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Hasan TercanView profile

Researcher

• Machine Learning
• Deep Learning
• Industrial Artificial Intelligence
• +7 more

Dr.-Ing. Hasan Tercan is a Scientific Researcher and Head of the Research Field 'Industrial Deep Learning' at the Institute for Technologies and Management of Digital Transformation, University of Wuppertal, where he has been since December 2018. His work focuses on the development and application of machine learning and artificial intelligence methods in industrial environments, particularly in production quality assurance and intelligent process control. Education: PhD (Dr.-Ing.) in Computer Science, University of Wuppertal (2023) Master's in Computer Science, Technical University of Darmstadt (specialization: Database Systems and Data Mining) Research Assistant, Chair of Information Management in Mechanical Engineering, RWTH Aachen University His research interests lie at the intersection of machine learning and industrial applications. He specializes in transfer learning , lifelong learning , deep reinforcement learning , and predictive quality modeling in manufacturing. A key focus is bridging simulation and real-world data through simulation-to-reality approaches and enabling continuous model adaptation in dynamic production environments. His recent publications (2025) reflect a strong trend in applying advanced AI techniques—such as AttentiveGRUs, GANs, Decision Transformers, and deep reinforcement learning—to industrial challenges like radar-based object detection, job shop scheduling, robot end-effector control, and process configuration. These works demonstrate a consistent emphasis on deploying scalable, adaptive AI solutions in real-world industrial systems. Scientific Awards: Ph.D. Prize from the Friends and Alumni Association of the University of Wuppertal (FABU) Hasan Tercan leads the 'Industrial Deep Learning' research group, indicating active mentoring and project leadership. While specific grant details are not mentioned, his multiple 2025 publications in high-impact journals (e.g., Procedia CIRP, AI, Autonomous Agents and Multi-Agent Systems) suggest involvement in funded research projects. He collaborates closely with Prof. Dr. Meisen and other researchers, contributing to a robust research ecosystem in industrial AI. He is affiliated with the 'Industrial Deep Learning' research group, which focuses on advancing deep learning methodologies tailored for industrial transformation, including intelligent planning, control, and quality assurance in manufacturing and assembly processes.

Wolfgang MalzkornView profile

Professor

• Banking
• Finance
• Risk Management
• +10 more

Wolfgang Malzkorn is a Professor at Fachhochschule der Wirtschaft (FHDW) , serving as Vice President for Organization and Didactic Development since October 2024. His academic and professional journey spans roles as a strategic consultant in financial risk management at Oliver Wyman, KPMG, and McKinsey & Company (2000–2014) before transitioning to academia. Education : Master of Arts in Mathematics, Philosophy, and Logic from the University of Bonn (1992); Ph.D. (Dr. phil.) from the University of Bonn (1998). Research Areas : Focus on credit risk modeling , Economic cycles , text sentiment analysis , social ontology of companies , ESG risks , and financial ethics . Leadership : Vice President for Organization and Didactic Development at FHDW since October 2024. Publications : Author of works on credit risk, financial ethics, and mathematical logic, including contributions to die bank journal (2019–2020) and books on Kantian cosmology and formal sciences.

• Electrical Engineering
• Renewable Energy Systems
• Control Systems
• +3 more

M. Elmorshedy is an academic researcher specializing in Electrical Engineering and Renewable Energy Systems , with a focus on Control Systems , Power Electronics , and Hybrid Energy Optimization . His work often involves collaborations with co-authors like D. Almakhles, Kotb M. Kotb, and S. M. Allam. Elmorshedy's research spans applications in Photovoltaic Systems , Wind Energy , and Model Predictive Control for inverters and electrical drives. He has contributed to advancements in Seventeen-Level Switched Capacitor Inverters and Hybrid Renewable Energy Systems for residential and microgrid contexts. Recent publications highlight trends in Power System Optimization , Grid Integration , and Emission Reduction through innovative control strategies. Key collaborations include studies on Linear Induction Motors and PMSG Control under unbalanced conditions.

• High Performance Computing (HPC)
• Computer Architecture
• Energy-efficient Computing
• +5 more

M. Schulz is a prolific researcher in Computer Science and Engineering, with over 350 publications and an h-index of 53. His work focuses on High Performance Computing (HPC), energy efficiency, and performance analysis. His research spans critical areas such as: High Performance Computing Energy-efficient Computing Parallel and Distributed Systems Software Engineering Runtime Optimization Monte Carlo Methods The trends in his publications include predictive modeling for architectural design spaces (2006), energy savings in HPC applications (2009–2016), and innovative tools like IPAS and ARCHER for error detection and runtime optimization. While no explicit scientific awards are mentioned in the scraped text, his work has garnered over 8,991 citations, reflecting significant impact.

• Educational Psychology
• Self-Regulated Learning
• Academic Risk-Taking
• +3 more

Vanessa Hübner is a Scientific Associate at the Chair of Empirical Educational Research within the Faculty of Humanities at Otto-Friedrich University of Bamberg. She holds a PhD in Educational Research (2025) and an M.Sc. in Empirical Educational Research (2021), with a B.A. in Education (2018). Her work focuses on academic risk-taking, self-regulated learning, and error beliefs in educational contexts. PhD: "Between mistakes and learning potential: An investigation of individual and contextual predictors of students' academic risk-taking" (2025) M.Sc.: Empirical Educational Research (2021) B.A.: Education (2018) Her research explores how error beliefs, teaching quality, and motivational factors influence academic risk-taking in higher education. Publications highlight disparities in risk-taking across gender and socioeconomic groups, the role of webtools in self-regulated learning, and psychometric assessments of risk-taking scales. Current projects include analyzing extracurricular tutoring effects and developing tools like the Open-vhb e-learning course for self-regulated learning. She collaborates with Prof. Dr. Maximilian Pfost and contributes to the University Teaching journal.

Felix BlankenburgView profile

Professor

• Cognitive Neuroscience
• Neuroimaging
• Somatosensory Processing
• +5 more

Felix Blankenburg is Professor and Chair of the Neurocomputation and Neuroimaging Unit at the Department of Education and Psychology, Freie Universität Berlin. His research investigates neural mechanisms of perception, cognition, and action using multimodal neuroimaging (fMRI, EEG) and neurostimulation (TMS, tDCS), with emphasis on somatosensory processing, sensory attenuation, and perceptual decision-making within virtual reality environments. His primary research focuses on cognitive neuroscience and neuroimaging, exploring how the brain processes tactile information, integrates multisensory cues, and generates conscious perception. Key areas include Bayesian models of sensory prediction, neural correlates of working memory for tactile stimuli, cross-modal influences (e.g., language on touch), and computational mechanisms of perceptual decisions. His work bridges experimental neuroscience with theoretical frameworks like active inference. Recent publications (2023-2025) reveal strong trends in multimodal integration methodologies, predictive coding applications in somatosensation, and clinical translation through neurostimulation. He pioneers concurrent TMS-fMRI and virtual reality paradigms to probe causal brain mechanisms, with growing emphasis on therapeutic applications for mental health as evidenced by the PREACT research unit. Professor Blankenburg supervises PhD students including: Gianluigi Giannini Guido Caccialupi Jona Förster He serves as principal investigator for the DFG-funded Research Unit 5187 PREACT, developing neuroimaging biomarkers to predict psychotherapy response in non-respondent patients through fMRI-based signatures. The Neurocomputation and Neuroimaging Unit, under his leadership, integrates computational modeling with empirical neuroscience. The team comprises postdocs, PhD students, and research staff collaborating on projects spanning tactile perception, decision neuroscience, and clinical applications of neuroimaging.

Laura NohrView profile

Researcher

• Transcultural Clinical Psychology
• Psychotraumatology
• Refugee Mental Health
• +6 more

Laura Nohr is a Researcher at the Department of Clinical Psychological Intervention within the Faculty of Education and Psychology at Free University of Berlin. Her work focuses on transcultural clinical psychology , refugee mental health , and digital health interventions , with particular attention to social inequality , women's health , and depression . Education: 2021: PhD in Clinical Psychology, University of Münster 2020: Approbation as Licensed Psychotherapist (CBT) 2015: Master of Science in Psychology, Humboldt-Universität zu Berlin 2012: Bachelor of Science in Psychology, Humboldt-Universität zu Berlin Research Interests: Laura’s research explores transcultural explanatory models of mental disorders , digital interventions for refugees , and social determinants of mental health . Her work addresses stigma , help-seeking behaviors , and cross-cultural adaptation of psychological tools. Recent articles emphasize Internet-based interventions for emotional disorders among Arabic- and Farsi-speaking refugees, role conflicts in interpreter mental health, and transcultural comparisons of causal attributions for mental illness. She also investigates microaggressions in therapy and ambulatory care disparities for migrant populations. Scientific Awards: 2023: Research Promotion Award (Elfriede Dietrich Foundation, 6,000 €) 2023: Scholarship to attend DeGPT Annual Meeting 2022: Scholarship for DeGPT Summer School 2017: DGPPN Poster Award Labs & Collaborations: Laura contributes to the I-REACH consortium (Internet-based Refugee Mental Health Care) and collaborates with institutions in Germany, Cuba, and Peru. Her research integrates transcultural psychology with digital health innovations .

Alexandra TsimpidiView profile

Researcher

• Atmospheric Chemistry
• Aerosol Science
• Air Quality
• +4 more

Dr. Alexandra Tsimpidi is a Senior Scientist and Head of the "Atmospheric Aerosol Modelling" group at the Institute of Climate and Energy Systems (ICE) , Forschungszentrum Jülich. Her research focuses on improving atmospheric aerosol representation in chemistry climate models to predict global air quality and climate impacts, particularly through advancing organic aerosol formation descriptions.

Gernot SchullerusView profile

Professor

• Electrical Drives
• Power Electronics
• Motion Control
• +7 more

Prof. Dr.-Ing. Gernot Schullerus is a W2-Professor for Electrical Drives at the Faculty of Engineering, Reutlingen University, where he has been employed since September 2010. He currently serves as the Collegial Head of the Electronics & Drives Research and Teaching Center since March 2023 and will assume the scientific leadership of the Reutlingen Research Institute starting September 2024. His office is located in Building 2, Room 018, and his office hours are Tuesdays from 8:00 to 9:00 AM. Professor Schullerus completed his doctorate at the University of Karlsruhe (TH) in 2004. His academic and professional journey includes: 2004: PhD (Dr.-Ing.) at University of Karlsruhe (TH) January 2004 - August 2010: Development Engineer, Electronics Development, SEW-EURODRIVE, Bruchsal September 2010 - present: W2-Professor for Electrical Drives, Faculty of Engineering, Reutlingen University March 2023 - present: Collegial Head of the Electronics & Drives Research and Teaching Center September 2024 - present: Collegial Scientific Head of the Reutlingen Research Institute Professor Schullerus specializes in electrical drive technology and power electronics. His research focuses on new motor concepts, particularly harmonically excited synchronous machines, power electronic components for drive applications, and inverters using gallium nitride power switches. He investigates sensorless operation of switched reluctance machines and energy-efficient control of rotating field machines. His work extends to condition monitoring and predictive maintenance in drive systems, with recent emphasis on green hydrogen production and flexible hydrogen logistics. The Reutlingen Research Institute under his leadership explores decentralized energy systems and energy efficiency. His recent publications demonstrate a strong focus on power electronics innovations, particularly with gallium nitride technology, and applications in energy systems. Key trends include advanced modulation techniques for power converters, optimization of PCB layouts for high-power applications, and the integration of renewable energy with hydrogen production systems. His work bridges theoretical control concepts with practical industrial applications, particularly in drive systems and energy conversion. Professor Schullerus leads the Electronics & Drives research group at Reutlingen University, which is actively involved in multiple research projects. Current projects include CleMoSy (Clean Motor Supply), Trusted Handling (Predictive Maintenance for roller chains), H2FLEX (Flexible Interoperable Hydrogen Logistics), and the Model Region Green Hydrogen: Lighthouse H2-Grid (Networking decentralized hydrogen production and consumption). Previously, he has led projects on modular scalable power electronics based on gallium nitride components, dynamically energy-efficient operation of induction machines, and model-based hierarchical condition monitoring. His laboratory facilities include the Electrical Drives Laboratory and the Laboratory for Control and Drive Technology, where his team focuses on optimal control/regulation and diagnosis of drive and grid converters. The research environment supports both fundamental investigations and applied industrial projects, with strong connections to industry partners in the drive technology sector.

Lars PenterView profile

Researcher

• Machine Tools
• Adaptive Control Systems
• Industrial Internet of Things
• +6 more

Lars Penter is a Senior Engineer Research and Education at the Chair of Machine Tool Development and Adaptive Controls , Dresden University of Technology, and a Research Associate at the Fraunhofer Institute for Machine Tools and Control Technology (Department of IIoT Controls and Technical Cybernetics). His career spans roles since 2018 in machine tool development, adaptive control systems, and industrial IoT applications. Current affiliations: Dresden University of Technology, Fraunhofer Institute Research focus: Digital twin technology, friction modeling, damping analysis, sheet metal forming, process-machine interaction Scientific contributions include 15 recent articles (2025–2022) covering AI-driven deep drawing process prediction, digital twin applications in forming, multifractal surface analysis, and composite material damping. Publications appear in journals like At-Automatisierungstechnik and Robotics and Computer-Integrated Manufacturing . He contributes to industrial simulation and thermo-elastic analysis , addressing challenges in forming press behavior, elastic tool deformations, and chatter detection. His work bridges mechanical engineering with industrial IoT and AI-driven optimization .

Jens MüllerView profile

Researcher

• Mechatronics
• Machine Tools
• Control Systems
• +7 more

Jens Müller is a Researcher at the Technische Universität Dresden in the Control and Feedback Control Systems department. His research spans interdisciplinary domains including Mechatronics , Machine Tools , Thermal Analysis , and Biomedical Engineering . Key contributions to thermo-elastic correction and motion accuracy optimization in industrial systems. Extensive publications in both mechanical engineering (e.g., Annals of Production Engineering ) and biomedical fields (e.g., Neurosurgery ). Recent work (2025–2020) highlights a shift toward epileptic seizure prediction and neurosurgical imaging , alongside continued focus on machine tool dynamics and compliant joint design . No explicit student or award data provided.

André HardtmannView profile

Researcher

• Forming Technology
• Finite Element Analysis
• Hybrid Composites
• +4 more

André Hardtmann is a Research Associate and Senior Engineer at the Institute of Forming Technology within Dresden University of Technology . His work focuses on advanced forming process modeling, machine-process interactions, and hybrid composite development.

Abdullah MakkehView profile

Researcher

• Information Theory
• Computational Neuroscience
• Artificial Neural Networks
• +4 more

Dr. Abdullah Makkeh is a Senior Scientist at the University of Göttingen's Department of Data-driven Analysis of Biological Networks, headed by Michael Wibral, and a Guest Scientist at the Max Planck Institute for Dynamics and Self-Organization in Göttingen under Viola Priesemann's Complex Systems Theory group. Previously, he served as a Postdoc at the University of Tartu in both Theoretical Computer Science (Dirk Oliver Theis) and Computational Neuroscience (Raul Vicente) groups. Education: PhD in Informatics (2018, University of Tartu, Supervisor: Dirk Oliver Theis) MSc in Mathematics (2013, Lebanese University, Supervisor: Bassam Mourad) BSc in Mathematics (2011, Lebanese University) His research focuses on extending information theory to study computation in intelligent systems like the brain and artificial neural networks (ANNs). He has developed interpretable information-theoretic learning rules for ANNs (Makkeh et al., 2025) and analyzed reinforcement learning agents to reveal emergent computation mechanisms (Engel et al., 2022; Ehrlich et al., 2023). Current work applies these methods to enhance large language model (LLM) interpretability. His publications span information-theoretic frameworks, predictive coding, and neural oscillation analysis, with a 2024 Royal Netherlands Academy of Arts and Sciences (KNAW) recognition. He co-teaches courses in Bayesian Inference, Information Theory, and Discrete Mathematics, and organizes annual workshops on information theory in computational neuroscience. Scientific Awards: Royal Netherlands Academy of Arts and Sciences (KNAW) (2024) Dr. Makkeh contributes to open-source research tools via GitHub and collaborates with interdisciplinary teams across neuroscience, computer science, and mathematics. His work bridges theoretical foundations with applied machine learning through rigorous mathematical frameworks.

Dirk KalmringView profile

Professor

• Business Informatics
• Business Process Management
• Decision Analytics
• +5 more

Prof. Dr. Dirk Kalmring is a Professor of Business Informatics at the Faculty of Business Studies, Hochschule Düsseldorf (University of Applied Sciences Düsseldorf). His academic work focuses on business process management, decision analytics, and IT applications in business contexts. He teaches courses including Business Process Management, Business Informatics, and IT Applications, and leads project modules on topics like supply chain analysis and IT-supported business methods. Prof. Kalmring's research spans several key areas in business informatics: business process management (BPM), decision analytics, process mining, robotic process automation (RPA), information security, and knowledge management. His work integrates theoretical frameworks with practical applications, particularly in modeling business processes using standards like BPMN and DMN. He has developed strong industry partnerships with companies such as UiPath, Signavio, and Fluxicon to enhance practical learning experiences for students. His recent publications reflect trends toward digital transformation in business processes, with increasing focus on decision automation, process optimization through RPA, and security in digital business environments. The research shows a progression from foundational work in knowledge management to current applications in process mining and decision analytics. Scientific awards include nominations for 'Professor des Jahres' (Professor of the Year) in 2023, 2018, and 2015, recognizing his excellence in practical-oriented teaching that prepares students for professional careers. Prof. Kalmring actively engages students through project modules where they work with real industry partners like Tsurumi Europe GmbH and GBTEC Software AG. These projects focus on practical applications of business process modeling, supply chain optimization, and digital transformation. His teaching approach emphasizes bridging academic theory with industry practice. He leads a working group that collaborates with various industry partners including UiPath, Signavio, Fluxicon, and MID GmbH on research and teaching initiatives related to business process management, decision modeling, and enterprise architecture.

Torsten LilgeView profile

Researcher

• Nonlinear Systems
• Discrete-Time Control Systems
• Observer Design
• +3 more

Dr.-Ing. Torsten Lilge is a Senior Researcher/Lecturer at the Institute of Automatic Control Engineering , part of Leibniz University Hannover 's Faculty of Electrical Engineering and Computer Science. He has been a core member of the university since 1991 and held senior roles including Deputy Faculty Information Officer and Mentor at the Mechatronics Center Hannover. Research focus on nonlinear systems and discrete-time control Specialized in observer design for systems with unknown inputs and data fusion applications Key contributor to human-robot interaction and mechatronic stabilization solutions Publications show expertise in: 2025 : Global reactive obstacle avoidance using circular fields 2024 : Robust predictive control for energy systems 2023 : Real-time sampling techniques and educational robotics applications 2021-2019 : Multi-agent trajectory planning and antagonistic impedance control His work bridges theoretical control systems with practical implementations in robotics, with a particular emphasis on nonlinear observer design and dynamic modeling for real-world applications.