جست‌وجوی استاد و پژوهشگر

Ralf Romeikeمشاهده پروفایل

استاد

• Artificial Intelligence Education
• Computer Science Education
• Data Literacy
• +۷ مورد دیگر

Prof. Dr. Ralf Romeike is a Professor of Computer Science Education specializing in Didactics of Computer Science at the Free University of Berlin, within the Department of Mathematics and Computer Science. His office is located at Königin-Luise-Str. 24-26, Room 019, 14195 Berlin, where he holds consultation hours every Wednesday from 12-1 p.m. by appointment. Romeike's research focuses on innovative approaches to computer science education, with particular emphasis on artificial intelligence education, data literacy development, and constructionist learning methodologies. His work bridges theoretical educational frameworks with practical classroom applications, especially in teacher training contexts. He has developed numerous educational frameworks including AI-PACK, which adapts the DPACK model for AI-related digital competencies for teachers. His publication record shows a clear trend toward AI education in K-12 settings, teacher professional development in AI and data literacy, and the integration of constructionist principles in computing education. The research spans multiple educational contexts from primary through higher education, with particular attention to how non-computer science students and teachers can develop meaningful AI competencies. His scientific contributions include significant work on: AI literacy frameworks for school education Data literacy as a component of AI education Constructionist approaches to AI/ML learning Debugging education methodologies Agile methods in computer science education Romeike leads or participates in multiple significant research projects including ENKIS (Establishment of sustainable AI-related study programs), Digi4All (Interdisciplinary Digital Education), AMI (Agile Methods for Computer Science Education), and TrainDL (Teacher training for Data Literacy & Computer Science competences), demonstrating his leadership in shaping computer science education policy and practice in Germany and internationally.

Mathias Staudiglمشاهده پروفایل

استاد

• Optimization Theory
• Operations Research
• Machine Learning
• +۴ مورد دیگر

Prof. Dr. Mathias Staudigl serves as Professor and Chair in Mathematical Optimization within the School of Business Informatics and Mathematics, leading research at the intersection of theoretical and applied optimization. His research program emphasizes fundamental mathematical optimization theory, game theory, and complexity analysis, alongside practical algorithm design for societal challenges—particularly in energy systems control and AI optimization methods. The group maintains active collaborations with the Chair of Scientific Computing (Prof. Simone Göttlich) and the Chair in Probability Theory (Prof. Leif Döring) for integrated research and teaching initiatives. Teaching responsibilities include specialized courses in optimization and artificial intelligence, directly contributing to the academic curriculum of the school through both theoretical instruction and real-world application development.

• Mathematical Data Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Leonardo Galli is a Researcher at the Mathematical Institute of Ludwig-Maximilians-University Munich within the Faculty of Mathematics, Computer Science and Statistics. He is actively affiliated with the Mathematical Data Science and Artificial Intelligence working group, contributing to cutting-edge interdisciplinary research at the intersection of mathematics and computational intelligence. His research program focuses on foundational aspects of data-driven methodologies, with core expertise in: Mathematical frameworks for artificial intelligence Statistical learning theory Algorithmic optimization in high-dimensional spaces Probabilistic modeling for complex systems Dr. Galli's work bridges theoretical mathematics with practical AI applications, developing rigorous mathematical tools for data science challenges. He maintains his research operations at the Mathematical Institute (Theresienstr. 39, Munich), where he can be contacted via email galli@math.lmu.de or telephone +49 (0)89 2180-4613 from office 421.

Marko Bachlمشاهده پروفایل

استادیار

• Computational Social Science
• Political Communication
• Online Behavior
• +۴ مورد دیگر

Marko Bachl serves as an Assistant Professor at the Institute of Sociology within the Department of Political and Social Sciences at Freie Universität Berlin. Based at Garystr. 55, Room 274, Berlin, he co-directs the DiMES (Digital Methods Innovation Lab) initiative, focusing on advancing computational approaches in social science research. His research centers on computational social science with emphases on political communication dynamics, gendered online interactions, and health information behavior. Key investigations include the impact of sexism on women's political participation, trust allocation in digital news ecosystems, and methodological innovations in computational text analysis. His work bridges theoretical communication frameworks with cutting-edge digital methods to address contemporary societal challenges. Analysis of his recent publications reveals a consistent integration of computational techniques with substantive social science questions, particularly regarding social media's role in political engagement and public health communication. His scholarship demonstrates methodological rigor through preregistered experiments, large-scale behavioral data analysis, and critical evaluation of measurement error in digital contexts. As DiMES co-director, Bachl leads initiatives developing innovative research tools like the MediaLiveTracker for real-time response measurement and promotes interdisciplinary collaboration through workshops on computational methods.

Andreas Heuerمشاهده پروفایل

استاد

• Theoretical Physical Chemistry
• Polymer Electrolytes
• Ionic Liquids
• +۷ مورد دیگر

Prof. Dr. Andreas Heuer is a leading theoretical physical chemist at the University of Münster , holding the W3 Professorship for "Theory of Complex Systems" since 2012. As a full member of the Helmholtz Institute Münster (HIMS) and former Dean of Chemistry and Pharmacy (2014-2016), he bridges fundamental research and applied energy storage solutions. Key research areas include polymer electrolytes, glass-forming liquids, lipid membranes, and ion transport in batteries Develops multiscale simulation frameworks connecting molecular dynamics to macroscopic behavior Active in science communication and teaching excellence (2014 Goldener Brendel award) Research Themes: His work on polymer electrolytes reveals lithium ion transport mechanisms through segmental dynamics (τ2 reduction with ionic liquid additives). In lipid membrane studies , he demonstrates cholesterol's structural role in raft formation using coarse-grained models. His glass transition analysis via potential energy landscapes explains dynamic heterogeneities and fragility-to-strong crossovers. Scientific Leadership: • Coordinates BATTERY 2030+ European roadmap for next-gen battery research • Chairs multiple supervisory boards and commissions • Develops reactive molecular dynamics methodology ( rs@md ) Awards & Collaborations: Feodor-Lynen Stipend (post-PhD international phase) Stiftung des Deutschen Volkes member (early career recognition) Collaborates with experimental groups on SEI formation, membrane proteins, and organic electronics

• Machine Learning Theory
• Game Theory
• Information Theory
• +۹ مورد دیگر

Wouter M. Koolen is a Professor of Mathematical Machine Learning at the University of Twente and a Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. Appointed to his professorship on June 1, 2022, he delivers his expertise across both institutions with offices in Enschede and Amsterdam. He is actively engaged in academic leadership through his organization of the Machine Learning Theory Research Semester Programme at CWI in Spring 2023 and serves as an ELLIS Scholar since December 2020. Dr. Koolen's research spans machine learning theory with particular focus on pure exploration in multi-armed bandit models , game tree search algorithms , and provably accelerated learning in both statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His work bridges theoretical foundations with practical applications, especially in safe statistical testing using e-values. He maintains active collaborations through INRIA-CWI associate teams 6PAC with Inria Lille and 4TUNE with Inria Paris and Grenoble. His recent publications reveal a strong emphasis on developing theoretically sound methods for statistical inference that maintain validity under optional stopping and continuation, representing a significant shift from traditional p-value based approaches. This work has important implications for fields requiring rigorous statistical guarantees in adaptive experimental settings. NWO VENI grant recipient QUT Vice-Chancellor's postdoctoral research fellowship awardee ELLIS Scholar (elected December 2, 2020) Member of ACM Future of Computing Academy Professor Koolen has supervised numerous PhD students to completion, including Hongwei Wen, Clément Lezane, and Tyron Lardy in 2025, and formerly Rianne de Heide who won the VVSOR Willem R. van Zwet award. He has served on program committees for major conferences including COLT, ICML, and ALT, and actively organizes workshops on cutting-edge topics in machine learning theory. His research group at CWI hosts regular reading groups and seminar series, fostering a vibrant theoretical machine learning community in the Netherlands.

• Philosophy
• Computational Philosophy
• Artificial Intelligence
• +۴ مورد دیگر

Mohammad Yeghaneh Abkenar is a Researcher at the Department of Philosophy, Humboldt University of Berlin, affiliated with the DFG-Emmy-Noether-Research Group "A Sensible World" and the Chair of Social Critique under Prof. Rahel Jaeggi. His work bridges computational methods with philosophical and historical inquiry. Research Assistant in the Department of Philosophy Contributor to interdisciplinary projects combining AI with humanities His research interests span computational philosophy , machine learning applications , and history of science . Recent work includes developing AI models for argumentation mining and digitizing historical scientific texts like Kepler's Astronomia Nova , alongside medical informatics projects for epilepsy prediction using EEG data. Email: yeghanem@hu-berlin.de Publications reveal trends in applying computational tools to both philosophical discourse analysis and historical scientific documents, while also contributing to health monitoring technologies through mathematical modeling.

Jan Schürمشاهده پروفایل

پژوهشگر

• Microwave Engineering
• Terahertz Technology
• Non-Destructive Testing (NDT)
• +۴ مورد دیگر

Jan Schür, Dr.-Ing., is a Senior Research Associate and Academic Director at the Chair of High Frequency Engineering within the Department of Electrical Engineering, Electronics and Information Technology (EEI) at Friedrich-Alexander University Erlangen-Nürnberg (FAU). He leads the research group focused on Non-Destructive Testing (NDT), Material Characterization, and Terahertz (THz) Technology. Chair of High Frequency Engineering Faculty Council Member (Faculty of Engineering) Advisory Board of Regional Computing Center Erlangen (RRZE) Head of THz Laboratory Laboratory Management: Antenna Design, EM Material Characterization, Network & Spectrum Analysis His research spans microwave engineering, THz technology, and non-destructive material evaluation. Key trends in his 2022-2024 publications include 3D-printed microwave components, high-frequency interconnect designs, and THz imaging systems for composite materials and historical document analysis. He has served in leadership roles for international conferences including: Operational Office Chair of German Microwave Conference (GeMiC, 2015) Operational Office Co-Chair of European Microwave Week (EuMW, 2017) Financial Chair of IEEE International Conference on Microwaves for Intelligent Mobility (ICMIM, 2018) Scientific awards include: Best Podium Presentation Award at GeMiC 2008 He contributes to technical committees: "Microwave and Terahertz Techniques" (German Society for NDT) "8.17 THz Systems" (VDE/VDI Measurement & Automation Society)

Lennart Binkowskiمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Quantum Algorithms
• Theoretical Physics
• +۴ مورد دیگر

Lennart Binkowski is a doctoral candidate and scientific staff member at the Institute of Theoretical Physics , part of the Faculty of Mathematics and Physics at Leibniz University Hannover. His research focuses on quantum computing, particularly quantum algorithms and combinatorial optimization. University: Leibniz University Hannover School: Faculty of Mathematics and Physics Department: Institute of Theoretical Physics Email: lennart.binkowski@itp.uni-hannover.de Lennart's research interests span quantum algorithms, quantum walks, and optimization frameworks. His work explores quantum programming languages, Pauli transfer matrices, and hybrid quantum-classical systems. Recent publications highlight advancements in QAOA, quantum permutation generation, and tensor network applications. Lennart's 15 most recent articles focus on quantum computing trends, including algorithm design, constraint handling, and tensor structures. No scientific awards are mentioned in the provided data. Contact details: Schneiderberg 32, 30167 Hanover, Germany (Building 3702, Room 013).

• Computational Optimization
• Mathematical Optimization
• Numerical Analysis
• +۴ مورد دیگر

Alberto De Marchi is a Research Associate at the Institute of Applied Mathematics and Scientific Computing at Universität der Bundeswehr München (UniBw M) in Germany. He holds a doctoral degree (Dr.rer.nat.) in Applied Mathematics from UniBw M (2021), an M.Sc. in Mechatronics Engineering (2016), and a B.Sc. in Industrial Engineering (2014) from the University of Trento (UniTn) in Italy. In Fall 2022, he was a Visiting Research Associate with Ryan Loxton at Curtin University, Australia. Education Dr.rer.nat., Applied Mathematics, UniBw M (2021) M.Sc., Mechatronics Engineering, UniTn (2016) B.Sc., Industrial Engineering, UniTn (2014) His research spans computational optimization, mathematical modeling, numerical analysis, and control systems, with a focus on developing robust numerical optimization tools. Recent work includes applications in IoT digital twins, blockchain-based anonymity frameworks, and hybrid optimal control methods. His 2025–2023 publications emphasize nonlinear and mixed-integer optimization techniques, constrained composite optimization, and advanced algorithms for control systems. These works explore topics like augmented Lagrangian methods, proximal gradient approaches, collision avoidance, and blockchain ethics. Scientific Awards COAP 2022 Best Paper Prize Alberto collaborates internationally and is intellectually curious about interdisciplinary topics, including the philosophy of mind and literature.

• Computational Aesthetics
• Architectural Design
• Digital Fabrication
• +۴ مورد دیگر

Prof. Mirco Becker is a Professor at the Leibniz University Hannover, holding roles as Executive Director of the Institute for Design and Presentation and leading the Department of Digital Methods in Architecture within the Faculty of Architecture and Landscape Sciences. His expertise focuses on computational aesthetics, digital fabrication, and robotic systems in architectural design. He manages the 3D printing facilities for the faculty and serves as the BAföG representative for Architecture students. His research integrates artificial intelligence, virtual reality, and advanced materials to explore innovative architectural solutions. Becker’s research interests emphasize computational methods for evaluating architectural aesthetics, immersive design environments, and modular robotic construction systems. His work bridges theoretical frameworks with practical applications, such as using machine learning to quantify aesthetic preferences and developing materials like felt-concrete composites. He has contributed to projects like 'VRoxel' (an immersive design tool) and 'Project DisCo' (VR-based modular block choreography). His publications highlight themes such as algorithmic aesthetics, robotic fabrication, and sustainable materials, reflecting a commitment to advancing digital tools in architecture. He actively participates in academic leadership roles, overseeing institutional facilities and student support systems.

• Machine Learning
• Artificial Intelligence
• Stochastic Games
• +۵ مورد دیگر

Xian Chen is an active academic researcher with an extensive publication record spanning over 30 years from 1994 to 2025. Their research demonstrates significant contributions across multiple disciplines within computer science, engineering, and applied mathematics. The publication pattern indicates sustained scholarly activity at a high level, with numerous papers in top-tier journals and conferences. Chen's research interests span a remarkably diverse range of fields, with particular emphasis on machine learning, artificial intelligence, stochastic processes, and their applications across various domains. Their work bridges theoretical foundations with practical applications, as evidenced by publications in both theoretical journals like SIAM Journal on Control and Optimization and applied venues like IEEE Access . The research portfolio shows evolution from early work in parallel computing and wireless networks toward contemporary AI and machine learning applications. The publication record reveals significant trends in Chen's research trajectory. Early work focused on parallel computing, wireless networks, and database systems. Over time, there's a clear shift toward machine learning applications, optimization techniques, and interdisciplinary work connecting computer science with fields like biomedical engineering, renewable energy, and education technology. Recent publications (2024-2025) show strong focus on large language models, federated learning, hate speech detection, and educational AI applications, reflecting current trends in artificial intelligence research. The consistent output across multiple domains suggests a highly collaborative research approach with numerous co-authors across different institutions. Chen has established productive collaborations with researchers worldwide, as evidenced by the diverse author lists across publications. The research demonstrates both theoretical depth in areas like stochastic games and Markov decision processes, as well as practical applications in fields ranging from medical diagnostics to renewable energy systems. The interdisciplinary nature of the work indicates versatility in applying computational methods to solve domain-specific problems.

• Programming Education
• Novice Programming
• Educational Tools Development
• +۴ مورد دیگر

Neil Christopher Charles Brown is an Associate Professor in the School of Computing at the University of Kent, where he has been actively contributing to computing education research since completing his PhD in 2011. His work bridges theoretical computer science concepts with practical educational applications, focusing on how novices learn to program and how educational tools can better support this process. Dr. Brown's research interests center on programming education, with specific expertise in novice programming behavior, educational tool development, and computing education research methodologies. His early work focused on programming language theory and concurrency models, but he has increasingly specialized in computing education research, particularly examining how students interact with programming languages and environments. He has made significant contributions to understanding novice Java programming mistakes through large-scale data analysis and has developed frame-based programming environments to ease the transition from block-based to text-based programming. His recent publications (2023-2025) show a strong focus on AI applications in programming education, registered reports methodology in computing education research, and the development of tools like Strype that support novice programmers. Brown has published extensively in top venues including SIGCSE, ICER, and ACM Transactions on Computing Education, often collaborating with Michael Kölling on projects related to BlueJ and educational programming environments. Among his notable contributions is the Blackbox data repository project, which has collected millions of student programming attempts to analyze learning patterns. His 2018 paper 'Ten quick tips for teaching programming' in PLoS Computational Biology has been widely influential in the computing education community. Dr. Brown has served in leadership roles for major computing education conferences, including co-organizing ICER 2025 and contributing to SIGCSE workshops on research methodologies. His work demonstrates a commitment to improving both the science and practice of computing education through rigorous research and practical tool development.

• Machine Learning
• Optimization Algorithms
• Data Mining
• +۳ مورد دیگر

Václav Snásel is a Professor at the Department of Informatics, VSB - Technical University of Ostrava, Czech Republic. He holds a PhD from Masaryk University (Brno, Czech Republic). His research focuses on optimization algorithms, machine learning, metaheuristics, data mining, and their applications in engineering and computational intelligence. Key research interests include developing novel metaheuristic algorithms (e.g., Walrus Optimizer, Artificial Protozoa Optimizer), optimization frameworks for engineering problems, and applications in wireless sensor networks, power systems, and medical diagnostics. He also explores computational methods for data analysis, including graph-based techniques and surrogate-assisted evolutionary algorithms. His recent work emphasizes multi-objective optimization, algorithm design for high-dimensional problems, and interdisciplinary applications in agriculture, energy systems, and bioinformatics. Collaborations span institutions globally, with frequent co-authorship on topics like swarm intelligence and evolutionary computation.

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +۴ مورد دیگر

Ali Zarezade is a researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on interdisciplinary research spanning algorithms, machine learning, and social computing. His work integrates theoretical foundations with practical applications in cyber-physical systems, distributed networks, and human-centric AI. Key research areas include human-in-the-loop machine learning, optimal control strategies for social networks, and spatio-temporal modeling of social media activity. His methods often combine stochastic control theory, sparse regression techniques, and online algorithm design to address challenges in education technology, security, and information diffusion. Prior contributions involve developing algorithms like RedQueen and Cheshire for optimizing social network activity, probabilistic models for hyperspectral unmixing, and visual tracking systems resilient to occlusions. His research bridges computational theory with real-world systems, emphasizing scalable solutions for distributed and networked environments.