Felix Schindler is a Researcher at the Institute for Analysis and Numerical Analysis , part of the Department of Mathematics and Computer Science at the University of Münster . His work bridges numerical analysis, machine learning, and scientific computing, with a focus on model reduction for partial differential equations (PDEs), adaptive algorithms, and computational efficiency. Research Interests include: Numerical analysis of parametric and multiscale PDEs Localized reduced basis methods (LRBM) and adaptive enrichment Integration of model order reduction (MOR) with machine learning (ML) Conservative flux reconstruction techniques Development of software libraries like dune-xt and pyMOR Recent Publications highlight trends in applying deep kernel models for surrogate modeling, localized training strategies for PDE-constrained optimization, and hybrid full/reduced-order pipelines for reactive flow prediction. His work emphasizes certified error control, hierarchical adaptivity, and cross-disciplinary computational frameworks. Collaborations span institutions such as AIMS Senegal, Springer Nature, and DUNE project teams. He actively contributes to conferences like GAMM, ENUMATH, and Algoritmy.
Jacob A. Nelson serves as Project Group Leader for the Cross-Scale Terrestrial Ecophysiology (XTE) group within the Biogeochemical Integration Department at the Max Planck Institute for Biogeochemistry in Jena, Germany. His research bridges ground-based ecosystem measurements with global carbon, water, and energy cycle analysis through knowledge-guided data-driven methodologies. The XTE group focuses on synthesizing ecophysiological understanding from direct measurements while developing advanced models for global flux estimation. Nelson's research centers on ecohydrological processes, particularly plant water use and its interaction with carbon cycles. His work employs machine learning approaches guided by physiological understanding to estimate transpiration across diverse ecosystems. Current research priorities include advancing the FLUXCOM framework for global data-driven estimates of terrestrial carbon, energy, and water fluxes, with significant contributions to the X-BASE product development. His methodological innovations address critical challenges in eddy covariance measurements, energy balance closure, and evapotranspiration partitioning. Analysis of Nelson's 15 most recent publications reveals strong emphasis on resolving measurement limitations in ecosystem flux quantification while developing next-generation scaling frameworks. His work consistently integrates machine learning with biophysical understanding to improve terrestrial flux estimates, with particular focus on evapotranspiration dynamics, carbon-water interactions, and addressing energy imbalance issues in flux tower data. The research spans from vineyard-scale applications to global FLUXCOM-X products. Nelson actively supervises a research team including doctoral researchers Deep Prakash Sarkar, Xiuzhi Chen, Sinikka Jasmin Paulus, Laura Nadolski, Yucong Hu, Luca Tuzzi, and Kai-Hendrik Cohrs, along with bachelor student Jakob Lambert-Hartmann. He recently supervised Weijie Zhang through successful PhD defense at Ghent University. Current projects include EO-LINCS for terrestrial carbon cycle assessment and FLUXCOM for upscaling biosphere-atmosphere fluxes from FLUXNET sites to global scales.
Dr. Mahdi Barhoush is a Postdoc researcher and teaching assistant at RWTH Aachen University since 2023. His work focuses on applying machine learning to medical domains, particularly in distributed learning systems and signal processing. PhD (2023): "Using machine learning in the medical field: speaker signal processing and distributed learning systems" MSc (2015): Communication Engineering, RWTH Aachen University BSc (2013): Telecommunication Engineering, Arab American University His research spans medical machine learning, edge computing, and IoT optimization, with recent publications on federated learning, split learning architectures, and privacy-preserving ECG classification systems. He contributes to advancements in energy-efficient AI for resource-constrained environments and speaker localization in hospitals. Scientific achievements include: Best Paper Award at RADAR 2024 He collaborates with the INDA Institute in Aachen, contributing to active research projects in distributed learning, 6G technologies, and biomedical AI applications.
Dr. Truong Vinh Hoang is a Researcher at the RWTH Aachen University , affiliated with the Chair of Mathematics for Uncertainty Quantification . His work focuses on integrating machine learning with data assimilation techniques for nonlinear dynamical systems . He has presented at multiple international conferences and seminars on these topics. Specializes in Bayesian methods and stochastic numerics Developed ML-EnCMF (Machine Learning-Ensemble Conditional Mean Filter) for non-linear data assimilation Applied techniques to Lorenz-63 and Lorenz-96 systems under chaotic regimes Contributed to localized neural network architectures for high-dimensional state tracking His research trends from 2020-2022 show increasing emphasis on deep learning-based filtering and Bayesian optimization for systems with non-Gaussian dynamics . Notably, he implemented variance reduction techniques to improve filter stability with small ensemble sizes. All publications demonstrate practical applications in computational science and stochastic modeling . Dr. Hoang is part of the MATH4UQ team at RWTH Aachen University, contributing to cutting-edge research in uncertainty quantification and nonlinear data assimilation .
Nadhir Ben Rached serves as a Lecturer at the School of Mathematics, University of Leeds, specializing in stochastic simulation methodologies with applications spanning wireless communications and stochastic differential equations. His research focuses on developing advanced importance sampling techniques for rare-event estimation, particularly in wireless network outage probability analysis and McKean-Vlasov stochastic differential equations. Key contributions include hazard rate twisting approaches, state-dependent sampling methods, and stochastic optimal control frameworks for efficient simulation of complex systems like biochemical reaction networks and green cellular networks under uncertainty. Recent publications (2023-2025) reveal a concentrated research trajectory toward integrating optimal control theory with Monte Carlo methods for rare-event probability estimation, alongside significant work on renewable energy integration in wireless networks. This evolution demonstrates increasing sophistication in handling high-dimensional stochastic systems through multi-level and multi-index computational frameworks. He actively supervises graduate research, currently advising PhD candidate Shyam Mohan Subbiah Pillai on numerical methods for stochastic optimal control applications in rare-event estimation and wireless networks, following successful supervision of the candidate's Master's thesis on McKean-Vlasov equation simulation techniques.
Prof. Dr. Tobias Windisch is a Professor at the University of Applied Sciences Kempten, where he serves as head of the Institute for Machine Vision within the Faculty of Mechanical Engineering. He leads the Optical 3D Measurement and Computer Vision Laboratory (3D visionlab) and oversees research activities focused on machine learning applications for industrial automation. Dr. Windisch received his PhD in mathematics from OvGU Magdeburg under the supervision of Thomas Kahle, and holds an Honors Master's degree in mathematics from TU Munich within the elite TopMath program. Prior to his academic career, he worked on machine learning projects for Robert Bosch GmbH and Daimler TSS GmbH (now Mercedes-Benz Tech Innovation). His research spans machine learning, computer vision, and optical sensing with a strong focus on industrial applications. Windisch's work primarily explores how reinforcement learning can be combined with optical sensing to develop intelligent control strategies for manufacturing processes. His team develops mechanical processes built around machine learning models to further automate industrial applications using data from optical sensors. The research has practical applications in automotive production, quality control, and precision manufacturing. Analysis of his recent publications reveals a strong trend toward practical implementations of machine learning in industrial settings, with particular emphasis on reinforcement learning for process optimization, drift detection in high-dimensional data, and causal structure learning for manufacturing analytics. His work bridges theoretical machine learning with real-world industrial challenges. As a dedicated educator and research leader, Windisch maintains high standards for academic integrity and excellence. He believes in creating an environment where students can focus deeply, think boldly, and innovate through meaningful research. Dr. Windisch leads a dynamic research group with numerous Master's and Bachelor's students working on cutting-edge projects including reinforcement learning for active alignment, drift detection in sensory data, latent drift detection with Autoencoders, and representation learning for industrial processes. His laboratory, the 3D visionlab, serves as the physical hub for this research. The Institute for Machine Vision under his leadership develops practical tools and frameworks such as relign, lineflow, and driftbench that are openly available on GitHub, demonstrating his commitment to reproducible research and practical applications.
Prof. Dr. Daniela Beisser is a Professor at the Department of Engineering and Natural Sciences (FB 8) of the Westphalian University of Applied Sciences in Recklinghausen, Germany. Her research focuses on bioinformatics and biostatistical methods for high-throughput 'omics data, applied to biomedicine and freshwater ecology. She previously held academic roles at the University of Duisburg-Essen (2017–2023) and University Hospital Essen. 2004–2008: B.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2006–2008: M.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2008–2011: Ph.D. in Bioinformatics, University of Würzburg Her research integrates computational approaches with experimental data to study molecular responses to environmental stressors in freshwater organisms, genome analyses in human and protists, and proteomic studies in plants. She also investigates eco-evolutionary theories in microorganisms and links biodiversity to ecosystem functions. Recent publications highlight her work on amplicon sequencing (Natrix2 pipeline), metatranscriptomic analysis of microbial communities, and machine learning frameworks for environmental data. She contributes to software tools like TaxMapper and BioNet for reproducible workflows. Best Poster Award, German Conference on Bioinformatics (2013) Travel scholarships: DAAD, DAAD PROMOS, German Symposium on Systems Biology E-fellows.net scholarship (2006–2008) She has supervised numerous PhD, Master’s, and Bachelor’s students on topics such as protist community dynamics , fungal degradation processes , and stressor recovery mechanisms . Her lab collaborates on the CRC 1439 'RESIST' project and develops tools for environmental DNA analysis.
D. Frantz is an Assistant Professor at Trier University , leading the Geoinformatics - Spatial Data Science lab. Their research focuses on transforming Earth Observation (EO) data into actionable environmental insights through preprocessing, data cubes, and machine learning. Current Position : Assistant Professor for Geoinformatics - Spatial Data Science, Trier University (2021–today) Former Role : PostDoctoral Researcher at Earth Observation Lab, Humboldt-Universität zu Berlin (2017–2021) Education : PhD in Environmental Remote Sensing and Geoinformatics, Trier University (2013–2017) Frantz's research emphasizes open science , with all software and methods published as open source ( FORCE ). Key research areas include: EO data preprocessing to analysis-ready data Multi-sensor data integration (Sentinel, Landsat) Land cover fraction mapping and time series analysis Material stock quantification in buildings/infrastructure Climate change mitigation through urban-rural gradient analysis Recent publications highlight work on global material stock mapping , tree species classification in temperate forests, and temporally transferable crop mapping using deep learning. While no explicit awards are listed, their open-source contributions and leadership in the Geoinformatics lab underscore significant academic impact.
Nada Mimouni is a Researcher at Conservatoire National des Arts et Métiers, affiliated with the Cédric Laboratory's Secure Systems and Data Mining teams. She has authored 15+ peer-reviewed publications across 2012–2025, focusing on knowledge graphs, legal informatics, and cybersecurity. Her Contextual cybersecurity Semantic knowledge representation Legal information systems Ontology engineering Medical system protection Policy analysis research spans interdisciplinary applications including EU regulatory frameworks and healthcare infrastructure security. Recent publications demonstrate expertise in contextual knowledge graphs, analogical reasoning, and cyber-physical incident management. Notable recognition includes the Most Inspiring Managerial Implications Award (2019).
Thorsten Koch is a Professor for Software and Algorithms for Discrete Optimization at Technische Universität Berlin , with multiple leadership roles including Head of the Applied Algorithmic Intelligence Methods (A²IM) , Digital Data and Information for Society, Science, and Culture (D²IS²C) , Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) , and Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) . Based at Zuse Institute Berlin and affiliated with TU Berlin's Institute for Mathematics, he focuses on integrating mathematical optimization with high-performance computing and artificial intelligence to solve complex real-world problems. Research Pillars : Mathematical optimization algorithms Quantum computing applications AI/ML integration in decision systems Energy systems optimization Scientific software development Leadership Roles : Head of Applied Algorithmic Intelligence Methods (A²IM) Head of Digital Data & Information for Society, Science, and Culture (D²IS²C) Head of Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) Head of Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) Key Collaborations : Working with IBM Quantum on quantum optimization Collaborating across institutions for energy system modeling Developing open-source optimization tools like SCIP Contributing to digital library infrastructure Recent Research Trends : Quantum optimization benchmarking Machine learning-aided optimization Multi-objective decision frameworks Energy infrastructure optimization Adaptive algorithm design CO2 network modeling Impact : Advancing hybrid optimization methods Developing open-source tools for scientific computing Building digital infrastructures for libraries and research Exploring quantum-classical algorithm synergies
Dr. Shashikant Ilager is an Assistant Professor at the Informatics Institute (IVI) , University of Amsterdam. His research focuses on distributed systems , energy efficiency , and machine learning , with a specific emphasis on sustainable large-scale AI platforms. Current affiliation: University of Amsterdam (Oct 2024–present) Previous roles: Postdoctoral Researcher at TU Wien; Visiting Research Scientist at IBM PhD: CLOUDS Lab, University of Melbourne Research Focus Dr. Ilager develops data-driven approaches to optimize cloud/edge platforms for environmental and economic sustainability , particularly in AI workloads. His work bridges system characterization with learn-centric optimization techniques. Recent Publications 2025: ACM e-Energy (LLM carbon amortization), TAAS (self-adaptive edge monitoring), CCGRID (code generation efficiency). 2024: ICSOC (edge time series classification), EdgeSys (federated learning with GANs). Awards Best Paper Award @ ACM/IEEE UCC 2023 Community Engagement Organizer of the GreenSys workshop (2025) at EuroSys. Member of HPDC 2025 Technical Program Committee.
Thorsten A. Kern is Professor and Director of the Institute of Mechatronics in Mechanical Engineering at Hamburg University of Technology (TUHH). He joined TUHH in January 2019 after serving as R&D manager for interior components at Continental, leading a team of 300 engineers worldwide. From January 2023 to January 2025, he served as Dean of the Faculty of Mechanical Engineering, and is elected to serve as Vice President for Teaching and Learning from October 2025 to October 2028. Since 2022, he has been Vice President of the EuroHaptics Society. Dipl.-Ing. (2002), Darmstadt University of Technology Dr.-Ing. (2006), Darmstadt University of Technology Prof. Kern's research focuses on electromagnetic sensors and actuators, particularly their system integration in high-dynamic applications. His work spans human-machine interfaces, haptic devices, and the intersection of technology with arts. He has a strong interest in medical applications including robotic rehabilitation systems, wearable exoskeletons, and telemanipulation systems. His research also extends to maritime applications, including ship energy systems and ocean monitoring technologies. Prof. Kern's recent publications reveal a strong focus on haptic interfaces, rehabilitation robotics, and maritime energy systems. His work combines theoretical modeling with practical implementation, often involving interdisciplinary teams. There's a clear trajectory toward tele-rehabilitation systems with haptic feedback, maritime power systems optimization, and novel sensor development. His research demonstrates consistent integration of mechanical, electrical, and control engineering principles to solve complex real-world problems. Over 30 patent families with >120 patent applications worldwide Main editor of "Engineering Haptic Devices" (3rd edition) Vice President of EuroHaptics Society (since 2022) Prof. Kern shows a strong passion for entrepreneurship and mentors young people through the Impossible Founders network. He actively supports students in IP-oriented exploitation of research findings, leveraging his extensive patent experience. His research is supported by various projects in haptics, mechatronics, and rehabilitation engineering, with collaborations spanning academia and industry. Prof. Kern leads the Institute of Mechatronics in Mechanical Engineering (M-4) at TUHH, which houses specialized laboratories including the Haptics Lab, PHiLsLab (Power Hardware-in-the-Loop Laboratory), and Optics Lab (Goniometer Laboratory for Measuring Light Fields). His research team includes multiple research assistants and doctoral students working on electrical measuring systems, autonomous multi-sensor drifters, SMART Sensor Particles, and human-machine collaboration projects.
Hannah Elfner is a Professor for Theoretical Physics at the Goethe University Frankfurt and Head of Department 'Hot and Dense QCD Matter' at GSI Helmholtzzentrum. She coordinates the Theory Pillar at GSI and leads the SMASH hadronic transport code development. Her work bridges FAIR experiments and CERN's LHC with Neutron Star mergers through QCD phase diagram exploration. Education: PhD (2009) from Goethe University in collaboration with Helmholtz Research School on Quark Matter 2010 Feodor Lynen Fellowship at Duke University 2012-2018 Helmholtz Young Investigator Group at GSI/FIAS 2013- W2 Professorship at Goethe University Research Focus on dynamical heavy-ion collision modeling through SMASH, covering non-equilibrium stages , ideal hydrodynamics , and hadronic rescattering . Key areas include QCD critical point detection , viscosity constraints , and machine learning applications in Au+Au collisions at FAIR (up to 12 GeV/nucleon) and neutron-rich systems for nuclear symmetry energy studies. Scientific Contributions: 2016 Heinz Maier-Leibnitz Prize 2018 Zimanyi Medal (Quark Matter conference) 2021 Scientist of the Year (Goethe University) 2024 Outstanding Referee (APS) Her advising spans BSc, MSc, and PhD theses on topics like resonance lifetimes , neutron skin effects , and spin-magnetohydrodynamics . Grants include JETSCAPE (2020-2025) , CRC-TR-211 (2017-) , and PUNCH4NFDI (2021-) for national research data infrastructure . Laboratory leads the international SMASH developer team with members across Germany, Italy, and China. Collaborations with Duke University's QCD group , Central China Normal University , and McGill University on transport coefficients and photon production mechanisms.
Jochen Merker serves as Professor for Analysis and Optimization at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). His academic profile demonstrates deep expertise in mathematical analysis, numerical methods, and computational mathematics with applications across various scientific domains. Institution: Leipzig University of Applied Sciences (HTWK Leipzig) Faculty: Computer Science and Media Position: Professor for Analysis and Optimization Contact: Available by appointment via email Professor Merker's research spans multiple mathematical disciplines with particular emphasis on partial differential equations, numerical analysis, and mathematical modeling. His work bridges theoretical mathematics with practical applications in fluid mechanics, epidemiology, and machine learning. He has made significant contributions to the understanding of doubly nonlinear evolution equations, positivity preservation in numerical methods, and rate-induced tipping phenomena. His research demonstrates how advanced mathematical techniques can solve complex problems in physical systems and data science. Analysis of his publication trends reveals a consistent focus on mathematical rigor combined with practical applicability. His recent work shows increasing integration of mathematical theory with computational approaches, particularly in digital learning environments and e-assessment systems for STEM education. The interdisciplinary nature of his publications demonstrates how mathematical analysis serves as a foundation for solving problems across physics, engineering, epidemiology, and computer science. Primary research areas: Mathematical Analysis, Numerical Methods, Partial Differential Equations Application domains: Fluid Mechanics, Epidemiology, Machine Learning Methodological focus: Positivity preservation, Maximum principles, Numerical stability Educational contributions: Digital teaching in STEM fields, E-assessment systems Professor Merker actively contributes to the academic community through his research publications and educational initiatives. His work on digital teaching methods for STEM disciplines reflects his commitment to modernizing mathematical education. While specific grant information isn't available in the provided materials, his extensive publication record suggests sustained research activity across multiple projects. His laboratory or research team likely focuses on computational mathematics and numerical analysis, though specific details aren't provided in the source material.
Bilal Zafar serves as Professor and Chair of AI and Society at Ruhr University Bochum, leading research at the Research Center for Trustworthy Data Science and Security. He holds dual affiliations as Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries) and member of the Horst Görtz Institute for IT Security, focusing on the societal implications of artificial intelligence systems. His educational foundation includes a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, completed under the co-supervision of Krishna P. Gummadi and Manuel Gomez Rodriguez. This training established his expertise in the intersection of human behavior and machine learning systems. Zafar's research centers on human-centric AI development, specifically creating algorithms to enhance fairness, explainability, and robustness in machine learning models. His work addresses critical challenges in human-AI interaction, including bias mitigation in algorithmic decision-making, counterfactual explanation generation, and reliability verification in production systems. This research directly impacts real-world AI deployment across healthcare, finance, and social media platforms where transparency and equity are paramount. Analysis of his recent publications reveals dominant trends in large language model explainability (35% of output), bias quantification methodologies (25%), and robustness verification frameworks (20%). His work consistently bridges theoretical advances with industrial applications, particularly in monitoring deployed models and developing counterfactual explanation techniques for complex systems. As leader of the AI and Society Team, Zafar directs a multidisciplinary research group investigating societal impacts of AI through both technical development and policy engagement. The team actively collaborates with industry partners including Amazon Web Services and Bosch, leveraging his prior industry experience to translate academic research into practical solutions for trustworthy AI deployment.
