The “Big data analytical methods for complex systems” will be held on October 6-7, 2022 in Wrocław, Poland. The workshop is co-organized by the Center for Advanced Systems Understanding (CASUS) at Helmholtz-Zentrum Dresden-Rossendorf and the University of Wrocław, Poland.
The workshop will provide an international audience to present, discuss and exchange on research progress and ideas relating to methods for analyzing complex systems and big data. It will bring together students and researchers to explore how methodologies from computer science, data science and artificial intelligence can be applied to a wide range of research topics, from fundamental physics to ecology to biology and medicine.
The primary goal of this workshop is to increase the scope and intensity of scientific and educational cooperation between partner institutions. Furthermore, the purpose of the workshop is to enable implementation of student’s projects in interdisciplinary and international research teams from Polish and German institutions.
The venue of the workshop is the Faculty of Mathematics and Computer Science in Wrocław, Poland. Conference language is English.
Confirmed Invited Speakers
Jerzy Marcinkowski, University of Wrocław, Poland
Michael Bussmann, CASUS/HZDR, Germany
Grzegorz Karch, University of Wrocław, Poland
Malgorzata Bogdan, University of Wrocław, Poland
Marcin Bieńkowski, University of Wrocław, Poland
Paweł Rychlikowski, University of Wrocław, Poland
Justin Calabrese, CASUS/HZDR, Germany
Artur Yakimovich, CASUS/HZDR, Germany
Michael Hecht, CASUS/HZDR, Germany
Attila Cangi, CASUS/HZDR, Germany
Tobias Dornheim, CASUS/HZDR, Germany
Tyll Krüger, Wrocław University of Science and Technology
Witold Rohm, Wrocław University of Environmental and Life Sciences
Krzysztof Graczyk, University of Wrocław, Poland
Marek Adamczyk, University of Wrocław, Poland
Piotr Wnuk-Lipiński, University of Wrocław, Poland
Please download the full program here.
|Thursday, October 6, 2022||9:15 – 9:30 CET||
Introduction Tomasz Jurdziński, University of Wrocław, Poland
|9:30 – 9:45 CET||Jerzy Marcinkowski, University of Wrocław, Poland|
|9:45 – 10:00 CET||Michael Bussmann, HZDR/ CASUS, Germany|
|10:00 – 11:00 CET||
Morning Session, Chair: Attila Cangi
|10:00 – 10:20 CET||Grzegorz Karch, University of Wrocław, Poland|
|From biology and models of carcinogenesis to mathematical theorems|
|10:20 – 10:40 CET||Małgorzata Bogdan, University of Wrocław, Poland|
|Statistical learning at UWr|
|10:40 – 11:00 CET||Marcin Bieńkowski, University of Wrocław, Poland|
|11:00 – 11:20 CET||Paweł Rychlikowski, University of Wrocław, Poland|
|Discovering the structure: unsupervised methods in speech recognition|
|11:20 – 11:50 CET||
|11:50 – 13:10 CET||
Midday session, Chair: Leszek Pacholski
|11:50 – 12:10 CET||Justin Calabrese, HZDR/CASUS, Germany|
|Animal movement research as a cross-cutting theme at CASUS|
|12:10 – 12:30 CET||Artur Yakimovich, HZDR/CASUS, Germany|
|Machine learning in biomedical images to study infection and disease|
|12:30 – 12:50 CET||Michael Hecht, HZDR/CASUS, Germany|
|minterpy – Multivariate Interpolation in Python|
|12:50 – 13:10 CET||Attila Cangi, HZDR/CASUS, Germany|
|Physics-informed and data-driven modeling of matter under extreme conditions|
|13:10 – 14:40 CET||
|14:40 – 15:40 CET||
Afternoon Session, Chair: Justin Calabrese
|14:40 – 15:00 CET||Tobias Dornheim, HZDR/CASUS, Germany|
|Frontiers of computational quantum many-body theory|
|15:00 – 15:20 CET||Tyll Krüger, Wrocław Univeristy of Science and Technology|
|15:20 – 15:40 CET||Witold Rohm, Wrocław University of Environmental and Life Sciences
|Future GNSS troposphere remote sensing, next Big Data for weather forecasting?|
|15:40 – 16:10 CET||
|16:10 – 17:30 CET||
|Friday, October 7, 2022||9:00 – 10:20 CET||
Morning Session, Chair: Michael Bussmann
|9:00 – 9:20 CET||Krzysztof Graczyk, University of Wrocław, Poland|
|Predicting porous medium properties by deep neural network|
|9:20 – 9:40 CET||Marek Adamczyk, University of Wrocław, Poland|
|How to price Uber fares using submodularity|
|9:40 – 10:00 CET||Piotr Wnuk-Lipiński, University of Wrocław, Poland|
|Computational intelligence in mining temporal data with hidden structure discovering|
|10:00 – 10:20 CET||Grzegorz Wyłupek, University of Wrocław, Poland|
|Nonparametric tests for selected testing problems with applications|
|10:20 – 11:20 CET||
Student talks, Chair: Jarek Byrka
|10:20 – 10:30 CET||Krystyna Grzesiak, University of Wrocław, Poland|
|A novel semiparametric model for hydrogen deuterium exchange monitored by mass spectrometry data|
|10:30 – 10:40 CET||Mateusz Staniak, University of Wrocław, Poland|
|Statistical methods for protein quantification based on mass spectorometry data with shared peptides|
|10:40 – 10:50 CET||Rodrigo Cofre, University of Wrocław, Poland|
|Regularization methods for gene identification|
|10:50 – 11:00 CET||Maciej Grabias, University of Wrocław, Poland|
|Classification with imbalanced data: Predicting instances of severe knee and back pain|
|11:00 – 11:10 CET||Kamil Smolak, Wrocław University of Environmental and Life Sciences|
|Explaining predictability of human movement trajectories through the sequence matching algorithms|
|11:10 – 11:20 CET||Bartosz Chmiela, University of Wrocław, Poland|
|Locally-informed proposals in Metropolis-Hastings algorithm with applications|
|11:30 – 12:30 CET||
Round table discussions with Coffee break
|12:30 – 13:30 CET||
|13:30 – 14:00 CET||Scientific speed dating|
|14:00 – 14:30 CET||
The book of abstracts can be downloaded here.
The complete list of profiles can be downloaded here.
Plasma physics, accelerator physics, high-energy-density physics, cancer therapy, ML, HPC, in-transit large scale data analytics
Scientific Head CASUS, Topic Lead Data Management & Analysis in the Helmholtz Research Field Matter
Application of HPC and ML to large-scale forward and inverse problems in physics, scalable data analytics on federated data
H Burau, et al., IEEE Transactions on Plasma Science 38 (10), 2831-2839
E Zenker, et al., 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 631-640
P Stiller, et al., 2020 Smoky Mountains Computational Sciences and Engineering Conference, 20-34
Analysis of Large Data Sets: Theoretical Foundations, Development of new algorithms, Applications in Medicine, Genetics, Astronomy and Finance
Asymptotic Statistics, Bayesian Statistics, Optimization Algorithms
M Bogdan, E Van Den Berg, C Sabatti, W Su, EJ Candès, SLOPE—adaptive variable selection via convex optimization, The Annals of applied statistics 9 (3), 1103-1140.
W Su, M Bogdan, E Candes, False discoveries occur early on the lasso path, The Annals of statistics, 2133-2150
D Brzyski, CB Peterson, P Sobczyk, EJ Candès, M Bogdan, C Sabatti, Controlling the rate of GWAS false discoveries, Genetics 205 (1), 61-75
Online and approximation algorithms, from a theory viewpoint but with a special focus on applications in networking and operations research.
Associate professor in Combinatorial Optimization Group
Competitive analysis, linear programming, analysis of algorithms complexity
M. Bienkowski, A. Kraska, H.-H. Liu: Traveling Repairperson, Unrelated Machines, and Other Stories About Average Completion Times. ICALP 2021
M. Bienkowski, J. Byrka, C. Coester, Ł. Jeż: Unbounded lower bound for k-server against weak adversaries. STOC 2020
M. Bienkowski, J. Byrka, M. Mucha: Dynamic Beats Fixed: On Phase-based Algorithms for File Migration. ACM Trans. Algorithms (2019)
M. Bienkowski, N. Sarrar, S. Schmid, S. Uhlig: Online Aggregation of the Forwarding Information Base: Accounting for Locality and Churn. IEEE/ACM Trans. Netw. (2018)
Natural language processing, Speech recognition, Information retrieval
Applying machine learning techniques to different problems, combining ML/DL approaches with more traditional methods
Quantitative ecology; Animal movement; Data science/statistics; Epidemiology
Research Group Leader, Earth System Science
Statistical methods and software development, statistics for autocorrelated data, process-based modeling, nonlinear optimization
Silva, I., Fleming, C. H., Noonan, M. J., Alston, J., Folta, C., Fagan, W. F., & Calabrese, J. M. (2022). Autocorrelation‐informed home range estimation: A review and practical guide. Methods in Ecology and Evolution, 13(3), 534-544.
Calabrese, J. M., & Demers, J. (2022). How optimal allocation of limited testing capacity changes epidemic dynamics. Journal of Theoretical Biology, 538, 111017.
Calabrese, J. M., Fleming, C. H., Noonan, M. J., & Dong, X. (2021). ctmmweb: A graphical user interface for autocorrelation‐informed home range estimation. Wildlife Society Bulletin, 45(1), 162-169.
Dormann, C. F., Calabrese, J. M., Guillera‐Arroita, G., Matechou, E., Bahn, V., Bartoń, K., … & Hartig, F. (2018). Model averaging in ecology: A review of Bayesian, information‐theoretic, and tactical approaches for predictive inference. Ecological Monographs, 88(4), 485-504.
Machine Learning and Deep Learning
Young Investigator Group Leader
Deep Learning with Microscopy and Multi-modal datasets
Galimov, Evgeniy, and Artur Yakimovich. “A tandem segmentation-classification approach for the localization of morphological predictors of C. elegans lifespan and motility.” Aging (Albany NY) 14.4 (2022): 1665.
Yakimovich, Artur, et al. “Infectio: a generic framework for computational simulation of virus transmission between cells.” Msphere 1.1 (2016): e00078-15.
Beerli, Corina, et al. “Vaccinia virus hijacks EGFR signalling to enhance virus spread through rapid and directed infected cell motility.” Nature microbiology 4.2 (2019): 216-225.
Multivariate Interpolation and Approximation Theory
Variational PDE Solvers on Complex Geometries
Non-linear Black Box Optimization
Graph Theory and Network Dynamics
Young Investigator Group Leader
Mathematical Foundations of Complex System Science, CASUS
Applied Mathematics and Numerics
Multivariate Interpolation in Non-Tensorial Nodes Lifts the Curse of Dimensionality for Trefethen Functions,Michael Hecht, Krzysztof Gonciarz, Jannik Michelfeit, Vladimir Sivkin and Ivo F. Sbalzarini, (in review) IMA Journal of Numerical Analysis, 2022
Fast interpolation and Fourier transform in high-dimensional spaces.
M. Hecht and I. F. Sbalzarini (2018). In Intelligent Computing. Proc. 2018 IEEE Computing Conf., Vol. 2, volume 857 of Advances in Intelligent Systems and Computing, pages 53–75, London, UK, Springer Nature
High-energy-density physics, Electronic structure theory, Computational physics, Machine Learning
Research Group Leader
Methods development in electronic structure theory (density functional theory), Software development for multiscale modeling of materials including machine learning (Gaussian processes, neural networks), optimization, automated and reproducible computational workflows.
J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, and S. Rajamanickam, Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks, Phys. Rev. B 104, 035120 (2021).
Nikolov, S., Wood, M.A., Cangi, A. et al. Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics. npj Comput Mater 7, 153 (2021).
L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Training-free hyperparameter optimization of neural networks for electronic structures in matter, arXiv:2202.09186 (2022).
Computational physics and high performance computing
Warm dense matter
Quantum many-body simulation
Young Investigator Group Leader
Method development (Quantum Monte Carlo, Density Functional Theory, …) and application
T. Dornheim et al., The Uniform Electron Gas at Warm Dense Matter Conditions, Physics Reports 744, 1-86 (2018)
T. Dornheim et al., Ab initio Path Integral Monte Carlo Results for the Dynamic Structure Factor of Correlated Electrons, Phys. Rev. Lett. 121, 255001 (2018),
T. Dornheim et al., Effective static approximation: A fast and reliable tool for warm-dense matter theory, Phys. Rev. Lett. 125, 235001 (2020)
T. Dornheim et al., Accurate temperature diagnostics for matter under extreme conditions, Nature Comm. (submitted), arXiv:2206.12805
Problems on which I focus in my research are combinatorial in nature but come from the context of stochastic optimization, online optimization, mechanism design, algorithmic game theory and core machine learning. Simply speaking, optimization under uncertainty.
Combinatorial optimization, stochastic optimization, statistics, probability theory
Submodular stochastic probing on matroids, M Adamczyk, M Sviridenko, J Ward
Mathematics of Operations Research 2016
Improved analysis of the greedy algorithm for stochastic matching M Adamczyk
Information Processing Letters 2011
Improved approximation algorithms for stochastic matching M Adamczyk, F Grandoni, J Mukherjee
Algorithms-European Symposium on Algorithms 2015
Random order contention resolution schemes M Adamczyk, M Włodarczyk
Annual Symposium on Foundations of Computer Science (FOCS) 2018
Computational intelligence, evolutionary algorithms, data mining, machine learning, time series and temporal data analysis, recommender systems, decision support systems
Head of the Computational Intelligence Research Group
Applying computational intelligence / data mining / machine learning approaches
 P. Filipiak, P. Lipinski, Making IDEA-ARIMA efficient in dynamic constrained optimization problems, EvoStar, 2015.
 P. Lipinski, Evolutionary approach to optimization of data representation for classification of patterns in financial ultra-high frequency time series, GECCO, 2017.
 P. Lipinski, E. Brzychczy, R. Zimroz, Decision tree-based classification for Planetary Gearboxes’ condition monitoring with the use of vibration data in multidimensional symptom space, Sensors, 2020.
 J. Stanczyk, J. Kajewska-Szkudlarek, P. Lipinski, P. Rychlikowski, Improving short-term water demand forecasting using evolutionary algorithms, Scientific Reports, 2022.