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Personal profile

Research areas

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Data Science & Statistical Signal Processing  (SSP)

The mission of my research is to use Artificial Intelligence (AI), Machine Learning (ML) and Data Science to:

  • Improve people's health and welfare by making better medical solutions, based on the design and development of mathematical algorithms (predictive clinical algorithms and support tools) and hardware solutions (miniature medical robots) 
  • Protect our environment by optimizing the production of green energy generated from renewable resources harvested by wind turbines

Research interests


As AI is beginning to make its mark on healthcare, a few questions keep raising:

  • How can we best advance AI in healthcare and healthy living to the benefit of people, patients and care professionals?
  • To what extent we can rely on AI algorithms when it comes to matters of safety-critical applications (life and death) or personal health and well-being?”
  • How do we ensure that AI does not inadvertently discriminate against minorities, race and gender, or other groups, thereby perpetuating inequalities in access and quality of care?

My research is centered around answering these questions keeping in mind that we should move from a reactive sick care to a proactive health care. This requires AI in its outmost definition, featuring adaptive and embedded solutions while being precise, transparent, interpretable, adaptive, versatile and robust. A useful demarcation line that makes the distinction between our research and others crisp and easy to apply can be formulated as follows. In addition to the design of predictive algorithms for early detection and prediction of high-impact diseases and their associated complications, our research benefits from the use of causal inference to interpret the outcome of the algorithms. This enables us that the data-crunching power of AI goes hand in hand with domain knowledge from human experts and established clinical sciences, equipping them with a strong decision-support tool while keeping human oversight over its recommendations.

Danish Electronic Health Records is the gold mine to make better clinical predictions and explanations, which we extensively use in areas such as endocrinology with focus on diabetic complications, urology (prostate cancer), hepatology (liver disease) , gynecology (preeclampsia), gastroenterology (colorectal cancer, Crohn’s disease), neurology (epilepsy & seizure) and cardiology with focus on Atrial fibrillation.



Our efforts center around monitoring the behavior of wind turbines under normal and abnormal conditions and operational states. The goal is to provide performance benchmarks for the manufactures and their customers, and a predictive framework to facilitate remaining lifetime estimations and optimal operation and maintenance plans.


Education/Academic qualification

Electrical Engineering, Control Theory, Ph.D., Aalborg University

15. Feb 20051. May 2008

Award Date: 1. May 2008

Electrical Engineering, Control Theory, MSc., Sharif University of Technology

1. Feb 20021. Feb 2004

Award Date: 1. Feb 2004

External positions

Professor at Center of Excellence of Wireless Capsule Endoscopy, Odense University Hospital

1. Nov 20191. Nov 2024

Chair Data Analytics @ SDU eScience Center

1. Jan 2019 → …

Visiting Prof. , Harvard University

1. Feb 201129. Dec 2011

Research areas

  • Modern Artificial Intelligence
  • Machine Learning
  • Medical image processing
  • Mathematic modelling
  • Statistics
  • Computer Vision
  • Stochastic models


Fingerprints are formed from scientific publications and create an index of weighted keyword concepts for each individual researcher.
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