جست‌وجوی استاد و پژوهشگر

• Geographic Information Systems (GIS)
• Parallel Computing
• GPU Acceleration
• +۳ مورد دیگر

Jianting Zhang is an Assistant Professor of Computer Science at the City College of New York (CUNY). His research focuses on geospatial data management, parallel computing, and GPU acceleration techniques for handling large-scale spatial datasets. He specializes in optimizing spatial query processing, indexing methods for moving objects, and high-performance computing frameworks for web-based GIS applications. His work bridges theoretical algorithm design with practical implementations in distributed and cloud environments. Key research interests include parallel indexing of geospatial rasters and trajectories, efficient spatial join algorithms, and GPU-based acceleration of data compression and visualization. He has explored applications in biodiversity analysis, urban mobility (e.g., NYC taxi data), and query-driven web-GIS systems. His contributions emphasize scalable solutions for big data challenges in geographic information systems. His publications highlight advancements in GPU-accelerated spatial processing, including novel approaches to quadtree construction, sparse matrix operations, and top-K trajectory similarity queries. While no scientific awards are explicitly mentioned, his active publication record reflects sustained contributions to the field of geospatial computing.

• Computer Networks
• Control Systems
• Optimization
• +۳ مورد دیگر

A. Kevin Tang is a Professor of Electrical and Computer Engineering (ECE) at Cornell University, affiliated with the School of ECE. His research focuses on computer networks, control systems, optimization, and information theory. He teaches advanced courses such as ECE 5800 (Control and Optimization of Information Networks) and ECE 6960 (Interplay between Economics and Systems). His work bridges theoretical foundations and practical network applications, including network coding, distributed control, and protocol design. Recent contributions address privacy-preserving data sharing, network routing stability, and optimization techniques for heterogeneous systems. Tang’s publications span top venues like NeurIPS, ICML, NSDI, and IEEE Transactions. His research group explores cutting-edge topics in networked systems, with applications to distributed storage, software-defined networking, and multipath communication protocols. He advises on interdisciplinary projects combining control theory, optimization, and networking. His lab collaborates closely with industry partners to translate theoretical insights into real-world network solutions.

• Smart Grids
• Distributed Generation
• Electrical Network Control
• +۴ مورد دیگر

Raphael Caire is a Senior Lecturer at Grenoble INP - National Polytechnic Institute of Grenoble, affiliated with the School of Energy, Water and Environment Engineers (ENSE3) and the Grenoble Electrical Engineering Laboratory (G2Elab). He serves as coordinator of the European DREAM project (FP7) and the International Master Smart Grid and Building program under KIC InnoEnergy. His educational background includes an Accreditation to Supervise Research from the University of Grenoble (2012), Doctorate from the National Polytechnic Institute of Grenoble (2004), and Engineering degree from the National School of Electrical Engineers of Grenoble (2000). Prior to his academic career, he worked at the Center for Power Electronic Systems (CPES) in the US and EDF research centers in Germany and France. Caire's research focuses on Smart Grid evolution, distributed generation integration, and critical infrastructure interdependencies. His work spans three key areas: new grid architectures, advanced operational modes, and ICT integration. Recent publications emphasize voltage control, microgrid protection, digital twin applications, and inverter-based resource management, reflecting his focus on grid flexibility and renewable integration challenges. His team's output shows strong industry collaboration with EDF, Schneider Electric, and European research entities. Notable scientific contributions include two patents: 'Determining the Frequency of an Alternating Signal' (US 2018/0059154 A1) and 'Method of Stabilizing an Electrical Network by Load Shedding' (PCT/EP2014/077815). He has coordinated major EU projects including DREAM, evolvDSO, and INSTINCT. Caire has co-supervised 21 completed PhD theses with a 41% average co-supervision rate and currently oversees 5 doctoral candidates. His teaching spans electrical networks, grid stability, and smart grid technologies at multiple institutions including Ecole Centrale Lyon and Mines ParisTech. He pioneered the Smart Grids MOOC with Enedis and developed experimental platforms like the RD-PREDIS demonstrator for hands-on Smart Grid education.

Luis Ricardez-Sandovalمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Multiscale Modelling
• Computer-Aided Catalyst and Materials Design
• +۷ مورد دیگر

Luis Ricardez-Sandoval is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo, Canada. He holds the Tier II Canada Research Chair in Multiscale Modelling and Process Systems. His research focuses on developing theoretical and computational tools for optimizing chemical processes, with a strong emphasis on CO2 capture technologies, energy systems, and heterogeneous catalysis. His work integrates multiscale modeling, machine learning, and advanced control strategies to enhance process efficiency and sustainability. Education : Doctorate in Chemical Engineering, University of Waterloo (2008) Master of Science in Chemical Engineering, Instituto Tecnologico de Celaya (2000) Bachelor of Science in Chemical Engineering, Instituto Tecnologico de Orizaba (1997) Research Interests : Process Systems Engineering and Multiscale Modelling Computer-Aided Material Design for CO2 Utilization Advanced Process Control and Uncertainty Management Optimal Integration of Process Design, Control, and Scheduling Energy Systems and CO2 Capture Technologies Selected Awards : 2024 Canadian Society of Chemical Engineering D.G. Fisher Award 2017 Canada Research Chair (Tier II) 2015 Early Researchers Award (Ontario) Teaching : CHE 341: Introduction to Process Control CHE 521: Process Optimization CHE 522: Advanced Process Dynamics and Control Labs & Collaborations : Ricardez-Sandoval collaborates with industry and governments to advance carbon reduction technologies. His research group has partnerships with institutions like CONACyT (Mexico), China Scholarship Council, and Tampere University (Finland). Key projects include optimizing aquaculture systems, chemical looping combustion, and catalyst design.

Ningfang Miمشاهده پروفایل

دانشیار

• Capacity Planning
• Resource Management
• Storage Systems
• +۹ مورد دیگر

Ningfang Mi is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University. She holds a Ph.D. from the College of William and Mary (2009), an M.S. from the University of Texas at Dallas (2004), and a B.S. from Nanjing University (2000). Her research focuses on capacity planning, resource management, storage systems (particularly NVMe flash devices), distributed data processing, and cloud computing. She directs the Northeastern University Computer Systems Research Laboratory (NUCSRL), which specializes in performance evaluation and system modeling for large-scale computing systems. Education: Ph.D. Computer Science (2009, College of William and Mary), M.S. Computer Science (2004, University of Texas at Dallas), B.S. Computer Science (2000, Nanjing University). Research Interests: Her work bridges theoretical performance modeling and practical system optimization. Key areas include improving data center efficiency, managing heterogeneous architectures, and developing scheduling algorithms for Hadoop/YARN clusters. She has pioneered techniques for flash storage optimization and containerized application scheduling on NVMe SSDs. Publications: Over 50 peer-reviewed articles in top venues like IEEE Transactions on Computers, ACM Transactions on Modeling and Computer Simulation, and conferences such as IEEE Cloud, HPEC, and IPCCC. Recent work addresses serverless computing scalability, GPU idleness reduction in distributed deep learning, and federated learning client selection policies. Awards: NSF CAREER Award (2015), AFOSR YIP Award (2014), IBM Faculty Award (2010), and multiple best paper awards (2017-2020). Grants: Led NSF, AFOSR, and Samsung-funded projects totaling over $1M. Current focus includes AI-enabled educational data processing frameworks and NVMeOF zoning configurations for storage systems. Labs/Teams: NUCSRL collaborators include PhD students (e.g., Janki Bhimani, Danlin Jia) and industry partners like Samsung Semiconductor. Current initiatives explore disaggregated storage systems and elastic resource management in cloud infrastructures.

Cem Yukselمشاهده پروفایل

دانشیار

• Computer Graphics
• Real-Time Rendering
• Physically-Based Simulations
• +۵ مورد دیگر

Cem Yuksel is an Associate Professor at the Kahlert School of Computing, University of Utah, and founder of Cyber Radiance LLC. His research focuses on computer graphics, including real-time rendering, physically-based simulations, global illumination, and hair/cloth modeling. He holds a PhD from Texas A&M University and has been a postdoc at Cornell University. Education: PhD in Computer Science (2010, Texas A&M), MS in Computer Engineering (2003, Bogazici University), BS in Physics (2000, Bogazici University). Research interests include Hair Meshes for real-time hair rendering, high-performance polynomial solvers, mesh color textures, and wearable 3D knitting. He has authored over 100 peer-reviewed papers and received awards such as the Wolfgang Straßer Best Paper Award (2022) and multiple Dean’s commendations for teaching. Courses taught include Introduction to Computer Graphics and Rendering with Ray Tracing. Key projects: Hair Meshes (SIGGRAPH 2024), Stochastic Lightcuts (HPG 2019), Mesh Color Textures (2020), and high-performance polynomial solvers (SIGGRAPH 2022). His work has been implemented in commercial software like Hair Farm for 3ds Max. Grants and service roles include steering committee member for High-Performance Graphics (HPG) and organizing SIGGRAPH conferences. He runs a graphics research lab at the University of Utah.

Terrence W.K. Makمشاهده پروفایل

مدرس

• Mathematical Optimization
• Machine Learning
• Energy Systems
• +۵ مورد دیگر

Terrence W.K. Mak is a Lecturer in the Department of Data Science & AI at Monash University, within the Faculty of IT. He is affiliated with the Monash Energy Institute and Monash Data Futures Institute, focusing on interdisciplinary research at the intersection of mathematical optimization, machine learning, and energy systems. His work addresses climate change challenges in the energy sector, such as net-zero emissions and climate-resilient infrastructure. Academically, he holds a PhD from the Australian National University (2018), MPhil and BSc from the Chinese University of Hong Kong (2011, 2009). He has held postdoctoral and research roles at Georgia Tech and the University of Michigan, collaborating extensively with Prof. Pascal Van Hentenryck and Prof. Jimmy Lee. His research emphasizes combining machine learning and optimization to solve energy sector problems, including smart grid technologies, disaster management, and sustainable informatics. Key collaborations include ARPA-E projects (e.g., Grid Optimization Competition), US national laboratories, and industry partners like RTE France and Origin Energy. Recent projects include Grid Guru (AI-driven grid optimization) and consultancy for Origin Energy. He supervises PhD students focusing on deep learning and nonlinear optimization for smart grids.

Ismail Ariمشاهده پروفایل

استادیار

• Big Data
• Cloud Computing
• Networked Storage Systems
• +۷ مورد دیگر

Ismail Ari is an Assistant Professor at Ozyegin University in Turkey and a co-founder of Havooz. He previously served as a senior research scientist at Hewlett Packard Laboratories in Palo Alto for five years. His academic background includes a Ph.D. from the University of California, Santa Cruz, and an M.S. in Computer Science from the University of Maryland, Baltimore County. Ph.D., University of California, Santa Cruz (2004) M.S., University of Maryland, Baltimore County (Computer Science) His research focuses on big data and cloud computing, with a strong emphasis on performance improvement and automation in networked storage systems. Additional interests include web services, service-oriented architectures, data mining, and event stream processing. Much of his published work explores adaptive caching techniques for storage and network systems. The recent publications highlight a consistent focus on adaptive caching, performance optimization, and workload management in storage and distributed systems. Key themes include automated caching in SANs, flash crowd management, and intelligent refetching strategies using multiple expert models. These works span disciplines in computer science, networking, and distributed systems. While no scientific awards are listed in the provided text, Ismail Ari has contributed significantly to the fields of storage systems and network performance through his research and industry experience. He has advised or collaborated with researchers including Prof. Ethan L. Miller, Scott A. Brandt, Darrell D. E. Long, and others during his doctoral and post-doctoral career. There is no mention of funded grants in the provided information. His work at HP Labs and academic contributions suggest potential involvement in large-scale system research projects. Ismail Ari’s work on adaptive caching systems, particularly the ACME and SANBoost projects, indicates deep engagement in building intelligent, self-optimizing storage infrastructures. These efforts were likely developed within research labs or collaborative teams at UC Santa Cruz and HP Labs, though specific lab names are not provided.

Samit Royمشاهده پروفایل

استاد

• Composite Materials
• Advanced Materials
• Carbon Nanotubes
• +۱۵ مورد دیگر

Dr. Samit Roy is the William D. Jordan Professor in the Department of Aerospace Engineering and Mechanics at The University of Alabama, College of Engineering. He is actively involved in research through multiple centers including the Center for Advanced Vehicle Technologies and the Polymers and Soft Materials Research Center. Ph.D., Engineering Science and Mechanics, Virginia Tech, 1988 M.S., Aerospace Engineering, Virginia Tech, 1986 B.Tech, Aerospace Engineering, Indian Institute of Technology, Kharagpur, 1981 Dr. Roy's research focuses on advanced composite materials, with a strong emphasis on multiscale modeling, nanotechnology, and self-healing polymers. His work spans computational mechanics, material characterization, and manufacturing of high-performance composites for aerospace applications. He investigates fracture mechanisms, interfacial strength enhancement using carbon nanotubes, and structural health monitoring using embedded sensors. His recent publications highlight a strong trend in nanocomposites, multiscale modeling, and self-healing materials, particularly focusing on carbon nanotube-wrapped fibers and thermoset composites. These works employ molecular dynamics and finite element methods to understand material behavior at micro and nanoscales, contributing to the development of durable, lightweight, and intelligent materials for extreme environments. Dr. Roy has received numerous accolades for his contributions to composites research and academic leadership: ASC/DEStech Award in Composites, 2023 UA President’s Senior Research Scholar Award, 2024 Fellow of ASME, 2010 Associate Fellow of AIAA, 2007 Summer Faculty Fellowship at Air Force Research Laboratory, 2016 He has secured significant research funding, notably from the Air Force Office of Scientific Research, where he served as a program officer. His leadership extends to mentoring Ph.D. students and guiding research teams in composite material innovation. He has been elected to leadership roles in technical divisions of ASC and ASME, reflecting his national impact. Dr. Roy leads research initiatives within interdisciplinary centers focused on advanced materials and vehicle technologies, collaborating with teams on scalable manufacturing, damage prognosis, and smart material systems.

• Geospatial Data Management
• Database Systems
• Cluster Computing
• +۵ مورد دیگر

Jia Yu is a researcher affiliated with Arizona State University , Tempe, AZ, USA. Their work focuses on geospatial data management, database systems, and cluster computing frameworks like Apache Spark. They have collaborated extensively with Mohamed Sarwat and other researchers on projects such as GeoSpark , GeoSparkViz , and GeoSparkSim , contributing to scalable spatial data processing and visualization systems. Key research areas include Learned indexing mechanisms (e.g., GLIN) Microscopic traffic simulation Parallel and distributed data processing Interactive geospatial dashboards Column correlation exploitation for database efficiency Integration of visualization with backend data systems Recent publications (2014-2024) demonstrate expertise in geospatial analytics, database indexing, software testing, and Apache Spark-based systems. Notable projects include Turbocharging Visualization Dashboards , HERMIT Indexing , and Spindra Knowledge Graph Management . Work emphasizes both theoretical innovation and practical implementation for handling massive-scale spatial data.

John R. Langeمشاهده پروفایل

دانشیار

• High Performance Computing
• Operating Systems
• Virtualization
• +۳ مورد دیگر

John R. Lange is an Associate Professor in the Computer Science Department at the University of Pittsburgh's School of Computing and Information. Currently on leave at Oak Ridge National Laboratory, he has established himself as a leading researcher in high-performance computing and systems software. His work bridges academic research and practical supercomputing applications, with extensive collaborations across national laboratories and research institutions. PhD in Computer Science from Northwestern University (2006) M.S. in Computer Science from Northwestern University (2010) B.S. in Computer Engineering from Northwestern University (2003) B.S. in Computer Science from Northwestern University (2003) Dr. Lange's research centers on High Performance Computing systems, with particular expertise in operating systems for supercomputing environments, virtualization technologies, and network optimization. His work explores how specialized operating systems can enhance performance in large-scale computing infrastructures, addressing challenges in resource management, fault tolerance, and system composition. He investigates the intersection of traditional HPC architectures with emerging virtualization techniques to create more flexible and efficient computing environments. His publication record demonstrates a consistent focus on advancing the state of the art in high-performance systems, with recent work emphasizing exascale computing challenges, network optimization for leadership-class supercomputers, and innovative approaches to system composition. His research shows an evolution from foundational virtualization techniques toward more sophisticated solutions for next-generation supercomputing architectures, with increasing emphasis on practical implementations in real-world leadership computing facilities. Best Paper Finalist at SC 2024 for network cost optimization research Best Paper Finalist at SC 2023 for Frontier exascale supercomputer architecture Best Paper Nominee at HPDC 2012 for VNET/P overlay networking As an educator, Dr. Lange has taught numerous systems courses including Introduction to Systems Software (CS 0449), Computer and Network Security (CS 1652), and Operating Systems (CS 2510 and CS 3510). His teaching spans both undergraduate and graduate levels, focusing on practical systems knowledge with hands-on projects. His research has been supported by collaborations with Oak Ridge National Laboratory and other national computing facilities, enabling his work to directly impact real-world supercomputing infrastructure. Dr. Lange is actively involved with The Prognostic Lab and maintains strong connections with the Oak Ridge Leadership Computing Facility (OLCF), where his current leave appointment allows him to work at the forefront of exascale computing research and development.

• Neuromorphic Computing
• Spintronics
• Reservoir Computing
• +۷ مورد دیگر

Dr. Matt Ellis is a Lecturer in Machine Learning at the University of Sheffield , affiliated with the Machine Learning Group within the Department of Computer Science . He is also the Director of UG Admissions and Admissions Tutor for the School. Education PhD in Physics (2015), University of York MPhys in Theoretical Physics (2011), University of York Research Interests focus on neuromorphic computing and spintronics , particularly energy-efficient machine learning through physical reservoir computing and magnetic systems . His work explores using nanomagnetic devices as processing units, leveraging stochastic synapses , magnetic domain walls , and GPU-accelerated modeling . He investigates terahertz phonon pumping for minimally dissipative switching and emergent magnetization dynamics for computational capability. Publications span 2014–2024 , with recent articles on magnetic stochastic synapses , device-agnostic network optimization , and voltage-controlled superparamagnetic ensembles . His research integrates machine learning with physical computing to address challenges in edge computing , speech recognition , and chaotic time series prediction . Grants Spintronic Reservoir Fusion (2025–2027), EPSRC , £563,599 (Principal Investigator) MARCH: Magnetic Architectures for Reservoir Computing Hardware (2021–2025), £936,815 (Co-Investigator) From Stochasticity to Functionality: Probabilistic Computation with Magnetic Nanowires (2019–2023), £755,424 (Co-Investigator) Contact : m.o.ellis@sheffield.ac.uk

Kevin Skadronمشاهده پروفایل

استاد

• Computer Architecture
• Memory Architecture
• Processing in Memory (PIM/PNM)
• +۷ مورد دیگر

Kevin Skadron is the Harry Douglas Forsyth Professor of Computer Science at the University of Virginia's School of Engineering and Applied Science, where he has been faculty since 1999. He previously served as department chair from 2012-2021 and has made significant contributions to computer architecture research. Dr. Skadron received his B.S. in Electrical and Computer Engineering and B.A. in Economics from Rice University in 1994, and his Ph.D. in Computer Science from Princeton University in 1999. He spent the 2007-08 academic year on sabbatical at NVIDIA Research. His research focuses on computer architecture, particularly novel heterogeneous processor organizations, accelerator architecture, processing in memory, and automata processing. He has pioneered work in processing-in-memory (PIM) architectures, automata processing for pattern matching, and heterogeneous computing systems. His work addresses critical challenges in thermal management, power delivery, process variations, and wear-out in modern computing systems. Current projects include Fulcrum, Gearbox, Sieve, and DRAM-CAM architectures, along with the development of the PIMeval simulation framework and PIMbench benchmark suite. Skadron's recent publications reveal a strong focus on processing-in-memory architectures, with numerous papers on PIM design, benchmarking, and applications. His research also emphasizes automata processing for pattern matching, with contributions to FPGA-based implementations and programming models. Many of his recent works address graph processing acceleration, bioinformatics applications, and memory system optimization, demonstrating the practical impact of his theoretical contributions. Dr. Skadron has received numerous accolades including the 2023 SRC/SIA University Research Award for lifetime research contributions to the U.S. semiconductor industry, the 2011 ACM SIGARCH Maurice Wilkes Award, and is a Fellow of both IEEE and ACM. He was also named a University of Virginia Teaching Fellow for 2003-04. Skadron has advised numerous graduate students, with recent PhD graduates now working at leading companies like IBM, AMD, Apple, and Black Sesame. His research has been supported by the National Science Foundation, Semiconductor Research Corporation, DARPA, and industry partners including NVIDIA, Intel, and Micron. He co-founded IEEE Computer Architecture Letters and served as editor-in-chief from 2010-2012. He is actively involved in the UVA Center for Automata Processing (CAP) and has served as director for the SRC JUMP 1.0 Center for Research on Intelligent Storage and Processing in Memory (CRISP). He is currently a member of the SRC JUMP 2.0 Center for Research on Processing in Storage and Memory (PRISM).

• XML Database Systems
• Cloud Computing Security
• Data Stream Mining
• +۵ مورد دیگر

Eric Pardede is a professor at the School of Computing and Mathematics of Western Sydney University . He has an extensive publication record in data management, cybersecurity, and technology-enhanced learning, often collaborating with researchers like Wenny Rahayu, David Taniar, and A. S. M. Kayes. Research Focus: XML database systems, cloud computing security, data stream mining, and educational technology Award-Winning Contributions: Notable work on IoT security frameworks, blockchain risk assessment, and adaptive learning systems Recent Publications: 15 articles from 2021-2025 address topics like concept drift adaptation, cybersecurity in multi-cloud environments, and smart education tools Key Collaborations: Frequent partnerships with researchers in Australia, Indonesia, and Austria

• Distributed Stream Processing
• Data Stream Analytics
• Database Optimization
• +۳ مورد دیگر

Dr. Boris Novikov is a prominent Russian computer scientist affiliated with St Petersburg University and the Higher School of Economics . His research focuses on distributed stream processing, temporal databases, and XML query optimization. Key collaborators include Artem Trofimov , Elena Mikhailova , and Anna Yarygina . Research Areas : Distributed Systems (stream processing, substream management) Database Optimization (vertical partitioning, XML caching) Temporal Data Management Machine Learning (concept drift detection) His recent work on distributed stream processing (2023-2022) explores substream bounding, deterministic models, and resource allocation strategies. Earlier contributions (2012-2008) focused on XML query performance, similarity indexing, and access control frameworks. Technical Trends : Event time alignment in distributed architectures Dynamic residual projection for cybersecurity Bi-objective optimization in database systems Speculative execution with conflict resolution Incremental Lagrangian dual solutions Matrix clustering for partitioning problems