جست‌وجوی استاد و پژوهشگر

Julia Lanzlمشاهده پروفایل

پژوهشگر

• Digital Work
• Digital Responsibility
• Organizational Digitalization
• +۳ مورد دیگر

Dr. Julia Lanzl serves as a postdoctoral researcher at the University of Hohenheim’s Department of Digital Management within the Faculty of Business, Economics and Social Sciences. She is also a member of the Fraunhofer Institute for Applied Information Technology (FIT), specifically its Business and Information Systems Engineering branch, and co-heads the Research Institute for Information Management (FIM). Her work bridges research, teaching, and practice, focusing on digital work (including hybrid work, digital leadership) and digital responsibility (digital stress, inclusion). Dr. Lanzl holds a doctorate summa cum laude from the University of Hohenheim (2021) and a degree in law and economics from the University of Augsburg. Her research employs quantitative methods (e.g., structural equation modeling) and qualitative approaches (e.g., interviews), alongside design science. She has published in top journals like Business & Information Systems Engineering and conferences such as the International Conference on Information Systems, and serves as a reviewer and guest editor. Her research aims to empower organizations to leverage digitalization’s potential while fostering human-centered work designs. Key areas include technostress mitigation, hybrid work dynamics, and ethical digital leadership. Awards include the Südwestmetall Award (2021) and the Hohenheim Teaching Award (2022). Research Focus: Digital Work, Technostress, Human-Centered Design Key Projects: “Healthy Digital Work” studies, prevention of digital stress in SMEs Teams: Fraunhofer FIT, FIM Research Institute

Shashank Agnihotriمشاهده پروفایل

پژوهشگر

• Adversarial Robustness
• Out-of-Distribution (OOD) Robustness
• Neural Architecture Search
• +۵ مورد دیگر

Shashank Agnihotri is a Researcher & PhD Candidate at the Chair for Machine Learning within the Data and Web Science Group at the University of Mannheim. He is affiliated with the School of Business Informatics and Mathematics . His research focuses on adversarial and OOD robustness of vision models, pixel-wise prediction tasks, and neural architecture search. He has contributed to projects like CosPGD (an efficient adversarial attack for pixel-wise tasks) and Improving Feature Stability during Upsampling . Education: PhD Candidate in Computer Science, University of Mannheim (2023–Present) PhD Candidate in Computer Science, University of Siegen (2022–2023) MSc. Computer Science, Albert-Ludwigs Universität Freiburg (2018–2021) B.E. Computer Engineering, VESIT, University of Mumbai (2014–2018) Research Interests: Adversarial and OOD robustness of deep learning models Sensor layout optimization and task-specific camera parameters Signal processing impact on model reliability Neural architecture search (NAS) methodologies Publications & Awards: Published at ICML, ECCV, ICCV, NeurIPS, and ICCP Outstanding Reviewer (CVPR 2025) and Notable Reviewer (ICLR 2025) Organized the 45th DAGM German Conference on Pattern Recognition (GCPR 2023) Labs & Collaborations: Part of the Machine Learning Group at Mannheim and previously at the Machine Learning Group in Freiburg under Prof. Frank Hutter and Prof. Thomas Brox.

Dirk Pflügerمشاهده پروفایل

استاد

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +۳ مورد دیگر

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

Dieter Schmalstiegمشاهده پروفایل

استاد

• Augmented Reality
• Virtual Reality
• Computer Graphics
• +۲ مورد دیگر

Dieter Schmalstieg is the Alexander von Humboldt Professor of Visual Computing at the University of Stuttgart's Faculty 5: Computer Science, Electrical Engineering and Information Technology, leading the Institute for Visualization and Interactive Systems (VIS). He also holds an adjunct professorship at Graz University of Technology's Institute of Computer Graphics and Vision. His research focuses on augmented reality (AR), virtual reality (VR), computer graphics, visualization, and human-computer interaction. He earned his Dipl.-Ing. (1993), Dr. techn. (1997), and Habilitation (2001) from Vienna University of Technology. He has authored/co-authored over 400 peer-reviewed publications, with over 29,000 citations and numerous awards, including the IEEE ISMAR Career Impact Award (2020) and the IEEE Virtual Reality Technical Achievement Award (2012). His professional roles include editorial leadership in top journals like IEEE Transactions on Visualization and Computer Graphics, and steering committee memberships in major conferences like IEEE ISMAR. Research interests span AR/VR applications, situated visualization, coherent rendering, spatial interaction, and medical visualization. Notable projects include the Christian Doppler Laboratory for Handheld Augmented Reality (2008–2015), and collaborations with industry partners like Qualcomm and VRVis. He has advised over 50 PhD students, many now professors or researchers globally. Grants include the Alexander von Humboldt Professorship (2023), SNAP Mixed Reality Lab funding, and EU projects like MiReBooks (mining education). His work bridges theory and application, with impactful contributions to AR/VR systems, medical visualization tools, and GPU techniques for real-time rendering.

Jörg Mainمشاهده پروفایل

استاد

• Theoretical Physics
• Condensed Matter Physics
• Quantum Optics
• +۶ مورد دیگر

Jörg Main is a full Professor at the University of Stuttgart , leading the research group at the Institute for Theoretical Physics I . His work bridges condensed-matter theory, quantum optics, and nonlinear dynamics, with a strong emphasis on Rydberg excitons in cuprous oxide and the foundations of non-Hermitian quantum systems. Research Interests: Exciton Physics & Semiconductor Spectroscopy: Extensive studies of Rydberg excitons in cuprous oxide, including quantum-defect theory, oscillator strengths, and fine-structure effects. Quantum Chaos & Semiclassical Methods: Application of periodic-orbit theory and phase-space analysis to understand complex spectra and classical-quantum correspondence. Non-Hermitian & PT-Symmetric Systems: Investigation of exceptional points, resonance phenomena, and gain-loss balanced potentials in Bose-Einstein condensates and photonic structures. Machine Learning in Physics: Development of Gaussian-process and neural-network approaches for predicting quantum dynamics and locating exceptional points in high-dimensional spectra. Recent Publication Trends: Over the last two years, Prof. Main has published prolifically on cuprous oxide excitons, unveiling new quantum-well phenomena, resonance linewidth behavior, and semiclassical descriptions. A parallel thrust leverages machine-learning techniques to analyze complex spectra and time-dependent systems, demonstrating an innovative blend of theoretical physics and data science. Laboratory & Group: Professor Main heads the Theory Group at the Institute for Theoretical Physics I, fostering an environment for advanced analytical and computational research in quantum many-body physics.

Vanessa Borstمشاهده پروفایل

پژوهشگر

• Deep Learning
• Machine Learning
• AI-based Image Processing
• +۳ مورد دیگر

Vanessa Borst is a Researcher at the Chair of Software Engineering (Informatik II) within the Department of Computer Science at the University of Würzburg, led by Prof. Dr. Samuel Kounev. Her work focuses on AI-driven solutions in healthcare, including medical image analysis, time series forecasting, and mobile health applications. Education: Master of Computer Science (University of Würzburg, 2017–2021), Bachelor of Computer Science (University of Würzburg, 2015–2018). She has conducted studies abroad at the University of the Basque Country (2018–2021) and worked as a student trainee at Siemens AG (2016–2019). Research Interests: Medical Image Segmentation, AI in Healthcare, Lightweight Neural Networks, Time Series Analysis, and Patient-Centered Mobile Applications. Recent projects include WoundAIssist (mobile app for wound care) and MACRO (ECG disease classification). Teaching: Regular supervision of Bachelor/Master theses, coordination of the 'Seminar Software Engineering,' and contributions to courses like 'Advanced Programming' and 'Operating Systems.' Awards: 2023 Institute Award for Master's Excellence, 2022 Medicine Award (€25k), CAIDAS Best Master Thesis Prize (2021), and Siemens Abitur-Preis (2015). Professional Activities: Reviewer for KI, ECML PKDD, ACM IUI, and other conferences. Active in the Symposium on Software Performance (SSP) since 2021.

Sven Hertlingمشاهده پروفایل

استاد

• Knowledge Graph Generation
• Ontology Matching
• Knowledge Graph Integration
• +۳ مورد دیگر

Dr. Sven Hertling is a Substitute Professor for Data Science at the University of Mannheim, affiliated with the Data and Web Science Group. His research focuses on Knowledge Graph Generation, Ontology Matching, and Knowledge Graph Fusion. He teaches courses such as Knowledge Graphs, Data Mining, and Database Technologies. Notable awards include the Best Demo at ESWC 2021 and Best Paper at the Knowledge Graphs and Semantic Web Conference (KGSWC) 2022. Education: PhD in Computer Science (implied by role). His research interests span ontology alignment, large language model integration, and scalable knowledge graph systems. Recent work includes frameworks like KGrEaT for knowledge graph evaluation and tools like MELT for ontology matching analysis. He has contributed to over 50 publications, with a focus on ontology alignment evaluation (OAEI) and semantic web technologies. Key projects include the Software Campus initiative's GUI search engine and DBkWik, integrating knowledge from thousands of Wikis. Grants: €100,000 from Software Campus for the SuGraBo project. Advising: Coordinates OAEI challenges and chairs ESWC 2023. He actively serves on program committees for ISWC, ESWC, and other conferences, emphasizing community engagement in semantic web research.

Richard Membarthمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Domain-Specific Compilers
• GPU Acceleration
• +۴ مورد دیگر

Richard Membarth is an academic researcher at the Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Computer Science. His work focuses on high-performance computing, domain-specific compilers, and GPU acceleration. He has contributed to projects like FLOWER (dataflow compiler), Hipacc (image processing DSL), and XEngine (neural network optimization). Membarth's research bridges compiler design, parallel algorithms, and heterogeneous hardware, with applications in medical imaging, bioinformatics, and autonomous systems. Co-developer of AnyDSL framework for partial evaluation Lead in GPU acceleration for molecular dynamics (tinyMD) Specialized in compiler techniques for FPGAs and GPUs Key areas include: - Domain-specific languages (DSLs) - Parallel algorithm optimization - Medical computing pipelines - Real-time graphics rendering

Anthony Steinمشاهده پروفایل

استادیار

• Agricultural Robotics
• AI in Digital Agriculture
• Swarm Robotics
• +۷ مورد دیگر

Anthony Stein is a Tenure Track Professor (equivalent to Assistant Professor) and Head of the Department of Artificial Intelligence in Agricultural Engineering at the University of Hohenheim's Faculty of Agricultural Sciences. He holds a Dr. rer. nat. from the University of Augsburg. Research develops intelligent agricultural systems through reinforcement learning, evolutionary computation, and distributed AI. Core applications include real-time weed detection, robotic crop monitoring, emission prediction, and resource-efficient AI for sustainable farming. Recent publications focus on optimizing vision systems for precision agriculture using generative AI and federated learning. Work emphasizes embedded system deployment and multi-objective neural architecture design. No awards documented. Leads research on AI-driven agricultural robotics without specific student advising details. Committee roles include GI Organic Computing group leadership and KTBL outdoor robotics standards.

Eric Schottمشاهده پروفایل

استاد

• Digital Transformation
• Project Management
• Organizational Change Management
• +۳ مورد دیگر

Prof. Dr. Eric Schott holds a dual role as a Professor at Technische Universität Berlin's Institute of Technology and Management and as founder of the international consultancy Campana & Schott. His expertise spans complex organizational change, digital transformation, and innovative collaboration models. He has authored over 50 publications on project management and co-led the German Social Collaboration Study with TU Darmstadt. Affiliations: TU Berlin (Technology and Innovation Management), Founder of Campana & Schott (600+ employees) Education: Dipl.-Wi.-Ing (engineering economics), Dr. phil (philosophy/management) His research focuses on digital workplace optimization, fusion teams, and AI-driven process automation. He advises Fortune 500 companies on sustainable transformation and has pioneered methods for hybrid work and frontline worker integration. Key Collaborations: TU Darmstadt (Social Collaboration Study), University of St. Gallen (Future Organization Report) Prof. Schott's work bridges academic research and industry implementation, emphasizing practical solutions for digital leadership and organizational resilience.

• E-Governance
• Government Information Technology Management
• Systems Thinking and System Dynamics
• +۳ مورد دیگر

Prof. Hsiao Nai-Yi is an Associate Professor in the Department of Public Administration at National Chengchi University, Taiwan. He specializes in E-Governance, Government Information Technology Management, and Systems Thinking. His research focuses on policy simulation, digital transformation in governance, and public sector IT frameworks. Education: Ph.D. in Public Administration and Policy (SUNY Albany, USA), MBA in Information Management (National Sun Yat-Sen University), and B.S. in Applied Mathematics (National Chao-Tung University). Professional roles include Director of the Taiwan E-Governance Research Center (2012–present) and Visiting Scholar at Harvard University’s Kennedy School (2009). He teaches courses on systems thinking, e-governance, and public policy analysis at both graduate and undergraduate levels. His work emphasizes cross-sector collaboration, digital service delivery models, and impact assessment frameworks for electronic governance programs. Key grants include studies on cross-sector e-governance collaboration (2012–2014), big data analysis for public decision-making (2013), and foresight studies on digital convergence (2013). Labs/Teams: Active in the Taiwan E-Governance Research Center, focusing on policy innovation and digital governance capacity building.

• Optimal Control
• Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Prof. Michael Hintermüller is a Professor at Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics, specializing in Applied Mathematics. He also holds an affiliation with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin. His research focuses on optimal control of partial differential equations (PDEs), variational inequalities, numerical analysis, and their applications in imaging and energy systems. Notable contributions include work on machine learning-informed PDEs, stochastic optimization, and mathematical modeling of coupled systems like hydrogen-electric markets. His recent publications emphasize data-driven methods in imaging, regularization techniques, and optimization algorithms for complex systems. He explores interdisciplinary topics such as quantum systems simulation, gas network control, and energy market modeling. His work often bridges theoretical analysis and computational implementation, with applications in engineering and medicine. Prof. Hintermüller’s research aligns with initiatives like MaRDI, aiming to build infrastructure for mathematical data sciences. His academic profile reflects a strong commitment to advancing applied mathematics through rigorous analysis and innovative numerical methods.

Dominik Dworschakمشاهده پروفایل

پژوهشگر

• Electrocatalysis
• High-throughput electrocatalysis
• Renewable Energy
• +۵ مورد دیگر

Dominik Dworschak is a researcher and team leader at the Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (HI ERN), where he heads the High Throughput Electrocatalysis group. His work is integral to advancing electrochemical energy conversion technologies, particularly through high-throughput methods for catalyst development and analysis. His research focuses on accelerating the discovery and optimization of electrocatalytic materials, with applications in sustainable energy systems such as water electrolysis and renewable fuel production. This involves the integration of automated experimentation, materials synthesis, and advanced characterization techniques to understand and improve interfacial processes in energy devices. Dominik Dworschak's scientific contributions are centered in the domain of electrocatalysis and renewable energy materials. His work emphasizes high-throughput methodologies, suggesting a strong trend toward data-driven and scalable approaches in materials development for energy applications. No scientific awards were mentioned in the provided text. There is no information available regarding student advising or research grants in the provided profile. Dominik Dworschak leads the High Throughput Electrocatalysis team at HI ERN, a research group dedicated to developing and applying rapid screening techniques for electrocatalytic materials. The team operates within a broader interdisciplinary environment focused on renewable energy solutions, collaborating across domains such as photovoltaics, hydrogen storage, and process engineering.

Anders Blomdellمشاهده پروفایل

استاد

• Control Systems
• Real-Time Systems
• Embedded Systems
• +۷ مورد دیگر

Anders Blomdell is a Professor in the Department of Control Engineering at the Faculty of Engineering, Lund University. His work bridges theoretical control systems with practical applications in real-time, embedded, and cloud-based environments. He is actively involved in research projects focusing on autonomous systems, industrial automation, and networked control. His research interests include Control Systems , Real-Time and Embedded Systems , Networked Control , Industrial Automation , Robotics , and Cloud-Controlled Systems . These areas reflect his focus on scalable, adaptive, and secure control solutions for complex, interconnected systems. The recent publications highlight a strong trend toward event-based control , large-scale optimization , autonomous adaptation , and secure cloud integration . His work often combines control theory with computer science, particularly in distributed and real-time settings, showing a consistent effort to address challenges in latency, scalability, and robustness. Scientific Awards: Advising and Grants: While specific students are not listed, Anders Blomdell leads and participates in major research initiatives such as WASP, ELLIIT, and the Nordic University Hub on Industrial Internet of Things (HI2OT). These projects involve significant funding and collaboration with industry and academia, indicating active mentorship and leadership in training next-generation researchers. Labs and Teams: He is associated with RobotLab LTH and contributes to the development of tools like TrueTime and LabComm , which are widely used in real-time and networked control research. His work supports both academic and industrial innovation in automation and intelligent systems.

Wolfgang Wiechertمشاهده پروفایل

استاد

• Bioprocess Optimization
• Metabolic Engineering
• Systems Biotechnology
• +۳ مورد دیگر

Prof. Wolfgang Wiechert is a Universitätsprofessor at Forschungszentrum Jülich GmbH, leading the Computational Systems Biotechnology group within the Institute of Bio- and Geosciences (IBG-1). His research focuses on integrating computational methods with experimental biotechnology to optimize bioprocesses, particularly in metabolic engineering and systems biology. Key areas include metabolic flux analysis, high-throughput screening, and Bayesian statistical methods for process optimization. Research interests span Bioprocess design and scale-up (e.g., itaconate production, fungal cultivation) Automated microbioreactor systems and robotic workflows Development of computational tools for metabolic modeling (e.g., hopsy, pyFOOMB) Application of machine learning in bioprocess analytics Recent publications emphasize Bayesian approaches for flux inference, strain characterization automation, and integration of omics data with process analytics. Collaborations span industries and academic partners, driving innovations in sustainable biomanufacturing and enzyme production systems.