About Us

Robert Kuipers

Robert is a hands-on CEO with over 10 years of experience in leading and growing high-tech companies within healthcare. Translating innovative technologies like Artificial Intelligence (AI) into added value solution for customers, closing commercial contracts with hospitals and building an organization around this is a key asset of Robert. Among his key achievements are securing over € 10 mio. dilutive and non-dilutive investment capital through road shows in Europe, Australia and Asia and preparing for an Independent Public Offering (IPO). Robert has also successfully realised an exit in one of the startup’s he has build.

Before becoming a CEO, Robert worked for 15 years for large Business/IT consulting firms. He became partner and was for 5 years responsible for leading multi-disciplinary teams of over 300 professionals. His biggest commercial win was closing a € 100 mio. deal with a large Utility company. His leadership style is characterized by focus on result, authenticity and success through development of others.

Evangelos Kanoulas

Prof. dr. Evangelos Kanoulas is co-founder of and Chief Operation Officer at Ellogon.AI. He leads the day-to-day administrative and operational functions towards the development and commercialisation of advanced AI algorithms. Outside Ellogon.AI, Evangelos Kanoulas is a full professor at the University of Amsterdam. He is lead of the Information Retrieval Lab at the Informatics Institute, while until recently he held a joint appointment with the Amsterdam Business School, researching ways that AI can make a real difference in society and business. Evangelos received the NWO VIDI grant 2017 and other national, European and industrial grants to research on AI algorithms that allow machines to understand human language and communicate with humans. He has extensive expertise in search engines, recommender systems and language technology, and his interest lies in building and evaluating human-machine intelligence. His drive and passion are in positioning AI systems in modern society to allow citizens to make fair, unbiased, and informed decisions.

Efstratios Gavves

Dr. Efstratios Gavves is co-founder of and Chief Science Officer at Ellogon.AI. He leads the AI research and development effort and pioneered the design of the Exclusive Deep Learning technology. Outside Ellogon.AI, dr. Efstratios Gavves is an associate professor at the University of Amsterdam and an ELLIS Scholar. He is a director of the QUVA Deep Vision Lab with Qualcomm, and the POP-AART Lab with the Netherlands Cancer Institute and Elekta. Efstratios received the ERC Career Starting Grant 2020 and NWO VIDI grant 2020 to research on the Computational Learning of Time for spatiotemporal sequences and video. He has extensive expertise in computer vision and deep learning, and his interest lies in temporal machine learning and systems dynamics, efficient computer vision, and machine learning for oncology. His drive and passion are in understanding and recreating the unique ability of humans of learning. What does it mean to learn and to extrapolate? It is his firm belief that recreating intelligence algorithmically will pave the way towards not only better healthcare, but also a better and freer society.

Jonas Teuwen

Jonas Teuwen is Advisor of Ellogon.AI and group leader at the Netherlands Cancer Institute and an expert on the use of artificial intelligence for the analysis of medical images in oncology. Before this, he obtained his PhD degree at the Delft University in Technology on the intersection of mathematics and applied physics. After his postdoc at the Netherlands Cancer Institute, he continued as a postdoc and subsequently as assistant professor at the Radboud University Medical Center, where he worked on AI for oncological imaging. He is principal investigator or co-investigator on multiple grants from the EU, the Dutch Cancer Society (KWF) or the Netherlands Organization for Scientific Research (NWO). Beyond scientific excellence, Jonas guarantees the necessary translational aspect of the use of AI in oncology, which requires a strong collaboration with experts in oncology and artificial intelligence. Further, one of his key strengths is the actual development of software for medical imaging and his knowledge on MDR and ISO approvals required.

Mart van Rijthoven

Leonardo Romor

Leonardo has a background in physics, high-performance computing, and artificial intelligence. With years of experience in operational and technical roles, he now wishes to enable research findings into impactful applications in the medical sector. He is a passionate software engineer with expertise in scientific simulations and product design. He’s excited to bring in the latest technologies to improve the odds of ill patients.At Ellogon.AI, he is an AI Software Engineer responsible to develop a secure, safe and compliant medical device software that will scale AI models and image capabilities to satisfy the market needs.

Gabriele Bani

Gabriele Bani brings to the company a unique blend of technical expertise and pragmatic problem solving skills. He has a background in Computer Science and Artificial Intelligence, and after working on 2 publications during his studies, he immediately turned towards the entrepreneurship world. He founded Intrical AI, a tech startup using AI and NLP to improve the research process of M&A analysts. Having founded his own company, he understands very well the importance of deeply understanding customers and end-users, along with the importance of focusing on the problems that matter while iterating quickly. He is now working at Ellogon.AI as an AI Engineer, focusing on creating state-of-the-art AI models that can be used reliably for pathology.

Pasan Missaka Gama Etige Don

Pasan Missaka is a Full Stack and Data Engineer at Ellogon.ai. He has a background in Software Engineering, with nearly five years of experience, working in diverse environments, including startups, enterprise, and mid-level companies. He brings extensive experience in developing Software as a Service (SaaS) products, designing robust platform architectures to ensure high throughput and availability, and building intricate systems tailored to support B2B operations from his previous roles. As a Software Engineer, he is passionate about creating seamless user experiences and have a keen interest in working with new technologies to provide the best solutions for clients. At Ellogon.AI he will be focusing on building the overall product which will be used by our potential clients for identifying right patients for immunotherapy.

Wangzhao Song

Wangzhao Song is Medical Director at Ellogon.AI. In 2016, she moved to the Netherlands to investigate molecular pathology in soft tissue and bone tumors at the University Medical Center of Groningen. In 2020, she achieved her Doctoral degree. As Medical Director, she is responsible for product development and Research & Development of Ellogon.AI from a medical perspective, translating relevant medical developments that add value to the company’s clients. She is working closely with pathology experts to develop the company’s AI solutions. She believes that scientific advances often drive medical science. She is proud to be part of the team. Her professional goal is to provide accurate information to assisting healthcare practitioners in making the right decision by the AI.

Georgia Riga

Georgia Riga is Regulatory Officer at Elllogon AI. As Regulatory Officer she is responsible for compliance of the software under the IVDD/IVDR.

Andreas Panteli

Andreas Panteli  is a PhD candidate under the HISTO-AI project (2020-2024) which is a collaborative project between Ellogon.AI, the University of Amsterdam, and the Netherlands Cancer Institute. Andreas is an experienced doctoral researcher with a demonstrated history of working in the industry and academia alike. He is skilled in biomedical computer vision and digital pathology using artificial intelligence and machine learning with extensive leadership background. Together with Ellogon.AI, Andreas works on developing tumor and stroma segmentation algorithms along with lymphocyte detection and stromal tumor-infiltrating lymphocyte (sTIL) score prediction. He is passionate about pushing the scientific frontier in AI especially when applied for important causes such as medical research.

Yoni Schirris

Yoni Schirris is a PhD candidate under the HISTO-AI project (2020-2024) which is a collaborative project between Ellogon.AI, the University of Amsterdam, and the Netherlands Cancer Institute. This project aims to develop deep learning methods to predict genomic and transcriptomic information directly from H&E WSIs which can be used as predictive biomarkers for immunotherapy. Yoni has an interdisciplinary background with a formal education in biomedical (neuro)science, economics, and artificial intelligence. With years of experience in operational and technical roles, he now wishes to bridge the gap between medical science, artificial intelligence, and its clinical implementation. He wishes to increase the life quality of cancer patients worldwide by developing simple-to-use AI-based diagnostic tests that require no expensive local infrastructure so that those patients can get access to the best possible therapy with the fewest side-effects. Such diagnostic automation can also reduce the burden on pathologists and doctors, humanizing medicine by allowing doctors to do what matters most: spending more time with patients and developing better treatments.

David Wessels

David Wessels finished a bachelor and master in Artificial Intelligence at the University of Amsterdam. During his master thesis, he worked on an equivariant reinforcement learning approach for the chemistry problem of conformer generation. Afterwards, he worked on other chemistry- and graph-based problems. During these projects he got really excited in Geometric Deep Learning and applying these methods on real-life problems. Within Ellogon.AI David will work as an AI-engineer on the HistoGeometry Ellis project. This project aims to develop geometric deep learning approaches within the Ellogon.AI framework. Specifically, he will work on implementing group-convolutions, creating representations which are equivariant against certain symmetries such as rotations or coloring. Ellogon.AI specifically interested David, since it gives him an opportunity to work in a research oriented team creating state-of-the-art deep learning models which really add something onto our society.

Ylva Weeda

Ylva Weeda is part of the SELECT-AI project (2023-2027), which is a collaborative project between Ellogon.AI, the University of Amsterdam, the Amsterdam UMC, and the Netherlands Cancer Institute. This project aims to develop deep learning methods to predict biomarkers from histopathology whole slide imaging for personalised immunotherapy selection of gastroesophageal cancer patients. Ylva finished a bachelor and masters in Technical Medicine at the TU Delft. Her master thesis focussed on the identification and evaluation of nuclear and computed-tomography imaging features to predict and monitor treatment response in patients with gastrointestinal stromal tumours. This project has fuelled her interest for research on personalised cancer treatments through medical imaging-based predictions. As a technical physician, she aims to bridge the gap between fundamental artificial intelligence research and clinical practice.

Hugo Horlings

Hugo Horlings is part of the Advisory Board of Ellogon.AI. Hugo is a Dutch certified pathologist at Antoni van Leeuwenhoek (2014). He is a surgical pathologist with broad diagnostic and molecular experience, internationally recognized author and translational researcher with an expertise and excellence in evaluation and implementation of novel diagnostic genetic technologies in pathology laboratories. He is principal investigator or co-investigator on multiple grants from the Dutch Cancer Society (KWF). In 2014 he was awarded a translational and applied cancer research fellowship from the Dutch Cancer Society. During this fellowship he focused on identification of the genetic make-up of the tumor contributing to therapy resistance and poor prognosis of patients with breast and ovarian cancer. He worked at the University of British Columbia and Stanford University. This fellowship gave him opportunities to obtain extensive skills in the application of large- scale and high-throughput genomics technologies in clinical settings, which will be essential for a “molecular” pathologist to make precision medicine a reality. In 2019, he was recruited as clinical group leader at the Netherlands Cancer Institute and his lab focuses on developing computational pathology approaches to personalize cancer treatment. The Netherlands Cancer Institute-Antoni van Leeuwenhoek, a dedicated comprehensive cancer institute which offers the best available research environment for cancer research in the Netherlands, with excellent research facilities and infrastructure.

Albert Suurmeijer

Albert Suurmeijer is part of the Advisory Board of Ellogon.AI. As Professor of Pathology at the University Medical Center Groningen (UMCG), Albert became specialized in oncopathology with special expertise in bone and soft tissue tumors and cardiovascular pathology. Over twenty years, he was Staff Member of the Department of Pathology at UMCG. Nowadays he is enjoying his retirement, but he is still Consultant for the Netherlands Committee on Bone Tumors. Albert’s passion is to translate biomarkers into better treatment options in oncology.

Erik Bekkers

Erik Bekkers is Scientific Advisor on geometric deep learning within Ellogon AI. Erik is an assistant professor in Geometric Deep Learning in the Machine Learning Lab of the University of Amsterdam (AMLab, UvA). Before this he did a postdoc in applied differential geometry at the dept. of Applied Mathematics at Technical University Eindhoven (TU/e). In his PhD (cum laude, Biomedical Engineering, TU/e), he developed medical image analysis algorithms based on sub-Riemannian geometry in the Lie group SE(2) using the same mathematical principles that underlie mathematical models of human visual perception. Such mathematics find their application in machine learning where through symmetries and geometric structure, robust and efficient representation learning methods are obtained. His current work is on generalizations of group convolutional NNs and improvements of computational and representation efficiency through sparse (graphs) and adaptive learning mechanisms. Erik is a recipient of a MICCAI Young Scientist Award 2018 and a VENI personal research grant (awarded by the Dutch Research Council (NWO)).