22nd ECMI Conference third day, special issue of Journal of Mathematics in Industry

The middle of the conference is a good moment to advertise submitting to the ECMI conference proceedings. Following a tradition of ECMI Conferences and courtesy of Springer Verlag the authors of up to 15 top ranking papers submitted for the proceedings will be invited to submit the extended version of their contribution for publication, up to 20 journal pages, in a special issue of Journal of Mathematics in Industry, a Springer Open Access journal, devoted to ECMI 2023. The selection will be based on the quality of the paper, in the light of recommendations and the evaluation of the Scientific Committee. Upon peer review, accepted papers will be published free of charge. We encourage all the participants to participate and thank in advance for all the effort!

Coming back to the conference, during the third day we will be given next two plenary lectures.

Claudia Schillings

A Professor in Numerical analysis of stochastic and deterministic partial differential equations at Free University Berlin. Her recent interest includes preconditioners for sampling techniques in the small noise or large data limit, data-informed approximations of the underlying model, and the analysis of particle-based methods for inverse problems. She will give a lecture PDE-constrained Optimization under Uncertainty.

During the talk she will discuss machine learning optimization techniques which can be used in the presence of uncertainty about model correctness, data relevance, and numerous other factors that influence the resulting solutions. The presented approach replaces the complex forward model by a surrogate, e.g. a neural network, which is learned simultaneously in a one-shot sense when estimating the unknown parameters from data or solving the optimal control problem. She will show an algorithmic framework is developed which ensures the feasibility of the parameter estimate and control w.r. to the forward model, which is linked to the Bayesian approach.

Rafał Weron

Professor of Management Science and Head of the Department of Operations Research and Business Intelligence at the Wrocław University of Science and Technology. He is an experts on energy forecasting and is engaged as a consultant to multiple financial, energy and software engineering companies. He will give a talk Recent Advances in Electricity Price Forecasting: A 2023 Perspective.

The talk will describe the recent advances in the field of electricity price forecasting. It is a branch of energy forecasting which is on the interface between econometrics, statistics, computer science and engineering, which focuses on predicting the spot and forward prices in wholesale electricity markets. Over the last 25 years, a variety of methods and ideas have been tried for this type of forecasting, with varying degrees of success. The talk will review recent developments in this fascinating area, including (but not limited to) probabilistic forecasting, combining forecasts and deep learning.

Research in Wrocław: models and statistical methods for single particle experiments data

Single particle tracking is a rapidly expanding field in which new microscopy methods allow to study single molecule dynamics with unprecedented accuracy. These tools open completely new ways of studying complex physical systems, crucial application being studying the dynamics of molecules inside and on the surface of biological cells, which is then relevant for numerous problems in biochemistry and medicine. On Wednesday and Friday talks about recent experimental discoveries by Diego Krapf and Yuval Garini will be supported by the discussion of modelling and data analysis techniques made by the numerous Wrocław team: Kacper Taźbierski, Monika Muszkieta, Jakub Ślęzak, Aleksei Chechkin, Michał Balcerek, Marek Teuerle and Joanna Janczura.

Large amount of data from single particle tracing measurements is a testing ground for modern mathematical methods. Models include stochastic differential equations, continuous random walks and fractional differential equations, hidden Markov processes, and others. Spectral and time series analysis, hypothesis testing and Bayesian interface are relevant statistical tools. Some of the recent achievements in the field resulted in a Nature paper Single-molecule imaging reveals receptor–G protein interactions at cell surface hot spots (doi:10.1038/nature24264) co-authored by Krzysztof Burnecki and Aleksander Weron from Wrocław.

Exemplary single particle tracking data. Figure from doi:10.1038/nature24264 discussed above. a) One frame from a fast single-molecule image sequence and corresponding trajectories. b) One trajectory as a function of time. c) Hidden Markov process analysis with the detected states.
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