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Jens StoyeView profile

Professor

• Bioinformatics
• Computational Biology
• Comparative Genomics
• +3 more

Jens Stoye is a Professor of Genome Informatics at the Faculty of Engineering , Bielefeld University, where he has held this position since 2002. He leads the Genome Informatics Working Group at CeBiTec and serves as Managing Director of the Bielefeld Institute for Bioinformatics Infrastructure (BIBI). Additionally, he is a member of the Board of Directors at the Center for Interdisciplinary Research (ZiF) and held numerous administrative roles, including Dean of the Faculty of Technology and Speaker of the DFG Research Training Group. Education: Diploma in Informatics in the Natural Sciences (1995) and PhD (1997) from Bielefeld University Current Roles: Full University Professor, Managing Director of BIBI, Executive Director of ZiF (2023-2025) His research focuses on computational genomics , including genome rearrangement, comparative genomics, and pangenomics. Recent work explores the double distance problem , natural genome phylogeny reconstruction, and efficient pangenome storage techniques. He has developed algorithms for genome assembly benchmarking (GABenchToB), rearrangement epidemiology (pling), and core genome detection. Scientific Contributions include best paper awards and editorial roles at IEEE/ACM Transactions on Computational Biology and Bioinformatics , BMC Bioinformatics , and Discrete Applied Mathematics . He has served on over 20 conference committees (WABI, ISMB, RECOMB) and advised 165+ theses (36 PhD, 71 Master/Diploma, 58 Bachelor). Leadership Roles: Vice-Speaker of German Bioinformatics Society (2002-2014) Dean of Faculty of Technology (2007-2009) Speaker of DFG Research Training Group (2013-2019) Member of DAAD Postdoc Fellowship Committee (2014-2025)

• Electromagnetic Sensors and Actuators
• Human-Machine Interfaces
• Haptic Devices
• +4 more

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.

• Database Systems
• Integration of heterogeneous databases
• Data quality and data integrity
• +6 more

Thomas Kudraß serves as Professor for Database Systems at the Faculty of Computer Science, Mathematics and Natural Sciences (IMN) at Leipzig University of Applied Sciences (HTWK Leipzig). With over 25 years of academic and industry experience, he teaches courses including Database Basics, Database Application Programming, Data Warehousing, and Big Data Technologies. His leadership roles include Internship Coordinator for Computer Science since 2001, Member of the Academic Senate since 2011, and Contact person for the IMN faculty alumni association since 2005. Dr. Kudraß earned his Dipl.-Ing. in Computer Science from Technical University of Dresden (1985-1990) and completed his doctorate at Darmstadt University of Technology (1992-1997), where his dissertation received the prestigious Jos Schepens Memorial Award. Prior to academia, he worked as an Information Systems Architect at UBS AG Zurich and Database Specialist at Swiss Bank Corporation Basel. His research spans database technologies with focus on heterogeneous database integration, data quality, data privacy, and modern database concepts. Recent work explores cloud-native billing applications for 5G, NoSQL databases, and privacy-preserving record linkage techniques. His publication record shows continuous evolution from traditional database systems to contemporary challenges in big data and distributed systems. Jos Schepens Memorial Award for doctoral dissertation (1997) Active participation in German Informatics Society (GI) specialist groups Organizer of multiple database-related workshops and conferences Professor Kudraß has significantly contributed to academic governance as E-Learning Officer (2000-02), Faculty Council member (2009-12), and liaison lecturer for the German Society for Computer Science (2001-14). He has coordinated numerous student projects and served on program committees for major conferences including INFORMATIK 2017 and BTW series.

Jochen MerkerView profile

Professor

• Mathematical Analysis
• Numerical Methods
• Partial Differential Equations
• +5 more

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 ZafarView profile

Professor

• Human-Centric AI
• Algorithmic Fairness
• Explainable AI
• +3 more

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.

• Collective Intelligence
• Robotics
• Animal Behavior
• +4 more

Liang Li is a Group Leader at the Max Planck Institute of Animal Behavior , leading the Embodied Collective Intelligence Lab . Her research bridges biology and robotics to study collective behavior through physical agent-environment interactions. Current position: Group Leader, Department of Collective Behavior Past roles: Project Leader at University of Konstanz and MPI Education: Ph.D. in Robotics and Control, College of Engineering, Peking University Research Interests Dr. Li's work focuses on embodied cognition , swarm intelligence , and fluid dynamics in biological and robotic systems. Key areas include hydrodynamic interactions in fish schooling, bio-inspired robotics design, and translating biological principles into robotic applications. Article Trends : Recent publications emphasize energy efficiency in swimming systems, hydrodynamic modeling for collective behavior, and virtual reality tools for behavioral studies. Interdisciplinary themes span robotics, fluid dynamics, neuroscience, and evolutionary ecology. Collaborations & Grants Her research involves international collaborations with institutions like University of Konstanz and Peking University. Grants include funding for robotic platforms (RoboTwin) and advanced pose estimation tools (DeepPoseKit). Labs & Teams The Embodied Collective Intelligence Lab is a small, interdisciplinary team specializing in bio-robotic systems. The group develops experimental robotic platforms and computational models to decode collective behavior rules.

• Reinforcement Learning
• Computational Decision-Making
• Machine Learning
• +3 more

Carlo D'Eramo is a Professor in Reinforcement Learning and Computational Decision-Making at the Center for Artificial Intelligence and Data Science (CAIDAS) of Julius-Maximilians-Universität Würzburg (JMU). He leads the LiteRL research group and the hessian.AI independent research group, focusing on lightweight methods for adaptive autonomous agents in complex real-world environments. B.Sc., M.Sc. in Computer Engineering - Politecnico di Milano (2011, 2015) Double M.Sc. in Computer Science - University of Illinois at Chicago (2015) Ph.D. in Information Technology - Politecnico di Milano (2019) His research spans multiple RL domains including multi-task learning , curriculum learning , adversarial RL , options learning , and multi-agent coordination . Recent work explores physics-informed ML, neural network distillation, and uncertainty-driven exploration. 2025 publications highlight advancements in: Bellman update optimization via adaptive distillation uncertainty propagation in tree search multi-agent policy gradients real-world educational applications The group's work shows increasing specialization in physics-integrated RL and efficient neural architectures. Spotlight Presentation - ICML (2025) Spotlight Presentation - ICLR (2024) Oral Presentation - NeurIPS (2020) As Senior Area Chair for RLC and Area Chair for major AI conferences (AAAI, NeurIPS, ICLR), he contributes to academic governance. His team includes researchers like Ahmed Hendawy, Théo Vincent, and Georgia Chalvatzaki, with collaborations spanning TU Darmstadt and hessian.AI.

Xiao BiView profile

Tutor

• Atmospheric Chemistry
• Environmental Sensor Development
• Air Quality Monitoring
• +2 more

Xiao Bi, M.Sc. , is a Tutor and researcher at the Professorship for Environmental Sensors and Modeling at the Technical University of Munich (TUM) . He is based in room N1506B and contributes to atmospheric pollution research through instrument development, data analysis, and teaching in environmental sensing. Research Focus : Development of NO 2 and air pollutant sensors using CAPS technology, analysis of ground-based and satellite data during the COVID-19 pandemic, and implementation of in-situ and mobile measurement systems in Munich. Teaching Role : Tutor for Lab Courses on Innovative Atmospheric Sensing Devices, Advanced Seminars in Environmental Sensing, and Joint Practical Courses on Electromagnetic Sensors and Measurement Systems. His publications (2018–2022) highlight trends in atmospheric pollution monitoring , sensor technology for greenhouse gases, and machine learning applications in artificial olfaction. Notable collaborations include work with Prof. Jia Chen and Frank Keutsch . Xiao Bi has been recognized with the Hans Fischer Fellowship , underscoring his contributions to urban environmental research and sensor innovation.

Dominic HenzeView profile

Professor

• Cyber-Physical Systems
• Smart Environments
• Machine Learning Applications
• +1 more

Dominic Henze is a Professor at the Technical University of Munich (TUM), affiliated with the Faculty of Informatics and the Chair of Software Engineering . He leads research initiatives in Cyber-Physical Systems , Smart Environments , and Machine Learning Applications , with a particular focus on Fog Computing architectures. His research spans theoretical frameworks and real-world implementations, including collaborations with institutions like Carnegie Mellon University and industry partners such as Siemens AG and Zeiss IMT . His work addresses challenges in resource allocation , predictive maintenance , and blockchain integration within industrial contexts. Dr. Henze's publications reveal a consistent focus on Fog Computing architectures, IoT resource management, and educational software engineering. Key trends include self-organizing network systems , decentralized supply chain traceability , and smart environment coordination . He has advised numerous students on topics ranging from QoS negotiation to autonomous drone coordination . As an educator, he has taught multiple iPraktikum courses and seminars on iOS development and Agile Project Management since 2014, with publications exploring team composition strategies and distributed programming pedagogy.

• Additive Manufacturing in Construction
• Building Information Modeling (BIM)
• Digital Twinning
• +3 more

Martin Slepicka is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich. His work focuses on bridging Building Information Modeling (BIM) with additive manufacturing and digital twinning technologies. Department: Civil and Building Engineering Research Group: Digital Twinning, Construction Robotics Research Highlights: Slepicka specializes in: Integrating BIM with digital fabrication workflows Developing closed-loop systems for additive manufacturing Real-time data exchange in construction robotics Automated parameter calibration using machine learning Semantic enrichment of BIM through multi-sensor platforms Academic Contributions: Recent publications demonstrate expertise in: Extrusion-based additive manufacturing control Non-planar path planning for 3D-printed components Autonomous robot grasping solutions From fabrication models to simulation frameworks Laboratories: Active in: BIM-Lab Robotic Fabrication Lab Mobile Machinery development

Peiman Shah NazarView profile

Researcher

• Rehabilitation Robotics
• Biomedical Engineering
• Intelligent Control Systems
• +3 more

Peiman Shah Nazar is a Researcher at the Institute for Medical Device Technology, University of Stuttgart, Germany. He holds an M.Sc. in Mechatronics and Robotic System Design from Azad University, Ahar Branch, Iran, and is currently pursuing his Dr. Ing. at the University of Stuttgart. Research focus: Rehabilitation robotics for ankle, hip, and knee joints Expertise in intelligent control systems and biomedical signal processing His work emphasizes sustainable medical device design and optimization of robotic rehabilitation systems using neural networks and genetic algorithms. Recent publications highlight advancements in home-care physiotherapy robotics and chaotic control methodologies. At the Institute for Medical Device Technology, he contributes to reprocessing procedures and sustainable device development in laboratory settings.

• Urban Climate Research
• Thermal Comfort
• Urban Air Quality
• +6 more

Prof. Dr. Uwe Schlink is a leading Professor at the Institute of Meteorology, University of Leipzig, and Senior Researcher at the Department of Urban & Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ. His work focuses on urban climate research , thermal comfort , urban air quality , and statistical modelling with Bayesian inference. He leads the working group on urban climate and personal exposure, bridging environmental science with societal resilience. Affiliation: University of Leipzig (since 2009) and UFZ (since 2013) Research Themes: Urban heat islands, personal exposure to environmental stressors, statistical climate models, and health impacts of air pollution His research spans environmental health , urban climatology , and resilient city planning , with significant contributions to understanding thermodynamic interactions between urban structures and climate. He has pioneered methods for high-resolution land surface temperature analysis and green infrastructure performance in mitigating heat stress. Recent publications (2023-2025) highlight his work on PM2.5-bound PAH exposure , anthropogenic heat impacts in Beijing, and Asian plateau climate dynamics . Collaborative projects address urban heat stress , green roofs , and health-focused urban planning .

Georgina HallView profile

Assistant Professor

• Polynomial Optimization
• Semidefinite Programming
• Convex Relaxations
• +3 more

Georgina Hall serves as Assistant Professor of Decision Sciences at INSEAD, holding the prestigious Patrick and Valentine Firmenich Fellowship for Business and Society. She officially joined the institution in September 2019 after completing a postdoctoral position at INRIA's DYOGENE team from January to September 2019. Her educational background includes a PhD from Princeton University's Department of Operations Research and Financial Engineering (2018), where she was a Gordon Y.S. Wu fellow under Professor Amir Ali Ahmadi's supervision. She earned both her Bachelor of Science (2011) and Master of Science (2013) from Ecole Centrale Paris, where she was valedictorian. Dr. Hall's research focuses on optimization theory, particularly polynomial optimization , semidefinite programming , and convex relaxations of NP-hard problems . Her work bridges theoretical mathematics with practical applications in business decision-making. She has made significant contributions to shape-constrained regression using sum of squares polynomials, demonstrating how semidefinite programming hierarchies can effectively fit shape-constrained polynomials to noisy data. Her recent publications reveal a research trajectory that increasingly connects optimization theory with machine learning applications and supply chain decision-making. The 2025 Operations Research paper on shape-constrained regression represents her theoretical contributions, while the Management Science paper demonstrates practical business applications of her optimization framework. Her scientific recognition includes: Médaille de l'Ecole Centrale from the French Académie des Sciences Princeton School of Engineering and Applied Sciences Award for Excellence 2016 Informs Computing Society Prize for Best Student Paper Multiple teaching awards from Princeton University Patrick and Valentine Firmenich Fellowship for Business and Society Dr. Hall teaches Probability and Statistics at the PhD level and has received significant recognition for her teaching excellence, including the Princeton University's Engineering Council Teaching Award and the Excellence in Teaching Award from the Princeton Graduate School. Her research has practical applications in areas including optimal transport maps for color transfer tasks and estimating optimal value functions for conic programs, with real-time applications in inventory management contract negotiation.

• Scientific Software Development
• High Performance Computing
• Molecular Dynamics
• +3 more

Dr. Torsten Stuehn serves as IT Group Leader at the Max Planck Institute for Polymer Research (MPI-P) in Mainz, Germany, leading scientific software development and HPC infrastructure since joining in 2003. He oversees the ESPResSo++ simulation package and collaborates with the University of Mainz and Max Planck Compute and Data Facility (MPCDF). Education: Diploma in Physics, University of Mainz, 1999 Doctorate in Physics, University of Mainz, 2005 His research focuses on scientific software engineering for exascale computing, developing neural network-based force fields, adaptive resolution methods, and load balancing algorithms to advance molecular simulation capabilities. This work addresses critical challenges in maintaining computational leadership for soft matter physics. Recent publications reveal a clear evolution in ESPResSo++ toward exascale readiness, integrating machine learning with multiscale modeling and parallel computing innovations. The software's progression reflects broader trends in computational physics where AI-driven methods and heterogeneous architecture optimization are becoming indispensable. Stuehn directs MPI-P's computational infrastructure team and contributes to major initiatives including Transregio SFB 146 and the European E-CAM project, driving open-source scientific software development for the global research community.

Gregory GayView profile

Associate Professor

• Software Testing
• Search-Based Software Engineering
• AI for Software Engineering
• +4 more

Gregory Gay is an Associate Professor in the Interaction Design and Software Engineering division within the Department of Computer Science and Engineering at Chalmers University of Technology and the University of Gothenburg, Sweden. His academic profile spans numerous software engineering conferences where he has served as committee member, program chair, and active researcher since at least 2018. Dr. Gay's research focuses on the intersection of software engineering and artificial intelligence, with particular emphasis on: Software Testing and Analysis Search-Based Software Engineering AI for Software Engineering (AI4SE) AI Engineering Automation of development tasks Software Carbon Footprint and sustainability His recent publications demonstrate a strong trend toward applying AI and optimization techniques to software testing challenges, with increasing focus on sustainability aspects of software development. Many studies take an industrial perspective, examining real-world applications in automotive software systems. His work blends theoretical foundations with practical applications, making significant contributions to both academic research and industrial practice in software engineering. Dr. Gay has been actively involved in numerous top software engineering conferences including ASE, ICSE, ESEC/FSE, ISSTA, and ICST, serving on program committees and organizing tracks. His research methodology typically combines optimization, artificial intelligence, and machine learning to help developers deliver complex systems in a safe, secure, and efficient manner.