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Dr.Ir. L. (Lejla) Alic


  
    
Assistant Professor
Lejla Alic
Technohal room: 2180
Phone: +31 534898731
E-mail: l.alic@utwente.nl

General information

Lejla Alic studied Electrical Engineering at Delft University of Technology, the Netherlands. She graduated under Prof. Babuska (BSc 1999) on fuzzy clustering of medical signals, and under Dr. ir. van Wijk van Brievingh, Prof. Ince, and Prof. Lelieveldt (MSc 2001) on analysis of colonoscopy images. In 2013 she earned, under supervision of Prof. Niessen, a PhD from Erasmus Universiteit Rotterdam (Biomedical imaging Group Rotterdam) where she has worked on a broad range of methods for assessment and prediction of treatment outcome in pre-clinical and clinical oncology data. Her thesis entitled ‘Quantification of tumour heterogeneity in MRI’ was defended at advanced school for computing and imaging. In the time before and after her graduate school, she was a research fellow at Leiden University Medical Center (Division of Image Processing), at Academic Medical Center university of Amsterdam (Experimental Anesthesiology), and at Engineering Science (University of Oxford - Robotics Research Group, headed by Sir. Brady) . Furthermore, she was also a scientific researcher and project leader at TNO (Dutch contract research institute). In 2017 she started at Texas A&M University at Qatar (Department of Electrical and Computer Engineering) as an Assistant Research Scientist responsible for a variety of imaging applications including analysis of MRI and histology data, initiating new collaborations in the field of medical signal/image processing and analytics with a focus in machine learning.

In 2019 she was appointed as an Assistant Professor at the Department of Magnetic Detection and Imaging. She has (co-)authored over 30 publications (conference abstracts excluded) in the field of medical image analysis, computer vision, and laparoscopy.

Research interests

Magnetic particle detection, magnetic particle imaging; laparoscopy; dual ladling of particles; hyperspectral imaging; magnetic resonance imaging; image registration; image analysis; texture analysis; heterogeneity; machine leaning; data science.

 Teaching responsibilities

M8-De bewegende mens en gezondheidsrecht

Publications