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Silke GlasView profile

Research Fellow

• Reduced basis method
• Variational inequalities
• Wave equations
• +3 more

Silke Glas is a Postdoctoral Researcher at the Institute of Numerical Mathematics, Ulm University, a position she has held since July 2018. Previously, she served as a Research Assistant at the same institute from April 2016 to July 2018 and at the Chair of Energy Trading and Finance, University of Duisburg-Essen (2012-2016), funded by the German Research Foundation's Priority Programme 1324. Her research visits include the Institut Henri-Poincaré in Paris and SISSA in Trieste. Her educational background features: Diploma in Mathematics and Economics from Ulm University (2006-2012), thesis on "Reduced Basis Method for Variational Inequalities" Master of Mathematics from the University of South Florida (2009-2010) Dr. Glas specializes in model reduction for nonlinear problems, with core expertise in reduced basis methods applied to variational inequalities, wave equations, Hamilton-Jacobi-Bellman equations, and space-time formulations. Her work bridges theoretical numerical analysis with practical applications in energy markets, particularly intraday electricity trading. She has developed novel approaches for noncoercive and parabolic systems, addressing challenges in error estimation and computational efficiency. Her publication trajectory reveals evolving sophistication in handling time-dependent nonlinear systems, with increasing emphasis on financial applications. Early work focused on theoretical foundations of variational inequalities, while recent publications integrate model reduction with optimal control for energy trading problems, demonstrating cross-disciplinary impact. Scientific recognition includes: No formal awards documented in source material Research funding has been secured through the German Research Foundation's Priority Programme 1324. No student advising activities are mentioned, though her collaborative work involves prominent researchers like K. Urban and Anthony T. Patera. Her primary research environment at Ulm University's Institute of Numerical Mathematics supports her focus on computational mathematics and real-world applications.

Edmund SpencerView profile

Associate Professor

• Space Plasma Physics
• Space Weather
• Radio Frequency Instrumentation
• +3 more

Dr. Edmund Spencer is an Associate Professor in the Department of Electrical and Computer Engineering at the University of South Alabama , with research focused on space plasma physics and space weather . He designs advanced instruments for space science, develops theoretical frameworks for plasma characterization, and applies stochastic optimization algorithms to complex systems. Ph.D. Electrical and Computer Engineering, University of Texas at Austin M.S. Electrical and Computer Engineering, University of Texas at Austin B.S. Electrical and Electronics Engineering, University of Leicester, UK His work bridges space instrumentation with nonlinear magnetospheric dynamics , particularly in geomagnetic substorms and solar wind-earth magnetosphere interactions . Current projects include onboard space weather modules for satellites and advanced antenna systems for CubeSats . Recent research trends from his 15 most recent publications (2019-2025) include: Development of time-domain impedance probes for ionospheric electron density measurements Applications of machine learning in substorm prediction Hybrid physics-black-box modeling for Dst index forecasting Advanced antenna designs for small satellites 3D Particle-in-Cell simulations for RF instruments Collisional effects in plasma probe measurements Scientific contributions include: NSF CAREER Award (2013) for RF impedance probe development Key role in NASA's USIP CubeSat missions (e.g., JAGSAT I) Leveraging WINDMI model for substorm dynamics analysis He teaches graduate and undergraduate courses in electromagnetics and stochastic processes , contributing to the department's space science integration in engineering education.

• Mechanism Design
• Voting Theory
• Game Theory
• +1 more

Andreas Kleiner is a Professor in the Department of Economics at the University of Bonn. His research focuses on Mechanism Design , Voting Theory , Game Theory , and Political Economy . Research Interests Mechanism Design Voting Theory Game Theory Political Economy Recent Publications (2018-2025) Kleiner's work explores multidimensional delegation with stochastic mechanisms, convex choice properties in mechanism design, and extreme point analysis in fusions. He investigates sequential veto bargaining dynamics, interdependent voting models, and costly verification in resource allocation. His research connects linear programming to monopoly pricing duality and examines historical legislative agenda-setting strategies. Contact Email: andreas.kleiner@uni-bonn.de Email: andykleiner@gmail.com

• Embedded Systems
• Internet of Things
• Wireless Sensor Networks
• +5 more

João Luís Marques Pereira Monteiro is a Full Professor at the Department of Industrial Electronics, School of Engineering, University of Minho, Portugal. He has been with the university since 1980, progressing from Assistant Trainee to his current position. He has held significant leadership roles including Pro-rector (2005-2009), Director of the Algoritmi Center (1998-2006, 2010-2013), and Dean of the School of Engineering (2013-2019). He currently coordinates the Embedded Systems Research Group (ESRG) and is a Senior Researcher at the Algoritmi Research Center. His educational background includes: Bachelor's degree in Electrical Engineering and Computers from the University of Porto (1980) PhD in Informatics and Systems Engineering - specialization in Computer Engineering - from the University of Minho (1991) Habilitation (Dr habil) from the University of Minho (2003) Professor Monteiro's research spans multiple domains in engineering and computer science, with a strong focus on practical applications. His work in embedded systems has led to innovations in real-time processing, sensor networks, and hardware-software co-design. In medical applications, he has developed textile-based sensors for vital sign monitoring. His recent work focuses on computer vision for autonomous vehicles, particularly 3D object detection from point clouds. He also contributes to educational initiatives in data science and AI, particularly in developing countries. His recent publications show a clear progression toward applications requiring real-time processing on resource-constrained devices. There's a strong emphasis on autonomous systems, particularly self-driving vehicles, with multiple papers on 3D object detection from point clouds. His work bridges embedded systems, computer vision, and wireless communications, often focusing on practical implementations for real-world applications like emergency responder localization and medical monitoring. Professor Monteiro has supervised more than a dozen doctoral students, many of whom have gone on to become faculty members at national and international universities. His research has been supported by various funding agencies including FCT (Portuguese), ADI (Portuguese), and FP7 (European funding), as well as industry partners. He leads the Embedded Systems Research Group (ESRG), which is part of the Algoritmi Research Center. The ESRG focuses on developing embedded solutions for various applications including autonomous vehicles, medical devices, and industrial monitoring systems. The group works on both hardware and software aspects of embedded systems, with particular expertise in real-time processing and resource-constrained environments.

• Logistics
• Supply Chain Management
• Operations Research
• +2 more

Maria Sameiro Faria Brandão Soares Carvalho is an Associate Professor at the Department of Production and Systems of the School of Engineering, University of Minho, Portugal. She serves as a Senior Researcher with PhD at the Centro ALGORITMI and was Associate Director of the ALGORITMI Research Centre from 2018 to 2024. She is an active member of both the Industrial and Engineering (IEM) R&D Group and the SLOTS (Supply chain, Logistics and Transportation Systems) R&D Lab. Educational Background: Graduated in Computer and Systems Engineering at the University of Minho, Portugal MSc degree in Transportation Planning and Engineering PhD degree in Transportation Planning from the University of Leeds, UK Dr. Carvalho's primary research interests focus on Logistics and Supply Chain Management, with specific emphasis on techniques and applications of Operations Research in transportation, logistics, and supply chain contexts. Her work spans theoretical frameworks, empirical studies, and practical applications across various industries including automotive, construction, healthcare, and manufacturing. She has developed decision support systems for supply chain optimization, risk management, and transportation planning, with particular attention to the impacts of globalization and pandemic disruptions on supply chain performance. Her publication record shows a consistent research trajectory with 105 publications spanning from 2007 to 2025. Her recent work demonstrates an evolution from traditional operations research methods toward integrating artificial intelligence and machine learning approaches. Key research themes include supply chain quality management 4.0, explainable AI for business decision-making, service-oriented supply chains, risk propagation in global networks, and sustainable logistics solutions. Her publications appear in high-impact journals across operations research, supply chain management, and transportation fields. Scientific Contributions: h-index of 16 81 publications (according to profile, though the list shows 105) 23 publications in Q1/Q2 journals 1157 total citations Dr. Carvalho has supervised a large number of MSc dissertations and doctoral theses throughout her career, demonstrating her commitment to mentoring the next generation of researchers. She has served on the Director Board of the Doctoral Program in Industrial and Systems Engineering and contributed to the AESI (Advanced Engineering Systems for Industry) PhD program. Her professional service includes reviewing more than twenty papers for scientific journals and conference proceedings, reflecting her active engagement with the academic community. Her research is conducted within the ALGORITMI Research Centre, where she leads the SLoTS R&D Lab focused on Supply chain, Logistics and Transportation Systems. This lab serves as a hub for interdisciplinary research that bridges theoretical operations research with practical industry applications, particularly in the automotive, construction, and healthcare sectors.

George VairaktarakisView profile

Professor

• Project Management
• Production Chain Coordination
• Subcontracting and Outsourcing
• +7 more

George Vairaktarakis is a Professor at the Department of Operations, Weatherhead School of Management, Case Western Reserve University. His research focuses on subcontracting, production chain coordination, and project management, with impactful findings reducing production costs by 30% while improving service efficiency. He holds the William E. Umstattd Professorship in Industrial Economics and serves as Director of the Master of Science in Management - Operations Research and Supply Chain Management program. Education: PhD in Operations (University of Florida, 1994) MSc in Industrial Engineering (University of Florida, 1992) MSc in Applied Mathematics (University of South Carolina, 1990) BS in Applied Mathematics (University of Patras, 1988) Vairaktarakis investigates time/cost-sensitive project management, third-party logistics, and workforce planning using mathematical models like integer programming and game theory. His work addresses decentralized production coordination, contract design for subcontracting, and resource flexibility in manufacturing systems. His publications span top journals including Management Science, Operations Research, and Naval Research Logistics. Key research trends include optimization of outsourced operations, game-theoretic analysis of supply chain collaboration, and robust scheduling methodologies. His editorial roles include Senior Editor at Production & Operations Management and Associate Editor at Naval Research Logistics. Awards: William E. Umstattd Professorship (2014), Alcoa Teaching Excellence Award (2013) Teaching: Developed Lean Operations and project management courses for MBA/executive programs globally Service: Held leadership roles in Faculty Council, Academic Integrity Office, and editorial boards

• Computer Architecture
• Parallel Computing
• Compiler Technology
• +3 more

Keiji Kimura is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering. He earned his Doctor of Engineering from Waseda University and has held academic positions at the university since 1999, progressing from Research Associate to Assistant Professor (2004-2005), Associate Professor (2005-2012), and Professor (2012-present). He is affiliated with multiple professional organizations including ACM, IEEE Computer Society, The Institute of Electronics, Information and Communication Engineers, and Information Processing Society of Japan. His research focuses on computer architecture, particularly parallel computing systems and compiler technology. Kimura has made significant contributions to the development of the OSCAR (Optimally Scheduled Advanced Multiprocessor) automatic parallelizing compiler framework. His work spans multiple areas including multicore processor architecture, power reduction techniques for embedded systems, non-volatile memory systems, and parallelization methods for heterogeneous architectures. His research interests specifically include Multiprocessor Architecture and Parallelizing Compiler development, with applications in real-time systems and energy-efficient computing. Analysis of his recent publications reveals a strong focus on practical implementations of parallel computing technologies across diverse hardware platforms including RISC-V, ARM, and heterogeneous multicore systems. His work demonstrates a consistent trajectory from theoretical compiler development toward practical applications in embedded systems, security, and non-volatile memory technologies. The publications show increasing emphasis on RISC-V architecture, persistent memory programming, and power-efficient computing solutions. MEXT Award for Science and Technology (Research category), 2014.04 Ministry of Education, Culture, Sports, Science and Technology (MEXT) Kimura has served on numerous prestigious conference program committees including PACT, IPDPS, HPCA, and LCPC. His research has been supported through collaborations with major technology companies and government initiatives such as the METI/NEDO project entitled "Multicore Technology for Realtime Consumer Electronics." His work with the OSCAR compiler framework has demonstrated significant performance improvements and power reductions in real-world applications. He leads research in the APAL laboratory (http://www.apal.cs.waseda.ac.jp/) at Waseda University, focusing on advanced parallel processing technologies. His team works on compiler-directed approaches to solve challenges in heterogeneous multicore architectures, with particular emphasis on making parallel programming more accessible while optimizing for both performance and power efficiency. Current research directions include RISC-V secure boot verification, non-volatile memory systems, and GPU-based persistent memory solutions.

• Power System Stability
• Multi-terminal VSC-HVDC Systems
• Power Electronics in Power Grids
• +7 more

Dr. Francisco Javier Renedo Anglada is a Guest Researcher at the Instituto de Investigación Tecnológica (IIT), affiliated with the Higher Technical School of Engineering (ICAI) at Comillas Pontifical University, Spain. He has been actively involved in research at IIT since 2012, with a formal position since 2021. His work is centered on electrical systems, particularly in the domain of power system stability and HVDC technologies. Industrial Engineer (Electrical), ICAI - Comillas Pontifical University (2010) Master in Mathematical Engineering, Carlos III University of Madrid (2013) PhD in Engineering Systems Modeling, ICAI - Comillas Pontifical University (2018) His research interests focus on the stability and control of modern power systems. Key areas include multi-terminal VSC-HVDC systems , power system stability (especially transient and small-signal), power electronics applications in grids, and challenges in systems with high renewable energy content and low inertia. He has developed simulation tools and control strategies to enhance grid resilience. The analysis of his recent publications reveals a strong trend toward solving stability issues in hybrid AC/DC grids, particularly using DC segmentation, fast voltage boosters, and coordinated control of VSC-HVDC systems. His work addresses critical challenges like intra-area oscillations, transient stability in 100% inverter-based systems, and frequency support in low-inertia grids, reflecting a consistent focus on enabling high renewable integration. His scientific achievements include: Honorary Distinction for the best Doctoral Thesis in Engineering, Comillas Pontifical University (2019) Dr. Renedo has supervised PhD students and contributed to numerous national and international research projects funded by the European Commission (Horizon 2020), Spanish Ministry of Science, IRENA, IADB, and industry partners like Iberdrola and Endesa. He serves as a reviewer for IEEE Transactions on Power Systems and is an active member of IEEE and CIGRE Working Group C4B4.52, contributing to guidelines on sub-synchronous oscillations in power electronics-dominated systems. He has been involved in several research laboratories and teams, primarily within the IIT’s Electrical Systems area, focusing on modeling, control, and simulation of hybrid power systems. His work often involves collaboration with international universities and industry stakeholders.

• Deep Learning
• Distributed Machine Learning
• Federated Learning
• +7 more

Tao Lin is a Tenure-Track Assistant Professor and Principal Investigator of LINs Lab at Westlake University, School of Engineering. He leads cutting-edge research in deep learning optimization, generalization, and robustness, particularly in distributed and federated settings. Prior to this, he was a Ph.D. student at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, under the supervision of Prof. Martin Jaggi and Prof. Babak Falsafi. Doctor of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2017–2022) Master of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2014–2017) Bachelor of Engineering (with honors), College of Electrical Engineering, Zhejiang University, China (2010–2014) His research focuses on the intersection of optimization and generalization in deep learning, leveraging theoretical and empirical insights into loss landscapes and training dynamics to design efficient and robust learning and inference methods. This includes work on decentralized and federated learning under noisy, heterogeneous, and hardware-constrained environments. His work spans algorithmic innovation, theoretical analysis, and practical system integration. The recent publications from his lab demonstrate a strong trend in advancing federated learning, efficient inference for large language models, multimodal foundation models in pathology, and robust training under distribution shifts. Key themes include communication efficiency, model personalization, gradient tracking, and hardware-aware learning. His group has published at top venues including NeurIPS, ICML, ICLR, CVPR, and ECCV, with several papers receiving oral or spotlight presentations. ECCV Best Paper Candidate, 2024 Top 2% Scientists Worldwide 2024 (Stanford University) Doctoral Program Thesis Distinction Award, EPFL, 2022 Outstanding Performance Bonus, EPFL, 2021–2022 Top Reviewer: NeurIPS, ICML, AISTATS He advises multiple Ph.D. and master’s students, including Yongxin Guo, Futing Wang, Peng Sun, and Yuxuan Sun, whose work has been accepted at premier conferences. He has secured competitive grants as PI and participant, including the National Natural Science Foundation of China for Excellent Young Scientists Fund (Overseas) and the Science and Technology Innovation 2030 – Major Project. He also contributes to the community through service as an area chair (NeurIPS, ICML), reviewer for top journals and conferences, and organizer of workshops and academic events. His open-source contributions, such as Post-local SGD, have been integrated into PyTorch. Tao Lin teaches graduate courses such as Research Methodology of Computer Science and Technology and Deep Learning at Westlake University. He is actively involved in academic governance, serving on committees for student seminars, academic exchange, doctoral studies, and teaching leadership. The LINs Lab runs a regular research seminar on Deep Learning and Optimization, fostering a collaborative and dynamic research environment.

Louis-Pierre ChaintronView profile

Research Fellow

• Probability Theory
• Stochastic Processes
• Optimal Transport
• +12 more

Louis-Pierre Chaintron is a Research Fellow at the École Normale Supérieure de Paris (ENS-PSL), affiliated with the Department of Mathematics and Applications (ENS DMA). His work bridges probability theory, stochastic processes, and applied mathematics, with a focus on constrained measure-valued dynamics, stability analysis, and approximation methods. PhD supervised by Julien Reygner (CERMICS) and Philippe Moireau (Inria M3DISIM) from 2022–2024 Member of the Probability and Statistics research team at ENS DMA Teaching roles include assistant for PDE courses and organizer of applied mathematics workshops Research Interests : His research explores connections between mean-field theory , large deviations , and viscosity solutions for Hamilton-Jacobi equations. He investigates filtering theory in dynamic systems, optimal transport frameworks, and Schrödinger bridge problems for high-dimensional systems. Applications span diffusion models in machine learning, muscle contraction modeling , and non-linear estimation under constraints. Recent Publications analyze convergence rates in stochastic control, stability of Gibbs principles, and jump-diffusion formalisms for biological systems. His work often combines calculus of variations , stochastic control , and viscosity solutions to address stability and approximation challenges. Contact : Email: lchaintron@dma.ens.fr Office: Bureau C11, Espace Cartan, 45 Rue d'Ulm, Paris

Ahalya RavendranView profile

Research Fellow

• Computer Vision
• Artificial Intelligence
• Robotics
• +3 more

Dr. Ahalya Ravendran is a Research Fellow at Data61 , Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia. Her work focuses on advancing computer vision , artificial intelligence , and distributed sensing for robotics, particularly in light-constrained environments. She previously held a postdoctoral position at the University of Sydney in collaboration with SCION, a New Zealand Crown Research Institute. PhD in Engineering and Information Technology (2023), University of Sydney Master's (Thailand), Bachelor's in Mechatronics Engineering (Sri Lanka) Her research spans robotic vision , 3D reconstruction , and burst imaging techniques. Recent publications highlight applications in low-light navigation , deep learning domain adaptation , and AI for forestry innovations . Scientific accolades include: IEEE Young Professional Fellowship (2023) Career Advancement Award (University of Sydney, 2023) Engineering and Information Technologies Research Scholarship (2019-2023) Dr. Ravendran actively advocates for women in technology as a WomenTechMaker Ambassador . Her interdisciplinary approach connects robotics , climate innovations , and sustainable technology .

Robin PurshouseView profile

Professor

• Computational Modelling of Complex Social Systems
• Agent-Based Modelling
• Machine Learning
• +6 more

Professor Robin Purshouse is a leading academic at the University of Sheffield , currently serving as Professor of Decision Sciences in the Department of Automatic Control and Systems Engineering within the School of Electrical and Electronic Engineering . With a career spanning academia and industry, his work bridges computational modelling , optimization , and systems science to address complex challenges in public health and engineering. His research has been pivotal in developing mechanisms for agent-based modelling and evolutionary multi-objective optimization . Education: PhD in Control Systems (2004), MEng in Control Systems Engineering (1999) from the University of Sheffield Professor Purshouse's research focuses on computational modelling of complex social systems , decision analytics for population health policy , and Bayesian optimization . He has pioneered the integration of machine learning and uncertainty quantification in social science simulations, with notable projects like the Sheffield Alcohol Policy Model and CASCADE initiative. His work spans interdisciplinary domains, including health economics , policy evaluation , and engineering design . Recent publications highlight his expertise in agent-based modelling for smoking/vaping dynamics , intersectional disparities in alcohol consumption , and inclusive economy frameworks . He has secured substantial funding (exceeding £16 million) through grants from NIH , CRUK , UKPRP , and MRC , including his role as co-PI in the HealthMod cluster. His contributions to multi-objective optimization and evolutionary algorithms have advanced methodologies in both engineering and public health domains. Scientific Awards: ESRC Future Research Leaders Award (2012-2015) As a co-developer of the Liger optimization environment , Purshouse has fostered open-source tools for complex decision-making. He leads the SIPHER consortium for systems science in public health and serves on editorial boards for journals like Environmental Modelling & Software . His teaching includes Agent-Based Modelling (ACS6132), and he maintains professional memberships in the Association for Computing Machinery and Research Society on Alcohol .

David MinhView profile

Associate Professor

• Computational Chemical Biology
• Protein Dynamics
• Molecular Interactions
• +3 more

David Minh is an Associate Professor of Chemistry and the Robert E. Frey, Jr. Endowed Chair in Chemistry at Illinois Institute of Technology (IIT), affiliated with the Lewis College of Science and Letters. He serves as Associate Director of the Center for Interdisciplinary Scientific Computation (CISC). His research focuses on computational chemical biology, developing methods to predict protein dynamics and molecular interactions for structure-based drug design. Education: Ph.D. in Chemistry, University of California, San Diego M.S. in Chemistry, University of California, San Diego B.A. in Chemistry, University of California, Berkeley Research Interests: Dr. Minh's group specializes in computational methods to study small molecule-biological interactions, including: Structural mechanisms of G protein-coupled receptors (GPCRs) and signaling proteins Advanced binding free energy calculations incorporating entropy Enhanced sampling in molecular simulations Bayesian statistical integration of experimental data Modeling bacterial metabolic enzymes and inhibitor development Articles Trends: Recent work emphasizes antiviral drug discovery (e.g., SARS-CoV-2 protease inhibitors), Bayesian analysis of binding data, and computational tools like AlGDock for free energy predictions. Collaborations with biologists (e.g., Oscar Juárez) drive antibiotic discovery targeting pathogenic bacteria. Advising & Grants: Leads interdisciplinary projects funded by NIH and industry partnerships. Mentors students in computational modeling and experimental validation. Active in open science initiatives like the D3R Grand Challenge in drug design. Labs & Teams: Directs the Minh Computational Chemistry Lab at IIT, focusing on molecular simulations, machine learning, and interdisciplinary collaborations to address biomedical challenges.

• Computer Architecture
• Parallel Processing
• FPGA Design
• +3 more

Frank Hannig is a Professor at the University of Erlangen-Nuremberg, Germany, specializing in computer architecture and high-performance computing. His research focuses on FPGA acceleration, hardware-software co-design, neural network optimization, and embedded systems. He has collaborated extensively with co-authors such as Jürgen Teich and Oliver Reiche, producing over 200 publications since 2001. His work emphasizes domain-specific languages (DSLs) for image processing (e.g., Hipacc) and compiler optimizations for FPGAs. Key contributions include techniques for quantized neural networks on microcontrollers, CGRA toolchain evaluation, and efficient mapping of CNNs onto processor arrays. Hannig also explores energy-efficient architectures and reconfigurable computing for emerging applications like edge AI and automotive systems. Publications span conferences like ASAP, FPL, and ARC, reflecting his interdisciplinary approach to bridging algorithm design and hardware implementation. His research often addresses practical challenges in deploying machine learning models on resource-constrained devices while maintaining performance and energy efficiency.

• Mathematical Optimization
• Nonsmooth Optimization
• Multiobjective Optimization
• +4 more

Bennet Gebken is a researcher at the Department of Mathematics , Technical University of Munich , affiliated with the Chair of Mathematical Optimization led by Prof. Ulbrich. His work focuses on nonsmooth and multiobjective optimization, particularly in PDE-constrained problems and numerical continuation methods. Research Interests: Bennet specializes in nonsmooth optimization, multiobjective optimization, and regularization techniques. His recent work explores convergence analysis in nonsmooth settings, second-order gradient sampling, and inverse optimization methods for data-driven decision criteria. Publications: His research spans topics like PDE-constrained multiobjective optimization, L1 penalty terms, and the hierarchical structure of Pareto critical sets. Key trends include reduced-order modeling, computational efficiency, and the interplay between optimization and machine learning. Contact: bennet.gebken@tum.de . Based at Boltzmannstr. 3, Garching b. München, Germany.