Claudia Schillings, Freie Universität Berlin
The research group "Mathematical Statistics & Machine Learning" headed by Prof. Dr. Alexandra Carpentier is part of the Institute of Mathematics, University Potsdam. We are focusing on problems in mathematical statistics and machine learning:
Anomaly detection, High or Infinite-Dimensional Statistical Inference, Inverse Problems and Compressed Sensing, Adaptive Estimation and Confidence Sets, Uncertainty quantification, Sequential Sampling, Bandit Theory, Optimisation of Computational Resources, Matrix Completion, Extreme Value Theory, Applications in Engineering, Neuroscience and Quantum Physic.
Alexandra Carpentier is an Emmy Noether research group leader for the project MuSyAD (CA 1488/1-1) "Anomaly Detection in a Multi-System Setting: Theoretical and Computational Objective", funded by the Deutsche Foschungsgemeinschaft (DFG, German Research Foundation). Andrea Locatelli and Maurilio Gutzeit are also funded by this project. Our project is on the topic of anomaly detection. Anomaly detection is an interdisciplinary domain, borrowing elements from mathematics, computer science, and engineering. The main aim is to develop efficient techniques for detecting anomalous behaviour of systems. In the classical scenario a monitor receives data from a system and compares this data to a reference system with some single normal behaviour. Ideally no strong assumptions are made on the nature of anomalous behaviours, so the problem of anomaly detection is by essence a non parametric problem. Here we propose to study a more complex scenario, which will be referred to as multi system anomaly detection. In this setting, reference systems can have a variety of normal behaviours, and moreover, there are many systems under the surveillance of the monitor, and the monitor must allocate its resources wisely among them. In this situation new theoretical and computational challenges arise. The overall objective of this proposal is to find efficient methods to solve the problem of multi system anomaly detection. This aim will be reached by addressing the following sub-objectives. First, we will generalise the theoretical framework of anomaly detection to the broader setting of multi-system anomaly detection. Second, multi-system anomaly detection methods will be developed, by taking ideas from the non parametric testing field and applying them to the new framework. Third, we will study optimal monitoring strategies for cases where the multiple systems cannot be monitored simultaneously. Here, it is important that the monitor allocates its resources among the systems in a way that is as efficient as possible. To this end, sequential and adaptive sampling methods that target the anomaly detection problem will be designed. Since anomaly detection is a non parametric problem, elements in the theory of non parametric confidence sets will be used. Finally, the newly developed methods will be applied to practical problems: a methodological example in extreme value theory, an econometric application for speculative bubble detection and two applications in a Brain Computer Interface framework.
The group is also funded by the DFG on the Research UnitFOR 5381 "Mathematical Statistics in the Information Age - Statistical Efficiency and Computational Tractability".
The project RE-BCI was awarded in the beginning of 2020 by the Land Sachsen Anhalt, more pre-
cisely by the Sachsen-Anhalt WISSENSCHAFT Spitzenforschung/Synergien. The objective of RE-BCI is to prepare preliminary results supporting the BCI (Brain-Computer Interfaces, i.e. a technology for connecting a human user with a computer through the lectrical impulses emitted by her/his brain) application to shared authority situations.
The group is also funded by the Deutsche Foschungsgemeinschaft (DFG, German Research Foundation) on the SFB 1294 Data Assimilationon “Data Assimilation - The seamless integration of data and models" on Project A03 together with Prof. Gilles Blanchard.
This project is concerned with the problem of learning sequentially, adaptively and in partial information on an uncertain environment. In this setting, the learner collects sequentially and actively the data, which is not available before-hand in a batch form. The process is as follows: at each time t, the learner chooses an action and receives a data point, that depends on the performed action. The learner collects data in order to learn the system, but also to achieve a goal (characterized by an objective function) that depends on the application. In this project, we will aim at solving this problem under general objective functions, and dependency in the data collecting process exploring variations of the so-called bandit setting which corresponds to this problem with a specific objective function.
As a motivating example, consider the problem of sequential and active attention detection through an eye tracker. A human user is looking at a screen, and the objective of an automatized monitor (learner) is to identify through an eye tracker zones of this screen where the user is not paying sufficient attention. In order to do so, the monitor is allowed at each time t to flash a small zone a t in the screen, e.g. light a pixel (action), and the eye tracker detects through the eye movement if the user has observed this flash. Ideally the monitor should focus on these difficult zones and flash more often there (i.e. choose more often specific actions corresponding to less identified zones). Therefore, sequential and adaptive learning methods are expected to improve the performances of the monitor.
The group is also funded by the Deutsche Foschungsgemeinschaft (DFG, German Research Foundation) on the GRK 2297 MathCoRe on “Mathematical Complexity Reduction" 314838170, GRK 2297 MathCoRe. The objective of this GRK is to investigate the problem of complexity reduction across the different areas of mathematics. In our group, we bring to this project some expertise on the field of sequential learning, in order to reduce the complexity of given problems by adapting the sampling strategies.
The group is also funded by the Deutsche Foschungsgemeinschaft (DFG, German Research Foundation) on the GRK 2433 DAEDALUS. The main goal of DAEDALUS is the analysis of the interplay between incorporation of data and differential equation-based modeling, which is one of the key problems in model-based research of the 21th century. DAEDALUS focuses both on theoretical insights and on applications in life sciences (brain-computer interfaces and biochemistry) as well as in fluid dynamics. The projects cover a scientific range from machine learning, mathematical theory of model reduction and uncertainty quantification to respective applications in turbulence theory, simulation of complex nonlinear flows as well as of molecular dynamics in chemical and biological systems. In our group, we cover mathematical statistics and machine learning aspects.
The group is also funded by Amazon Research postdoctoral program. Dr. Claire Vernade is the concerned postdoc and is sharing her time between Amazon Research in Berlin and the OvGU.