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Researcher and director of research engineering at IARAI. His background is geo-spatial big data processing and analytics, geo-spatial search and transport optimization. As an engineer and researcher he is interested in the intersection between ML for geo-spatial problems and their application at scale.
Researcher and engineer at IARAI. His background is theoretical computer science (algebraic information theory) and transport engineering. He is interested in bridging the gap in research between traffic domain and ML, and in temporal-spatial processes. He has worked as a software engineer and researcher in the railway industry and was involved in the FLATland NeurIPS challenges 2019/2020 on the problem of train scheduling in a simple grid world environment.
PhD Student in Geoinformatics at the Chair of Geoinformation Engineering at ETH Zurich and at IARAI. He was was part of the team that won the second place at the NeurIPS 2019 Traffic4cast competition. In his PhD he is interested in applying modern data analysis methods to spatio-temporal problems related to human mobility and investigating the role of mobility in our way towards a more sustainable energy system.
Pre-doctoral research fellow at IARAI and a PhD candidate at the Polytechnic University of Catalonia in Barcelona. His research focuses on rule detection and generalization in deep learning with applications to spatiotemporal data. Previously he worked as a machine learning consultant modeling mobility at SEAT, S.A., and on the explainable AI of lifestyle patterns in egocentric images at the University of Barcelona.
Researcher at HERE Technologies with a background in the geo-spatial and transportation domains. With his research interests lying mainly in the intersection of computational movement analytics and deep learning, he has extensive scientific experience in the field of knowledge extraction from large trajectory datasets.
Postdoctoral researcher at IARAI, with a background in theoretical condensed matter physics. His main interests concern the conceptual and theoretical understanding of existing machine learning approaches as well as their
extension towards biologically performant frameworks.
Postdoctoral researcher at IARAI, with a background in data science, data analysis and particle physics. During his PhD, he worked within the CMS collaboration at the Large Hadron Collider at CERN as a physicist and data scientist, where he developed machine learning-based analysis techniques to probe Higgs boson decays to tau leptons.
Deep learning research practitioner, experienced in implementing production grade data-driven models in natural language processing and computer vision. He holds an MSc. and an MBA Summa Cum Laude from Ben Gurion University, Israel. He and many of his students have won international awards and raised capital for their ventures.
Researcher Engineer at IARAI. His background focuses on software and machine learning engineering, MLOPs, and deep learning. As a research engineer, he is interested in reinforcement learning, distributed computing, Big Data and ways to combine these disciplines to solve real world problems.
Researcher at HERE Technologies. Her background lies in physics, quantitative finance and machine learning. She is interested in tackling geo-spatial problems with machine learning techniques such as reinforcement learning, Bayesian deep learning, and unsupervised learning etc.
Researcher and engineer at HERE Technologies. With a back-ground in GIS science and 3 years of working experience in the industry, his main interest lies in the intersection between big data technologies for handling large amounts of geospatial data and machine learning methods to analyze such data.
Currently works with the research team of HERE Technologies on topics related to machine learning, HPC and map automation. He has a background in applied mathematics and parallel and scientific computing.
Software engineer at HERE Technologies. He has a background in data management for research team working primarily with probe and lidar data. Prepared data for T4C 2020 and 2021.
Professor in machine learning at the Silesian University of Technology, Gliwice, Poland. Her research focuses on predictive models and the integrated analysis of large-scale heterogeneous patient proles. In addition she researches statics for challenging data sets (strong correlation structures, high dimensionality, . . . )
Assistant Professor at the Institute for Machine Learning at the Johannes Kepler University in Linz. Currently, he is also an ELLIS visiting postdoctoral researcher at the Amsterdam Machine Learning Lab. His research interests comprise physics-inspired and physics-informed deep learning, geometric deep learning, and few-shot learning.
Director of the Institute of Advanced Research in Artificial Intelligence (IARAI). He is also the head of the Institute for Machine Learning and the LITAI Lab and a professor at the Johannes Kepler University of Linz, Austria.
Director of IARAI. He is also a professor at Boku University Vienna, Austria. He runs a bioinformatics research group that focuses on analyzing, calibrating, and bench-marking genome-scale quantitative assays.
Director of IARAI. His background is in pure mathematics, mathematical modelling in finance and machine learning. Advancing and applying new strands of the latter to solving real world problems currently occupies his interest.
Senior Full Stack Developer specialised in WordPress coding, responsible for IARAI web platform development.