My research is located in the area of machine learning, a modern branch of artificial intelligence research. This is an interdisciplinary research topic in between computer science, statistics, and optimization, with connections to the neurosciences and applications in robotics, engineering, medicine, economics, and many more disciplines. Within this wide area I am focusing on two aspects: supervised learning (including modern deep learning), and optimization with simple gradient-based methods and evolutionary algorithms.
Supervised learning is a learning paradigm with endless (mostly technical) applications. A learning machine (algorithm) builds a predictive model from data provided in the form of input/output pairs. This allows for the automated solution of classification and regression problems. A primary example is classification of objects in images, a classic computer vision task. I have recently started to reach out to reinforcement learning problems in 3D environments for fully autonomous behavior learning of robots or computer game agents (bots). My research activities include both theoretical and practical aspects.
Gradient-based optimization, particularly relatively simple first order methods like (stochastic) gradient descent and coordinate descent, are at the heart of many modern training procedures for learning machines, in particular for (possibly regularized) empirical risk minimization. This includes backpropagation based training of (deep) neural networks, as well as convex (primal or dual) optimization, e.g., for support vector machine training.
Evolutionary Algorithms (EAs) are a class of nature-inspired algorithms that mimic the process of Darwinian evolution. This process is resolved into the components inheritance, variation, and selection. It has been widely recognized that EAs are useful for search and optimization, in particular when derivatives are not available. Formally they can be understood as randomized direct search heuristics. They are suitable for tackling black-box optimization problems. I focus on evolution strategies, a class of optimization algorithms for continuous variables, and on multi-objective optimization.