Meet our first-class research and development team, working to develop advanced methods tailored to each of our speciality sectors.
Dr. Sebastian Fitzek, PhD
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With over 16 years of experience in teaching and research in social and political sciences, Dr. Sebastian Fitzek obtained his Ph.D. in sociology at the West University of Timisoara in 2010 and successfully completed his postdoctoral studies at the Romanian Academy in October 2015.
Since October 2022, Sebastian Fitzek has been a full-time researcher at Medical Image Analysis & Artificial Intelligence (MIAAI) in the Department of Healthcare Evaluation, playing an important role in developing research projects, writing scientific articles from the perspective of medical sociology, evaluating and promoting results, supporting the whole team in different development goals, teaching and supporting specialty courses at Danube Private University (DPU), as well as other strategic implementation tasks in the medium and long term. Sebastian Fitzek has been an expert member in several international projects and is an author of articles and books in the fields of sociology, political science, anthropology, data science and social work. Sebastian Fitzek has participated in more than 50 conferences and is the author and co-author of nationally and internationally scientifically indexed articles and chapters.
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Dr. Geevarghese George, PhD
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Geevarghese George obtained his PhD in Physics from the University of Strasbourg, France, for his work conducted at the Institut Charles Sadron (Theory and Simulation of Polymers group), CNRS, France. His thesis was generally on the study of statistical and dynamical properties of freestanding polymer films using numerical methods. Building on this experience and the experience gained through other self-directed machine learning projects, he started as a researcher at the MIAAI centre in May 2022. At MIAAI, in close collaboration with other researchers and specialists, his main focus is on the design and development of machine learning and deep learning pipelines to analyze medical images.
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Ass.-Prof. Dr. Amirreza Mahbod, MSc. MSc.
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"Ass.-Prof. Dr. Amirreza Mahbod holds a BSc and two MSc degrees in electrical engineering, which he earned at the Iran University of Science and Technology in Tehran, Iran. Additionally, he obtained a second MSc degree in biomedical engineering from the KTH Royal Institute of Technology in Stockholm, Sweden. In 2020, he successfully completed his Ph.D. studies at the Medical University of Vienna, Austria, where he also served as an industrial PhD fellow. During this period, he conducted collaborative research at the Medical University of Vienna and TissueGnostics GmbH. His doctoral research primarily focused on the segmentation and classification of various structures and tissues in microscopic images.
Following the completion of his Ph.D., from 2020 to 2022, he served as a postdoctoral fellow at the Institute of Pathophysiology and Allergy Research at the Medical University of Vienna. He joined the Medical Image Analysis & Artificial Intelligence group at Danube Private University as an AI researcher, and since September 2023, he has been appointed as an assistant professor.
His primary research interests encompass a wide array of topics, including medical image analysis, computer vision, machine learning, and deep learning. His scholarly contributions extend to several peer-reviewed journals and conferences, and he also has played a significant role in securing successful grant applications. He is particularly interested in developing novel deep learning-based methods for histological image analysis."
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Dr. Florian Schwarzhans, PhD
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Florian Schwarzhans holds an MSc degree in Medical Informatics from the Medical University of Vienna.
He has a background in informatics with a focus on programming using C, C++, MatLab and Python, as well as a background in electronics specializing in biomedical engineering.
His research interests include medical image processing with a special focus on automatic graph-based segmentation algorithms, deep learning methods for both image classification and segmentation, and the development and implementation of parallel algorithms for medical image processing and analysis using CUDA.
He has developed real-time retinal tracking software for a prototype PS-OCT system and software for fast parallel reconstruction of OCT volumes from raw data via the GPU, which is actively being used in multiple OCT systems.
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Dr.in Inna Servetnyk
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Inna Servetnyk is a board certified pathologist (Fachärztin für klinische und molekulare Pathologie) teaching pathology and histology at the DPU and collaborates in research projects with MIAAI.
After graduation in Medicine from the Medical University of Charkiw in Ukraine she completed her medical training in anatomical and surgical pathology in Bayern und Berlin, Germany.
Her background is predominantly clinical and she has a broad expertise in routine surgical pathology.
Tumor pathology was the main area of her work: broad spectrum of tumors, especially in the digestive system, urinary, male genitals, female genitals, breasts, lungs, head and neck tumors. Including preoperative biopsy diagnosis with histological and immunohistological tumour typing, postoperative staging and grading, assessing tumour regression grade as response to neoadjuvant therapy.
Other duties in surgical pathology were macroscopic evaluation, cross sectioning of surgical specimens and clinical autopsies.
In addition, she was active in the field of epidemiological cancer registration, as Head of the Trust Center Common Cancer Registry of the New Federal States of Germany.
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S M Ragib Shahriar Islam, MSc
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Mr. S M Ragib Shahriar Islam received his master’s degree through an Erasmus Mundus Joint Master's program in "Medical Imaging and Applications (MAIA)," a collaboration between the University of Burgundy (France), the University of Cassino & Southern Lazio (Italy), the University of Girona (Catalonia, Spain), and Forschungszentrum Jülich (Germany). The program was fully funded by European Union. He worked on the Conditional Generative Adversarial Network (cGAN) for image modality transfer between Polarized Light Imaging (PLI) images and Cytoarchitectonic images in his master's thesis. He has experience working with medical sensors, digital signal processing, medical image analysis and processing (segmentation, registration, classification, etc.), statistical analysis, machine & deep learning, and medical robotics.
He is currently employed by Austrian Center for Medical Innovation and Technology (ACMIT) and is performing a Ph.D. on a joint research project (project number: GLF21-1-001) between ACMIT, Medical Image Analysis & Artificial Intelligence (MIAAI) group at Danube Private University, the Medical University of Vienna, Department of Medical Physics and Biomedical Engineering and Landesklinikum Wiener Neustadt hospital. His Ph.D. mainly deals with Cone Beam Computed Tomography (CBCT) imaging trajectory optimization for dental and medical applications.
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Ass.- Prof. Dr.in Olgica Zaric, PhD
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Dr. Olgica Zaric did her PhD studies at the Medical University of Vienna, where she worked as a researcher on the development and implementation of multi-parametric magnetic resonance imaging (MRI) techniques. Together with conventional T1, T2, and contrast-enhanced MRI, this included diffusion-weighted imaging (DWI), sodium imaging (23Na-MRI) and chemical exchange saturation transfer (CEST) MRI at ultra-high fields. Her major focus was investigating the feasibility of these imaging modalities and their translation into clinical practice.
The research was based on establishing biochemical imaging modalities and corresponding quantitative imaging markers, which are reliable and applicable for breast lesion diagnosis and characterization as well as treatment monitoring. She has worked on protocol optimization, fast image acquisition methods, evaluation and verification of image post-processing methods, quantitative imaging data analysis, etc.
In the future, she plans to extend her expertise in the area of image analysis, processing and deep learning, and branch out to other methods that may be potentially applicable and transferrable to different diagnostic and therapy approaches.
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