Computer Scientist

A highly skilled computer scientist with a specialization in machine learning for healthcare
Email: holger kunz@yahoo.com

Skills
operating systems: Linux and MS-Windows

programming: Java (Oracle Certified), C/C++, Python, Unix shell
machine learning: ML applied to image analysis (Convolutional Neural Networks) using Keras and Tensorflow
image analysis: image formats (e.g. DICOM) and modalities CT, MRI, X-ray

experience in medical imaging evidenced in scientific papers

Responsibilities

Module leader: Software Development in Python for Health Data Science

Module leader: Machine Learning in Healthcare and Biomedicine

Professional
Lecturer, University College London, Institute of Health Informatics, UK, since 2015

Teaching Fellow, Imperial College London, eHealth Unit, UK, 2013 to 2015

Software developer, msg systems Cologne, Germany, 2012 to 2013

Education
Doctoral degree in Medical Informatics, Charite University Medicine, Corporate Member of
Freie Universitat & Humboldt Universitat, Germany, 2004 to 2008

Diplom-Informatik, (German equivalent of MSc in Computer Science), Institute of Mathematics
and Computer Science, Freie Universitat Berlin, Germany, 1998 to 2002

External
Director of company 'Health Informatics Ltd' with projects in Saudi-Arabia and China

Selected Publications

• Belete S., Batta V., Kunz H., Automated Classification of Total Knee Replacement Prosthesis on Plain Film Radiograph using a Deep Convolutional Neural Network, Informatics in Medicine Unlocked 25 (2021), https://doi.org/10.1016/j.imu.2021.100669, 2021
• Kunz, H., Pasea, L., Denaxas, S. (2019). Classification of atherothrombotic events in myocardial infarctions survivors with supervised machine learning using data from an electronic health record system. International Conference on Informatics, Management and Technology in Healthcare, ICIMTH, Greece, Athens, 2019
• 
  Nguyen, V., Kunz, H., Taylor, P., Acosta, D. (2018). Insights into Pharmacotherapy Management for Parkinson's Disease Patients Using Wearables Activity Data. Studies in health technology and informatics, 247, 156-160.
• 
  Kunz, H. et al. (2004). XML knowledge database of MRI-derived eye models. Computer Methods and Programs in Biomedicine, 73(3), 203-208.
• Kunz, H. et al. (2004). Feature extraction and supervised classification of MR images to support proton radiation therapy of eye tumors. Computer Methods and Programs in Biomedicine, 73(3), 195-202