جست‌وجوی استاد و پژوهشگر

Sami Al Daghistaniمشاهده پروفایل

دانشیار

• Islamic Economics
• Environmental Ethics
• Islamic Law
• +۵ مورد دیگر

Sami Al Daghistani is an Associate Professor in Contemporary Islamic Studies at Lund University's Centre for Theology and Religious Studies, a Researcher at the Centre for Advanced Middle Eastern Studies (CMES), and holds associate appointments at Columbia University (Middle East Institute) and the Brooklyn Institute for Social Research. His work bridges Islamic economics, environmental ethics, legal philosophy, and intellectual history, emphasizing interdisciplinary methodologies and cross-cultural analysis. Ph.D. (2017) in Islamic Studies from Leiden University, co-supervised by Columbia University and WWU Münster Postdoctoral Fellow at Norwegian School of Theology (2020-2024) Visiting Researcher at Istanbul University and Swedish Research Institute in Istanbul His research focuses on decolonizing economic thought through Islamic traditions, integrating environmental ethics, moral philosophy, and legal theory. Recent publications analyze intersections between Islamic economic cosmology, pre-modern intellectual traditions, and contemporary societal challenges. Key projects include: SWEMENA (Swedish Middle East and North Africa Network), Critical Islamic Studies consortium (founded 2021), and GreenMENA. He has received the 31st World Book of the Year Award in Islamic Studies (2024) and Islamic Economics Research Award (2024). Academic roles span teaching at Lund University (Islamology courses), guest lectures at Istanbul Forum, and leadership in international research collaborations. His translations of Ibn Baṭṭūṭa and Ibn Ṭufayl into Slovenian demonstrate linguistic expertise and cultural bridge-building.

Yogananda Isukapalliمشاهده پروفایل

استاد آموزشی

• Signal Processing
• Computer Engineering
• Digital Design
• +۵ مورد دیگر

Yogananda Isukapalli is a Teaching Professor and Vice Chair in the Computer Engineering Program at the Electrical and Computer Engineering Department , University of California, Santa Barbara . He joined the faculty in Winter 2017 after a career as a staff scientist at Broadcom (2010–2017), where he designed Wi-Fi chips (11n/11ac/11ax) and worked on underwater wireless communication models during a postdoctoral stint at Scripps Institution of Oceanography (2009–2010). His PhD in Communication Theory and Systems from UC San Diego (2009) forms the basis of his expertise in wireless systems and digital design .

Phillip B. Gibbonsمشاهده پروفایل

استاد

• Parallel Computing
• Distributed Systems
• Databases
• +۴ مورد دیگر

Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.

Hyosang Leeمشاهده پروفایل

استادیار

• Tactile Sensing
• Robotics
• Soft Robotics
• +۴ مورد دیگر

Hyosang Lee is an Assistant Professor in the Robotics Section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He holds a PhD from KAIST and has held research positions at the Max Planck Institute and University of Stuttgart. His work focuses on tactile sensing technologies, including artificial skin development, soft robotics, and integration of sensory systems with AI. Bachelor's: Mechanical Engineering, Korea University Master's: Robotics and Mechanical Engineering (double major) PhD: Mechanical Engineering, KAIST (2017) Research interests span tactile sensor design, electrical impedance tomography (EIT), and human-robot interaction. His group emphasizes creating scalable, flexible tactile systems for robots. Recent work includes air pressure sensing for force estimation and biomimetic skin materials. Publications highlight innovations in multi-directional force sensing, soft component technologies, and haptic interfaces for autism therapy. He teaches 'Dynamics and Control of Robotic Systems' and serves on the editorial board of npj Robotics . No formal student advisees are listed, though his lab, the Tactile Sensing and Robotic Skin Group , likely involves graduate researchers. His research contributes to UN Sustainable Development Goals related to health and technology.

Andrew Warfieldمشاهده پروفایل

استاد مدعو

• Computer Systems Software
• Virtualization
• Distributed Storage
• +۳ مورد دیگر

Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.

James Andersonمشاهده پروفایل

استادیار

• Optimal Control Theory
• Robust Control
• Mathematical Programming
• +۴ مورد دیگر

James Anderson is an Assistant Professor in the Department of Electrical Engineering at Columbia University, with affiliations to the Data Science Institute (DSI) and multiple research centers including the Computing Systems for Data-Driven Science and Foundations of Data Science. Prior to Columbia, he was a Senior Research Scientist at Caltech’s Computing + Mathematical Sciences division (2016–2019) and held a Junior Research Fellowship at the University of Oxford’s Department of Engineering Science (pre-2012). He earned his DPhil (PhD) in Engineering Science from Oxford in 2012. His research focuses on optimal/robust control theory, mathematical programming, data privacy, and cyber-physical systems, with applications in smart grids, systems biology, and power systems. Recent work emphasizes energy storage strategies, distributed control algorithms, and cybersecurity in critical infrastructure. His publications span advanced control methodologies (e.g., reinforcement learning for LQR problems), energy market dynamics, and resilient system designs. Notable contributions include frameworks for decision-focused energy storage arbitrage and defenses against false data attacks in power grids. He actively collaborates on federated learning approaches for distributed systems and has pioneered techniques for system-level synthesis in cyber-physical architectures. Anderson’s affiliations include the Data Science Institute (DSI) and specialized centers focused on data-driven science and energy systems. His work bridges theoretical control advancements with real-world applications in energy and healthcare.

Ye Zhishengمشاهده پروفایل

دانشیار

• Inventory Control
• Reliability Engineering
• Emergency Response Systems
• +۴ مورد دیگر

Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.

Mihaela van der Schaarمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Healthcare
• +۸ مورد دیگر

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, leading the van der Schaar Lab. She holds dual affiliations with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Centre for Mathematical Imaging in Healthcare. Her research focuses on healthcare AI, machine learning, and operations research. She has authored over 250 journal articles and 275 conference papers, with notable contributions to synthetic data for privacy, causal inference, and clinical decision-making. Her work has led to 35 U.S. patents, including foundational innovations in streaming video compression (MPEG-4 standards). Awards include the Oon Prize (2018), IEEE Fellow (2009), and recognition as the UK's most-cited female AI researcher (2019). Leadership roles include Director of the Cambridge Centre for AI in Medicine and Co-Director of the European Laboratory for Learning and Intelligent Systems. She has mentored global academic leaders and pioneered initiatives like the Inspiration Exchange for early-career researchers. Key projects include predictive models for hospital resource allocation during pandemics and AI tools for personalized medicine. Publications span machine learning theory, healthcare applications, and interdisciplinary fields like network science. Her lab's impact includes tools like AutoPrognosis (automated ML for clinical prediction) and SynthCity (synthetic healthcare data generation).

Duminda Wijesekeraمشاهده پروفایل

استاد

• Cybersecurity
• Cyber-Physical Systems
• Digital Forensics
• +۵ مورد دیگر

Duminda Wijesekera serves as Professor in the Department of Cyber Security Engineering and Department of Computer Science at George Mason University, where he was inaugural chairman of the Cyber Security Engineering Department until December 2022. He concurrently held the position of visiting research scientist at the National Institute of Standards and Technology (NIST) from 2007-2022 and maintains status as a fellow at the Potomac Institute of Policy Studies. He leads the Mason Innovation Laboratory at Mason Square, driving translational research in cyber-physical security. His educational foundation includes: PhD in Computer Science, University of Minnesota (1997) PhD in Mathematical Logic, Cornell University (1990) BSc in Mathematics, University of Colombo Professor Wijesekera's research centers on cyber-physical system security , with pioneering work in Intelligent Transportation Systems spanning trains, aircraft, and connected vehicles. His digital forensics innovations establish frameworks for evidence-based scenario reconstruction and error management, while his formal methods research provides mathematical guarantees for safety-critical systems. Current projects address Next G-based edge services, digital twin vulnerability detection, and healthcare security architectures, consistently bridging theoretical rigor with real-world infrastructure protection. Analysis of his 2022-2025 publications reveals intense focus on autonomous vehicle security (38% of recent output), including traffic signal control optimization, ramming attack countermeasures, and CARLA-based scenario validation. Digital forensics using AI (20%) and secure manufacturing/edge computing (27%) constitute other major thrusts, demonstrating how formal verification and machine learning converge to solve complex cyber-physical security challenges across transportation, energy, and healthcare domains. His scientific recognition includes: CCI Impact Award (2022) for groundbreaking cyber-physical security contributions Fellowship at the Potomac Institute of Policy Studies for cybersecurity policy leadership Professor Wijesekera has secured substantial research funding through: NIST grants for health record security frameworks (2014-2015) US Department of Transportation projects on wireless frequency mapping for high-speed rail (2013-2014) Cyber Security Research Alliance funding for trust architectures in cyber-physical systems (2014) Commonwealth Cyber Initiative awards for autonomous vehicle security and energy-efficient manufacturing His industry partnerships with Honeywell and NIST ensure practical impact of theoretical research. The Mason Innovation Laboratory under his direction serves as an interdisciplinary hub for cyber-physical security, integrating researchers from computer science, electrical engineering, and policy studies to develop deployable solutions for transportation networks, power grids, and critical infrastructure protection.

• Network Security
• Privacy-Preserving Technologies
• Machine Learning for Networking
• +۵ مورد دیگر

Ke Xu is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology. With extensive research contributions in network security, privacy-preserving technologies, and machine learning applications for networking, Professor Xu has established himself as a leading researcher in computer science. Professor Xu's research interests span network security, privacy-preserving technologies, machine learning for networking, federated learning, internet protocols, encrypted traffic analysis, blockchain applications, and AI in networking. His work bridges theoretical foundations with practical implementations, focusing on real-world security challenges and network optimization problems. He has developed novel frameworks for secure network operations, privacy-preserving data sharing, and efficient AI deployment in distributed environments. Professor Xu's publication record shows a clear trend toward integrating artificial intelligence with traditional networking challenges. His recent work explores federated learning security, encrypted traffic analysis using deep learning, and novel approaches to network security that leverage machine learning techniques. The interdisciplinary nature of his research spans computer networking, security, privacy, and artificial intelligence. Professor Xu has received recognition for his contributions to network security and privacy-preserving technologies through publications in top-tier venues including IEEE journals, ACM conferences, and security symposia. His work has appeared in IEEE Transactions on Dependable and Secure Computing, IEEE/ACM Transactions on Networking, and security conferences like CCS and NDSS. Professor Xu actively collaborates with researchers across institutions, supervising students and junior researchers in exploring cutting-edge problems in network security and AI. His research has been supported by significant grants focusing on network security, privacy, and intelligent networking infrastructure. He leads projects that address fundamental challenges in secure communication, privacy-preserving data analysis, and intelligent network management. Professor Xu is involved with research laboratories focusing on network security and intelligent systems at Tsinghua University. His team works on developing practical security solutions, privacy frameworks, and AI-enhanced networking protocols that address real-world challenges in today's increasingly connected world.

Juergen Dingelمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Engineering
• Formal Methods
• +۷ مورد دیگر

Juergen Dingel is a Professor in the School of Computing at Queen's University, Canada. He joined the faculty in 2000 and holds a PhD in Computer Science from Carnegie Mellon University (1999). His research focuses on software modeling, model-driven engineering, formal methods, and formal verification, with applications in real-time systems and embedded systems. He leads the Modeling and Analysis in Software Engineering (MASE) research group. Education: PhD in Computer Science, Carnegie Mellon University (1999) M.Sc. in Pure and Applied Logic, Berlin University of Technology (1994) M.Sc. in Computer Science, Berlin University of Technology (1992) Research Interests: Model-driven engineering and transformation Formal specification and verification Automated testing and debugging Real-time and embedded systems Service composition and distributed systems His work emphasizes practical tools like Papyrus-RT and MDebugger , integrating formal methods into software development. Grants & Collaborations: Funded by NSERC, OCE, and industry partners (IBM, GM, Ericsson) Focus on automotive systems, IoT, and safety-critical applications Service: Editorial board member for SoSyM , STTT , and JOT Former chair of the MODELS Steering Committee (2016–2018) PC co-chair for MODELS 2014 and FMOODS/FORTE 2011 Labs & Teams: Leads the MASE group, which develops open-source tools for model-driven engineering. Collaborates with industry on automotive and IoT projects.

Ahmad Afsahiمشاهده پروفایل

استاد

• Parallel Processing
• High-Performance Computing
• Network-Based Computing
• +۵ مورد دیگر

Dr. Ahmad Afsahi is a Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada. He leads the Parallel Processing Research Laboratory (PPRL) and chairs the Graduate Studies committee in ECE. His research focuses on parallel processing, high-performance computing (HPC), and network-based systems, with emphasis on communication runtime systems, accelerated computing, and deep learning infrastructure. Education: Ph.D. (Electrical Engineering, 2000) from University of Victoria; M.Sc. (Computer Engineering, Sharif University of Technology); B.Sc. (Computer Engineering, Shiraz University). Research interests include parallel programming models, MPI optimization, GPU-aware communication, network-aware algorithms, and power-efficient HPC systems. He is a Senior Member of IEEE, ACM member, and licensed Professional Engineer in Ontario. Key Awards: Canada Foundation for Innovation Award, Ontario Innovation Trust Award. Over 50 publications in top venues like SC, EuroMPI, IPDPS, and IEEE journals. Current teaching includes cluster computing and digital systems. Labs/Groups: PPRL, Queen's Collaborative Graduate Specialization in Computational Science and Engineering, Data, Analytics, and Computing (DAC) Research Group.

Samuel Chengمشاهده پروفایل

دانشیار

• Information Theory
• Signal and Image Processing
• Remote Sensing
• +۴ مورد دیگر

Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.

Jiannong Caoمشاهده پروفایل

استاد

• Cloud and edge computing
• Parallel and distributed computing
• Big data analytics
• +۲ مورد دیگر

Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.

• Machine Learning
• Data Mining
• High-Performance Computing
• +۵ مورد دیگر

Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.