معرفی
Michael Riis Andersen is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute, Technical University of Denmark. His research focuses on Bayesian statistics, Gaussian processes, variational inference, and probabilistic programming, with applications in computer vision, natural language processing, and spatio-temporal modeling.
His recent work includes: (1) Bayesian optimization with uncertainty-aware frameworks, (2) recommender systems balancing accuracy with editorial constraints, (3) geometry-aware transformers for geospatial analysis, and (4) scalable inference methods for neural networks and Gaussian processes. Many articles explore variational inference and probabilistic programming to enhance computational reliability and efficiency.
Research keywords: Bayesian Statistics, Variational Inference, Machine Learning, Computer Vision, Probabilistic Programming, Spatio-Temporal Modeling.
