Richard Schulze is a Researcher at the University of Münster, contributing to projects such as SkelCL, PACXX, and dOpenCL. His work focuses on parallel computing, compiler optimization, and auto-tuning frameworks for high-performance and distributed systems. He explores portable code generation for heterogeneous architectures using Multi-Dimensional Homomorphisms (MDH) and develops abstractions for OpenCL/CUDA programming. His research interests include advancing scheduling languages and systematic composition models, alongside probabilistic data linkage techniques. Recent publications emphasize auto-tuning methodologies for Python and interdependent parallel program parameters. Publications since 2018 highlight contributions to portable compiler design, performance optimization, and cross-platform parallelism. No scientific awards are explicitly mentioned. Consultation hours are by appointment, and he is affiliated with the university's computer science research groups.
Nico Bohlinger is a PhD researcher at TU Darmstadt specializing in Intelligent Autonomous Systems . He employs Deep Reinforcement Learning (DRL) for advanced robot locomotion across diverse morphologies. Current focus on multi-embodiment learning Developing neural architectures for scalable DRL Experienced in humanoid and quadrupedal robots (Unitree H1, A1, Go2) Research contributions include: Unified Robot Morphology Architecture (URMA) Zero-shot policy transfer between simulated and real-world environments Vertical ground perturbation analysis for locomotion robustness His 8 publications since 2022 demonstrate expertise in cross-morphology policy learning and morphology-aware value function scaling . Nico actively supervises master's theses and teaches Robot Learning courses while leading the RL-X research framework development.
Prof. Dr.-Ing. Alexander Verl is a leading academic at the University of Stuttgart , serving as Principal Investigator at the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) . His work bridges control engineering , industrial robotics , and digital twin technologies to enhance precision in manufacturing systems. Research Interests : Improving positioning accuracy of industrial robots through transmission error modeling and compliance compensation. Developing adaptive preload control mechanisms for cable-driven parallel robots and rack-and-pinion systems. Advancing IT/OT convergence via Time-Sensitive Networking (TSN) and cloud-edge integration. Creating digital twin platforms for real-time simulation and quality monitoring in CNC machining. Exploring deep learning applications for perception of deformable linear objects in automation. Recent Work Trends show expertise in: smart manufacturing , Industry 4.0 , and data-driven control systems . Publications emphasize practical validation through industrial testbeds (e.g., KUKA KR210–2 robotics, CNC machine simulations) and theoretical contributions to elastokinematic models and nonlinear dynamics . Advising & Grants : Collaborates extensively with researchers like Armin Lechler and Michael Neubauer. Projects funded through academic-industry partnerships in automotive production, precision engineering, and Gaia-X-based data ecosystems.
Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .
Dr. Sebastian Schmelzle is a researcher at the Department of Biology, Technische Universität Darmstadt , where he works on functional morphology and biomechanics of arthropods. Since 2022, he has also served as CEO of Small World Vision GmbH . His academic career includes a magna cum laude PhD in Biology (2015) and research roles in DFG- and BMBF-funded projects like LIS, Waldklimafonds, NOVA, and ASTOR. He specializes in 3D imaging techniques, particularly synchrotron microtomography , and has contributed to open-source platforms like Biomedisa . Education : PhD in Biology (2015, TU Darmstadt), Diploma in Biology (2008, Eberhard Karls University Tübingen) Research interests focus on arthropod defensive mechanisms , notably the ptychoid defense in oribatid mites. His work explores how evolutionary lineages independently develop exoskeletal and muscular adaptations for survival. He bridges entomology , materials science , and computational imaging to study biomechanics, surface chemistry, and structural efficiency. Publications highlight his expertise in 3D microtomography (e.g., analyzing ant mandibles, insect cuticular hydrocarbons, and primate retinal structures). Collaborative projects span biomedical image segmentation , insect biomechanics , and synchrotron radiation applications . His recent work integrates machine learning and real-time data filtering for enhanced visualization. Additional contributions include public engagement initiatives like the AR Museum Station and Insect Excursions , aiming to democratize scientific knowledge through interactive exhibits and field studies.
Peter Münch is a postdoctoral researcher at the Chair of Numerical Methods for Partial Differential Equations within the Institute of Mathematics at Technical University of Berlin (TU Berlin), Faculty II - Mathematics and Natural Sciences. He has held research positions at Uppsala University, University of Augsburg, Helmholtz-Zentrum Hereon, and Technical University of Munich. Dr. Münch's research focuses on high-performance scientific computing with expertise in matrix-free computations, dynamic sparse communication patterns, node-level optimization, iterative solvers including multigrid and block preconditioners, and efficient algorithms for high-dimensional partial differential equations. His work spans discontinuous Galerkin methods, computational fluid dynamics, and simulation of additive manufacturing processes including solid-state sintering and melt-pool modeling. He is one of the principal developers of the deal.II finite-element library, which won the SIAM/ACM Prize in Computational Science and Engineering in 2025. His recent publications demonstrate significant contributions to matrix-free finite element methods, multigrid solvers, and applications in computational fluid dynamics and materials science. The research shows a strong trend toward high-performance implementations of numerical methods for extreme-scale computing, with particular emphasis on matrix-free approaches that avoid explicit storage of large sparse matrices. SIAM/ACM Prize in Computational Science and Engineering 2025 (for deal.II) Dr. Münch has supervised numerous student projects including Master's theses, Bachelor's theses, and term papers on topics ranging from immersed boundary methods to high-order discontinuous Galerkin methods. His teaching activities include courses on Numerical Methods for ODEs, PDEs, and High-Performance Parallel Computing. He has contributed to multiple deal.II tutorial programs (steps 19, 68, 75, 76, 87) demonstrating advanced finite element techniques. As a principal developer of the deal.II finite element library, Dr. Münch is actively involved in the open-source scientific computing community, contributing to one of the most widely used finite element frameworks in computational science and engineering. His GitHub profile shows consistent contributions to deal.II and related projects, with significant activity in 2025.
Sujoy Bhore is affiliated with the Indian Institute of Technology Bombay (Department of Computer Science & Engineering), Université libre de Bruxelles, and Technische Universität Wien. His research focuses on algorithms , computational geometry , and graph theory , with a strong emphasis on geometric optimization , dynamic data structures , and parameterized algorithms . Recent publications highlight advancements in Euclidean spanners for sparse network design online algorithms for dynamic geometric problems Steiner trees and tree covers in planar domains k-median/means approximation using coresets His collaborative work spans institutions, with frequent joint research on geometric intersection graphs , map labeling , and planar graph embeddings . Co-authors include prominent researchers like Csaba D. Tóth, Martin Nöllenburg, and Timothy M. Chan. While no formal awards are documented here, his contributions to algorithmic complexity and geometric networks remain significant.
Witold Andrzejewski is an active researcher in computer science, focusing on data deduplication pipelines, co-location pattern mining, and GPU-accelerated algorithms. His work bridges academia and industry, with publications analyzing customer record deduplication in the financial sector, performance optimization of spatial data processing, and comparative studies of statistical modeling versus machine learning approaches. 2025: Co-location pattern mining with Euclidean metrics 2024: Customer data deduplication parameter tuning 2023: Text similarity measures in financial applications
Andreas Wagner is a researcher affiliated with Helmholtz-Zentrum Dresden-Rossendorf , with a focus on interdisciplinary research spanning computational biology, systems biology, computer science, and materials science. His work explores genotype-phenotype mappings, evolutionary innovation, and robustness in biological systems, while also contributing to machine learning, numerical methods, and positron annihilation spectroscopy in physics. Wagner collaborates internationally, with co-authors from institutions in Germany, Austria, Finland, and beyond. Research Interests : Wagner's research bridges computational biology and systems biology, analyzing evolutionary processes through genotype networks, metabolic innovation, and gene regulatory circuits. He applies machine learning techniques to energy systems, such as solar power forecasting in federated learning frameworks. His physics work involves positron annihilation spectroscopy for material defect analysis, particularly in alloys and thin films. Publications & Data Science : He has published extensively on topics like robust numerical algorithms, adaptive cruise control optimization, and data-driven approaches for systematic reviews. His recent work includes matrix-free preconditioning methods and physics-regularized multi-modal image assimilation for medical imaging. Wagner contributes to open data initiatives, including datasets on radiation damage and material porosity via RODARE.
Benjamin Recht is a Professor at the California Institute of Technology , affiliated with the Center for the Mathematics of Information . His work spans Machine Learning , Control Systems , Reinforcement Learning , and Optimization , with a focus on theoretical guarantees, adaptive algorithms, and real-world applications. His research includes: Control Systems : Certainty equivalence, adaptive control, LQR, and robustness in dynamic environments. Machine Learning : Generalization bounds, interpolation in classifiers, test set overuse, and ethical frameworks for systemic harm detection. Neural Rendering : K-Planes for explicit radiance fields in space-time-appearance modeling. Recent publications (2025-2018) highlight trends in automating adaptive control , ethical machine learning , distributed computing , and 3D reconstruction . No student lists, awards, or lab details are explicitly mentioned.
Professor Thomas Kühne is a computational systems scientist affiliated with the University of Paderborn's Faculty of Computer Science and Institute of Artificial Intelligence. He holds a joint appointment with the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and works at CASUS Görlitz, a center investigating complex systems through data science methods. Education B.Sc. in Computer Science from ETH Zurich Diploma in Computational Sciences from ETH Zurich PhD in Computational Sciences (2008) from ETH Zurich His research focuses on developing advanced numerical algorithms for simulating chemical and physical processes, with specialization in aqueous systems, heterogeneous catalysis, and sustainable energy materials like CIGS solar cells and hydrogen storage systems. His work leverages supercomputers and quantum computing platforms. Notable achievements include authoring 175+ peer-reviewed publications and co-developing the open-source simulation package CP2K. Current leadership roles include Vice-Chair of the Paderborn Center for Parallel Computing (PC2) and Center for Sustainable Systems Design (CSSD), plus DFG Review Board membership. Scientific Awards ERC Starting Grant (2016) University of Paderborn Research Award (2020)
Prof. Dr. Alexander A. Auer is a research group leader at the Max Planck Institute for Chemical Energy Conversion, specializing in Molecular Theory and Spectroscopy and Theoretical Methods and Heterogeneous Reactions. His work integrates computational chemistry, spectroscopy, and catalysis to address fundamental questions in energy conversion and material science. Research Focus: Auer's expertise spans: Development of quantum chemical methods (e.g., contributions to ORCA software) Electrocatalysis (oxygen evolution, fuel cell interfaces) NMR spectroscopy and hyperpolarization techniques Heterogeneous catalysis reaction mechanisms Polymorphism in molecular crystals Recent Publications: His 2024-2025 articles emphasize computational studies of transition metal catalysts, NMR methodology advancements, and energy-related electrocatalysis. Dominant themes include mechanistic insights into hydrogenation, oxygen evolution, and solid-state NMR applications. Collaborations: Frequently partners with experimental groups (e.g., Fürstner, Neese, List) to validate theoretical models. Leads interdisciplinary projects combining synthesis, spectroscopy, and computation.
Amin Totounferoush serves as a Researcher within the Analytic Computing group at the University of Stuttgart, Germany. His institutional affiliation places him at Universitätsstraße 32, room 2.201, 70569 Stuttgart, with office hours available by appointment. Contact is facilitated through direct phone lines +49 711 685 88409 and +49 711 685 78409. His research portfolio centers on advanced computational methodologies, with primary expertise in data-intensive systems and scalable computing architectures. Key focus areas include: Algorithm optimization for distributed environments Parallel processing frameworks Large-scale data analytics pipelines High-performance scientific simulations As a core member of the Analytic Computing research unit, Dr. Totounferoush contributes to developing next-generation computational tools for complex scientific problems. His work integrates theoretical computer science with practical engineering solutions for data-intensive challenges across academic and industrial applications.
Prof. Dr. Ulrich Göhner is a Professor and Laboratory Head at the Faculty of Computer Science of Kempten University of Applied Sciences. He serves as Vice Dean of the Faculty of Computer Science and chairs the program committees for both the Bachelor and Master programs in Computer Science. His office hours are on Thursdays at 9:45 a.m. (pre-registration by email required). University: Kempten University of Applied Sciences Role: Professor, Vice Dean, Laboratory Head Research Expertise: Algorithms and Data Structures Compilers Parallel Computer Systems He leads the Parallel Computer Systems Laboratory and is associated with the Institute for Production and Informatics (IPI). Contact details include phone numbers +49 (0) 831-2523-198 and +49 (0) 831-2523-9283, and office location S3.06.
Prof. Dr.-Ing. Horst Schulte is a Professor at the Department of Engineering I, HTW Berlin - University of Applied Sciences. His expertise lies in Control Systems Engineering, Electrical Engineering, and Renewable Energy Systems, with a focus on modeling, fault-tolerant control, and computational intelligence applications. Department of Engineering I, HTW Berlin Chair in Control Systems Group ResearchGate profile with 214 publications Research Interests include: Model-based and data-driven control systems Wind and photovoltaic power plants Takagi-Sugeno fuzzy systems Robust and fault-tolerant control Dynamic virtual power plants (DVPP) Computational intelligence in energy systems Scientific Awards : 10th Annual ISGAN Award (2024) HTW Berlin Research Award (2018/19) Best Paper in Control Theory (2013) Best BMBF Project of the Month (2012) Key Contributions involve power tracking control for renewables, fault reconstruction in wind turbines, and innovative converter control schemes. His work bridges theoretical control methods with practical energy system implementations.
