جستجوی استاد راهنمای دانشگاهی

Chatzinikolaou Athanasiosمشاهده پروفایل

استاد

• Exercise Physiology
• Sports Training Adaptations
• Biochemistry of Exercise
• +۷ مورد دیگر

Professor Chatzinikolaou Athanasios is a faculty member at the Department of Physical Education and Sport Sciences, Democritus University of Thrace. His expertise lies in the Theory of Strength Training and Conditioning, with a focus on Sports Training Theory & Application. He teaches courses such as Principles of Planning and Guidance of Athletic Training, Biochemistry of Exercise, and Design of Resistance Training. His research interests span Sports Training Adaptations, Biochemistry of Exercise, Muscle Inflammation, Exercise-Induced Muscle Damage, and Obesity and Exercise. He has published extensively on topics like HIIT protocols, skeletal muscle recovery, and antioxidant responses to exercise. Recent work includes studies on vitamin D status in overweight adults, bioelectrical impedance equations for young athletes, and warm-up protocols in football and soccer. His research often explores practical applications in youth and elite sports, emphasizing evidence-based training strategies. Notable findings include the role of protein supplementation in muscle recovery, the impact of HIIT on metabolic health, and the biomechanical effects of warm-up techniques. Ongoing projects focus on optimizing training methods for different age groups and evaluating recovery kinetics post-exercise.

Michael Stonebrakerمشاهده پروفایل

استاد مدعو

• Database Systems
• Big Data
• Data Integration
• +۴ مورد دیگر

Michael Stonebraker is an Adjunct Professor at the Massachusetts Institute of Technology (MIT) and a leading figure in database systems research. He is affiliated with the MIT Computer Science and Artificial Intelligence Lab (CSAIL) and actively contributes to the Data Systems Group. His work focuses on designing scalable, efficient data management solutions for emerging technologies. Big Data Systems Predictive Elastic Database Systems Data Discovery & Integration Autonomous Vehicle Sensor Data Management Database Design Innovations Stonebraker's projects include Data Civilizer , which streamlines data preparation workflows, and systems for autonomous vehicle data management. His research emphasizes practical database design and elasticity in response to workload fluctuations. He collaborates extensively within CSAIL's Data Systems Group on topics spanning systems, networking, and cybersecurity. Stonebraker has been recognized for his contributions to database technology, though specific awards aren't listed in this text. He works with a dedicated team of researchers and students in the Data Systems Group, though no direct advisees are named. His lab focuses on addressing fundamental challenges in data management, including large-scale discovery, transformation, and predictive modeling for modern applications.

Tanvir Ahmed Khanمشاهده پروفایل

استادیار

• Computer Architecture
• Data Center Processing
• Compilers
• +۶ مورد دیگر

Tanvir Ahmed Khan is an Assistant Professor at Columbia University, specializing in computer architecture and systems. His research focuses on enabling efficient data center processing through hardware-software co-design, particularly in instruction and data caching, branch prediction, and profile-guided optimizations. His work has been adopted by industry leaders like Intel and ARM, and recognized with awards such as the MICRO 2022 Best Paper Award and the ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award. He holds a PhD from the University of Michigan, where he previously conducted research on techniques like I-SPY, DMon, and Ripple. His current projects aim to address bottlenecks in data center applications by leveraging repetitive execution patterns for predictive optimizations. Khan has mentored numerous students, including Kan Zhu (first place in MICRO 2022 SRC) and Yuxuan Zhang (first author of OCOLOS). His service roles include program committee memberships for ISCA, MICRO, and CGO, reflecting his influence in the field. Key achievements include the Rackham Predoctoral Fellowship, Qualcomm Innovation Fellowship finalist status, and multiple SRC Best Paper awards.

Saksham Agarwalمشاهده پروفایل

استادیار

• Networking
• Systems
• Hardware
• +۳ مورد دیگر

Saksham Agarwal is an Assistant Professor in the departments of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on the intersection of networking, systems, and hardware, with a particular emphasis on host congestion control, datacenter architectures, and network protocols. He received his Ph.D. in Computer Science from Cornell University under the supervision of Prof. Rachit Agarwal and completed his undergraduate studies at IIT Kanpur. Research Interests: Host congestion control, datacenter networks, hardware-software co-design, network protocols, and systems optimization. His work includes seminal contributions like hostCC (host congestion control architecture) and Harmony (congestion-free datacenter architecture). Awards: ACM SIGCOMM Doctoral Dissertation Award (2025), Cornell CS Dissertation Award (2025), IRTF Applied Networking Research Prize (2025), Google PhD Fellowship (2019), Best Student Paper Awards (SIGCOMM 2024 and SIGCOMM 2018). Teaching: CS 438/ ECE 438 Communication Networks, ECE 598MCI Modern Cloud Infrastructure. Advising: Current students include Midhul Vuppulapati (SIGCOMM'24 Best Paper recipient), Shreyas Kharbanda, and Omar Eqbal. Labs/Teams: Lead researcher on host congestion control and datacenter network projects. Collaborates on open-source implementations of hostCC and Harmony .

Katja Dlouhyمشاهده پروفایل

استادیار

• Career Development
• Professional Mobility
• Career Success
• +۳ مورد دیگر

Dr. Katja Dlouhy is an Assistant Professor of Human Resource Management at the University of Mannheim Business School. Previously, she served as Acting Professor for Strategic Management at Technical University of Munich (2020–2022) and worked as a research assistant at the University of Mannheim (2013–2018). Doctorate in Management from University of Mannheim (2017) Diploma in Psychology from University of Koblenz-Landau (2012) Her research explores: Organizational career dynamics and professional mobility Cultural and institutional influences on workforce development Human-animal relationships in organizational contexts Recent publications examine: Boomerang mobility frameworks Career ecosystem perspectives Human-animal work engagement Occupational turnover patterns Scientific recognitions include: Best Reviewer Award (Academy of Management 2023 & 2024) Excellence in Reviewing Award (Human Resource Management 2023) Carolyn B. Dexter Award Nominee (2020) Dissertation Prizes (Karin Islinger Foundation, Volksbank Weinheim 2018) Multiple Academy of Management award finalists (2015–2018) She serves as Associate Editor for Applied Psychology and on the Human Resource Management editorial board. Her work integrates psychology and organizational studies to address contemporary workforce challenges.

• Sports Sciences
• Youth Sports
• Talent Identification and Development (TID)
• +۳ مورد دیگر

Luís Miguel Massuça is a career Associate Professor at the Faculty of Physical Education and Sport (FEFD), Universidade Lusófona de Humanidades (UL-CUL) , and a guest Assistant Professor at the Higher Institute of Police Sciences and Internal Security (ISCPSI) . He is an Integrated Researcher at the ICPOL Research Center (rated Very Good by FCT) and collaborates with CIDEFES (Lusófona University), CIFI2D (University of Porto), and the First Responder Research Laboratory (University of Kentucky, USA). Education : Doctorate in Human Kinetics (Sports Sciences), Master’s in High-Performance Training, Postgraduate in High-Performance Training, Degree in Physical Education and Sport (5 years) Research Focus : Youth sports, Talent Identification and Development (TID) , and health/wellness/performance of tactical populations (police, firefighters, military). His work includes co-authoring a book and over 50 journal articles, book chapters, and conference papers. Recent studies examine police fitness assessment , stress biomarkers , and youth athlete career trajectories . He has supervised numerous theses on topics like police physical readiness , firefighter injury mechanisms , and elite sports performance . As a former professional handball player and EHF PRO Licence Coach , his applied expertise bridges academia and tactical professions. Current affiliations span institutions in Portugal and the USA, with a focus on physical training programs for law enforcement and morphological analysis in sports.

Odysseas Papapetrouمشاهده پروفایل

استادیار

• Streaming Data Processing
• Big Data Management
• Approximation Algorithms
• +۵ مورد دیگر

Odysseas Papapetrou serves as Assistant Professor in the Information Systems group within the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His academic journey spans multiple prestigious institutions across Europe, establishing him as a significant contributor to the field of data management and stream processing. Academic Credentials: BSc in Computer Science from University of Cyprus MSc in Advanced Information Technologies from University of Cyprus MSc in Computer Science from University of Saarland and Max Planck Institute (2005) PhD in Computer Science from University of Hannover and L3S Research Center (2011) Dr. Papapetrou's research focuses on cutting-edge challenges in data-intensive systems, with particular emphasis on real-time processing of streaming data. His work develops approximation algorithms for distributed Big Data management, distributed data streams processing, and sketching/summarization techniques. He has made significant contributions to cloud computing aspects including cloud federations, cloud-based stream processing, and privacy-preserving query execution. His research bridges theoretical foundations with practical implementations, addressing the growing need for efficient data processing in high-velocity environments. Analysis of his recent publications reveals a consistent focus on multivariate data analysis, with increasing specialization in spatial data streams and advanced similarity search techniques. His work demonstrates a progression from foundational stream processing to increasingly complex multivariate scenarios, reflecting the evolving challenges in Big Data analytics. The publications show strong collaboration patterns with researchers across Europe, particularly in the Netherlands and Switzerland. Dr. Papapetrou actively contributes to the academic community through service roles including frequent participation as PC member and reviewer for top-tier data management conferences and journals such as TKDE and VLDB Journal. He serves as a grants evaluator for the Hong Kong Research Grants Council (RCG) and other funding schemes. His current educational activities at TU/e include teaching Capita Selecta Databases, Seminar Datamanagement, Data-intensive Systems and Applications, Big Data Management, and Datamodeling and Databases. He currently leads research within the Database Group at TU/e, focusing on developing novel techniques for stream processing and Big Data management. His work has received funding from the European Union's Horizon Europe research and innovation program STELAR under grant agreement No. 101070122, supporting his research on advanced data stream processing techniques.

• Database Systems
• Stream Processing
• Internet of Things (IoT)
• +۷ مورد دیگر

Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.

Shensheng Tangمشاهده پروفایل

استاد

• Wireless Networking
• Computer Networking
• Cloud Computing
• +۱۵ مورد دیگر

Shensheng Tang is a Professor of Physics and Engineering at Bethel University, College of Arts and Sciences. He joined the university in 2024, bringing extensive academic and industry experience in electrical and computer engineering. He holds a Ph.D. in Electrical Engineering from the University of Toledo and a B.S. from Tianjin University. Ph.D. in Electrical Engineering, University of Toledo, 2006 B.S. in Electrical Engineering, Tianjin University, 1990 Dr. Tang's research focuses on wireless and computer networking, IoT, cloud/fog/edge computing, embedded systems, FPGA, cybersecurity, AI/ML, and performance modeling. His work integrates theoretical modeling with practical applications in smart grids, healthcare IoT, and 5G/Wi-Fi convergence. He has published over 100 peer-reviewed papers, with recent publications emphasizing fog-based IoT performance, cloud system modeling, FPGA acceleration, and network security using spatiotemporal models and machine learning. His publication record reveals a strong trend in applying analytical models—particularly queueing theory, Markov processes, and signal modeling—to optimize network performance, reliability, and security in distributed and resource-constrained environments. He frequently explores cross-layer design in wireless networks and integrates AI techniques for traffic analysis and threat detection. MWSU 2012 Dr. James V. Mehl Outstanding Faculty Scholarship Award Best Paper Award, IEEE/CIC ICCC 2016 Excellent Presentation Award, IEEE ICCSN 2016 Spotlight Paper, IEEE TPDS July 2013 Marquis Who's Who in America (2010–2016 editions) Marquis Who's Who in Science and Engineering (10th–12th editions) Dr. Tang has advised numerous students and researchers throughout his career, though specific names are not listed. He has secured significant research output and editorial leadership roles, serving as Editor-in-Chief for the Journal of Science and Engineering Research (JSER) and Journal of Smart Technology Applications (JSTA), and as editor for several international journals including MDPI's IoT. He has chaired program committees and organizing committees for major conferences such as CMVIT, CCISP, ASSE, and ICCSN. His professional service includes active roles in IEEE (Senior Member), ACM SIGMETRICS, INFORMS, and Sigma Xi. He leads research in performance modeling and optimization for next-generation networks and is involved in editorial and conference leadership that shapes discourse in smart technologies, IoT, and cybersecurity.

Lance Mentheمشاهده پروفایل

استاد

• Intelligence Analysis
• Artificial Intelligence
• Modeling and Simulation
• +۳ مورد دیگر

Lance Menthe is a Professor of Policy Analysis at the RAND School of Public Policy and a Senior Physical Scientist at the RAND Corporation. He serves as Quality Assurance Manager for the RAND National Security Research Division. His expertise spans intelligence analysis, artificial intelligence, and modeling/simulation, with a focus on U.S. Air Force and Army applications such as command-and-control systems, surveillance, and AI integration. He pioneered the SCOPEM agent-based model for air/ground/space domain analysis. Menthe holds a Ph.D. in Physics from UCLA (thesis on DNA conformation physics). Education: Ph.D. in Physics, University of California, Los Angeles. Research interests emphasize AI limitations in warfare, cybersecurity, and dynamic ISR systems. His work addresses challenges like adversarial evasion, distributional shifts in datasets, and wargame simulations. He evaluates airborne ISR value propositions and advocates for AI-driven intelligence modernization. Notable contributions include the Collections Operations Model (COM) for C3ISR analysis, methodologies for assessing ISR effectiveness, and frameworks for enhancing Air Force intelligence analysis through machine learning. His research prioritizes real-world operational relevance and has informed Pentagon strategies on technology integration.

• Emergency Medicine
• Acute Care
• Risk Prediction Modeling
• +۴ مورد دیگر

Patricia Stassen is an Associate Professor at the Faculty of Health, Medicine and Life Sciences , specializing in General Internal Medicine . She is affiliated with the Carim - Vessels and CAPHRI - Optimising Patient Care research groups. Emergency Department Systems Acute Care Pathways Machine Learning in Risk Prediction Sepsis and Mortality Analytics Her research focuses on optimizing patient care through: Emergency triage efficiency and cognitive load analysis Development of explainable AI models for risk stratification Primary-to-secondary care transition protocols Flash mob studies for real-time patient journey mapping Recent publications highlight trends in emergency patient management , with emphasis on: AI-driven risk prediction models Cognitive burden in acute care settings Regional healthcare coordination Telephone referral diagnostic accuracy

• Database Systems
• NoSQL Databases
• Cloud Data Management
• +۷ مورد دیگر

Norbert Ritter is a professor at the University of Hamburg, Germany, with a long-standing and active research career in database systems and data management. His work spans from foundational database theory to modern cloud and real-time systems, with significant contributions to NoSQL, polyglot persistence, and web performance optimization. He collaborates extensively with researchers such as Wolfram Wingerath, Felix Gessert, and Fabian Panse, and has published consistently in top-tier venues including VLDB, ICDE, and BTW. Research Interests: Database Systems NoSQL and Cloud Data Management Real-Time and Stream Processing Probabilistic and Incomplete Data Web Compression and Performance Spatiotemporal and Simulation Data His recent publications (2020–2024) show a strong focus on polyglot data management, adaptive systems, and efficient web encoding techniques. These works reflect trends toward heterogeneous storage, intelligent compression, and scalable simulation data handling, particularly in smart city and scientific contexts. The integration of machine learning with spatiotemporal data and graph-based modeling is also emerging as a key theme. Scientific Contributions: Co-authored influential tutorials and books on real-time data management and NoSQL systems. Contributed to reference works such as the Encyclopedia of Big Data Technologies . Editorial roles in journals like Datenbank-Spektrum and organizing roles in BTW workshops. Ritter has advised or collaborated with several researchers, though formal student listings are not available. His work often involves system design, benchmarking, and real-world deployment, suggesting involvement in grants and applied research projects. He has also contributed to the development of tools and frameworks for data integration, caching, and simulation environments.

• Fog Computing
• Internet of Things (IoT)
• Edge Computing
• +۷ مورد دیگر

Dr. Mohammed Al-Khafajiy is a researcher specializing in fog computing, Internet of Things (IoT), and intelligent systems, with a strong focus on healthcare, environmental monitoring, and distributed computing. His work frequently appears in high-impact journals and conferences, often in collaboration with researchers such as T. Baker and Z. Maamar. His research interests lie at the intersection of distributed computing and real-world applications. Key areas include: Fog and edge computing architectures Trust and security in decentralized systems IoT and IoE for smart healthcare and disaster response Cognitive models in IoT UAV-integrated monitoring systems Optimization of workload and resource allocation in fog environments The 15 most recent publications show a consistent trend towards enhancing performance, reliability, and intelligence in fog-based systems. His work emphasizes practical applications in healthcare (remote monitoring, emergency systems) and environmental resilience (disaster prediction), leveraging fog offloading, fog-to-fog collaboration, and cloud-edge integration. The publications span computer science, networking, and applied AI domains, reflecting interdisciplinary innovation. Dr. Al-Khafajiy has not been mentioned as receiving any specific scientific awards in the provided data. He has collaborated extensively with researchers across institutions, contributing to advancements in IoT frameworks, trust models, and cognitive systems. Although no formal advising or grant information is provided, his leadership in multiple publications suggests active involvement in research projects. He has contributed to the development of frameworks such as fog-2-fog coordination and cognitive fog models, indicating leadership in system design and innovation. There is no mention of specific labs or research teams in the provided text, but his collaborations suggest participation in multi-institutional research initiatives focused on smart systems and distributed computing.

Fahad Rafique Dogarمشاهده پروفایل

دانشیار

• Computer Networks
• Human-Computer Interaction (HCI)
• Generative AI
• +۴ مورد دیگر

Fahad Rafique Dogar is an Associate Professor in the Computer Science Department at Tufts University, also affiliated with the Jonathan Tisch College of Civic Life. He holds academic positions at the Tufts School of Engineering and has been a visiting assistant professor at LUMS, Pakistan. His research focuses on network systems, human-computer interaction, and generative AI, emphasizing social impact through technologies like TwIPS (autism support texting app) and AR-based educational tools. Education: B.S. from LUMS (Pakistan), Ph.D. from Carnegie Mellon University, Postdoc at Microsoft Research (UK) Research interests span network resource management, cloud computing, and accessibility technologies. He leads the NAT (Networking At Tufts) research group and has received grants including NSF awards and industry fellowships. Recent grants: NSF CNS Award ($850K, 2021-2025), Meta/Facebook support ($150K, 2020-2022) Notable awards include the LUMS Vice Chancellor Alumni Achievement Award (2021) and VMWare Early Career Fellowship (2019).

• Computer Architecture
• High-Performance Computing
• Hardware Transactional Memory
• +۷ مورد دیگر

Adria Armejach Sanosa is a Senior Lecturer in the Department of Computer Architecture at the Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing in Europe. His research spans computer architecture, high-performance computing, memory systems, and hardware acceleration for genomics and machine learning. PhD from UPC His research interests focus on optimizing computer systems for performance and efficiency, particularly in the areas of hardware transactional memory, cache optimization, RISC-V architectures, and acceleration of bioinformatics workloads. He investigates how to improve data movement, prefetching, and parallelism in large-scale heterogeneous systems. His work combines architectural innovations with practical implementations on real-world HPC platforms. The most recent articles reflect a strong trend towards high-performance computing for genomics, sparse data handling, and efficient hardware/software co-design. Topics include genomics benchmarking on ARM processors, tensor marshaling, RTL simulation scalability, and low-precision training for deep neural networks. These works demonstrate a consistent focus on bridging architectural research with real-world applications in science and AI. HiPEAC Paper Award 2024 HiPEAC Paper Award Armejach has advised several doctoral students, including J. Pavón, G. López, and J. Osorio. He has been involved in numerous competitive R&D+i projects such as Digital Autonomy for RISC-V in Europe, Laboratorio Zettaescala de Barcelona, and Genome Analysis Acceleration on HPC Architectures. These projects are often funded by national and European programs, indicating strong recognition and support for his research. He collaborates extensively within the CAP (High-Performance Computing) research group and with key figures like Miquel Moreto, Mateo Valero, and Osman Unsal. He is a member of the CAP research group and contributes to initiatives like the Laboratory for Open Computer Architecture and systems (RISC-V Chip Development) and the Barcelona Zettascale Lab. These labs focus on open hardware, European technology sovereignty, and next-generation supercomputing. His work on Metro-MPI for RTL simulation and hardware accelerators for databases highlights his contributions to both design automation and data-intensive computing.