020 for her master thesis on sub-Nyquist sampling of medical ultrasound data.
Now she works on representation learning, with applications in sleep medicine, in which she aims to combine signal processing algorithms with deep learning models. By creating meaningful representations of sleep recordings, she intends to learn about the sleeping brain, and therewith bridge the gap between technical models, and clinical interpretation. During the first year of her Doctorate, Iris conducted a research internship regarding deep learning for data compression in the team of Taco Cohen at Qualcomm AI Research, Amsterdam.
Quote
"Machine learning models can not only be used for automating labor-intensive processes, but also to teach us about the world."
Academic background
Iris Huijben studied Electrical Engineering at Eindhoven University of Technology (TU/e), where she received her MSc degree cum laude in 2019. Directly after, she joined TU/e as a Doctoral Candidate in the Signal Processing Systems group, where she works on representation learning for human sleep recordings.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Projects
- 1 Finished
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OP-SLEEP: Ontwikkeling Patiëntvriendelijk SLaap EEG Patch
Bergmans, J. W. M., van Sloun, R. J. G., Huijben, I., Huijben, I., van Gilst, M. M., Hermans, L. W. A., van der Hagen, D. & Kunkels, Y.
1/07/19 → 30/04/23
Project: Research direct
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A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
Huijben, I. A. M., Kool, W., Paulus, M. B. & van Sloun, R. J. G., 1 Feb 2023, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 45, 2, p. 1353-1371 19 p., 9729603.Research output: Contribution to journal › Article › Academic › peer-review
1 Citation (Scopus) -
Interpretation and Further Development of the Hypnodensity Representation of Sleep Structure
Huijben, I., Hermans, L. W. A., Rossi, A. C., Overeem, S., van Gilst, M. M. & van Sloun, R. J. G., 17 Jan 2023, In: Physiological Measurement. 44, 1, 21 p., 015002.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile12 Downloads (Pure) -
Certainty about Uncertainty in Sleep Staging: a Theoretical Framework
van Gorp, H., Huijben, I. A. M., Fonseca, P., van Sloun, R. J. G., Overeem, S. & van Gilst, M. M., 11 Aug 2022, In: Sleep. 45, 8, zsac134.Research output: Contribution to journal › Article › Academic › peer-review
1 Citation (Scopus) -
Contrastive Predictive Coding for Anomaly Detection of Fetal Health from the Cardiotocogram
de Vries, I. R., Huijben, I. A. M., Kok, R. D., van Sloun, R. J. G. & Vullings, R., 27 Apr 2022, 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers, p. 3473-3477 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
1 Citation (Scopus)1 Downloads (Pure) -
Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities
Gonzalez-Carabarin, L., Huijben, I. A. M., Veeling, B., Schmid, A. & van Sloun, R. J. G., 8 Jun 2022, (E-pub ahead of print) In: IEEE Transactions on Neural Networks and Learning Systems. XX, X, 12 p., 9790881.Research output: Contribution to journal › Article › Academic › peer-review
Press/Media
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Researchers from Eindhoven University of Technology Report on Findings in Machine Learning (A Review of the Gumbel-max Trick and Its Extensions for Discrete Stochasticity In Machine Learning)
1/03/23
1 item of Media coverage
Press/Media: Expert Comment