جستجوی استاد راهنمای دانشگاهی

• Database Management Systems
• Transaction Processing
• In-Memory Databases
• +۶ مورد دیگر

Andrew Pavlo is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on database management systems, particularly in the areas of transaction processing, in-memory databases, and self-driving database systems. He leads a productive research group that has published extensively in top database venues including VLDB, SIGMOD, and CIDR. Pavlo's research interests span database management systems, transaction processing, in-memory databases, non-volatile memory databases, and self-driving database systems. His work often bridges theoretical database concepts with practical system implementation, focusing on performance optimization, query processing, and system architecture. Recent work has explored machine learning applications for database tuning, novel storage techniques, and innovative approaches to transaction processing. An analysis of his recent publications reveals a strong focus on self-driving database systems, with significant work on the Database Gym framework for training machine learning models to optimize database performance. His research also examines columnar storage formats, transaction scheduling, and novel approaches to user-defined function optimization. The work demonstrates a consistent trajectory toward making database systems more autonomous and efficient through a combination of systems techniques and machine learning. Pavlo has been instrumental in mentoring numerous PhD students who have become active contributors to the database research community. His research has been supported by significant grants that have enabled the development of innovative database technologies and frameworks. His research group operates within CMU's vibrant database ecosystem, collaborating with other researchers on projects related to database systems, storage engines, and query processing frameworks. The group maintains close connections with industry partners to ensure practical relevance of their research contributions.

Thomas Wildمشاهده پروفایل

استاد

• Manycore system on chip (SoC) architectures
• Network processor (NPU) architectures
• On-chip communication architectures
• +۳ مورد دیگر

Dr.-Ing. Thomas Wild serves as an Academic Director at the Technical University of Munich (TUM), working within the TUM School of Computation, Information and Technology at the Chair of Integrated Systems. He maintains an active research and teaching role at the institution, with his office located in Building N1 (Theresienstr. 90), Room N2136 in Munich, Germany. Dr. Wild's research focuses on advanced computing architectures, with particular emphasis on manycore system on chip (SoC) architectures, network processor (NPU) architectures, on-chip communication architectures including networks on chip (NoC), and system level design methodologies. His work bridges theoretical research with practical implementation, often exploring design space exploration techniques to optimize system performance. The evolution of his research over two decades demonstrates a consistent focus on improving communication architectures and system-level design for embedded and high-performance computing platforms. His recent publications (2023-2025) reveal a growing integration of machine learning techniques with traditional hardware design, particularly in optimizing power-performance tradeoffs in embedded systems. There's a clear trend toward hardware-software co-design approaches, with significant work on SmartNICs, Linux system optimization, and network processing acceleration. His research shows strong interdisciplinary connections between computer architecture, networking, and machine learning. EUROPRACTICE representative for TUM city campus, facilitating access to commercial EDA tools for academic purposes Active collaborator with Professor Andreas Herkersdorf and other researchers at TUM Focus on practical implementations with FPGA-based prototyping and real system modifications Dr. Wild teaches several hardware design courses including VHDL Lab, SystemC Lab, and HW/SW Codesign, contributing to the education of next-generation computer engineers. His teaching directly complements his research in system design and hardware acceleration, providing students with hands-on experience in cutting-edge technologies.

• Internet of Things (IoT)
• Edge Computing
• Cloud-Edge-IO Continuum
• +۴ مورد دیگر

Carlos Enrique Palau is a prominent researcher in the field of Internet of Things (IoT), edge computing, and cyber-physical systems. His work focuses on interoperability, security, and scalability in distributed systems, particularly in industrial and smart city applications. He has contributed to frameworks for cloud-edge continuum integration, blockchain-based IoT solutions, and federated computing architectures. Key areas of research include: IoT interoperability and semantic frameworks Edge computing and distributed workload management Cybersecurity for IoT and critical infrastructure Smart port logistics and real-time data analytics Cognitive services in legacy port management systems His recent work explores: Data-as-a-Product frameworks for Industry 4.0/5.0 Autonomous workload scheduling in energy-efficient edge-cloud systems Deception mechanisms for IoT security Self-* capabilities in cloud-edge nodes Palau has collaborated extensively with institutions like Universitat Politècnica de València and international partners in projects funded by EU initiatives. His research addresses practical challenges in industrial IoT deployments, smart city infrastructure, and emergency management systems.

Karl-Erik Årzénمشاهده پروفایل

استاد

• Control Engineering
• Cyber-Physical Systems
• Real-Time Systems
• +۹ مورد دیگر

Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.

• Energy Efficiency in Communication Networks
• Quality of Service (QoS) and Quality of Experience (QoE)
• Network and Service Management
• +۵ مورد دیگر

Dr. Frank Loh is a researcher at the Department of Computer Science III, University of Würzburg, specializing in energy efficiency, network performance, and Quality of Experience (QoE) in communication networks. His work focuses on optimizing LoRaWAN deployments, serverless computing, and edge-cloud environments, with an emphasis on reducing message collisions and improving resource utilization. He actively contributes to methodologies for gateway placement, traffic modeling, and energy consumption metrics. Research Areas Energy Efficiency in Communication Networks Quality of Service (QoS) and Quality of Experience (QoE) LoRaWAN Network Planning Edge and Serverless Computing Network Resource Analysis Recent Publications 2025: Energy modeling for 6G base stations 2025: Server cluster resilience via Markov models 2024: Serverless computing in edge-cloud environments 2024: LoRaWAN channel access optimization

Alessandro Aliakbargolkarمشاهده پروفایل

استاد

• Federated Satellite Systems
• CubeSat Constellations
• Spacecraft Systems Architecture
• +۳ مورد دیگر

Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994

Andrea Bastoniمشاهده پروفایل

پژوهشگر ارشد

• Real-Time Systems
• Cyber-Physical Systems
• Operating System Design
• +۵ مورد دیگر

Dr. Andrea Bastoni is a Postdoctoral Researcher and Research Fellow at the Chair of Cyber-Physical Systems in Production Engineering at Technical University of Munich (TUM), Faculty of Mechanical Engineering. He is also the CTO and co-founder of Minerva Systems , developing operating system solutions for AI-ready embedded applications. His expertise spans real-time operating systems, cyber-physical systems, and predictable system design for heterogeneous platforms. His research focuses on enhancing predictability of memory hierarchies in complex SoCs through techniques like memory bandwidth regulation and cache partitioning. This work has industrial applications in safety-critical domains such as avionics and railways, where he contributes to certifiable hypervisors and operating systems. As former Software Architect of the PikeOS hypervisor at SYSGO GmbH (2012-2020), he specialized in DO-178C, IEC 61508, and EN 50128 standards. His academic background includes a Ph.D. in Computer Engineering from the University of Rome Tor Vergata (2007-2011), where he developed LITMUS^RT as part of UNC's Real-Time Systems Group during a visiting researcher period (2009-2010). His publications reflect ongoing work on Multicore Real-Time Scheduling , Mixed-Criticality Task Isolation, and Arm DynamIQ shared unit analysis. He actively participates in program committees for conferences like RTSS, DSN, and DATE.

• Algorithmic Game Theory
• Combinatorial Optimization
• Traffic Flow Equilibria
• +۴ مورد دیگر

Prof. Laura Vargas Koch serves as Junior Professor at RWTH Aachen University, leading the Teaching and Research Unit of Algorithmic Game Theory and Discrete Mathematics (GDM). Her interdisciplinary work bridges mathematics, computer science, and economics through rigorous theoretical frameworks. Her research focuses on: Algorithmic Game Theory : Analyzing fair pricing mechanisms and equilibrium structures in traffic flow systems Combinatorial Optimization : Developing approximation algorithms for clustering problems and graph-based optimization Analysis of her 2021-2025 publications reveals evolving expertise in dynamic traffic modeling, routing game equilibria, and auction mechanism design. Her work consistently addresses theoretical foundations while maintaining practical relevance to transportation networks and resource allocation systems. The GDM unit under her direction provides specialized coursework and fosters collaborative research at the intersection of discrete mathematics and economic modeling.

Yiting Xiaمشاهده پروفایل

استادیار

• Computer Networks
• Data Center Networking
• Optical Communications
• +۳ مورد دیگر

Dr. Yiting Xia is a tenure-track faculty member at the Max Planck Institute for Informatics (MPI-INF), leading the Network and Cloud Systems research group. She previously worked as a research scientist at Facebook and holds a PhD in Computer Science from Rice University (2018) and a B.S. in Telecommunications Engineering from Beijing University of Posts and Telecommunications and Queen Mary University of London (2011). Her research focuses on high-performance and energy-efficient networking for cloud computing, including reconfigurable data center networks, optical communications, and network protocols. Notable contributions include innovations in transport protocols, time synchronization for optical networks, and failure-resilient network design. Education: PhD in Computer Science, Rice University, 2018 M.S. in Computer Science, Rice University, 2014 B.S. in Telecommunications Engineering, BUPT & QMUL, 2011 Research Interests: Data center networking, optical communications, cloud systems, network protocols, distributed systems, and network security. Her work bridges theoretical contributions with practical implementations, addressing challenges in latency-sensitive flows, traffic engineering, and system reliability. Awards include the Ken Kennedy-Cray Fellowship and the N2Women Rising Star Award (2021). She has co-lectured courses on distributed systems and data networks at Saarland University and previously contributed to teaching at Rice University. Key projects include Aurora (for MoE inference optimization), Lighthouse (an open research framework for optical networks), and Occam (a reliable network management system). Grants & Projects: Focus on deployable optical network architectures and resilient backbone management during pandemic-driven traffic shifts. Labs/Teams: Leads the Network and Cloud Systems group at MPI-INF, collaborating with academia and industry on cutting-edge networking solutions.

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.

Nicole Megowمشاهده پروفایل

استاد

• Combinatorial optimization
• Discrete optimization
• Mathematical optimization
• +۱۱ مورد دیگر

Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich. Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems. Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS. Dissertation Award by the German Operations Research Society (2007) Berlin Science Award for Young Researchers (2013) Heinz Maier-Leibnitz Prize (2013) Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015) Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).

Gudrun P. Kiesmüllerمشاهده پروفایل

استاد

• Supply Chain Management
• Inventory Management
• Spare Parts Inventory Management
• +۳ مورد دیگر

Prof. Dr. Gudrun P. Kiesmüller is a full professor of Operations Management at TUM Campus Heilbronn since 2019. Previously, she held full professorships at Kiel University (Supply Chain Management) and Otto von Guericke University Magdeburg (Operations Management). She studied mathematics at Julius-Maximilians-University of Würzburg and later worked as a postdoc and assistant professor at Eindhoven University of Technology. Her research focuses on supply chain management, inventory management (particularly spare parts), maintenance process planning, and manufacturing system design. She develops optimization approaches for decision support, with publications in journals like IISE Transactions and Production and Operations Management . Key awards include an Honorary Doctorate (2023), ISIR Service Award (2022), and multiple teaching and reviewer awards. She has contributed to advancing stochastic inventory models and operational efficiency in complex systems. Her work integrates theoretical rigor with practical applications, addressing challenges in inventory routing, buffer allocation, and component reliability optimization. Current research emphasizes dynamic maintenance planning and capital goods design.

Gero Mühlمشاهده پروفایل

استاد

• Distributed systems
• Distributed algorithms
• Event-based systems
• +۷ مورد دیگر

Prof. Dr.-Ing. habil. Gero Mühl is a W2-Professor at the University of Rostock, where he holds the chair for "Architecture of Application Systems" since October 2009. His academic journey includes positions as a Heisenberg Fellow at the Technical University of Berlin (2009), postdoctoral research at TU Berlin (2002-2009), and doctoral studies at TU Darmstadt where he received his Dr.-Ing. degree with distinction in 2002. He completed dual Diplomas in Computer Science (Dipl.-Inform.) and Electrical Engineering (Dipl.-Ing.) from FernUniversität in Hagen in 1998. Prof. Mühl's research focuses on Self-Organizing Distributed Systems , with particular expertise in distributed systems, distributed algorithms, event-based systems, middleware, energy-efficient systems, organic computing, sensor networks, web services, and electronic commerce. His work bridges theoretical foundations with practical implementations in real-world distributed environments. His recent publications show a strong trend toward time-sensitive networking, content-based publish/subscribe systems, and P4 programmable data planes. These works address critical challenges in industrial communication, real-time systems, and network reliability. His research group has made significant contributions to making distributed systems more autonomous, reliable, and efficient. Scientific awards and recognitions include: Nomination for the Berlin Science Award for Young Scientists (2008) Heisenberg Fellowship by the German Research Foundation (DFG) (2008) Best paper award in System Software and Security at SAC 2015 Prof. Mühl has been actively involved in numerous research projects and collaborations, particularly focusing on self-organizing and self-stabilizing systems. His work on the REBECA publish/subscribe middleware represents a significant contribution to autonomous distributed systems. He has supervised numerous students and researchers, contributing to the development of the next generation of computer scientists specializing in distributed systems. His laboratory at the University of Rostock focuses on practical implementations of self-organizing distributed systems, with current projects investigating time-sensitive networking, publish/subscribe systems, and energy-efficient distributed computing. The team combines theoretical analysis with practical system development to address real-world challenges in industrial and commercial applications of distributed systems.

• Embedded Systems
• Hardware-Software Co-Design
• Invasive Computing
• +۷ مورد دیگر

Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.

Tsun-Ming Tsengمشاهده پروفایل

استاد

• Microfluidic Large-Scale Integration
• Optical Network-on-Chip
• Microfabrication
• +۳ مورد دیگر

Tsun-Ming Tseng is a Professor and principal investigator at the Chair of Electronic Design Automation at the Technical University of Munich (TUM). He leads the Emerging Technology Group and oversees multiple DFG/BMBF-funded research projects in the areas of microfluidic large-scale integration, optical network-on-chip design, and novel microfabrication techniques. Dr. Tseng's research focuses on design automation for emerging technologies, with particular expertise in three main areas: microfluidic large-scale integration, optical network-on-chip systems, and novel microfabrication processes. His work bridges the gap between electronic design automation and cutting-edge applications in bioengineering, photonics, and advanced manufacturing. His research group develops sophisticated algorithms and tools for optimizing design, reliability, and performance in these emerging domains. Analysis of Dr. Tseng's recent publications reveals a strong focus on practical implementation challenges in emerging technologies. His work spans both theoretical algorithm development and practical system implementation, with particular emphasis on reliability, performance optimization, and manufacturing considerations. The research shows increasing integration between different technology domains, particularly the convergence of microfluidics, optical networking, and electronic design automation. Dr. Tseng has been awarded multiple significant research grants including: "DE-TW-CloudWRONoC" (BMBF-NSTC project, PI, 2025-2028, EUR 797.7K) "DE-TW-PI3D" (BMBF-NSTC project, PI, 2024-2027, EUR 391.6K) "Physical Design for Microfluidic Large-Scale Integration" (DFG research grant, PI, 2024-2026, EUR 331.9K) Multiple other DFG and industrial projects totaling over EUR 3 million in funding He has successfully supervised numerous doctoral researchers and postdoctoral fellows, with current group members including Jiahui Peng, Debraj Kundu, Liaoyuan Cheng, and several others. Dr. Tseng leads the Emerging Technology Group at TUM, which focuses on developing design automation methodologies for next-generation technologies. The group maintains strong collaborations with international institutions, including partnerships with researchers in Taiwan and Hong Kong. The team operates state-of-the-art facilities for research in microfluidics, optical networking, and advanced microfabrication techniques.