The Ellogon.AI team shares a single passion: contribute to improving the outcome of cancer. To realize this we have put together a team with all the right ingredients. We are happy to introduce you to our team!
Robert KuipersChief Executive Officer
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 KanoulasChief Operating Officer
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 GavvesChief Scientific Officer
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.
Arun MukundanAI Tech Lead
Arun Mukundan is AI Tech Lead at Ellogon.AI. He oversees the machine learning part of the product, and is responsible for the technology that quantifies biomarkers in whole slide images. After getting his PhD in Computer Vision from the University of Czech Republic, he joined Ellogon.AI. He graduated with a masters in electrical engineering from IIT Bombay, India. Subsequently, he worked as a patent engineer at Sony Japan in Tokyo, and in a robotics lab in Mumbai before starting his PhD. He enjoys the challenge of working with novel technologies while ensuring they remain reliable for real world use.
Leonardo RomorAI Software Engineer
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.
Timo KootstraDeep Learning Engineer
Timo has a formal education in computational neuroscience, from which he transitioned into the field of Artificial Intelligence. Showing academic prowess, Timo had (co)-authored 3 peer-review scientific publications by the time he started his master thesis’ graduation project. During this project, he did research at the Netherlands Cancer Institute. During this time, he was supervised by dr. Jonas Teuwen, prof. dr. Jelle Wesseling and dr. Hugo Horlings. The project’s focus was developing deep learning methods for predicting recurrence risk of DCIS; early-stage breast cancer. As such, he is experienced in developing state-of-the-art deep learning applications for pathology. Although having academic potential, Timo decided to not pursue a PhD in favor of using his talents to bridge the gap between early-stage deep learning R&D and mature, commercial applications that are deployed in the clinic. Following this decision, Timo is now a Deep Learning Engineer at Ellogon.AI. At Ellogon.AI, his primary focus is developing deep learning systems for applications in whole-slide images stained for immunohistochemistry. Additionally, Timo is an experienced data engineer and is experienced in developing A.I. systems as medical software devices under the IVDD / IVDR. Finally, his talent is working together with a diverse set of stakeholders and has entrepreneurial ambitions.
David WesselsAI engineer
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.
Wangzhao SongMedical Director
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.
Jessica van der HoekMedical & Regulatory Officer
Jessica van der Hoek is Medical & Regulatory Officer at Ellogon.AI. As Medical Officer she is working closely with pathology and oncology experts on digital pathology annotations, clinical validation and clinical implementation of the software. Moreover, she updates the research and development team on the (bio)medical background and latest developments in immuno-oncology research. As Regulatory Officer she is responsible for compliance of the software under the IVDD/IVDR. Jessica has formal education in Biomedical Sciences with focus on cancer research. During her masters she started as research assistant at the Princess Máxima Center for Pediatric Oncology, where she worked on immuno-oncology, molecular profiling and high throughput drug screening. Jessica’s passion is to bridge the gap between academic research and clinical relevance. Her interest lies in the application of fundamental research into novel treatment opportunities.
Andreas PanteliPhD candidate HISTO-AI
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 SchirrisPhD candidate HISTO-AI
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.
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.
Hugo HorlingsPathologist at NKI
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 SuurmeijerMedical Advisor
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 BekkersAssistant Professor at UvA
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)).
Incorporation as spin-off University of Amsterdam
Focus on immunotherapy and € 1 mio. grant win with Netherlands Cancer Institute
Obtained another € 1 mio in non-dilutive funding
Grown team from 2 to 5 FTE
Grown team from 5 to 9 FTE
Obtained CE certification under IVDD