جست‌وجوی استاد و پژوهشگر

Trung Q. Duongمشاهده پروفایل

استاد

• Wireless Communications
• Quantum Communications
• Quantum Machine Learning
• +۸ مورد دیگر

Trung Q. Duong is a Canada Excellence Research Chair and Full Professor at Memorial University of Newfoundland, Canada. He specializes in wireless and quantum communications, with a focus on 5G/6G networks, IoT applications, and quantum machine learning. His research addresses challenges in network optimization, digital twin integration, and UAV-based communication systems. Education: PhD in Telecommunications Systems from Blekinge Institute of Technology, Sweden. Research Interests: Quantum machine learning, real-time optimization for UAVs, integrated satellite-terrestrial networks, and security in communications. His work spans applications in healthcare, disaster management, and smart cities. Key Achievements: Over 500 publications (20,000+ citations, h-index 76), $54M+ in grants, and prestigious awards like the Newton Prize and IEEE Fellowship. He serves as an editor for top journals such as IEEE Trans. on Wireless Communications and IEEE Wireless Communications Letters. Global Collaborations: Visiting Professorships at National Chung Cheng University (Taiwan), Kyung Hee University (South Korea), and Thuyloi University (Vietnam). Active in international grant review panels across 20+ countries.

Tian Guoمشاهده پروفایل

دانشیار

• Cloud Computing
• Edge Computing
• AR/VR
• +۴ مورد دیگر

Tian Guo is an Associate Professor in the Computer Science Department at Worcester Polytechnic Institute (WPI), where he leads research in systems for machine learning and augmented reality. He joined WPI after completing his PhD at the University of Massachusetts Amherst in 2016. His educational background includes: Bachelor of Engineering from Nanjing University (2010) Master of Arts from University of Massachusetts Amherst (2013) PhD from University of Massachusetts Amherst (2016) Professor Guo's research focuses on designing systems mechanisms and policies to handle trade-offs in cost, performance, and efficiency for emerging applications, particularly in the areas of: Cloud/edge resource management for machine learning workloads Deep learning inference optimization for mobile applications Augmented reality systems with focus on lighting estimation Distributed training frameworks for deep learning models His recent publications demonstrate a strong trend toward improving system support for deep learning applications, with particular emphasis on mobile and edge computing environments. His work bridges the gap between theoretical machine learning advances and practical system implementations. Professor Guo has received significant recognition for his research: National Science Foundation CAREER AWARD (2023) National Science Foundation CRII AWARD (2018) Outstanding Achievement by a Young Alum, Manning College of Information & Computer Sciences, UMass Amherst (2022) Best Paper Award, ACM Multimedia Systems Conference (2020) He actively mentors graduate students and has advised several PhD and Master's students to completion. His research is supported by major funding from the National Science Foundation, Google Cloud, and VMware Research. Professor Guo leads The Cake Lab at WPI, which focuses on creating efficient and effective systems for emerging applications, particularly those involving machine learning and augmented reality.

Rezaul Chowdhuryمشاهده پروفایل

دانشیار

• Algorithms and Data Structures
• Cache-efficient Computing
• Computational Biology
• +۸ مورد دیگر

Rezaul Chowdhury is an Associate Professor in the Department of Computer Science at Stony Brook University (SBU), with a joint appointment at the Institute for Advanced Computational Sciences (IACS). His research focuses on algorithms and data structures for efficient serial and parallel computing, computational biology, and experimental algorithmics. He leads the Theoretical and Experimental Algorithmics (TEA) Group, emphasizing both algorithm design and engineering. Notable contributions include the Pochoir stencil compiler and the AutoGen system for dynamic programming algorithms. Chowdhury earned his Ph.D. from UT Austin, working on cache-efficient algorithms, and held postdoctoral positions at MIT and UT Austin. He has received prestigious awards, including the NSF CAREER Award and a Best Paper Award at IPDPS 2010. His work spans parallel programming, cache-oblivious algorithms, and bioinformatics applications like protein-protein docking (F2Dock). Teaching includes advanced courses on algorithms, parallel computing, and supercomputing. He advises multiple Ph.D. and master’s students and actively contributes to competitive programming through the SBU teams. His research projects are NSF-funded, focusing on stencil computations and resource-oblivious algorithms. Key software contributions include Pochoir (for stencil computations), F2Dock (protein docking), and AutoGen (automated algorithm discovery). His work bridges theory and practice, addressing challenges in multicore and distributed systems.

Dongyoon Leeمشاهده پروفایل

دانشیار

• Computer Systems
• Software Reliability
• Software Security
• +۹ مورد دیگر

Dongyoon Lee is an Associate Professor in the Department of Computer Science at Stony Brook University. His research focuses on building reliable, secure, and efficient software systems, spanning compiler design, operating systems, security, and hardware architecture. He holds a Ph.D. (2013) from the University of Michigan, Ann Arbor, and has held positions at Virginia Tech (2014-2019) and internships at Microsoft Research, NEC, and IBM. Education: Ph.D. in Computer Science & Engineering, University of Michigan, Ann Arbor (2013) M.S. in Computer Science & Engineering, University of Michigan, Ann Arbor (2009) B.S. in Electrical and Computer Engineering, Seoul National University (2004) Research Interests: Lee's work addresses challenges in software reliability (e.g., concurrency bugs, transient faults), security (e.g., memory safety, kernel permissions), and runtime systems (e.g., stream processing, distributed storage). He leads the COSY Lab (Compilers, Operating Systems, and Security), emphasizing cross-stack solutions. Recent Article Trends: Recent publications focus on homomorphic encryption compilers (e.g., DaCapo , HECATE ), energy-efficient systems ( Write-Light Cache ), and security tools like DevFuzz for device driver testing. Themes include optimizing performance-security trade-offs and addressing vulnerabilities in edge computing and embedded systems. Awards: Distinguished Paper Awards (ESEC/FSE 2018, ASE 2019), ICTAS Junior Faculty Award (2017), Google Research Award (2015), and ProQuest Distinguished Dissertation Award (2013). Advising & Grants: Supervises students in Ph.D., MS, and undergraduate programs. Secured grants including NSF SaTC funding for homomorphic encryption compilers and projects with the South Korean Agency for Defense Development (ADD). Leads research on ARM TrustZone security and energy harvesting systems. Labs/Teams: COSY Lab (Compilers, Operating Systems, and Security) at Stony Brook, collaborating on cross-disciplinary projects with industry and government partners.

Derek Eagerمشاهده پروفایل

استاد

• Computer System Performance Evaluation
• Distributed Computer Systems
• Computer Networks
• +۵ مورد دیگر

Dr. Derek Eager is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on computer system performance evaluation, distributed systems, and internet multimedia applications. He holds a Ph.D. from the University of Toronto (1984) and has held academic positions since 1984. His work includes contributions to content delivery systems, video streaming optimization, and cache algorithms. He has served in leadership roles at ACM SIGMETRICS and has authored numerous influential publications. Education: B.Sc. (Hons.) in Computer Science from the University of Regina (1979), M.Sc. and Ph.D. from the University of Toronto (1981, 1984). Research Interests: Design and performance evaluation of internet applications, particularly in content distribution and multimedia systems. Recent work includes optimizing cache strategies for 360° video and analyzing user behavior patterns. Professional Contributions: Served as SIGMETRICS Chair (2001-2003), program chair for SIGMETRICS 1987, and co-chair for SIGMETRICS 2005. Current roles include program committee memberships in ACM SIGMETRICS, MAMA, and MASCOTS (2025). Awards: Best Paper Award at ACM SIGCOMM 2013 Workshop on Future Human-Centric Multimedia Networking (FhMN 2013). Labs/Teams: Active in distributed analytics and multimedia systems research, collaborating on projects such as Spark resource management and agricultural IoT systems.

• High Performance Computing (HPC)
• Data Locality
• GPU Computing
• +۷ مورد دیگر

Dr. Didem Unat is an Associate Professor at Koç University, College of Engineering, Department of Computer Engineering . Her research focuses on High Performance Computing (HPC) , GPU Computing , and Compiler Design , with particular emphasis on data locality optimization, multi-GPU communication, and adaptive mesh refinement. She leads the Parallel and Multicore Computing Lab , driving innovations in exascale co-design and heterogeneous system optimization. ACM SIGHPC Emerging Woman Leader in Technical Computing Award (2021) ERC Starting Grant (1.5M EUR) for the SparCity Project (2025) EuroHPC Grant for Extreme Scale Technologies and Applications (2025) 2025 BAGEP Award (Science Academy) Her work bridges Computer Architecture and Machine Learning , addressing challenges in data locality , multi-GPU systems , and performance portability . Recent publications highlight trends in sparse tensor optimization , asynchronous AMR execution , and compiler support for heterogeneous memory . She has developed tools like ComScribe for intra-node GPU communication analysis and ReuseTracker for multicore reuse distance analysis. Dr. Unat’s research is supported by grants from ERC and EuroHPC , reflecting her leadership in exascale technologies and GPU-centric systems . She contributes to the evolution of tiling abstractions , dynamic LLM training , and lightweight communication detection for modern architectures.

Stephen Blottمشاهده پروفایل

دانشیار

• Unix/Linux systems
• Computer networks
• Container technologies
• +۲ مورد دیگر

Stephen Blott is an Associate Professor at the School of Computing, Dublin City University. He holds a BSc in Computing Science from Glasgow University and a PhD from the same institution. Previously, he worked as a Senior Research Associate at ETH Zurich and as a Principal Investigator at Bell Labs in New Jersey. His research focuses on Unix/Linux systems, computer networks, container technologies, DevOps, and educational tools for computer science. Key areas include operating system optimization, network security protocols, container orchestration, and pedagogical innovations in computer science education. Publications show consistent focus on network security, data management, and computational efficiency. Recent work emphasizes internet infrastructure, privacy-preserving technologies, and biomedical informatics, with strong methodological foundations in simulation and algorithm design.

Martin Winterمشاهده پروفایل

استاد

• Electrochemistry
• Energy Storage
• Battery Technology
• +۳ مورد دیگر

Prof. Dr. Martin Winter is the Scientific Director of the MEET - Münster Electrochemical Energy Technology at the University of Münster, affiliated with the Faculty of Chemistry and Pharmacy and the Institute of Physical Chemistry. His research focuses on electrochemical energy storage systems, particularly lithium-ion batteries, solid-state batteries, and advanced battery materials. He leads interdisciplinary projects on battery safety, electrolyte design, and recycling technologies. Winter holds honorary doctorates (h.c.) from multiple institutions, reflecting his contributions to electrochemical energy research. His work spans experimental and analytical methods, including operando measurements, spectroscopy, and material characterization. Key research areas include SEI formation mechanisms, high-voltage battery optimization, and sustainable battery production. Recent articles highlight advancements in lithium plating detection, polysulfide distribution analysis in Li-S batteries, and thermal safety improvements in lithium metal batteries. Winter collaborates with institutions like the Helmholtz Institute Münster and Fraunhofer Research Institution for Battery Cell Production FFB, driving innovation in battery technology and industrial applications.

Henri Casanovaمشاهده پروفایل

استاد

• High Performance Computing
• Parallel and Distributed Systems
• Workflow Management Systems
• +۴ مورد دیگر

Henri Casanova is a Professor in the Information & Computer Sciences Department at the University of Hawaiʻi at Mānoa. His research focuses on High Performance Computing (HPC), including workflow management systems, distributed application simulation, and HPC storage systems. He teaches courses such as ICS 312 (Machine-Level and Systems Programming) and contributes to the development of tools like SimGrid and WRENCH for enabling scalable workflow research. Education and Affiliations: PhD in Computer Science (implied by academic rank and publications) Member of the HPC and distributed systems research community Research Interests: Casanova’s work bridges theoretical HPC concepts with practical implementations. He emphasizes workflow automation, simulator development (e.g., SimGrid for distributed systems), and energy-aware scheduling. His research addresses challenges in large-scale data management, fault tolerance, and resource optimization across HPC, cloud, and cyberinfrastructure platforms. Publications: Recent work includes advancements in workflow benchmarking (WfBench), automated workflow generator tools (WfChef), and simulation-driven scheduling methodologies. His articles frequently explore scalability, reproducibility, and interdisciplinary applications of HPC frameworks. Awards and Recognition: While no specific awards are listed, his extensive contributions to workflow and simulation tools reflect community impact. Grants and Collaborations: Involved in NSF-funded projects (e.g., CyberTraining) and collaborates with institutions like INRIA (France) on tools like SMPI and WRENCH. Labs and Teams: Leads or contributes to research groups focused on distributed systems simulation, workflow automation, and HPC infrastructure optimization at the University of Hawaiʻi.

Nodari Sitchinavaمشاهده پروفایل

استاد

• Parallel Algorithms
• Cache-efficient Computing
• I/O-efficient Algorithms
• +۴ مورد دیگر

Nodari Sitchinava is a Professor in the Department of Information and Computer Sciences at the University of Hawaii at Manoa, where he leads the Algorithms and Parallel Computing Group (AlgoPARC). His research focuses on designing efficient algorithms for modern parallel systems, particularly cache-efficient and I/O-optimized solutions for multicore and GPU architectures. His educational background includes advanced training in computer science with specialization in parallel algorithms. Research interests span: Parallel algorithm design for multicores and GPUs Cache-oblivious and I/O-efficient algorithms Communication-efficient distributed computing models Computational geometry and graph algorithms Lower bounds in computational complexity Publication analysis reveals consistent focus on parallel computing foundations, with recent work emphasizing GPU algorithm efficiency, external memory models, and geometric computations. Theoretical contributions frequently include novel lower bound proofs and practical implementations optimized for modern hardware constraints. Awards and honors include: Best Paper Award at the European Symposium on Algorithms (2019) for contributions to comparison-based algorithm complexity Teaching encompasses graduate and undergraduate courses in algorithms, parallel computing, and data structures. His instructional approach emphasizes fundamental principles of algorithm design and analysis across sequential and parallel computational models.

Weija Shangمشاهده پروفایل

استاد

• Parallel Processing
• Computer Architecture
• Parallelizing Compiler
• +۷ مورد دیگر

Weija Shang is a Professor at the School of Engineering, Santa Clara University, where she has been since 1994. She previously served at the Center for Advanced Computer Studies, University of SW Louisiana (1990–1993). Her research spans parallel processing, computer architecture, parallelizing compilers, algorithm theory, and non-linear optimization. PhD in Computer Engineering, Purdue University (1990) MS in Computer Engineering, Purdue University (1984) BS in Computer Engineering, Changsha Institute of Technology (1982) Her publications focus on parallel computing , GPGPU optimization , FPGA design , and video coding techniques. Key themes include supernode transformations , media distribution algorithms , and stack optimization in recursive programs. Scientific awards include: Clare Boothe Luce Professor (1994–2000) NSF Research Initiation Award (1991) NSF Career Award (1995) She has supervised numerous research projects in parallel programming and compiler design, contributing to high-performance computing and distributed systems through collaborations with institutions like Xilinx and NASA.

• Compiler Optimization
• High Performance Computing
• Computer Architecture
• +۲ مورد دیگر

Dr. J Nelson Amaral is a Professor in the Department of Computing Science within the Faculty of Science at the University of Alberta. His research focuses on compiler optimization techniques that enhance performance in modern computing architectures through clever code transformations. Educational background includes B.Sc. in Electrical Engineering (PUCRS), M.Sc. in Electrical Engineering (ITA), and Ph.D. in Electrical and Computer Engineering (University of Texas at Austin). Research interests span compiler optimizations for resource utilization, learning technology applications in compilation processes, and feedback-directed optimization efficiency improvements. Current investigations include instruction-level parallelism exploitation, code transformations for heterogeneous architectures, and machine learning-enhanced compilation. Publication analysis reveals strong focus on compiler techniques for performance optimization, particularly for matrix operations, convolution algorithms, and memory hierarchy management. Recent work emphasizes hardware-software co-design for specialized processors and auto-vectorization methods. University of Alberta Faculty of Science Excellence in Teaching Award (2015) Distinguished Engineer, Association for Computing Machinery (2014) Distinguished Speaker, Association for Computing Machinery (2012-2014) Interdepartmental Science Students' Society Award for Excellence in Teaching (2014) IBM Center for Advanced Studies Research Faculty Fellow of the Year (2012) Research includes industry collaborations through the IBM-CAS partnership. Contributes to computing education through curriculum development and student mentoring. Leads compiler optimization research at the IBM Center for Advanced Studies, focusing on performance improvements for enterprise workloads and specialized hardware.

• Communication Networks
• Routing Algorithms
• Switching Architectures
• +۳ مورد دیگر

Dr. Mike MacGregor is a Professor in the Department of Computing Science at the University of Alberta, where he directs the Master of Science in Internetworking program. His research focuses on fundamental networking challenges including terabit-scale packet processing, bandwidth management in non-cooperative environments, and generalized caching architectures for high-flow environments. Education includes: PhD in Computing Science (University of Alberta) MSc in Process Control (University of Alberta) BSc in Chemical Engineering (University of Alberta) His work explores the tension between simplified network cores versus functionally rich architectures, examining how core devices can balance forwarding efficiency with advanced functionality. Secondary interests include distributed architectures for internet services like streaming media and mail systems. He teaches the capstone MINT 709 Internet Project course involving design/analysis of significant internetworking systems. Previous industry experience includes serving as architect for a regional ISP, informing his research on network scalability and adaptability challenges.

Rakesh Ranjanمشاهده پروفایل

مدرس

• Cloud Computing
• Big Data Analytics
• Software Engineering
• +۲ مورد دیگر

Rakesh Ranjan is a part-time Lecturer in the Computer Engineering Department at San José State University, teaching Enterprise Software Overview and Software Testing & QA courses. His industry expertise complements his academic role, where he focuses on cloud data services, big data analytics, and enterprise software platforms. Ranjan holds extensive industry experience as a Cloud Engineering Manager at IBM Silicon Valley Lab, where he leads development of data and analytics services for IBM Bluemix. With over 20 years in software development, he specializes in database technologies (DB2), cloud architectures, and large-scale system design. His textbook 'Enterprise Software Platform' covers middleware, cloud computing, big data, and emerging web technologies for software engineering students. At SJSU, Ranjan oversees innovative student projects applying Hadoop, MapReduce, and distributed systems to real-world problems. Student teams have developed solutions including social media sentiment analysis, distributed caching systems, real-time analytics platforms, and accessibility testing frameworks under his guidance.

• Cloud Computing
• Mobile Networks
• Security and Privacy in Networks
• +۷ مورد دیگر

Dr. Melody Moh is a Professor and Interim Chair in the Department of Computer Science at San Jose State University, where she has been a faculty member since 1993. Her research focuses on cloud computing, mobile networks, security/privacy in networks, and machine learning applications. She coordinates Cyber Security Certificates and has published over 130 refereed papers in journals, conferences, and book chapters. Moh holds an MS and PhD in Computer Science from the University of California, Davis. Research Interests: Cloud and Network Security, 5G Networks, IoT Security, Blockchain Applications, Machine Learning for Cybersecurity, Smart Grid Authentication, and Fog Computing. Her work bridges theoretical advancements with practical implementations, such as developing efficient cache management for Cloud-RAN systems and adversarial defense mechanisms for deep learning models. Recent Publications: Recent work includes blockchain-based public key infrastructure, adversarial attacks on deep learning models, and autonomous driving systems using LiDAR data. Her research often involves student collaborators, with many publications featuring *SJSU students. Awards: Best Paper Runner-Up (2019 ACM Southeast Conference) for randomized load balancing research, and Honorable/Best Paper Award (2018 ACM Southeast) for cache management in 5G networks. Grants & Labs: Secured over $500K in NSF and industry grants. Leads research teams focusing on cybersecurity, edge computing, and AI-driven network optimization. Active in collaborative projects with industry partners and government agencies.