4/16/2023 0 Comments Hidata matsutaniThe IndoorAtlas R&D team has a lot of experience on this topic, and this presentation highlights some of the technical and privacy-related challenges that are central for the feasibility of digital contact tracing, but rarely in the focus in public discussion. Proximity detection - the technical problem of detecting when mobile devices are close to each other - is both the basic building block of many proposed digital contract tracing systems (e.g., Google/Apple, DP-3T, and TraceTogether) and an important concept in indoor positioning. Otto Seiskari: BLE and its alternatives for contact tracing from the point of view of indoor positioning Otto Seiskari: BLE and its alternatives for contact tracing from the point of view of indoor positioningĪlexander Törnroth: Sharing light on the work of the FAIA AI Task force Simo Särkkä, leader of AI across fields in FCAI and the following talks: Join us for a one-hour session chaired by prof. His research interests include machine learning and privacy, with applications especially in computational biology and medicine. Prior to his current position, he was an Assistant Professor of Statistics with a joint appointment at the Faculty of Science and the Faculty of Medicine at the University of Helsinki. He is the coordinating professor of the Research Programme in Privacy-preserving and Secure AI at the Finnish Center for AI (FCAI). Such data would help epidemic modelling and monitoring the impact of non-pharmaceutical interventions needed for managing the epidemic.Īntti Honkela is Associate Professor of Data Science at the Department of Computer Science at the University of Helsinki. We propose additional opt-in sharing of numbers of daily contacts of users under strict privacy guarantees from differential privacy. Privacy is an absolute requirement for such apps, and typically they would only share a list of anonymous identifiers of recent contacts when someone is diagnosed positive. Many European countries are planning to deploy contact tracing apps to help COVID-19 epidemic management. The current projects relate to systems and personalized medicine and opportunities of big data analysis and modelling. His background is in biomedical engineering, data science, signal processing and neuroscience. Miika Koskinen (D.Sc., Docent) is a senior scientist working as the Head of Analytics at Helsinki Biobank, HUS Helsinki University Hospital. Miika Koskinen: Clinical data repositories, applicability and opportunities for biomedical researchĪntti Honkela: Privacy-preserving contact statistics collection for COVID-19 epidemic management Petri Myllymäki, vice director of FCAI and the following talks:
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