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Assoc. Prof. Dr. Dimiter Prodanov, MD, PhD
Associate professor, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences

Keynote on: Active Segmentation: Differential Geometry meets Machine Learning
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Biography: Assoc. Prof. Dr. Dimiter Prodanov, MD, PhD, obtained an MD degree from the Medical University - Sofia in 1999 and a PhD degree from the Twente University, Enschede, the Netherlands in 2006. During his PhD, Dr. Prodanov introduced several microscopic and computational techniques for anatomic analysis. He was awarded a postdoctoral fellowship from the International Brain Research Organization to continue his research in neuroprosthetics and brain computer interfaces at the Catholic University of Louvain, Belgium, in 2006 and later at the University of Liege, Belgium. Since 2008 Dimiter Prodanov joined IMEC, Belgium in the role of a senior scientist in neurotechnology. Since 2013 Dimiter Prodanov became also an affiliated researcher in Neuroscience Research Flanders. He supports actively Imec’s research programs in life sciences, health care and nanotechnology as an expert in safety and regulations. Since 2021 Dimiter Prodanov has been promoted as an associated professor at the Institute of Information and Communication Technologies at the Bulgarian Academy of Sciences. His current research interests include Computational Biology, Signal Processing, and Safety aspects of Nanotechnology and Biotechnology. At present, Dimiter Prodanov has authored more than 50 academic publications and 4 book chapters.

Bernhard Spanlang
Chief Technical Officer & Co-Founder, Virtual Bodyworks

Keynote on: Virtual Reality Embodiment to Foster Positive Behaviour

Biography: Bernhard Spanlang holds a Diplom Ingenieur degree (MSc) from Johannes Kepler University, Austria in Computer Science and an Engineering Doctorate degree from University College London in Computer Vision, Imaging and Virtual Environments. His research interest is in body perception, neuroscience and psychology using immersive virtual reality and the associated applications in training and rehabilitation. Bernhard has published c.60 papers in journals like Frontiers, Scientific Reports and SIGGRAPH. Prior to his role as Chief Technology Officer at Virtual Bodyworks, he was working in research at the University of Barcelona, designing experiments to evaluate the effects of virtual embodiment on the mind.

Prof. Amar Ramdane-Cherif
Full Professor at the University of Versailles - Paris Saclay

Keynote on: Dynamic Architecture for Multimodal Applications to Reinforce Machine-Environment Interaction

Biography: Amar Ramdane-Cherif received his Ph.D. degree from Pierre and Marie Curie University in Paris in 1998. In 2007, he obtained his HDR degree from Versailles University. From 2000 to 2007 he was associate professor at university de Versailles and worked in PRISM Laboratory. Since 2008, he is a Full Professor at the University of Versailles - Paris Saclay, working in the LISV laboratory. His research interests include:
- Software Ambient Intelligence - semantic knowledge representation, modelling of ambient environment, multimodal interaction between person, machine and environment, fusion and fission of events, ambient assistance
- Software Architecture - software quality, quality evaluation methods, functional and non-functional measurement of real-time, reactive and software embedded systems.

As of today, he has edited 2 books on computational intelligence and communications. He authored 10 book chapters, 50 international journals and about 150 international conference papers. He also has supervised 20 doctoral PhD theses and reviewed 30 PhD theses. He managed several projects and has been doing several national and intentional collaborations. He is currently a member of the Council Board of the Graduate School of Computer Science of the Paris-Saclay University.

Active Segmentation: Differential Geometry meets Machine Learning
Dimiter Prodanov

Abstract: Image segmentation and classification is an active area of research in the last 30 years. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The presentation will present a project supported in part by the International Neuroinformatics Coordination Facility through the Google Summer of code. The project resulted in an automated image segmentation and classification platform, called Active Segmentation for ImageJ (AS/IJ). The platform integrates a set of filters computing differential geometrical invariants based and combines them with machine learning approaches.