Benedikt Hofer is a researcher at the Technical University of Munich (TUM), affiliated with the Professorship for Thermofluid Dynamics. His work focuses on the intersection of civil engineering, computer science, and digital modeling. University: Technical University of Munich Role: Researcher Contact: benedikt.hofer@tum.de Hofer’s research interests include Digital Twin , Building Information Modeling (BIM) , Graph Rewriting , and Parametric Modeling . His work emphasizes automated model generation, infrastructure digitization, and optimization through AI-driven methods. Recent publications highlight advancements in BIM collaboration , point cloud analysis for bridges , and graph-based design systems . These studies focus on scalability, version control, and integration of digital fabrication with BIM. The Technical University of Munich’s Thermofluid Dynamics group, led by Prof. Wolfgang Polifke, supports Hofer’s research in thermoacoustic interactions and fluid dynamics.
Dr. Daniel Langenkämper is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering and the Center for Biotechnology (CeBiTec). He serves as a key member of the Biodata Mining Group, where he develops and applies advanced computational methods for marine biological data analysis. His office is located at UHG V10-107 with contact number +49 521 106-3678. Langenkämper's research focuses on the intersection of computer science and marine biology, with particular expertise in: Computer vision applications for marine ecosystem monitoring Deep learning approaches for diatom and coral classification Biodata mining from complex marine imagery Digital platform development for environmental monitoring systems Multi-sensor data analysis for marine infrastructure assessment His publication record shows consistent output through 2025, with recent work emphasizing expert-computer vision integration for coral status exploration and self-supervised learning techniques for diatom classification. The research demonstrates strong interdisciplinary collaboration across computer science, marine biology, and engineering disciplines, addressing critical challenges in marine environmental monitoring and infrastructure maintenance. His work contributes significantly to both theoretical advancements in image analysis and practical applications for marine conservation and industrial monitoring. Langenkämper actively participates in marine imaging workshops and contributes to the development of standardized image datasets for marine research. His work with the Biodata Mining Group at CeBiTec supports multiple research initiatives focused on transforming visual data into actionable ecological insights, particularly for deep-sea coral ecosystems and marine infrastructure maintenance.
Thorben Markmann is affiliated with the Faculty of Engineering at the University of Bielefeld , where he contributes to the Machine Learning Group under the CITEC research cluster. His research spans interdisciplinary domains, focusing on machine learning applications in fluid dynamics, robotics, and computational modeling. Research Trends: His recent publications highlight the intersection of reinforcement learning , Fourier neural operators , and Koopman-based surrogate models to address challenges in turbulent convection and haptic systems. Key subfields include adaptive kinematic modeling for hand posture estimation and open-source tool development for biomechanical applications. Labs & Collaborations: Markmann operates within the Center for Cognitive Interaction Technology (CITEC) , a multidisciplinary institute advancing human-robot interaction and AI-driven scientific discovery.
Prof. Dr. Stefan Kuhlins is a Professor of Business Informatics at Heilbronn University's Faculty of Economics. He specializes in Business Engineering Logistics and previously worked at the universities of Mannheim and Regensburg. His research focuses on E-commerce, XML technologies, and enterprise software integration. He also operates as a freelance business information technology specialist. Current: Professor at Heilbronn University (Faculty of Economics) Previous: University of Mannheim, University of Regensburg Specialization: Business Engineering Logistics (formerly Technical Logistics Management) Freelance: E-commerce and software projects Education : Graduate in Business Information Technology Research Interests : His work spans business informatics, information technology, and programming with particular emphasis on database systems, XML standards, and Robotic Process Automation (RPA). He has developed frameworks for enterprise application integration and explored wireless usability metrics through Java software agents. His publications reflect a strong focus on E-commerce systems, XML schema design, and software development methodologies. Publications Trends : His scholarly output from 1993-2018 covers XML technologies, Java applications in enterprise systems, simulation modeling for logistics, and web service integration. The articles demonstrate consistent engagement with software engineering challenges in E-commerce and enterprise contexts. Technical Leadership : Since 2014, he has served as Head of the Internship Office for Business Engineering Logistics (BEL) and Technical Logistics Management (TLM) programs at Heilbronn University. This administrative role complements his technical expertise in systems design. Programming Expertise : With deep knowledge of C++ standard libraries and Java technologies, he has contributed to areas like garbage collection, CORBA optimization, and multithreaded frameworks. His technical work spans from core programming challenges to enterprise-scale integration solutions.
Sergey Safonov serves as Deputy Director of the Project Center for Energy Transition and ESG Education and holds the academic rank of Professor of the Practice. With over 23 years of industrial R&D experience spanning academia and global energy organizations, he has led cross-disciplinary research initiatives and digital innovation programs. His educational foundation includes: BSc and MSc in Applied Physics and Mathematics from Moscow Institute of Physics and Technology (1998) PhD in Physics from the University of Exeter (2002) Safonov's research integrates Artificial Intelligence, Machine Learning, and Data Analytics with experimental physics, focusing on dynamic systems modeling for fluid mechanics and thermal processes. His work emphasizes practical implementation of digital solutions in energy applications, combining theoretical frameworks with laboratory validation to drive technological innovation in the sector. He has directed multinational research teams for 15 years at Schlumberger Research Center and spearheaded AI-driven software development for 5 years at Aramco Innovations' Moscow Research Center, demonstrating consistent leadership in translating academic research into industrial applications.
Prof. Dr. Christoph Meinel is a C4 Professor for Internet Technologies and Systems at the Hasso Plattner Institute (HPI) and the University of Potsdam. He served as President and CEO of HPI from October 2004 to March 2023 and was Founding Dean of the Digital Engineering Faculty at the University of Potsdam (2017-2021). He is a member of acatech (National German Academy of Science and Engineering), Governor of the Technion in Haifa, and has held visiting professorships globally. Meinel co-founded the openHPI MOOC platform in 2012 and led the HPI-Stanford Design Thinking Research Program (2008-2022). For the German Federal Ministry of Education and Research, he developed the HPI Schul-Cloud (2017-2021) and designed a new school subject, "Digital World" (2022). President and CEO of HPI (2004-2023) Founding Dean of Digital Engineering Faculty (2017-2021) C4 Professor for Internet Technologies and Systems Member of acatech, Governor of Technion Program Director for HPI-Stanford Design Thinking Research (2008-2022) Meinel’s research spans knowledge management , digital education , security engineering , sustainable AI , and design thinking . He has authored or co-authored over 750 scientific papers and 35 books/anthologies, focusing on security analytics, secure identity, cloud security, and machine learning. His work includes developing tools like the Tele-Lab and Identity Leak Checker to enhance cybersecurity education and awareness. He has organized numerous conferences and symposia, including the Potsdamer Konferenz für Nationale CyberSicherheit and the Sino-German Workshop on Cloud-based HPC . Meinel’s leadership extends to advisory boards (e.g., SAP Research, South Africa) and national initiatives like the German IPv6 Council. He supervises many PhD students, with over 70 successfully completing their theses.
Ismail Ahmed, M.Sc., is a Researcher at the Chair of Control Engineering (Lehrstuhl für Regelungstechnik) under Prof. Dr.-Ing. habil. Boris Lohmann at the Technical University of Munich's School of Engineering and Design. He has been working at TUM since April 2020, with previous research positions at the Technical University of Lübeck (May 2019-March 2020) and the University of Paderborn (January 2018-March 2019). Dr. Ahmed's educational background includes a Master's degree in Mechatronics from the University of Paderborn (2016-2019), a Diploma scholarship in Mechatronics at the Information Technology Institute in Cairo (2014-2015), and a Bachelor's degree in Electromechanics from Alexandria University, Egypt (2009-2014). His research focuses on nonlinear control, model order reduction, and optimal control with specific applications in freeform bending processes . His work centers on developing control systems for geometry and mechanical properties during tube manufacturing, with particular attention to residual stress management. Ahmed is actively involved in the Priority Program 2183 of the German Research Foundation (DFG-SPP 2183) focusing on 'Property-controlled process design of free-form bending taking into account semi-finished product properties.' Analysis of his publication record from 2021-2025 reveals a consistent research trajectory in applying advanced control techniques to manufacturing processes, particularly freeform bending. His work demonstrates increasing sophistication in integrating predictive modeling, soft sensors, and optimization strategies to control both geometric and material properties in real-time manufacturing environments. As an educator, Ahmed teaches practical courses in 'Modern Methods of Control Engineering' and 'Computer-aided control design,' focusing on hands-on experiments with physical systems. He currently supervises at least one Bachelor's thesis on Model Predictive Control applications for freeform bending processes. His research is conducted within the Control Engineering Chair environment at TUM, which appears to have strong connections to industrial manufacturing applications and collaborates with multiple research groups working on mechanical systems, model reduction techniques, and interconnected systems.
Josef Winter is a researcher at the Department of Aerodynamics and Fluid Mechanics within the TUM School of Engineering and Design at the Technical University of Munich. His work focuses on advanced numerical methods for fluid dynamics simulations, integrating quantum computing, machine learning, and Bayesian optimization to enhance computational efficiency and accuracy. University: Technical University of Munich School: TUM School of Engineering and Design Department: Department of Aerodynamics and Fluid Mechanics Academic Rank: Researcher Email: josef.winter@tum.de Dr. Winter's research interests include: Quantum algorithms for fluid dynamics Multi-fidelity and Bayesian optimization techniques High-order numerical methods Level-set-based sharp-interface simulations Deep reinforcement learning for flow optimization Complex flow modeling across scales His publication trends indicate a strong focus on merging quantum computing with classical fluid dynamics simulations, developing adaptive solvers like ALPACA, and optimizing numerical schemes for compressible and multiphase flows. Recent works explore dynamic circuits for quantum lattice-Boltzmann methods and multi-objective optimization frameworks. Dr. Winter's articles cover diverse aspects of fluid mechanics, including: Quantum computing applications in fluid simulation Level-set methods for interface dynamics BAYESIAN OPTIMIZATION OF NUMERICAL SCHEMES Multi-fidelity surrogate models for complex flows High-order methods for conservation laws Deep reinforcement learning for dynamic systems
Korbinian Zöls is a Researcher at the Chair of Materials Handling, Material Flow, and Logistics at Technische Universität München (TUM), focusing on AI-driven warehouse optimization and simulation modeling. University: Technische Universität München (TUM) Department: Chair of Materials Handling, Material Flow, and Logistics Role: Researcher Contact: korbinian.zoels@tum.de His research applies Deep Learning , Reinforcement Learning , and Multi-Agent Systems to develop AI-based storage and retrieval strategies, improve warehouse simulations, and optimize traditional material handling systems. He actively collaborates with Prof. Johannes Fottner and participates in conferences like the Deutscher Materialflusskongress . Recent publications highlight trends in automated bulk material transport , contextual AI decision-making , and self-learning logistics software . His work bridges theoretical algorithm development with practical implementation in industrial environments. Research Areas AI and Machine Learning in Warehousing Simulation-Based Optimization Automated Storage/Retrieval Systems Material Flow Analysis Logistics Scalability Studies
Ke-Thia Yao is a Research Professor at the University of Southern California's Information Sciences Institute (ISI), where he leads initiatives in the Artificial Intelligence Division. With a Ph.D. in Computer Science from Rutgers University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley, his career spans 25+ years in research and development across artificial intelligence, machine learning, and multidisciplinary engineering design. B.S., Electrical Engineering and Computer Science, UC Berkeley (with honors) M.S. and Ph.D., Computer Science, Rutgers University His research focuses on leveraging AI and machine learning for predictive modeling in engineering systems, quantum computing applications for neural networks, and data-driven approaches in environmental and medical contexts. He has authored influential work on knowledge graph augmentation, simulation data analysis, and automated machine learning pipelines. Recent trends in his publications highlight interdisciplinary applications, including quantum-assisted Boltzmann machines for materials science, environmental toxicology studies linking PM2.5 exposure to corneal diseases, and scalable cyber warfare test environments. His work bridges high-performance computing, neuromorphic systems, and quantum architectures for advanced AI solutions. Ke-Thia Yao is affiliated with the University of Southern California's Information Sciences Institute, collaborating with experts in computer science, quantum computing, and environmental health. His projects emphasize scalable simulation frameworks, failure prediction systems, and semantic interoperability in data management.
Benedikt Ehinger is a computational neuroscientist at the University of Stuttgart, leading an Emmy Noether research group on "EEG in motion" funded by DFG. He specializes in integrating EEG with eye-tracking, developing statistical methods for neuroimaging and creating open-source tools. DDFG Emmy Noether grant recipient (2019) Director of Computational Cognitive Science lab Co-developer of open-source tools: Unfold toolbox, ClusterDepth algorithm His research focuses on three main areas: 1. Methodological foundations of EEG/MEG analysis, particularly cluster-based statistics and deconvolution methods; 2. Eye-tracking methodology and visualization techniques; 3. Statistical modeling of human perception using linear mixed models and Bayesian approaches. Key trends in his recent publications include: Advancing EEG methodology with linear deconvolution and cluster permutation tests Developing open-source neuroscience tools in Julia/MATLAB Investigating perceptual inference and reliability estimation Creating art-science interfaces through "thesis art" projects Scientific contributions: Emmy Noether research group leadership Over 10 thesis supervisions (Master's/Bachelor's) Co-development of multiple open-source toolboxes Methodological innovations in ERP analysis and statistical testing As an educator, he creates interactive tutorials on statistical concepts and provides thesis art for each supervised student. His lab maintains strong software engineering practices with GitHub-hosted code repositories.
Jean-Noël Grad is a postdoctoral researcher at the Institute for Computational Physics , University of Stuttgart. His work focuses on computational physics and software engineering for soft matter simulations. Institute for Computational Physics, University of Stuttgart Research Interests: Grad specializes in computational physics and soft matter simulations, with a particular focus on molecular dynamics and simulation frameworks. He contributes to open-source software development and sustainability in research computing. Workshop Contributions: He regularly organizes and participates in workshops on soft matter simulation tools like ESPResSo, PyStencils, and LbmPy. His roles include co-organizer of events such as "Simulating soft matter across scales" and "Simulating energy materials with ESPResSo". Teaching Activities: Grad teaches the ESPResSo Block course annually, focusing on practical applications of soft matter simulation software. Software Contributions: Active in multiple software projects including ESPResSo (core contributor), pyMBE (co-developer), and tools like EESSI and waLBerla for cross-platform simulation environments.
Prof. Dr. Frank Leßke is a faculty member at the University of Applied Sciences Weihenstephan-Triesdorf (HSWT) , affiliated with the Department of Bioengineering (Sciences) . His roles include Chief scientist at the Computer Centre , CIO of HSWT , and leadership positions in the IT Commission , Research Commission , and IT Security Committee . Current affiliations: HSWT (Bioengineering, Computer Centre) Academic rank: Professor Prof. Leßke's research focuses on Digitalisation in Agriculture , Software Engineering , and Intelligent Systems . His work includes developing webBESyD and FNMS for farm nutrient management, leveraging machine learning and satellite data to optimize agricultural sustainability and reduce environmental impacts. Recent publications highlight his expertise in data-driven agricultural systems , with studies on nitrogen balancing , crop yield prediction , and metabolomics workflows . These articles demonstrate interdisciplinary approaches combining bioinformatics , environmental science , and software architecture . His supervised doctoral candidates include M.Sc. Fabian Weckesser and Dr. rer. nat. Michael Krappmann . While no specific scientific awards are mentioned, his leadership in research projects like digital soil management and data space development underscores his impact in applied agricultural technology. Prof. Leßke's work extends to climate change mitigation and resource-efficient farming , with projects addressing greenhouse gas emissions , biogenic residue materials , and urban biodiversity . His research aligns with HSWT's mission of interdisciplinary, practice-oriented, and sustainable innovation .
LiGuo Huang is an accomplished researcher and academic in the field of software engineering with a publication record spanning over two decades from 2003 to 2025. With 95 publications documented in the dblp database, Huang has established a significant presence in both traditional software engineering domains and emerging areas where machine learning intersects with software development practices. Huang's research has evolved from foundational work in value-based software engineering to cutting-edge applications of artificial intelligence in software analysis and maintenance. Huang's research interests encompass a broad spectrum of software engineering topics, with particular emphasis on value-based software engineering, software quality assurance, defect classification, and software process modeling. More recently, Huang has focused on applying machine learning and deep learning techniques to software engineering problems, including code summarization, vulnerability detection, and software maintenance. This evolution reflects the broader shift in the field toward data-driven approaches for software development and analysis. The publication trends reveal a consistent research trajectory with increasing publication rates in recent years, particularly in the application of machine learning to software engineering problems. Huang's work shows a strategic progression from theoretical foundations in software quality to practical applications of AI in software development. The research spans empirical studies, systematic literature reviews, and novel technical approaches to longstanding software engineering challenges, demonstrating both theoretical depth and practical relevance. Huang has collaborated extensively with researchers across multiple institutions, forming particularly strong partnerships with Jidong Ge, Bin Luo, Chuanyi Li, and Barry W. Boehm. The collaboration with Boehm in early career publications suggests mentorship that evolved into peer collaboration, while more recent work shows Huang mentoring newer researchers who now serve as primary authors on joint publications. Huang's research has practical implications for software development practices, particularly in improving software quality, enhancing developer productivity through AI-assisted tools, and providing empirical evidence for software engineering decision-making. The work bridges theoretical computer science with practical software engineering concerns, making significant contributions to both academic research and industry practice.
Gabriel Alves is a Brazilian computer science researcher affiliated with the University of Pernambuco and collaborating with institutions like Federal Higher Education Institutions and Pontifical Catholic University of Minas Gerais. His work focuses on performability evaluation, stochastic modeling, and learning analytics, particularly applied to education technology and transport logistics systems. Research Interests : Performability analysis, stochastic modeling, learning analytics, cyber-physical systems, cloud computing, and green supply chain optimization Key Contributions : Development of Tutoria platform for educational feedback, performability models for BRT systems, and frameworks for AI-driven instructor support tools His publications span journals like IEEE Transactions on Learning Technology and conferences including SMC and EC-TEL, with recent work exploring natural language processing for educational assessment. Current collaborations include researchers from Chile (Dragan Gasevic), Brazil (Rafael Ferreira Mello, Paulo Maciel), and Portugal (Taciana Falcão).
