جست‌وجوی استاد و پژوهشگر

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.

• 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.

Daniel Ka Chun Soمشاهده پروفایل

استاد

• Green Communications
• Beyond 5G/6G Networks
• Non-orthogonal Multiple Access
• +۹ مورد دیگر

Daniel Ka Chun So is a Professor of Communication Engineering in the Department of Electrical & Electronics Engineering at the University of Manchester. He joined the university in 2003 and holds a PhD from Hong Kong University of Science of Technology (2003) and a BEng from University of Auckland (1996). His industrial experience includes senior software engineering roles at Orion Systems. Research focuses on next-generation wireless technologies including green communications, 6G networks, machine learning applications, and network optimization. Key areas include: Energy-efficient wireless systems Non-orthogonal multiple access (NOMA) Reconfigurable intelligent surfaces Massive MIMO and D2D communications Recent publications demonstrate strong emphasis on federated learning optimization, cell-free massive MIMO, and cache-enabled D2D communications, reflecting cutting-edge work in wireless network efficiency. Awards include: Scottish Power Power Learning Award Two-time recipient of UMIST Electrical & Electronic Engineering Prize Extensive PhD supervision includes 19 current and former students working on NOMA systems, edge computing, and 6G optimization. Leadership roles include Director of Postgraduate Taught programs and Teaching Enhancement Lead.

Charles Leisersonمشاهده پروفایل

استاد

• Computer Science
• Algorithms
• Parallel Computing
• +۴ مورد دیگر

Charles Leiserson is the Edwin Sibley Webster Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. He is renowned for co-authoring the seminal textbook Introduction to Algorithms , now in its fourth edition, which is widely used globally. His research focuses on algorithms, parallel computing, and software performance engineering, with contributions to theoretical computer science and practical systems like the Cilk multithreaded language and the Connection Machine CM-5 architecture. Leiserson’s work spans compiler design (e.g., Pochoir for stencil computations) and high-performance computing frameworks. He explores post-Moore’s Law computational strategies, graph neural networks for financial forensics, and deterministic parallel algorithms. His educational contributions include courses like 6.172 (Performance Engineering of Software Systems) and initiatives in scalable graph learning. His research has led to innovations in parallel programming models, algorithm optimization, and hardware-software co-design. Leiserson’s interdisciplinary work bridges theory and practice, influencing both academic research and real-world applications in finance, cryptography, and supercomputing.

Mohammad Shahradمشاهده پروفایل

استادیار

• Cloud Computing
• Sustainability
• Large-Scale Computing Systems
• +۳ مورد دیگر

Mohammad Shahrad is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) and an Associate Faculty Member of UBC's Computer Science Department. He leads the UBC Cloud Infrastructure Research for Reliability, Usability, and Sustainability (CIRRUS) Lab. His research focuses on sustainable and efficient large-scale computing systems, particularly in cloud resource management and serverless systems. Education: Ph.D. in Electrical Engineering, Princeton University (2020) M.A. in Electrical Engineering, Princeton University (2016) B.Sc. in Electrical Engineering, Sharif University of Technology, Tehran (2014) Research Interests: Shahrad's work emphasizes energy-efficient cloud computing, serverless architecture optimization, and sustainability in distributed systems. His projects address challenges like carbon footprint reduction, resource allocation, and cold-start delays in serverless platforms. He employs interdisciplinary approaches, combining system design, algorithm development, and empirical analysis to tackle real-world cloud infrastructure problems. Recent Contributions: His research trends include carbon-aware scheduling, geospatial shifting for sustainability, and developer-centric compliance tools for serverless applications. These efforts aim to balance performance, cost, and environmental impact in modern cloud ecosystems. Scientific Awards: Community Award at USENIX ATC '20 for Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider Advising & Grants: As CIRRUS Lab lead, Shahrad mentors researchers in cloud infrastructure and sustainability. While specific grant details are not listed, his work reflects substantial industry collaboration (e.g., Microsoft Research, Azure). Labs/Teams: The CIRRUS Lab focuses on creating sustainable cloud solutions through open-source frameworks like OpenPiton and collaborative industry partnerships.

Cong Gaoمشاهده پروفایل

استاد

• Data Science
• Geospatial data management
• Spatio-temporal data mining
• +۷ مورد دیگر

Cong Gao is a Professor and Head of the Division of Data Science at Nanyang Technological University's College of Computing & Data Science. He also holds a courtesy appointment with the School of Physical & Mathematical Sciences. Previously, he served as an Assistant Professor at Aalborg University, Denmark, and worked as a researcher at Microsoft Research Asia. He co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU). His educational background includes: Ph.D. in Computer Science from National University of Singapore (2004) Master of Engineering from Tianjin University, China (1999) Bachelor of Engineering from Tianjin University, China (1996) Professor Gao's research focuses on Data Science, with particular expertise in geospatial data management, spatio-temporal data mining, recommendation systems, and social media data analysis. His work has significantly impacted areas like spatial-textual indexing, point of interest recommendation, and mining social networks. He has published extensively in top venues including VLDB, SIGMOD, ICDE, KDD, and WSDM, with over 14,000 citations and an H-index of 61. His recent publications demonstrate strong trends in applying machine learning to database systems, with particular focus on spatial and trajectory data management. Key research directions include learned indexing techniques, trajectory data analysis, and integrating large language models with database systems for improved query optimization. Professor Gao has received notable scientific recognition including: Best paper runner-up award at WSDM'22 Best paper award runner-up at WSDM 2020 He has advised numerous students who have become significant contributors in their own right, including Xin Cao, Lisi Chen, Kaiyu Feng, and Kaiqi Zhao. His research has been supported by substantial grants from Ministry of Education, NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft, including a S$42.4 million funding over 5 years for the SCALE@NTU lab. Professor Gao leads the Data Management Research Group (DANTE) and co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU), which develops market-leading AI and data science technologies.

Dejan Kosticمشاهده پروفایل

استاد

• Distributed Systems
• Computer Networks
• Operating Systems
• +۶ مورد دیگر

Dejan Kostic is a Chair Professor of Internetworking at KTH Royal Institute of Technology, leading the Networked Systems Laboratory. He is affiliated with RISE Research Institutes of Sweden and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on distributed systems, computer networks, operating systems, and machine learning applications in networking. Education: Ph.D. in Computer Science, Duke University M.S. in Computer Science, University of Texas at Dallas B.S. in Computer Engineering, University of Belgrade Research Interests: His work spans distributed systems , high-performance networking , smartNIC offload , AI-driven systems , and network function virtualization (NFV) . Recent projects include energy-efficient AI inferencing (Wallenberg Scholar Project) and scalable federated learning for healthcare applications. Key contributions include the PacketMill framework, FAJITA stateful processing, and the LineFS distributed file system. Publications & Awards: ERC Starting (2010) and Consolidator (2018) Awards Wallenberg Scholar (2024) Best Paper at SOSP 2021 and Community Award at NSDI 2022 Advising & Grants: Supervised over 15 doctoral students, including notable alumni like Hamid Ghasemirahni and Alireza Farshin. Leads projects funded by ERC, WASP, and Swedish Research Council (VR). Active in grant review panels for ERC and Swedish Foundations. Labs & Teams: Heads the Network Systems Laboratory (NSLab) at KTH, collaborating with Ericsson and RISE on industrial projects. Key initiatives include the Networked Systems for Machine Learning course and the Connected Intelligence unit at RISE.

Gianni Antichiمشاهده پروفایل

مدرس ارشد

• Networks
• Computer Architecture
• Programmable Hardware
• +۳ مورد دیگر

Dr. Gianni Antichi is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science, and an Associate Professor at Politecnico di Milano. His research focuses on network architecture, programmable hardware, and data plane optimization. He holds grants from EPSRC and Facebook Research, and has received multiple awards including Best Paper at SIGCOMM 2017. Antichi teaches modules on distributed systems, computer networks, and Internet protocols. Research Interests: End-host networking stacks, programmable hardware (SmartNICs/FPGA), network telemetry, data center optimization, and traffic analysis. His work spans from low-level data plane design to high-level network monitoring solutions. Key Awards: EPSRC New Investigator Award (2020), Facebook Networking Research Award (2020), Best Student Paper at EuroSys 2025, and ACM SIGCOMM Best Paper (2017). Grants & Projects: Leads NEAT (EPSRC-funded network telemetry project), and collaborates with industry on software data plane optimization. Active in mentoring PhD students and researchers in distributed systems and network hardware. Labs/Teams: Part of Queen Mary’s Centre for Networks, Communications and Systems, focusing on programmable networking and high-performance systems.

Huaiyu Daiمشاهده پروفایل

استاد

• Networking
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Huaiyu Dai is a Professor and University Faculty Scholar at the Department of Electrical and Computer Engineering, North Carolina State University. He holds editorial roles including Editor-in-Chief of IEEE Transactions on Signal and Information Processing over Networks. His research focuses on networking, machine learning, and communications, with over 300 publications. He has received IEEE Fellowships, AAIA Fellowships, and awards like the Qualcomm Faculty Award and William R. Bennett Prize. Education : Ph.D., Electrical Engineering, Princeton University (2002) M.S., Electrical Engineering, Tsinghua University (1998) B.E., Electrical Engineering, Tsinghua University (1996) Research Interests : His work spans federated learning, edge computing, network security, and graph neural networks. Recent advancements include decentralized federated learning frameworks, privacy-preserving techniques, and efficient scheduling algorithms for vehicular/cloud networks. Publications Trends : Recent articles address challenges in federated learning (data heterogeneity, privacy), edge computing (resource optimization), and UAV-enabled systems (fresh data collection, trajectory planning). Methodologies include reinforcement learning, coded computing, and graph-based approaches. Awards & Leadership : AAIA Fellow (2021) IEEE Fellow (2017) Multiple best paper awards at IEEE ICC, INFOCOM, and MASS Advising & Grants : Active in mentoring graduate students and securing grants for distributed machine learning, vehicular networks, and secure communication systems. Leads projects on hybrid defense mechanisms against Byzantine attacks in federated learning. Labs/Teams : Collaborates through interdisciplinary teams at NC State, focusing on AI-driven networking, edge computing architectures, and UAV communication systems.

Phillip R. Westmorelandمشاهده پروفایل

استاد

• Molecular modeling and reaction theory
• Pyrolysis and combustion chemistry
• Chemicals from biomass
• +۳ مورد دیگر

Phillip R. Westmoreland is a Professor in the Department of Chemical and Biomolecular Engineering at North Carolina State University, with affiliations as Honorary Professor at Nanjing University of Technology and Professeur invité at Université de Lorraine. He holds a B.S. from NC State, an M.S. from Louisiana State University, and a Ph.D. from MIT. His research focuses on molecular modeling, pyrolysis, combustion chemistry, biomass conversion, and data science. He has pioneered methods for polymer pyrolysis modeling and PFAS remediation through computational and experimental approaches. Education: B.S., Chemical Engineering, North Carolina State University (1973) M.S., Chemical Engineering, Louisiana State University (1975) Ph.D., Chemical Engineering, Massachusetts Institute of Technology (1986) Research Interests: His work integrates computational quantum chemistry, reactive flow modeling, and experimental techniques to explore reaction mechanisms at molecular scales. Key areas include chemical looping, biomass-to-chemicals conversion, and PFAS destruction. He emphasizes bridging fundamental science and industrial applications. Awards: 2023 AIChE Van Antwerpen Award 2019 NC State Alumni Association Outstanding Research Award 2018 Fellow of the Combustion Institute 2005 Fellow of AIChE Recipient of the NSF Presidential Young Investigator Award (1990) Leadership & Service: Served as 2013 AIChE President, NSF Director’s Award recipient (2009), and founding Chair of AIChE’s Computational Molecular Science Forum. Active in CACHE Corporation and the Combustion Institute. Labs/Teams: Leads a research group focused on molecular-level reaction engineering, with expertise in reactive molecular dynamics and computational chemistry. Collaborates globally on projects involving biomass conversion and environmental remediation.

Alex Sprintsonمشاهده پروفایل

استاد

• Networking
• Network Coding
• Security and Privacy
• +۴ مورد دیگر

Prof. Alex Sprintson is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering. He currently serves as a rotating Program Director at the NSF, managing networking research programs such as NeTS, SaTC, RINGS, and MLWiNS. His research focuses on algorithmic and information-theoretic aspects of networking, network coding, security, privacy, distributed storage systems, wireless communication, and software-defined networking. Sprintson holds a Ph.D., M.S., and B.S. in Electrical Engineering from Technion-Israel Institute of Technology (2003, 2001, 1995). His academic accolades include the Wolf Award, Viterbi Fellowship, TAMU Outstanding Contribution Award, and NSF CAREER Award. He has served on editorial boards (e.g., IEEE Transactions on Wireless Communications) and conference committees (e.g., IEEE Infocom). His work spans theoretical frameworks for secure communication, privacy-preserving protocols, and efficient distributed systems. Research highlights include pioneering contributions to private information retrieval, network coding applications, and coded computing. He leads projects on securing next-generation wireless networks and optimizing distributed storage systems. His NSF roles involve advancing research in machine learning for wireless systems and resilient cyber-physical infrastructure.

Abdullahمشاهده پروفایل

پژوهشگر

• Plant Breeding
• Agriculture
• Genetics
• +۳ مورد دیگر

Abdullah is a researcher in the field of plant breeding and genetics at Texas A&M University's College of Agriculture and Life Sciences. His academic background includes a B.S. and M.S. in Agriculture (Plant Breeding and Genetics) from the University of Agriculture, Faisalabad, Pakistan, completed in 2008 and 2010 respectively. He holds a Ph.D. in Plant Breeding and currently works in the Agronomy Lab, focusing on wheat plant breeding and genetic improvement strategies. His research emphasizes enhancing crop yields through genetic advancements and optimizing breeding techniques for wheat varieties. Notable areas of exploration include genotype-environment interactions, trait selection, and molecular breeding approaches. Abdullah's recent publications span interdisciplinary topics including compiler optimization, cache indexing strategies, and deep learning acceleration techniques—reflecting a unique blend of agricultural and computational research interests. These works address challenges in hardware-software co-design, parallel computing efficiency, and cybersecurity in distributed systems. Though no formal awards are listed, his work demonstrates contributions to both agricultural science and computer systems engineering. His current research program involves collaborations with computational scientists to integrate data-driven methods into plant breeding programs.