Machine Learning Scientist

Having scientific background in multidisciplinary fields such as Machine Learning, Deep Learning, Data Mining, Neural Networks, Computational Mathematics and Optimization, his goal is to apply these techniques into real-world problems that require efficient and practical solutions. To date, his expertise have been implemented into various application domains such as image/video segmentation, object detection/tracking, weather forecasting, traffic flow prediction and large scale data clustering/classification. Particularly the majority of his work has been dedicated towards development of several impactful advanced machine learning models including: Deep learning, Domain Adaptation, Transfer Learning, (Supervised, Unsupervised and Semi-) Supervised Learning. These models can be extensively and efficiently employed in many versatile sectors such as: Health & Medical imaging, Computer Vision & Robotics, Text Mining, Signal Processing, Neuroscience and etc.  

As an experienced data scientist researcher, he explicitly enjoys working in interactive & collaborative workplaces, where hard problems that require going beyond engineering solutions are tackled, and where there exists a healthy balance between research and development.