جستجوی استاد راهنمای دانشگاهی

Reza Salkhordehمشاهده پروفایل

مدرس

• Operating Systems
• Solid-State Drives
• Data Storage Systems
• +۵ مورد دیگر

Reza Salkhordeh is a Lecturer and Postdoctoral Researcher at Johannes Gutenberg University Mainz, Germany, where he leads the Efficient Computing and Storage Group. He holds a Ph.D. in Computer Engineering from Sharif University of Technology (2018) and has been affiliated with Ferdowsi University of Mashhad and Sharif University of Technology. His research focuses on operating systems, storage systems, and non-volatile memory technologies, with a particular emphasis on high-performance computing and I/O optimization. He has taught courses such as Storage Systems and Advanced Topics in Operating Systems since 2020. His academic journey includes a B.Sc. from Ferdowsi University (2011), M.Sc. and Ph.D. from Sharif University (2013, 2018). He has held roles such as Technical Lead for the High-Performance Data Storage System (HPDS) project in Tehran, Iran, and has mentored 4 M.Sc. and 6 B.Sc. students. His work spans patented technologies like Reconfigurable Cache Architectures and Load Balancers for I/O caching systems. Research interests include heterogeneous memory management, storage system design, and optimizing I/O performance in distributed environments. His recent publications address challenges in NVMM utilization, garbage collection in SSDs, and I/O tracing for HPC systems. He is actively involved in conference committees (e.g., FAST, ARCS, SC) and has reviewed for top journals like IEEE TPDS and ACM Transactions on Storage. Notable recognitions include membership in Iran’s National Elites Foundation (2012–2015) and top rankings in national exams (3rd in PhD, 35th in MSc). His contributions to storage systems have advanced enterprise-grade architectures and decentralized file systems, with a focus on practical implementations for modern computing challenges.

Jens Lechtenbörgerمشاهده پروفایل

مدرس

• Open Educational Resources (OER)
• Data Warehousing
• Business Intelligence
• +۴ مورد دیگر

Dr. Jens Lechtenbörger is a Faculty member and Lecturer at the Chair of Machine Learning and Data Engineering at the University of Münster's School of Business and Economics. He holds a Doctoral degree (2001) and a diploma in Computer Science (1997) from the University of Münster and Oldenburg, respectively. His roles include serving as Akademischer Oberrat since 2006, Acting Professor (2016), and Fellow for innovation in digital university teaching (2016–2017). He leads projects like Tutor.AI and OER infrastructure development, focusing on Open Educational Resources (OER), data warehousing, and educational technology. Research interests span OER creation, data integration, business intelligence, and machine learning applications in education. He has published extensively on data warehouse design, database systems, and educational technologies. Notable awards include the Best Evolutionary & Swarm Computation Paper Award (2019) and the Dissertation Prize 2003. Teaching responsibilities include courses on data structures, operating systems, and management information systems. He supervises theses on topics like AI-driven learning systems, data privacy, and business logic vulnerabilities. Active in academic service, he chairs committees on digital teaching and curriculum reform at the University of Münster.

Shuang Liuمشاهده پروفایل

پژوهشگر

• Embedded Systems
• Computer Architecture
• Networks-on-Chip (NoC)
• +۳ مورد دیگر

Shuang Liu is a Researcher at the Institute of Computer Architecture and Computer Engineering, University of Stuttgart. Their work focuses on embedded systems, networks-on-chip (NoC), and chip design methodologies. They specialize in deadlock-free routing, fault-tolerant computing, and application-specific system synthesis using advanced techniques like integer linear programming. Research interests include optimizing NoC architectures for chiplet-based systems, co-design of floorplanning and routing topologies, and energy-efficient signal processing in medical electronics. Their recent work emphasizes formal verification methods and performance optimization in hardware-software co-design. Publications span topics such as deadlock prevention in NoC, ILP-based routing synthesis, and trade-offs in hearing aid ASIP design. Office hours are Monday 2–3 p.m. in Room 1.001, Pfaffenwaldring 5b, Stuttgart.

Michael Stengerمشاهده پروفایل

پژوهشگر

• Synthetic Data Generation
• Time Series Analysis
• Data Science
• +۳ مورد دیگر

Michael Stenger is a Researcher at the Chair of Software Engineering (Informatik II) under the Department of Computer Science at the University of Würzburg. His current role since May 2023 involves research in synthetic data generation, time series analysis, and machine learning. He coordinates the lecture 'Softwaretechnik' and supervises bachelor/master theses and seminar topics in software engineering. Education: Master of Computer Science (2020-2023), University of Würzburg Bachelor of Computer Science (2017-2020), University of Würzburg Study Abroad: University of Saskatchewan, Canada (2021) Research Interests: Focuses on synthetic data generation, time series analysis, and machine learning applications. His work emphasizes evaluation methodologies for synthetic data quality and hybrid forecasting approaches. He also contributes to data science and deep learning techniques for real-world problem-solving. Publications & Conferences: Active contributor to venues like MASCOTS, ICPE, and VLDB. Recent work explores synthetic time series evaluation (STEB, 2025) and lightweight temporal feature encoding (TSRM, 2025). Trends show a strong emphasis on automated systems, benchmarking, and cross-domain applicability. Supervision & Grants: Supervises student projects in software engineering and data science. Engaged in interdisciplinary collaborations through his roles in multiple academic chairs. Labs/Teams: Part of the Software Engineering Group and Secure Software Systems Group at the University of Würzburg.

Mahendra Singhمشاهده پروفایل

پژوهشگر

• Energy Management
• Smart Buildings
• Energy Transition
• +۶ مورد دیگر

Dr. Mahendra Singh is a Researcher and Data Scientist in the Department of Energy Technologies and Energy Systems , currently affiliated with Fraunhofer ISI . He holds a joint doctorate from the Grenoble Institute of Technology, France, and the European Institute of Innovation & Technology (EIT). His research focuses on energy management, smart buildings, innovation clusters, and data-driven business models in the energy sector. Key projects include the Thai-German Cooperation on Energy, Mobility and Climate (TGC EMC) ; ENTEC Energy Transition Expert Network ; and Common European Energy Data Space . He has contributed to studies on energy transition policies, digitalization of energy flexibility, and cleantech startup ecosystems. His work integrates data analysis and diagnostic tools for building systems, with notable contributions to fault detection in smart buildings and energy consumption analysis of consumer electronics. Over 20+ peer-reviewed publications reflect his expertise in energy systems, sustainability, and innovation strategies. Mahendra’s academic trajectory includes a postdoc at the Maersk Mc-Kinney Moller Institute (Denmark) and collaborations across Europe and Asia. He advises on EU-funded initiatives and industry partnerships, emphasizing actionable insights for energy transition challenges.

• High-Performance Computing (HPC)
• Parallel Programming
• Performance Modeling
• +۶ مورد دیگر

Felix Wolf is a Full Professor at Technische Universität Darmstadt's Department of Computer Science since 2015 and leads the NHR4CES@TU Darmstadt HPC center. He served as Department Chair and Vice Department Chair at TU Darmstadt, with prior faculty roles at RWTH Aachen University (2006–2015) and adjunct positions at the University of Tennessee (2003–2008). His academic work spans performance analysis, parallel programming, and scalability modeling. Ph.D. in Computer Science, RWTH Aachen University (2003) M.Sc., RWTH Aachen University (1998) Research Interests : Wolf focuses on High-Performance Computing , including performance modeling , parallel programming tools , and I/O optimization . His work addresses GPU acceleration , machine learning integration into HPC systems, and structural plasticity simulation . Key projects involve tools like Scalasca, Score-P, and Extra-P for automated performance analysis. Article Trends : Recent publications emphasize HPC systems for AI-driven workflows , I/O contention reduction , and GPU port validation . His team explores empirical modeling for deep learning and scientific simulations , with applications in engineering and neuroscience .

Martin Lacknerمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Computational Social Choice
• Algorithms
• +۳ مورد دیگر

Martin Lackner is a Researcher at the Vienna University of Economics and Business (WU Wien) within the Institute for Data, Process and Knowledge Management. He previously held postdoctoral positions at the University of Oxford and TU Wien, and earned his doctoral degree (Dr. techn.) in computer science from TU Wien in 2014. His research focuses on artificial intelligence, computational social choice, and algorithm design, with a particular emphasis on multi-winner voting systems, approval-based methods, and fairness in decision-making processes. Education: PhD in Computer Science (TU Wien, 2014), Mathematics in Computer Science studies at TU Wien, and a semester at the University of Illinois at Urbana-Champaign (USA). Research interests include computational social choice topics such as multi-winner voting, approval-based committee rules, liquid democracy, and axiomatic analysis of voting systems. He has contributed to the development of the abcvoting Python library for implementing approval-based voting rules and co-authored the book Multi-Winner Voting with Approval Preferences (Springer, 2023). His work bridges theoretical computer science with practical applications in democratic processes and algorithmic fairness. Key contributions include studies on proportional representation mechanisms, participatory budgeting fairness, and long-term decision-making frameworks. Lackner's research frequently addresses the algorithmic aspects of collective decision-making, with publications in venues like Artificial Intelligence , Journal of Economic Theory , and top conferences such as AAAI and IJCAI. Grants and Projects: Principal investigator of the FWF-funded project Algorithms for Sustainable Group Decision Making (TU Wien, 2025–present), and co-developer of the abcvoting open-source software project.

Tobias Distlerمشاهده پروفایل

دانشیار

• Distributed Systems
• Byzantine Fault Tolerance
• Energy-efficient Computing
• +۷ مورد دیگر

Tobias Distler is an Associate Professor (Privatdozent) at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads research in resource-efficient fault tolerance, energy-aware systems, and distributed coordination. His work focuses on Byzantine fault tolerance, cloud computing, real-time systems, and embedded systems resilience. Research Projects: REFIT (Resource-efficient Fault and Intrusion Tolerance) EDC (Efficient Distributed Coordination) E³ (Energy-aware Execution Environments) TClouds (Trustworthy Clouds) Research Interests: His expertise spans distributed systems, fault-tolerant replication, energy-efficient computing, real-time systems analysis, and secure cloud infrastructure. He emphasizes practical system design with rigorous theoretical foundations. Publications: Over 100 peer-reviewed papers in top venues like Middleware, EuroSys, DSN, and ECRTS. Notable contributions include Egalitarian BFT, Strome (energy-aware stream processing), and TinyBFT for embedded systems. Awards: SIGACT Research Highlights Nomination (2024) FAU Teaching Award for Junior Researchers (2021) IBM Ph.D. Fellowship (2012-2013) Professional Activities: Served on program committees for Middleware, EuroSys, DSN, and IEEE Transactions. Chair of Artifact Evaluation for Middleware '20. Founded the BCRB workshop on Byzantine consensus and blockchains. Education: Habilitation (2021) and Ph.D. (2014) from FAU, focusing on resource-aware system software for replicated services. Advisor to numerous graduate students in distributed systems research.

• Bioinformatics
• Computational Biology
• Genomics
• +۳ مورد دیگر

Mark Robinson is a Full Professor for Statistical Genomics at the University of Zurich's Institute of Molecular Life Sciences. His research focuses on computational biology, bioinformatics, and genomics with expertise in RNA sequencing analysis and single-cell data science. He actively contributes to open-source tools and methodologies for genomic data processing. Key research interests include developing statistical frameworks for differential expression analysis, clustering of single-cell RNA-seq data, and integration of multi-omics datasets. His work emphasizes reproducible research practices and open-science initiatives. Notable contributions include peer-reviewed publications on single-cell data challenges and RNA-seq analysis workflows. His lab (robinsonlabuzh.github.io) develops software packages like 'conquer' and 'scRNAseq_clustering_comparison' for genomic analyses. Active on GitHub, he maintains repositories related to statistical genomics and bioinformatics tooling.

• Static Analysis
• Logic Programming
• Program Verification
• +۴ مورد دیگر

Manuel V. Hermenegildo is a Professor at the Universidad Politécnica de Madrid, Spain. He is a prominent researcher in the field of programming languages and static analysis, with significant contributions to logic programming, program verification, and formal methods. His work focuses on advancing static analysis techniques, compiler optimization, and energy-efficient computing. He has co-authored numerous papers and organized international conferences such as LOPSTR and SAS, demonstrating his leadership in academic and research communities. His research explores topics including abstract interpretation, runtime checking, and parallel logic programming systems. He is a key contributor to the Ciao Prolog system, emphasizing comprehensive tool integration and formal methods. His research interests span static analysis frameworks, program verification, resource usage analysis, and energy efficiency in computing. He has developed methodologies for optimizing program performance while ensuring correctness, with applications in both theoretical and applied domains. His work often bridges the gap between high-level program analysis and low-level hardware constraints, particularly in embedded systems. Recent trends in his publications include advancements in static cost analysis, dynamic inference of invariants, and tools for incremental assertion checking. His collaborations with institutions like the University of Copenhagen and the University of Kent reflect a global network in advancing computational logic and software engineering. He has been actively involved in academic service, including editorial roles for conference proceedings and journals. His contributions highlight a commitment to both foundational research and practical tools for the programming language community.

• Computational Display
• Virtual Reality
• Augmented Reality
• +۸ مورد دیگر

Bin Chen is a Lecturer at the School of Computing and Information Systems, University of Melbourne, where he conducts research at the intersection of computer graphics, computational imaging, and human perception. He was previously a postdoctoral researcher at the Max-Planck-Institut für Informatik and a visiting scholar at the University of Cambridge. Research Interests: His work spans Computational Display , focusing on glass-free 3D and VR/AR systems; Perception , studying how humans perceive virtual materials and gloss; and Computational Imaging , developing AI-driven methods for HDR, deblurring, and depth synthesis. He uses both optical hardware and software rendering to enhance visual fidelity. Recent Research Trends: His recent publications emphasize deep learning for image restoration (e.g., deblurring, tone mapping), neural representations for image stacks, and perceptual validation of material rendering. The integration of light field displays and self-supervised learning is a key theme across his recent work. Scientific Awards: CVPR Best Paper Award Finalist (Top 0.4%) – 2022 Service and Mentoring: Bin Chen has served on the Technical Paper Committees for SIGGRAPH, SIGGRAPH Asia, CVPR, and AAAI. He actively mentors PhD students and invites self-motivated candidates to join his research group. He has advised students such as Tao Huang, Lingyan Ruan, Chao Wang, and Jizhou Li. Laboratory and Teams: While no formal lab name is specified, his research group at the University of Melbourne focuses on visual computing, with strong collaborations extending from City University of Hong Kong to Max-Planck-Institut and the University of Cambridge.

• Multimedia Communications
• Self-organizing Systems
• Mobile Systems
• +۷ مورد دیگر

Prof. Ralf Steinmetz is a Full Professor of Multimedia Communications at Technische Universität Darmstadt since 1996 and head of the Multimedia Communications Lab. His research focuses on adaptive multimedia systems, self-organizing networks, mobile/sensor networking, and educational technologies. He has held leadership roles at IBM's European Networking Center and Fraunhofer IPSI. His work spans cybersecurity, IoT, disaster communication systems, and industrial networking. Education : Dr.-Ing (PhD) in Electrical Engineering, TU Darmstadt (1986) Habilitation in Computer Science, Goethe University Frankfurt (1994) Research Interests : He pioneers innovations in networked multimedia systems , including fault-tolerant communication, aerial-ground disaster monitoring (e.g., CAMON system), and 5G/Industry 4.0 infrastructure. His work emphasizes resilience, adaptive protocols, and cyber-physical integration. Recent Work Trends : Publications from 2022-2025 highlight advancements in: Wireless communication protocols (ESP-NOW, LoRa) Cybersecurity (intrusion detection via machine learning) Disaster response networks (aerial-ground cooperation) Time-sensitive networking for industrial applications Key Contributions : Developed Nature 4.0 sensor systems for biodiversity monitoring Pioneered P4-programmable network hardware solutions (P4-CODEL, P4-BNG) Advanced UAV-based emergency communication frameworks

• Geothermal Energy
• Structural Geology
• Geophysical Modeling
• +۳ مورد دیگر

Prof. Florian Wellmann is a University Professor at RWTH Aachen University's Chair of Numerical Geosciences, Geothermal Energy and Reservoir Geophysics within the Faculty of Georesources and Materials Engineering. His research focuses on integrating machine learning with geoscience applications, particularly in geothermal energy, structural modeling, and uncertainty quantification. He leads the CG³ research group, developing open-source tools like GemPy and GemGIS for 3D geological modeling. Notably, he received a KI-Campus fellowship for advancing AI in geoscience education. His work includes projects such as the Horizon Europe GeoHEAT initiative, exploring geothermal systems and repurposing idle wells for energy. Education details are not explicitly provided, but his academic roles indicate expertise in numerical geosciences and geothermal engineering. Research interests span physics-based machine learning, subsurface characterization, and probabilistic modeling. His fellowship highlights contributions to digital education, blending AI with geological curricula. Recent publications emphasize sensitivity analysis, fault modeling, and geothermal reservoir optimization. Prof. Wellmann's awards include the KI-Campus fellowship (2021). His grants and advising efforts are reflected in collaborative projects like GeoHEAT and software development. Labs/teams include the CG³ group at RWTH Aachen, equipped with advanced geophysical instruments for field and lab analysis.

Konstantin Nestmannمشاهده پروفایل

پژوهشگر ارشد

• Quantum Thermodynamics
• Open Quantum Systems
• Quantum Information
• +۳ مورد دیگر

Konstantin Nestmann is a Postdoctoral Researcher in the Department of Physics at Lund University's Faculty of Engineering since 2023, funded by an EU Marie Skłodowska-Curie fellowship. His research focuses on quantum thermodynamics and open quantum systems using techniques from statistical field theory and quantum information. His educational background includes a PhD in Physics (summa cum laude) from RWTH Aachen University (2018-2022) and a combined BSc and MSc in Physics from Dresden University of Technology (2011-2018). His doctoral thesis addressed the dynamics of open quantum systems from quantum information and statistical physics perspectives. Nestmann's research centers on non-perturbative quantum master equations, quantum non-Markovianity, and quantum thermodynamics. He develops theoretical frameworks and computational tools, such as the RealTimeTransport library, to study quantum transport in the strong coupling regime. His work bridges fundamental quantum dynamics with practical applications in nanoelectronics. His publication record from 2019 to 2024 reveals a consistent focus on the interplay between time-local and time-nonlocal descriptions of quantum dynamics, with significant contributions to renormalization group methods and fermionic duality. These works span exact solutions for open quantum systems, non-perturbative approximations, and numerical implementations. Scientific awards include: Marie Skłodowska-Curie fellowship Nestmann has served as a lead teaching assistant for Quantum Information and Introduction to Theoretical Physics, and as a teaching assistant for Statistical Physics and Quantum Mechanics. His research is supported by the Marie Curie fellowship and collaborative projects with institutions including RWTH Aachen and Forschungszentrum Jülich. He is an active member of the quantum transport and open systems research community, contributing to open-source software development and collaborative theoretical advancements.

Olaf Landsiedelمشاهده پروفایل

استاد

• Distributed Systems
• Wireless Sensor Networks
• Peer-to-Peer Systems
• +۴ مورد دیگر

Olaf Landsiedel is a Full Professor and head of the Distributed Systems Group at Kiel University, where he leads research in distributed computing, wireless sensor networks, and peer-to-peer systems. He has been a key contributor to the field since joining COMSYS in 2006 and earning his PhD in 2010. His research interests center on enabling accurate, scalable, and energy-efficient network simulations and protocols. He focuses on bridging the gap between simulation and real-world deployment, improving timing accuracy, modeling power consumption, and designing robust communication for unstable wireless environments. His work emphasizes modularity, flexibility, and reliability in distributed systems. The 15 most recent publications reflect a strong trend toward enhancing simulation fidelity, addressing bursty wireless links, enabling dynamic software updates in sensor networks, and developing tools for realistic experimentation. His work spans both theoretical models and practical implementations, often validated through simulation frameworks and testbeds. Olaf Landsiedel has advised numerous students on topics ranging from routing protocols to modular communication frameworks and ontology design, primarily during his time at RWTH Aachen University. While no specific grants are mentioned, his sustained publication record suggests consistent research funding and collaborative projects. He leads the Distributed Systems Group, which conducts cutting-edge research in network simulation, protocol development, and sensor network reliability. The group's work includes tools like KleeNet for bug detection and Horizon for parallel simulation, indicating a strong focus on practical system building and validation.