جست‌وجوی استاد و پژوهشگر

• Wireless Communication
• Network Optimization
• Machine Learning
• +۳ مورد دیگر

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

Laurens Deboمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Management
• Operations Strategy
• +۵ مورد دیگر

Laurens Debo is the C.V. Starr Professor of Operations Management and Area Chair in the Operations and Management Science Department at Tuck School of Business, Dartmouth College. He holds a PhD (2002) and MSc (2000) from INSEAD, France, and dual MSc degrees in Operations Management (1995) and Electrical Engineering (1994) from Université Catholique de Louvain, Belgium. Prior to Tuck, he taught at Carnegie Mellon University and the University of Chicago Booth School of Business. Research Interests: Debo specializes in service operations, strategic consumer behavior, and discretionary service management. His work explores how consumer decisions shape service demand and how providers optimize service delivery in contexts where value increases with service duration. Recent focus areas include queueing systems, tipping behavior, and pandemic-era retail operations. Awards & Editorial Roles: He received the MSOM Meritorious Service Award (2008, 2010) and served as associate editor for Management Science , Manufacturing & Service Operations Management , and Operations Research . He currently edits Production and Operations Management and advises the IIE Transactions . Grants & Impact: NSF-funded research has informed his work on discretionary service design and retail safety protocols during pandemics. His tipping behavior studies have been featured in WSJ , Harvard Business Review , and Marketplace . Labs & Collaborations: Collaborates with global institutions like MIT, Stanford, and the Chinese Academy of Sciences on behavioral operations projects. Active in presenting at leading business schools including Wharton, Kellogg, and INSEAD.

Bryan Wardمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems Security
• Cyber-Physical Systems
• +۳ مورد دیگر

Bryan Ward is an Assistant Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on enhancing the security and resilience of real-time and embedded systems in critical domains such as industrial control systems and space systems. Ward holds a Ph.D. in Computer Science from the University of North Carolina at Chapel Hill, along with dual bachelor's degrees in Computer Science and Engineering and Mathematics from Bucknell University. His academic contributions span multiple areas including operating system security, real-time scheduling, and hardware-software co-design for safety-critical systems. Key research themes include control-flow integrity, secure resource management in multi-core environments, and mitigating timing-based attacks in real-time embedded systems. Ward's work has explored advanced defense mechanisms like dynamic address space randomization, tagged architecture implementations, and retry-free transactional memory models. He has also investigated vulnerabilities in kernel scheduling and cache-sharing behaviors in multi-core platforms, proposing novel solutions to ensure predictability and security. Current projects emphasize securing cyber-physical systems through adaptive defenses that maintain strict timing guarantees required for safety-critical applications.

Orran Kriegerمشاهده پروفایل

استاد

• Cloud Computing
• Operating Systems
• Virtualization
• +۳ مورد دیگر

Orran Krieger is a Professor in the Department of Electrical and Computer Engineering at Boston University. He serves as the Founding Director of the Cloud Computing Initiative (CCI) and Resident Fellow of the Hariri Institute for Computing and Computational Science & Engineering. His work focuses on cloud computing infrastructure, operating systems, and virtualization technologies. Prior roles include leading the Advanced Operating System Research Department at IBM T.J. Watson and contributing to VMware's vCloud initiatives. Education: PhD and MASc in Electrical Engineering from the University of Toronto. Research interests include cloud resource management, unikernel-based systems, security in distributed environments, and performance optimization. His leadership in the Massachusetts Open Cloud project and Open Cloud Testbed (OCT) demonstrates expertise in scalable cloud platforms. Key contributions span innovations in OS design (e.g., EbbRT framework), cloud marketplace architectures, and hardware-as-a-service models. His research bridges theoretical advancements with practical implementations in modern data centers.

• High-Performance Computing
• Parallel Programming
• Computer Architecture
• +۳ مورد دیگر

Prof. Dr. rer. nat. Matthias S. Müller is a Universitätsprofessor and Director of the IT Center at RWTH Aachen University. His research focuses on High-Performance Computing (HPC), parallel programming models, correctness verification, energy-aware computing, and tools for distributed systems. He leads the High-Performance Computing group, contributing to advancements in HPC resource management, runtime systems, and sustainable computing practices. Key areas of expertise include MPI and OpenMP correctness checking, static and dynamic analysis techniques, performance optimization for heterogeneous architectures, and energy footprint modeling. Müller has extensively collaborated on projects like MUST (MPI correctness tool), OMPT tools, and frameworks for analyzing hybrid parallel applications. His work bridges theoretical computer science with practical implementation challenges in large-scale computing environments. Notable contributions include developing methods for data race detection in Remote Memory Access (RMA) programs, latency-aware power management models, and educational frameworks for HPC lab courses. His research often emphasizes tool development, runtime systems, and interdisciplinary applications of HPC across engineering domains. Müller's lab is part of RWTH Aachen's IT Center, which provides infrastructure and expertise for computational research. He actively publishes in top-tier conferences and journals, addressing challenges in parallel programming, energy efficiency, and distributed computing systems.

Young Leeمشاهده پروفایل

مدرس ارشد

• Edge Computing
• Blockchain Technology
• Fog Computing
• +۳ مورد دیگر

Young Lee is a Senior Lecturer in Computing at Macquarie University, affiliated with the School of Computing and three research centers: Data Horizons Research Centre, Future Communications Research Centre, and Smart Green Cities Research Centre. His research focuses on edge computing, blockchain technology, fog computing, and resource scheduling in distributed systems, with a strong emphasis on applications in IoT, healthcare, and sustainability. He has led or contributed to over 150 research outputs since 2005, including influential works on edge-based video analytics, blockchain frameworks, and cloud-edge resource optimization. Dr. Lee's projects include 'Extreme-Scale Computing for Big Data Analytics' (2016–present) and 'Synergising the Power of the Cloud with the Power of the Crowd' (2016–present), demonstrating expertise in scalable computing and cloud-crowd integration. His work bridges theoretical advancements in scheduling algorithms with practical implementations in energy-efficient data centers and mobile edge environments. Research interests span edge computing architectures, blockchain oracles, and fog caching strategies, with recent contributions to carbon-conscious travel systems and malware detection frameworks. His interdisciplinary approach addresses challenges in resource allocation, network security, and distributed system efficiency.

Norbert Trautmannمشاهده پروفایل

استاد

• Combinatorial Optimization
• Project Management
• Production Planning
• +۳ مورد دیگر

Prof. Dr. Norbert Trautmann is a Full Professor of Quantitative Methods in Business Administration at the University of Bern, leading the Group for Business Analytics, Operations Research, and Quantitative Methods. He holds a PhD from the University of Karlsruhe (2000) and a Habilitation degree in Operations Research from the University of Karlsruhe (2004). His research focuses on Combinatorial Optimization , Project Management , Production Planning , and Portfolio Optimization . Prof. Trautmann has extensive professional experience, including roles as Head of the Department of Business Administration (2010–2012), and leadership in academic societies like the Swiss Operations Research Society (President since 2017). He has authored over 100 peer-reviewed publications and supervised numerous PhD students, including Philipp Baumann, Adrian Zimmermann, and Tom Rihm. His research has led to awards such as the 2010 IEEE Honourable Mention Award and the 2001 German Society of Operations Research Doctoral Thesis Award. Key projects include scheduling optimization for assessment centers, production planning in process industries, and portfolio tracking models for small investors.

Mehrdad Ghandhari Alavijhمشاهده پروفایل

استاد

• Power System Dynamics
• Power System Stability and Control
• FACTS and HVDC Systems
• +۳ مورد دیگر

Mehrdad Ghandhari Alavijh is a Full Professor in Electric Power Systems at KTH Royal Institute of Technology. He leads research in power system dynamics, stability, and control, with a focus on FACTS (Flexible AC Transmission Systems), HVDC systems, and advanced control strategies. His work addresses challenges in grid reliability, renewable energy integration, and environmental sustainability. Education: Received M.Sc. (1995) and Ph.D. (2000) in Electrical Engineering from KTH. His research group develops innovative methods for secure, efficient power system operation, leveraging advanced monitoring and communication technologies. Research Interests: Power System Dynamics and Stability Transient Stability Assessment HVDC and FACTS Technologies Renewable Energy Integration Publications: Recent works focus on HVDC systems, battery storage, transient stability algorithms, and frequency control. His studies emphasize real-time monitoring and control strategies to enhance grid resilience against outages. Teaching: Supervises courses in power systems engineering, including degree projects in Electric Power Systems and Energy Innovation. Serves as an examiner and course responsible for multiple graduate programs.

Timo Hönigمشاهده پروفایل

استاد

• Operating Systems
• System Software
• Energy-Aware Computing Systems
• +۳ مورد دیگر

Prof. Timo Hönig is a Professor leading the Bochum Operating Systems and System Software (BOSS) Research Group at Ruhr-Universität Bochum (RUB). Previously, he served as an Assistant Professor at Friedrich-Alexander-University Erlangen-Nürnberg (FAU), where he was part of Department of Computer Science 4. His research focuses on Energy-Aware Computing Systems, Operating Systems, and System Software design with applications in embedded and real-time systems. Key research projects include the DFG Collaborative Research Center/TR 89 (Invasive Computing) and the DFG SPP 1914 (Latency- and Resilience-Aware Networking). He has received notable awards such as the SOSP SRC Gold Medal (2019) and the ISORC Best Paper Award (2017). He actively contributes to conferences like ACM EuroSys and USENIX ATC, and has led initiatives like the Albatross runtime system for energy-efficient HPC clusters. Teaching includes courses on Energy-Aware Computing and Operating Systems Technology. His work bridges theoretical system software design with practical applications in energy efficiency and heterogeneous architectures. The BOSS group explores future system software challenges for many-core and NVM-based systems.

Daniel Mosseمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Cyber-Physical Systems
• +۸ مورد دیگر

Dr. Daniel Mosse is a Professor in the Department of Computer Science at the University of Pittsburgh. He holds a PhD from the University of Maryland, College Park, and has been a faculty member since 1993. His research focuses on distributed systems, real-time systems, power management, security, and fault tolerance, bridging operating systems and networking research. He has held leadership roles, including Chair of the Department of Computer Science since 2009 and Visiting Professorships in Brazil. His work spans grants totaling millions, addressing energy-efficient computing, secure infrastructure, and embedded systems. Education: PhD (1993), MS (1990), and BS (1985) in Mathematics and Computer Science. Research interests include distributed real-time systems, multicore systems, embedded systems, and wireless networks. Notable awards include the 2007 Provost's Innovation in Education Award and the 2006 Bellet Teaching Award. Key grants include NSF-funded projects on energy-efficient data centers, secure emergency management systems, and power-aware scheduling. He advises numerous students and teaches courses on operating systems and distributed systems. His contributions to real-time systems, fault tolerance, and energy management have been recognized through over 150 publications and international collaborations.

Rabah Aoufiمشاهده پروفایل

مدرس ارشد

• Digital Signal Processing
• Microcontroller Systems
• Control Systems
• +۳ مورد دیگر

Rabah Aoufi is a Senior Lecturer in the Department of Engineering Technology & Industrial Distribution at the College of Engineering, Texas A&M University. His primary affiliation includes the Electronic Systems Engineering Technology program. He can be reached at raoufi@tamu.edu and is located in FERM 304AA. His work focuses on digital signal processing, microcontroller systems, control systems, and engineering entrepreneurship. Research interests span embedded systems design, real-time data processing, and innovative educational projects. His publications reflect expertise in both foundational engineering principles (e.g., signal processing fundamentals) and applied technologies (e.g., microcontroller programming and big data frameworks). His articles demonstrate a career-long engagement with evolving technologies—from foundational 1980s multiprocessor architectures to modern big data systems. No scientific awards or grants are explicitly mentioned in the provided materials.

Ambika Chandraمشاهده پروفایل

استاد

• Turfgrass Breeding
• Molecular Genetics
• Turfgrass Genomics
• +۴ مورد دیگر

Dr. Ambika Chandra is Professor and Assistant Director of Texas A&M AgriLife Research & Extension in Dallas, specializing in turfgrass genetics and breeding. Her research adopts a holistic systems approach to cultivar development for home lawns, athletic fields, and golf courses, integrating molecular genetics, physiology, entomology, pathology, and socio-economics. Her research focuses on developing high-throughput phenotyping methods for evaluating biotic and abiotic stress tolerances in breeding populations. She has established interdisciplinary collaborations across turfgrass science domains to enhance sustainable turf production. Recent publications demonstrate advancements in drought resistance, salinity tolerance, and sustainable turf management, with field trials spanning multiple U.S. regions. Her work emphasizes genetic improvements for environmental resilience and reduced resource inputs. Her grants include projects on water-efficient turfgrasses and multi-state cultivar development initiatives. Dr. Chandra contributes to graduate education, training future plant breeders and turfgrass professionals.

Srinivas Shakkottaiمشاهده پروفایل

استاد

• Networking
• Wireless Networks
• Reinforcement Learning
• +۶ مورد دیگر

Srinivas Shakkottai is a Professor in the Department of Electrical and Computer Engineering (ECE) and affiliated faculty in the Department of Computer Science and Engineering (CSE) at Texas A&M University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2007), followed by a postdoctoral stint at Stanford University. Since 2008, he has been at Texas A&M, advancing through roles from Assistant to Associate Professor before attaining full Professor status. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign (2007) Postdoctoral Associate, Stanford University Research Interests: Focus on algorithms for communication, energy, and transportation networks. Key areas include wireless networks, reinforcement learning, caching/content distribution, multi-agent learning, game theory, networked markets, and systems design. He co-directs the LENS Laboratory and the Texas A&M Initiative on Connected Intelligence (TICI). Key Awards: NSF CAREER Award (2012) Google Faculty Research Award (2010) Defense Threat Reduction Agency Young Investigator Award (2009) Grants & Labs: Leads research projects funded by NSF, including collaborative efforts on EdgeRIC (NextG cellular networks) and Caching systems. Active in labs focused on AI-driven network optimization and intelligent control systems. Lab & Teams: Co-director of LENS Lab and TICI, emphasizing real-time intelligent control, edge computing, and networked intelligence.

Donniell E. Fishkindمشاهده پروفایل

استاد پژوهشی

• Graph Theory
• Matrix Analysis
• Medical Imaging
• +۳ مورد دیگر

Donniell E. Fishkind is an Associate Research Professor and Director of Undergraduate Studies in the Department of Applied Mathematics and Statistics at Johns Hopkins University. He is affiliated with the Institute for Computational Medicine and the Johns Hopkins University Human Language Technology Center of Excellence. Education: BS and MS in Mathematics from the University of Maryland, Baltimore County (1991, 1993); MSE and PhD in Applied Mathematics and Statistics from Johns Hopkins University (1995, 1998). Research Interests Graph theory and matrix analysis Topological aspects of medical imaging Graph matching and vertex nomination Probabilistic path planning Random graph models for social networks Scheduling problems Awards 2023 Excellence in Teaching, Advising, and Mentoring Award His advising and mentoring contributions are highlighted by student projects such as optimizing baseball schedules for the Frontier League. Teaching responsibilities include course materials hosted on Blackboard.

Ioannis Kyriakouمشاهده پروفایل

استاد

• Actuarial Science
• Quantitative Finance
• Financial Mathematics
• +۱۳ مورد دیگر

Professor Ioannis Kyriakou is a leading academic in actuarial finance and quantitative methods at Bayes Business School, City St George's, University of London, where he serves as Professor of Actuarial Finance and Director of the MSc in Actuarial Science and MSc in Actuarial Management. He holds a visiting professorship at the University of Eastern Piedmont and has previously served as affiliate faculty at the Cyprus International Institute of Management. His research spans actuarial science, derivatives pricing, risk management, computational finance, and machine learning applications in finance and insurance. His research interests focus on quantitative finance , stochastic modeling , numerical methods , and machine learning , particularly in the context of derivative pricing , pension product design , energy and commodity markets , and investor sentiment . He has developed advanced computational techniques such as moment-based approximations and transform methods for financial modeling. His work integrates simulation, optimization, and data-driven approaches to solve complex financial and actuarial problems. The recent publications reflect a strong trend toward interdisciplinary research, combining machine learning with financial economics , energy efficiency forecasting , mutual fund performance , and climate risk . His work frequently appears in top journals like Operations Research , Journal of Financial and Quantitative Analysis , and European Journal of Operational Research , showcasing expertise in both theoretical and applied finance. European Journal of Operational Research (2020) Editors' Award for Excellence in Reviewing Cass Business School (2014) Prize for Excellence in Teaching and Learning Dimitris N. Chorafas Foundation (2009) Prize for outstanding PhD research work EPSRC Doctoral Training Award (2008) He supervises several PhD students in areas such as derivatives pricing, machine learning in actuarial science, and pension optimization. He has received research support through editorial leadership, consultancy (e.g., with Lloyd’s Treasury), and academic collaboration. He is actively involved in organizing conferences and workshops, including the Finance and Business Analytics Conference. He is affiliated with research groups focusing on financial modeling , actuarial computation , and machine learning in insurance , contributing to both academic and industry-facing initiatives. His co-authored book Machine Learning in Insurance (MDPI, 2020) highlights his leadership in bridging data science with actuarial applications.