Dr Arno Solin is an Assistant Professor (tenure-track) in Machine Learning at the Department of Computer Science at Aalto University, Finland. He also holds an Adjunct Professorship (Title of Docent) at Tampere University and is the coordinating professor of the ‘Next-generation Data-efficent Deep Learning’ program of the Finnish Center of Artificial Intelligence (FCAI). His research interests are in data-efficient machine learning, with special interest in probabilistic methods for real-time inference and sensor fusion.
At Aalto, Arno leads a research group in machine learning. He gave a tutorial on Machine Learning with Signal Processing at ICML 2020 and was one of the organisers of MLSP 2020. He has acted as a program committee member/reviewer for, e.g., NeurIPS, ICML, AISTATS, and AAAI. He is an Editorial board reviewer for JMLR. He is a winner of the ISIF 2018 Jean-Pierre Le Cadre Best Paper Award, and he won the MLSP 2014 Schizophrenia Classification Challenge on Kaggle.
Previously Arno worked as a team-lead in industry (2015–2017) and held an Academy of Finland post-doctoral fellowship (2017–2020). He has also held visiting researcher positions in Prof. Neil Lawrence’s group at University of Sheffield (2013), the Computational and Biological Learning Lab (CBL) at University of Cambridge (2017–2018), and Prof. Thomas Schön’s group at Uppsala University (2019). He is a co-author of the book Applied Stochastic Differential Equations, published by Cambridge University Press.
Machine Learning: Probabilistic methods, data-efficient machine learning, Gaussian processes, approximate inference, deep probabilistic models, real-time inference, reinforcement learning.
Signal Processing: Sequential methods, nonlinear state estimation, Kalman filtering, time-series modelling, dynamical systems, system identification.
Sensor Fusion: Real-time inference, computer vision, inter-frame reasoning, perception, odometry, tracking.