Assistant Professor

Xi Long

I always believe opening my eyes to see different worlds will encourage me to think about things that better our life.

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Department / Section
Electrical Engineering
Building
Flux
Floor / room
7.104

Research Profile

Xi Long is Assistant Professor in the Signal Processing Systems group, where he advises, coordinates and participates in many collaborative projects (with, for example, Philips, Maxima Medical Center, Kempenhaeghe, UMC Utrecht, KU Leuven, Fudan University and others) combining signal processing, data analysis, and healthcare. Long’s expertise is in signal processing, time series analysis, machine learning, data analytics, deep learning and mathematical modeling. His research interests include engineering for biomedical applications such as unobtrusive sensing, patient monitoring, vital signs monitoring, sleep, physical activity, perinatal and pregnancy monitoring, cardiorespiratory dynamics, psycho-physiological analysis, epilepsy, and brain activity. In addition, Long has published over 80 scientific articles and reports, generated more than 20 IPs or patent filings and supervised more than 15 PhD or MSc students. He is a peer reviewer of more than 20 prestigious international journals/conferences in his research areas.

Academic Background

Xi Long holds a BSc with honor in Electronic Information Engineering from Zhejiang University (China) and obtained his MSc in Electrical Engineering from Eindhoven University of Technology (TU/e) in 2009. He worked at Philips as a researcher from 2008 to 2009 and at Tencent (China) as project manager and UX engineer from 2009 to 2011. He then went on to do his PhD at TU/e and Philips Research, on signal processing and machine learning in unobtrusive sleep monitoring, which he completed cum laude in 2015. Long currently wears several hats: he is a Senior Scientist and Lead FCS Data Analytics Cluster at Philips Research, a Project Coordinator at Maxima Medical Center and UMC Utrecht, and an Assistant Professor at TU/e.

Ancillary Activities

No ancillary activities