جستجوی استاد راهنمای دانشگاهی

Syed Ali Razaمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Syed Ali Raza is a researcher affiliated with the University of Technology, Sydney (PhD 2018), with a former affiliation at the Institute of Business Administration, Karachi. His work focuses on reinforcement learning, robotics, and human-robot interaction. Key contributions include studies on social robots' question-answering services, human feedback integration in AI systems, and optimization of robotic movements in multi-agent scenarios. He has collaborated extensively with researchers like Mary-Anne Williams and Sajjad Haider. His research spans theoretical advancements in machine learning algorithms and practical applications in robotics competitions (e.g., RoboCup). Research interests emphasize computational reinforcement learning, reward shaping techniques, and ethical design of autonomous systems. Notable projects include privacy-first approaches for social robots and hybrid methods for humanoid robot locomotion. His work bridges theoretical AI advancements with real-world robotic implementations.

• Artificial Intelligence
• Computational Intelligence
• Neural Networks
• +۵ مورد دیگر

Lakhmi C. Jain is a distinguished academic affiliated with the University of South Australia. As a Professor, she has made significant contributions to the fields of Artificial Intelligence, Computational Intelligence, and Fuzzy Systems. Her research spans neural networks, decision support systems, robotics, and data analysis, with a focus on interdisciplinary applications. Her career includes over 445 publications, including books like Complex Networks in Software, Knowledge, and Social Systems (2019) and E-Learning Systems - Intelligent Techniques for Personalization (2017). She has held editorial roles in journals such as the International Journal of Intelligent Decision Technologies (IDT) and the Journal of Intelligent & Fuzzy Systems. Jain's work emphasizes practical applications of computational intelligence, including efforts in software development, biomedical signal processing, and multi-agent systems. She has collaborated extensively with researchers globally, contributing to advancements in AI-driven technologies and decision-making frameworks.

Andres M. Escorciaمشاهده پروفایل

پژوهشگر

• Molecular Dynamics Simulations
• Quantum Mechanics/Molecular Mechanics (QM/MM)
• Enzyme Catalysis
• +۳ مورد دیگر

Dr. Andres M. Escorcia is a researcher at the Max Planck Institute for Dynamics of Complex Technical Systems, leading the Molecular Simulations and Design (MSD) group. He holds a PhD in Chemistry from Universidad Industrial de Santander (UIS), Colombia, and has held academic positions including Assistant Professor at Universidad Icesi (2021–2023). His research focuses on enzyme reaction mechanisms using molecular dynamics (MD) simulations and hybrid quantum mechanics/molecular mechanics (QM/MM) methods, targeting enzymes such as deubiquitinases, hydrogenases, terpene synthases, and lytic polysaccharide oxygenases. He collaborates extensively with interdisciplinary teams and co-supervises PhD students. Previous postdoctoral positions include work at the University of Bristol (UK), Max-Planck-Institut für Dynamik komplexer technischer Systeme (Germany), and Max-Planck-Institut für Kohlenforschung (Germany). His educational background includes a BSc in Chemistry (UIS, 2008) and a PhD (UIS, 2015), advised by Professors Markus Doerr and Martha Daza. His research interests span computational enzymology, reaction mechanism elucidation, and enzyme engineering. He has published extensively in top journals like Inorg. Chem. , J. Chem. Inf. Model. , and ACS Catal. . His work bridges theoretical modeling and experimental validation, with contributions to understanding catalytic pathways in biocatalysts and protein-ligand interactions. Recent projects include studies on Cezanne-2 deubiquitinase and taxadiene synthase mechanisms. Dr. Escorcia is affiliated with the MSD group, contributing to both computational infrastructure and experimental collaborations. His contributions to enzyme modeling and reaction design have advanced understanding of enzymatic selectivity and catalytic efficiency.

• Process Systems Engineering
• Chemical Engineering
• Energy Conversion Systems
• +۴ مورد دیگر

Prof. Kai Sundmacher is a Full Professor and Chair of Process Systems Engineering at Otto-von-Guericke University Magdeburg, and Director of the Max Planck Institute for Dynamics of Complex Technical Systems. His expertise spans model-based analysis of complex process systems, integrated chemical processes, energy conversion (fuel cells/electrolysis), and biosystems engineering. He holds a Dr.-Ing. habil. from Clausthal University of Technology and has held visiting positions at institutions worldwide. Research focuses include catalyst design for CO2 methanation, digital twins for power-to-X processes, and sustainable chemical production. Awards include the Arnold Eucken Prize (1999) and Carl Zerbe Prize (1998). He leads initiatives like the Max Planck Research Network for Synthetic Biology and chairs the German Chemical Energy Research group. Teaching covers advanced process systems engineering, fuel cell technology, and bioprocess design. Over 500 publications include 400 peer-reviewed articles, with contributions to journals like Chemical Engineering Science and Fuel Cells. Active in academic service, he reviews ERC grants, chairs selection committees, and advises the Alexander von Humboldt Foundation.

• Telecommunications
• 5G/6G Networks
• Network Slicing
• +۷ مورد دیگر

Christian Wietfeld is a Professor at TU Dortmund, Germany, specializing in telecommunications, 5G/6G networks, and robotics. His research focuses on network slicing, machine learning for communications, vehicular networks, and intelligent reflecting surfaces. Affiliations: TU Dortmund Key Collaborations: Stefan Böcker, Benjamin Sliwa, Manuel Patchou His work spans 6G multi-X communications, private industrial networks, and disaster response robotics. Recent projects include mmWave reflector systems, predictive uplink slicing, and AI-driven network planning. 2024-2025 publications highlight advancements in 6G IRS, energy-efficient 5G, and vehicular connectivity. Sub-fields include beam management, digital twins, and non-terrestrial networks. He contributes to experimental frameworks like Open RAN and ns-3 simulations, emphasizing scalable solutions for industrial and emergency applications.

• Smart Grids
• Energy Transition
• Cybersecurity in Energy Systems
• +۵ مورد دیگر

Prof. Andreas Ulbig is a Universitätsprofessor at RWTH Aachen University's Institute of Electric Installations and Networks, Digitization and Energy Economics (IAEW), situated in Aachen, Germany. His work focuses on advancing smart grid technologies, energy transition strategies, and cybersecurity in power systems. He is affiliated with the Department of Active Energy Distribution Networks within the Faculty of Electrical Engineering and Information Technology. Ulbig's research emphasizes resilient communication protocols, grid topology identification, multi-use energy storage systems, and electrification of transportation. His contributions include co-authoring studies on adaptive protection systems, local energy markets, and cyber-physical resilience frameworks. He leads projects addressing municipal energy system transformations and grid-friendly energy sharing concepts. Ulbig holds a doctoral degree (Dr.) and has extensive experience in energy infrastructure digitization and grid stability. His recent publications span 2023–2025, focusing on technical and economic aspects of energy systems, with particular attention to cyberattack simulations, distribution grid flexibility, and hydrogen integration in gas networks. Collaborations include IEEE conferences and institutions like Westenergie AG, where he contributed to studies on grid-forming converters. His research is methodologically diverse, combining machine learning, stochastic control, and experimental validation with industry partners. In academic service, Ulbig advises on energy policy implications and co-supervises research projects at IAEW. His work bridges theoretical advancements with practical grid solutions, addressing challenges such as curative distribution measures, tariff design for local markets, and infrastructure expansion costs. Key technical interests include broadband powerline communication, digital twin applications, and optimization of DC/AC grid integration.

• Applied Econometrics
• Agricultural Land Markets
• Renewable Energy (Wind Energy)
• +۳ مورد دیگر

Dr. Matthias Ritter is a Senior Researcher at the University of Tübingen's Faculty of Economics and Social Sciences, holding a position at the Chair of Marketing. Previously, he served as an Associate Professor and Assistant Professor at Jönköping International Business School (Sweden) and held roles including Junior Professor of Quantitative Agricultural Economics at Humboldt University Berlin. He earned his PhD in Economics (Dr. rer. oec.) from Humboldt University Berlin in 2011, focusing on econometrics and economic statistics. Education: PhD in Economics, Humboldt University Berlin (2011) Postdoctoral Researcher, Technical University Berlin (2013) Diploma in Mathematics (university unspecified) Research Interests: Ritter’s work centers on applied econometrics, agricultural land markets, renewable energy (especially wind energy), and weather risk management. His research bridges economic theory with practical applications in climate change adaptation, land valuation, and energy policy. Recent studies include analyzing bargaining power in agricultural land markets and modeling wind energy potential in urban areas. Key Awards: Best Paper Award (GEWISOLA, 2021) Humboldt Award for Excellence in Teaching (2020 shortlist) Stifterverband Prize (2018) for land market research Finanzkompass Innovation Award (2013) for dissertation Teaching & Advising: Ritter teaches courses in data science, agricultural economics, and renewable energy economics. He has advised students in both bachelor’s and master’s thesis seminars but no named advisees are listed. His research has explored interdisciplinary topics such as wind turbine efficiency, climate modeling, and digital teaching strategies post-pandemic. Labs/Teams: Engaged with agricultural economics and energy policy research groups, focusing on collaborative projects involving land markets, renewable energy integration, and climate risk assessment.

• Power Systems
• Smart Grids
• Renewable Energy Integration
• +۳ مورد دیگر

Prof. Ferdinanda Ponci is a Professor at RWTH Aachen University, leading the Department of Monitoring and Distributed Control for Energy Systems. Her work focuses on smart grid technologies, renewable energy integration, and control systems for modern power networks. She actively contributes to advancing grid resilience, EV charging infrastructure, and cybersecurity in energy systems. Key research interests include hybrid AC-DC grids, distributed energy resources coordination, and application of AI in grid management. She has pioneered frameworks like datafev for EV charging management and SMU platforms for synchronized measurements. Her interdisciplinary approach also addresses diversity in engineering and societal impacts of energy systems. Prof. Ponci’s laboratory activities involve hardware-in-the-loop testing, blockchain-based grid solutions, and development of educational toolboxes for emerging technologies. She collaborates on projects such as submarine transmission systems and grid interoperability testing, contributing to both academic and industrial advancements in power systems engineering.

Karin Nachbagauerمشاهده پروفایل

استاد

• Multibody System Dynamics
• Numerical Mathematics
• Inverse Dynamics
• +۳ مورد دیگر

Karin Nachbagauer is a Professor of Applied Mathematics at the University of Applied Sciences Upper Austria, affiliated with the Faculty for Engineering's Mechanical Engineering Department. She holds a Hans Fischer Fellowship at the TUM Institute for Advanced Study (since 2020). Her research focuses on multibody system dynamics, numerical mathematics, optimal control, and inverse dynamics, with applications in mechanical engineering and robotics. She earned her PhD in Engineering Sciences (2012) and Diploma in Industrial Mathematics (2009) from Johannes Kepler University Linz. Notable awards include the 2020 Best Paper Award for optimal control research and 2019 Excellence in Teaching Award. Her work emphasizes adjoint gradient methods for optimization problems, parameter identification in multibody systems, and time-optimal control applications. Current projects include the VRoboCoop initiative for human-robot collaboration and IOMMS for innovative optimization in multibody systems. Publications span journals like Journal of Computational and Nonlinear Dynamics and Multibody System Dynamics , with over 80 peer-reviewed articles. She actively participates in international conferences and serves on editorial boards.

August Wilhelm Scheerمشاهده پروفایل

استاد

• Business Informatics
• Process Management
• Digital Transformation
• +۵ مورد دیگر

Prof. Dr. August-Wilhelm Scheer is a Professor of Business Informatics at Saarland University and founder of the Scheer Group, a leading IT enterprise. He pioneered the ARIS method for integrated information systems and established the School of Digital Sciences at Saarland University. His work bridges academic research and industry application through ventures like IDS Scheer Holding and IMC AG. Educations: Founded Institute of Business Informatics at Saarland University Doctoral supervision of 76 theses (over 3/year average) Research collaborations with German Research Center for Artificial Intelligence (DFKI) Research Interests: Focuses on business process management, digital transformation, and AI-driven enterprise solutions. His ARIS method revolutionized IT-business alignment through process modeling. Current work explores composable enterprise architectures and AI agent systems in business contexts. Awards: German Research Hall of Fame Inductee (2017) Lifetime Achievement Award (Global University Alliance) Advising & Innovation: Supervised 76 doctoral students and spun-off 1,300+ client companies through Scheer Group solutions. Spearheaded digital learning platforms via IMC AG. Labs/Teams: Leads August-Wilhelm Scheer Institute for Digital Products and Processes, fostering research-practice partnerships in digital innovation.

Timo Speithمشاهده پروفایل

مدرس

• Machine Ethics
• Machine Explainability
• Artificial Intelligence
• +۲ مورد دیگر

Dr. Timo Speith is a fixed-term Lecturer at the University of Bayreuth's Faculty of Cultural Studies, affiliated with the Professorship for Philosophy, Computer Science and Artificial Intelligence. He holds a B.A. in Philosophy and M.Sc. in Computer Science from Saarland University, where he also completed his doctoral studies in Philosophy. His research focuses on machine ethics, machine explainability (XAI), and non-speculative AI systems, alongside interests in ancient philosophy and the philosophy of science. He has contributed to interdisciplinary work on XAI evaluation methods, fairness in AI systems, and explainable hardware (XHW). Speith has organized workshops on explainable systems requirements engineering and co-authored a manifesto outlining XAI 2.0 challenges. His work bridges philosophy, computer science, and ethics to address AI transparency and accountability. Education: B.A. Philosophy, Saarland University M.Sc. Computer Science, Saarland University Ph.D. in Philosophy, Saarland University (2023 dissertation) Research Interests: Machine Ethics, XAI Methodology, AI Transparency, Philosophy of Science, Ancient Philosophy. Grants and Collaborations: Active in international workshops (e.g., RE4ES), IEEE conferences, and collaborations with institutions like Saarland University and the University of Bayreuth's Philosophy Institute. His work addresses practical and theoretical challenges in AI explainability across hardware and software systems. Lab/Team Affiliation: Associated with the Philosophy, Computer Science, and Artificial Intelligence research group at the University of Bayreuth, focusing on interdisciplinary AI ethics and technical implementation.

Tommaso Calarcoمشاهده پروفایل

استاد

• Quantum Optimal Control
• Quantum Computing
• Quantum Technologies
• +۳ مورد دیگر

Tommaso Calarco serves as Director of the Institute for Quantum Control (PGI-8) at Jülich Research Centre, leading cutting-edge research in quantum optimal control methodologies for next-generation quantum technologies. His work focuses on developing transformative computational frameworks applicable to natural sciences, logistics, and high-performance computing through advanced quantum device engineering. His research spans quantum optimal control for computation and many-body systems, emphasizing physical model development, model reduction techniques, and machine learning integration for scalable quantum hardware. Key focus areas include spin-qubit optimization, diamond quantum register engineering, and error suppression in gate operations, with significant contributions to ultracold atom systems and semiconductor-based quantum platforms. Analysis of his 2024-2025 publications reveals concentrated efforts on hardware-specific challenges across multiple quantum modalities: spin shuttling fidelity in semiconductor systems, gate optimization for nitrogen-vacancy centers, and photon-spin interface engineering. This work demonstrates a unifying thread of optimal control solutions tailored to platform-specific decoherence mechanisms and scalability constraints. As Director of PGI-8 within the Peter Grünberg Institut, Calarco oversees a dedicated research team advancing quantum control theory and applications, contributing substantially to European quantum technology roadmaps including the Quantum Flagship initiative and strategic European Commission reports.

Lorenz Linhardtمشاهده پروفایل

پژوهشگر

• Explainable AI
• Robust Machine Learning
• Machine Learning for Medical Applications
• +۳ مورد دیگر

Lorenz Linhardt is a Researcher and PhD candidate at the Machine Learning Group of Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on robustness of deep neural networks, spurious correlations, and representation learning, with applications in explainable AI and medical domains. Educational Background: M.Sc. in Computer Science, 2019 – ETH Zürich B.Sc. in Computer Science, 2016 – University of Vienna Research Interests: Robust Machine Learning : Addressing vulnerabilities in neural networks caused by spurious correlations and adversarial examples. Human Alignment : Bridging gaps between AI outputs and human judgments via representation learning and similarity metrics. Medical Applications : Leveraging machine learning for counterfactual inference in healthcare and dose-response modeling. Article Trends: Recent work emphasizes alignment between human judgments and AI systems, with studies on latent diffusion models, adversarial robustness, and forensic analysis of malware classifiers. Earlier contributions span astronomy surveys and decision tree optimization. Labs/Teams: Active in BIFOLD and the TU Berlin Machine Learning Group, focusing on foundational AI research.

Marina Fiedlerمشاهده پروفایل

استاد

• Artificial Intelligence in Organizations
• Information Systems and Organizational Behavior
• Sustainable Management Practices
• +۳ مورد دیگر

Prof. Dr. Marina Fiedler holds the Chair of Management, People and Information at the University of Passau since 2010. She is an Associate Editor at the Schmalenbach Journal of Business Research (SBUR) and heads the Passau experimental laboratory PAULA . Her academic journey includes a PhD (2004) and habilitation (2008) from the University of Munich, with visiting professorships at Corvinus University and State University of Management.

Andrea Tetiمشاهده پروفایل

استاد

• Gerontology
• Public Health
• Aging and Health
• +۴ مورد دیگر

Prof. Dr. Andrea Teti is the Head of the Department of Gerontology - Aging and Health at the University of Vechta. He leads the Institute for Gerontology (IfG) and focuses on medical, health, and nursing care for older adults. His research emphasizes age-related diseases, prevention strategies, care needs, and housing conditions in old age. Teti earned a PhD in Medical Sociology from Charité-Universitätsmedizin Berlin (2016), followed by roles as a Post-Doc at the Robert Koch Institute and a Junior Professor in Gerontology (2017-2020). He holds a Master’s in Public Health (2014) and a Diploma in Sociology from TU Dresden and Università degli Studi di Trento (2009-2016). Research areas include diabetes prevention (ActiVAtE_Prevention project), residential mobility as a health strategy, and caregiver support. His work integrates epidemiological methods and interdisciplinary approaches. Awards include a poster prize at the 11th Vienna Geriatrics Congress (2010). Teti supervises numerous bachelor’s and master’s theses and teaches courses like Biological Aspects of Aging and Geriatric Psychiatry . He collaborates on national projects, including IMOA (health monitoring for older adults) and diabetes surveillance initiatives. Key Projects: ActiVAtE_Prevention, IMOA, National Diabetes Surveillance System Teaching: Focus on biological aging, geriatric care, and quantitative research methods Grants: Drittmittelprojekte funding for diabetes and aging research His lab, the Institute for Gerontology, explores aging-related health challenges and policy implications, emphasizing interdisciplinarity and community engagement.