جست‌وجوی استاد و پژوهشگر

Matthias Thieleمشاهده پروفایل

پژوهشگر

• Electromigration-aware Integrated Circuit Design
• Photonic Integrated Circuits
• Mechatronic Systems Reliability
• +۴ مورد دیگر

Matthias Thiele is a Research Associate at the Institute of Precision Engineering and Electronic Design, Dresden University of Technology. His work focuses on electromigration-aware integrated circuit design, photonic integrated circuits, and mechatronic systems reliability. He leads projects funded by the DFG (Scalable Ising Machines in Silicon) and BMBF (Robust Mechatronic Systems Simulation). Research Associate, Dresden University of Technology DFG-funded project on CMOS-based photonic Ising machines BMBF-funded project on mechatronic system reliability His research spans electromigration analysis, finite element method applications, wear and aging effects, 3D-IC integration, and layout design optimization. Publications emphasize reliability challenges in future technologies and security implications of aging effects. Current projects analyze electromigration using advanced simulation techniques and explore photonic circuits for scalable Ising machines. Earlier work includes wear simulation tools and multiobjective optimization for 3D-ICs. Contact: matthias.thiele@tu-dresden.de

Emanuel Kitzelmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +۴ مورد دیگر

Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.

• Programming Languages
• Compilers
• Formal Verification
• +۷ مورد دیگر

Clément Pit-Claudel is an assistant professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the SYSTEMF lab in the School of Computer and Communication Sciences. His research focuses on programming languages, compilers, and formal verification, with broader interests spanning systems engineering, hardware design languages, security, performance engineering, and type theory. Education: Undergraduate studies at École Polytechnique PhD at MIT with Adam Chlipala, specializing in proof-producing compilers Pit-Claudel's research program is organized around three main axes: extensible compilation (teaching compilers domain-specific optimization tricks), hardware design languages and verification (creating ways to describe and verify hardware), and tooling for proof assistants (to support verification efforts and lower entry barriers). His work bridges theoretical foundations with practical applications, as evidenced by algorithms from his Elk project being merged into V8 (and hence Chrome and Node.js). Scientific Awards: Distinguished artifact, Untangling Mechanized Proofs, ACM SIGPLAN International Conference on Software Language Engineering (2020) William A. Martin Memorial Thesis Award for Outstanding Thesis in CS, MIT (2016) Frederick C. Hennie III Teaching Award in Recognition of Outstanding Contributions to Departmental Teaching, MIT (2016) Pit-Claudel has extensive experience mentoring students through MIT's Undergraduate Research Opportunities Program and currently teaches Software Construction to approximately 400 undergraduate students and Interactive Theorem Proving at the graduate level. He's deeply committed to educational excellence, having developed innovative teaching methods including continuous assessment through oral examinations and designing assignments that lead students to build concrete artifacts they can be proud of. The SYSTEMF lab, created in January 2023, focuses on building "small, fast, and completely verified components for critical systems, at reasonable cost." The lab's philosophy of "full assurance, without compromise" combines machine-checked proofs of correctness, hardware-software co-design, low-level compiler engineering, and new tools for interactive theorem proving.

Sebastian Otteمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Recurrent Neural Networks
• +۱۲ مورد دیگر

Sebastian Otte is a Professor at the Institute for Robotics and Cognitive Systems at the University of Lübeck, where he leads the Adaptive AI research group. Prior to this, he was a postdoctoral researcher and substitute professor at the University of Tübingen, contributing significantly to the Cognitive Modeling and Distributed Intelligence groups. University of Lübeck, Professor (since 2023) University of Tübingen, Postdoc and Substitute Professor (2016–2023) Centrum Wiskunde & Informatica (CWI), Humboldt Fellow (2022–2023) His research focuses on recurrent and spiking neural networks, bio-inspired computing, efficient learning, and adaptive AI systems. He explores how neural models can perform online learning, handle multiple time scales, and solve complex cognitive tasks such as binding, prediction, and motor control. His work bridges machine learning with cognitive science and robotics. The recent publications show a strong trend toward physics-informed neural networks, finite volume methods for PDE modeling, and explainable AI via counterfactual reasoning. His work integrates deep learning with scientific computing, emphasizing robust, interpretable, and efficient models for real-world applications. Scientific awards include: Best Paper Award at ICANN 2019 Humboldt Research Fellowship Editor's Highlight in Water Resources Research He has supervised over 70 bachelor’s and master’s theses and actively mentors students in areas such as spiking neural networks, reservoir computing, and robotics. His teaching includes core computer science and advanced neural network courses. He has been involved in research projects with industry partners like Daimler AG and Mercedes-Benz AG. Otte leads the Adaptive AI research group, which focuses on developing next-generation AI systems that learn efficiently, adapt dynamically, and model complex cognitive and physical processes using biologically inspired architectures.

Michael Kampمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Deep Learning Theory
• +۵ مورد دیگر

Michael Kamp is an Associate Professor for Machine Learning and Artificial Intelligence at TU Dortmund University and a faculty member of the Lamarr Institute for Machine Learning and Artificial Intelligence. He maintains affiliations with the Institute for Artificial Intelligence in Medicine (IKIM) at University Medicine Essen and collaborates with institutions in the UA Ruhr consortium. Formerly led Trustworthy Machine Learning group at IKIM Postdoctoral researcher at CISPA Helmholtz Center (2021) and Monash University (2019-2021) Doctorate from University of Bonn His research focuses on trustworthy machine learning through three pillars: deep learning theory (loss surfaces, flatness, generalization), causal representation learning for explainability, and federated learning for privacy-preserving decentralized systems. Applications span healthcare, autonomous driving, and cybersecurity. Recent publications at AAAI/ICLR/NeurIPS explore: federated optimization algorithms for non-IID data, privacy-preserving causal discovery, and flatness-aware regularization techniques. His work combines theoretical rigor with real-world deployment challenges. NeurIPS 2021 Outstanding Reviewer Award ICLR 2021 Reviewer Award Best Paper at PDFL'20 Workshop NeurIPS 2019 Best Reviewer Award ICML 2019 Reviewer Award He contributes to the editorial board of Springer's Machine Learning journal and serves in the ELLIS society. His group's tools address early disease detection, 3D medical shape analysis, and domain transfer challenges while maintaining ethical constraints.

Tino Waltherمشاهده پروفایل

استاد

• Construction Management
• Project Control
• BIM (Building Information Modeling)
• +۴ مورد دیگر

Prof. Dr.-Ing. Tino Walther is currently serving as Professor of Construction Management and Project Control at the Technical University of Mittelhessen (THM) since October 2023. Previously, he held roles at STRABAG AG and Bauhaus University Weimar where he completed his doctoral studies (2017-2021). His career spans academic research, industry leadership, and technical project management in civil and bridge construction. Education includes an M.Sc. in Management Studies (Construction/Real Estate/Infrastructure) from Bauhaus University Weimar (2009-2014), dual training as a civil and road construction worker with integrated studies (2007-2009), and civil engineering studies at the University of Kassel (2008-2009). Research focuses on digital transformation in construction , including BIM applications, automated data acquisition, 5D process management, and optimizing construction performance through algorithmic frameworks. His work emphasizes bridging gaps between theoretical models and practical implementation in infrastructure projects. Recent publications (2019-2024) highlight advancements in automated construction monitoring, VR-based training systems, and path-planning algorithms for heavy machinery. His methodologies integrate convex hull algorithms, point-cloud comparisons, and image recognition to improve site control and project outcomes. No scientific awards were explicitly listed in the text. His professional activities include leading construction processes departments and advising on linear infrastructure projects. He maintains active engagement with industry through collaborations like the IABSE Congress and ECPPM conferences.

Jürgen Scheibleمشاهده پروفایل

استاد

• Electronic Design Automation
• Analog Integrated Circuit Design
• Automation of Circuit and Layout Design
• +۵ مورد دیگر

Jürgen Scheible is a Professor at the Faculty of Technology, Reutlingen University, specializing in Electronic Design Automation (EDA). He leads the Electronics & Drives Lab and the Reutlingen Energy Center (REZ), focusing on analog integrated circuit design automation, machine learning integration, and power electronics methodologies. His career spans academic and industrial roles, including 18 years at Robert Bosch GmbH before joining Reutlingen University in 2010. 1987: Dipl.-Ing. in Electrical Engineering, Karlsruhe Institute of Technology (KIT) 1991: Dr.-Ing. in Electrical Engineering, KIT His research bridges procedural automation with artificial intelligence in analog IC design, addressing challenges in performance optimization, constraint handling, and thermal analysis of bond wires. Key projects include SWARM (self-organizing layout automation) and Bond Calculator (thermoelectrical simulation tool). Recent publications emphasize differentiable surrogate models, reinforcement learning applications, and knowledge-based methodologies in EDA. He received the Südwestmetall Award for Scientific Talent in 1992 and has authored 2+ textbooks on analog layout fundamentals. 2024: Keynote on analog/digital design divergence 2023: Co-authored foundational EDA textbook 2022: Reinforcement learning integration Labs and teams under his leadership conduct cutting-edge research in MEMS sensor parasitics, hybrid layout techniques, and full-custom IC design. His work establishes systematic frameworks for next-generation EDA tools.

Amar Hekaloمشاهده پروفایل

پژوهشگر

• Medical Image Processing
• Natural Language Processing (NLP)
• Information Extraction
• +۶ مورد دیگر

Amar Hekalo is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems (1992-2025) at the University of Würzburg , within the Faculty of Mathematics and Computer Science . Since November 2018, he has collaborated with the University Hospital of Würzburg to enhance medical documentation through speech/image processing , deep learning , and rule-based methods . Education: Master's degree in Physics His research focuses on automated image processing and NLP , covering information extraction , image classification , segmentation , object recognition , and medical applications . Recent work includes federated learning for private data reconstruction and AI applications in cardiac/radiological imaging. His publications reveal expertise in medical AI , particularly gastroenterology , cardiology , and radiology . He supervises Bachelor's and Master's students in AI applications for healthcare, logistics, and education. He contributes to open-source tools and actively seeks student assistants with Angular/NodeJS skills for radiology-focused documentation and e-learning tools.

Moritz Fleischmannمشاهده پروفایل

استاد

• Data-Driven Design
• Building Information Modelling (BIM)
• Sustainable Architecture
• +۴ مورد دیگر

Prof. Moritz Fleischmann is a faculty member at the Peter Behrens School of Arts (PBSA) , Hochschule Düsseldorf , appointed in 2021. His research focuses on data-driven design , Building Information Modelling (BIM) , and sustainable architectural practices . He serves as a board member of the Center for Digitalization and Digitality (ZDD) and Vice Dean of the Architecture Department. Research: Digitalization in architectural design, BIM, computational modeling, lightweight structures Teaching: Bachelor and Master programs in Architecture, Interior Architecture, and Data Science Awards: Recognized with multiple honors including the Mies van der Rohe Preis and Schindler Award His publications emphasize computational design methods for optimizing spaceframe structures , microclimate design , and material behavior integration . Collaborative works with institutions like ICD Stuttgart and firms such as HENN Architekten highlight his interdisciplinary approach. Scientific Awards: Sonderpreis Jongespreis (2016) Best Paper Award AAG 2014 Mies van der Rohe Preis (2011) Detailpreis (2010) Leichtbaupreis (2010) Arch+ Preis (2006) Schindler Award Anerkennung (2004) Professional Activities: Head of Research at HENN Architekten (2011–2015) Co-founder of DRX (Design Research Exchange) projects Contributor to international conferences and journals Exhibited at venues including the Beijing Biennale and AA End of Year Exhibitions

Dominika Langenmayrمشاهده پروفایل

استاد

• Tax Policy
• Public Finance
• International Economics
• +۳ مورد دیگر

Prof. Dr. Dominika Langenmayr is a Professor of Economics at the Catholic University of Eichstätt-Ingolstadt, specializing in Public Finance. She is also a Guest Professor in the DIBT Doctoral Program at WU Vienna and a Research Fellow at CESifo. PhD in Economics (summa cum laude), Ludwig Maximilian University of Munich (2013) M.Sc. in Economics and B.Sc. in Business Administration, LMU Munich Study visits at University of Oxford and Harvard University Her research focuses on how tax policy affects firms, individuals, and society, with particular emphasis on international taxation, tax havens, and the global taxation of multinational corporations. She advocates for evidence-based tax policy through empirical work and theoretical modeling. Recent publications span topics like international tax policy, VAT simplification, corporate tax avoidance, and fiscal responses to crises. She actively contributes to policy debates and serves on advisory boards, including the German Federal Ministry of Finance. Capital magazine's '40 under 40' (2021) Frankfurter Allgemeine Zeitung's 100 most influential economists in Germany Langenmayr is a co-editor of Perspektiven der Wirtschaftspolitik and associate editor of International Tax and Public Finance (2025–2028). She also holds roles in the International Institute of Public Finance and Tax Foundation Europe.

• Model-Driven Engineering
• Graph Transformation
• Software Engineering
• +۳ مورد دیگر

Jens Kosiol is a Lecturer in Computer Science at University of Marburg, specializing in theoretical aspects of software engineering with particular emphasis on model-driven approaches and formal methods. Dr. Kosiol's research spans several interconnected areas within computer science: Model-Driven Engineering and Software Development Graph Transformation and Rewriting Systems Formal Methods for Software Verification Model Consistency and Synchronization Software Engineering Tools and Frameworks Theoretical Foundations of Model Transformation Analysis of Dr. Kosiol's recent publications (2020-2024) reveals a cohesive research program focused on advancing the theoretical foundations of graph-based model transformation while addressing practical challenges in software engineering. His work consistently explores double-pushout rewriting, model consistency restoration, and constraint-preserving transformations. A significant portion of his research investigates how to maintain consistency across different model views while minimizing information loss during synchronization processes. His publications frequently appear in venues emphasizing rigorous theoretical frameworks with practical applications in model-driven development. Dr. Kosiol has established productive collaborations with researchers including Gabriele Taentzer, Lars Fritsche, and Stefan John, indicating active participation in international research communities focused on formal methods. His work demonstrates both theoretical depth in category theory and graph transformation, while maintaining relevance to practical software engineering challenges through implementations in frameworks like Eclipse Modeling Framework.

Tingting Hanمشاهده پروفایل

مدرس

• Formal Verification
• Probabilistic Systems
• Machine Learning in Verification
• +۳ مورد دیگر

Tingting Han is a Lecturer in Computer Science at Birkbeck, University of London since October 2013. She holds a BSc and MEng in Computer Science from Nanjing University (2003, 2006) and a PhD from RWTH Aachen University and University of Twente (2009), supervised by Joost-Pieter Katoen. Prior to her current role, she was a postdoc at RWTH Aachen University (2009–2011) and a research assistant at the University of Oxford (2011–2013) under the VERIWARE project. Her research focuses on formal verification of probabilistic systems, machine learning applications in verification, and software model checking. She has contributed to advancements in adversarial robustness, code generation, and automated analysis of probabilistic programs. Her work integrates formal methods with machine learning to enhance software correctness and security. Han has taught courses such as 'Problem Solving for Programming', 'Big Data Analytics using R', and 'Introduction to Programming'. She actively participates in academic service, including PC memberships in QAPL 2014 and SAC SVT 2015–2017.

Yann Disserمشاهده پروفایل

استاد

• Combinatorial Optimization
• Online Algorithms
• Graph Exploration
• +۶ مورد دیگر

Yann Disser is a Professor (W2) at the Department of Mathematics, Technische Universität Darmstadt. His research focuses on combinatorial optimization, online algorithms, and graph theory. He holds a PhD in Theoretical Computer Science from ETH Zurich (2011) and has held academic positions at TU Berlin and ETH Zurich. Disser has led numerous DFG-funded research projects, including work on incremental maximization and potential-based flow networks. His teaching includes courses on discrete optimization, algorithmic discrete mathematics, and online optimization. He supervises a research group with members such as Júlia Baligács and Annette Lutz, and has advised multiple PhD students including David Weckbecker. His recent work explores competitive analysis, algorithmic lower bounds, and graph exploration. Education: PhD in Theoretical Computer Science (ETH Zurich, 2011), MSc in Physics (TU Darmstadt), Diplom in Computer Science (TU Darmstadt). Research Interests: Combinatorial Optimization, Online Algorithms, Graph Exploration, Computational Complexity, Incremental Algorithms, Approximation Algorithms. Grants: DFG TRR 154 (2022–2026), DFG Individual Grants (2019–2023), EU EFRE Project (2016–2019). Labs: Deep Learning Lab (TU Darmstadt), Algorithms Lab (ETH Zurich).

• Network Security
• Software Defined Networking
• Traffic Engineering
• +۷ مورد دیگر

Xingang Shi is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With over 126 publications spanning from 2006 to 2025, he maintains an active and influential research program in computer networking, with recent expansion into quantum network optimization. His work consistently appears in top-tier venues including INFOCOM, IEEE/ACM Transactions on Networking, and SIGCOMM. Dr. Shi's research focuses on network security, software defined networking, traffic engineering, and network anomaly detection. His recent work shows increasing emphasis on quantum network optimization and advanced security verification techniques. He has developed innovative approaches to routing protocols, congestion control, and inter-domain routing analysis, with practical applications in real-world network infrastructure. His publication trends reveal a steady evolution from traditional networking topics toward more sophisticated security and optimization frameworks. The 15 most recent publications demonstrate strong integration of machine learning techniques with networking fundamentals, particularly in anomaly detection and traffic engineering. His work increasingly addresses quantum networking challenges while maintaining strong contributions to conventional network security and performance optimization. As a leading researcher in his field, Dr. Shi has made significant contributions to network verification methodologies and security protocol testing frameworks. His recent papers on model checking-based security testing and proactive network policy verification represent important advances in ensuring network reliability and security. Through his extensive collaboration network (particularly with Zhiliang Wang, Xia Yin, and Han Zhang), Dr. Shi has built a productive research ecosystem that bridges theoretical networking concepts with practical implementation challenges. His work on quantum network optimization represents an emerging frontier in his research portfolio.

Arham Muslimمشاهده پروفایل

استادیار

• Learning Technologies
• Learning Analytics
• Data Science
• +۳ مورد دیگر

Dr. Arham Muslim is an Assistant Professor at the National University of Sciences and Technology (NUST), Pakistan. Previously, he served as a researcher in the Social Computing Group at the University of Duisburg-Essen, Germany (since 2018) and held roles as a software developer at RWTH Aachen University's Center of Innovative Learning (CiL) and the Fraunhofer Institute of Production Technology (IPT). He holds a Ph.D. in Computer Science from RWTH Aachen University (2012–2018), a Master’s in Software Systems Engineering (2008–2011), and a Bachelor’s in Software Engineering (2003–2007). His research focuses on learning technologies, data science, and visual analytics, with an emphasis on human-centered learning analytics, information visualization, and big data applications. Key contributions include frameworks for open learning analytics and methodologies for personalized educational data analysis. Publications span topics like modular frameworks for learning analytics, rule-based indicator tools, and ecosystem design for educational data. His work bridges technical innovation with practical educational challenges. No scientific awards are explicitly mentioned in the provided texts. His advising and grant activities are not detailed, but his professional experience includes impactful software development projects in academia and industry. He is affiliated with the Social Computing Group and has collaborated with institutions like RWTH Aachen University and Fraunhofer IPT.