Interpretability in Deep Learning (PDF)

periods as a consultant for EC (Telemedicine Task Force) and ESA in the Satellite-Enhanced eHealth for sub-Saharan Africa (eHSA) program. Since 2011, he was also supporting the United Nations Office for Outer Space Affairs (UNOOSA) in its Human Space Technology Initiative (HSTI). He is author or co-author of numerous scientific publications and has supervised a dozen doctoral students. His professional expertise ranges from eHealth applications to medical decision support. He has led or was a partner in projects for teleservices in gastroenterology and other medical specialties, web-based multi-modal interactive teaching of tumor diagnostics, case-based ophthalmologic eLearning, early detection of malignant melanoma, quantitative measurement of tumors using tomography data, and accelerometry for physical activity measurements in population studies and clinical research. His current scientific focus is on data analytics applied to biosensor time series and biological images using classical and machine learning approaches.









Dilip K. Prasad is an associate professor in the Department of Computer Science at UiT The Arctic University of Norway. He received the Ph.D. from Nanyang Technological University, Singapore and B.Tech. degree in Computer Science and Engineering from Indian Institute of Technology (ISM) Dhanbad, India. He was a senior research fellow at Nanyang Technological University, Singapore and research fellow at National University of Singapore. He has 5years of industrial experience with IBM, Infosys, Mediatek and Philips. He was a Kauffman Global Scholarship fellow in 2011. He has received 'Rolls-Royce Inventor Award' and several research grants from European Union, Research Council Norway and UiT The Arctic University of Norway. He is a founding member of Bio-AI Research Group at UiT The Arctic University of Norway. His research interests include image processing, pattern recognition, computer vision and artificial intelligence. He is passionate about making artificial intelligence interpretable and scalable toward bridging the intelligence gap between human and machines.