جست‌وجوی استاد و پژوهشگر

Sheheeda Mariam Manakkaduمشاهده پروفایل

دانشیار

• Robotics
• Data Analytics
• Machine Learning
• +۴ مورد دیگر

Sheheeda Mariam Manakkadu is an Associate Professor in the Department of Computer Science at Southern Illinois University Carbondale. She teaches graduate courses in Data Structures, Object-Oriented Programming, Data Mining, Text Mining, and Cloud Architecture, along with undergraduate courses in Operating Systems and Data Analytics. Ph.D., Computer Engineering M.E., Biomedical Engineering Her research spans robotics, data analytics, and parallel computing. Key areas include adaptive control of robotic manipulators, big data processing via MapReduce, IoT resource allocation, and computational bioinformatics for protein networks. Recent publications focus on neuro-sliding mode control, cloud architecture, and scalable recommender systems. She actively participates in academic service as a committee member for graduate courses and the IEEE Erie Section. Her work integrates machine learning, optimization algorithms, and distributed systems across diverse domains.

Hong Wangمشاهده پروفایل

استاد

• Numerical Analysis
• Differential Equations
• Fractional Calculus
• +۳ مورد دیگر

Hong Wang is a Professor in the Department of Mathematics at the University of South Carolina, part of the McCausland College of Arts and Sciences. He specializes in numerical analysis and differential equations, with a focus on numerical methods for fractional and variable-order equations. His work addresses complex boundary conditions, optimal control, and scientific computing challenges in advection-diffusion systems. Education Ph.D. in Mathematics, University of Wyoming (1992) Research Interests His research emphasizes numerical approximation techniques for differential/integral equations, particularly fractional diffusion-wave equations, variable-order models, and stochastic systems. Key areas include finite element methods, spectral methods, and fast algorithms for solving high-dimensional and time-dependent problems. He explores applications in optimal control, viscoelasticity, and multi-scale modeling. Recent Work Trends Recent publications highlight advancements in fractional calculus applications, including variable-exponent diffusion, distributed-order equations, and stochastic fractional differential equations. His work often combines theoretical analysis with computational efficiency, addressing challenges like nonsmooth parameters and singular density functions. Grants and Advising No specific grants or advisees are listed, but his research collaborations span computational mathematics, applied physics, and engineering systems. Labs/Teams No dedicated lab or team is explicitly mentioned, though his research aligns with computational and applied mathematics groups at the University of South Carolina.

Matthew Hertzمشاهده پروفایل

استاد آموزشی

• Computer science education
• runtime systems
• dynamic memory management

Matthew Hertz is a Teaching Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on computer science education, runtime systems, and dynamic memory management. Education: PhD, Computer Science, University of Massachusetts Amherst, 2006 MS, Computer Science, University of Massachusetts Amherst, 2001 BA, Computer Science, Carleton College, 1997 Research interests span computer science education and systems optimization. His educational research investigates learning factors in introductory programming courses, develops pedagogical tools like CloudCoder for programming exercises, and analyzes failure rates in CS1 courses. In systems research, he focuses on memory management innovations including garbage collection algorithms, adaptive resource allocation, and performance optimization in shared environments. Publications show dual focus: recent work emphasizes educational data analysis and programming pedagogy while earlier research concentrates on memory management efficiency and runtime systems. Trends include automated assessment tools and adaptive algorithms for resource-constrained environments. No scientific awards reported. No advising or grant information available. No labs or teams mentioned in available data.

Igor Jankovicمشاهده پروفایل

دانشیار

• Groundwater Hydrology
• Environmental Engineering
• Contaminant Transport
• +۲ مورد دیگر

Igor Jankovic is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on groundwater flow and contaminant transport in heterogeneous aquifers, with particular emphasis on the impact of aquifer heterogeneity on solute movement and transport modeling. Education: PhD in Civil Engineering, University of Minnesota (1997) MS in Civil Engineering, University of Minnesota (1993) BS in Civil Engineering, University of Split, Croatia (1990) His work addresses critical issues in groundwater hydrology including: Advective transport mechanisms in heterogeneous media Breakthrough curve prediction and analysis Effective hydraulic conductivity modeling Upscaling of flow and transport parameters Application of the Analytic Element Method (AEM) for complex aquifer simulations Comparison of transport models (CTRW, MRMT) in heterogeneous environments Research trends in his publications reveal a focus on: Three-dimensional heterogeneous aquifer modeling Non-Fickian and anomalous transport behavior Impact of spatial variability on contaminant migration Development of numerical algorithms for large-scale groundwater simulations Validation of stochastic transport theories against field experiments (e.g., MADE and Borden aquifers) Interaction between physical and chemical heterogeneity in reactive transport

• Cybersecurity
• Computer Security
• Information Security
• +۶ مورد دیگر

Matthew Allen Bishop is a Professor in the Department of Computer Science at the University of California, Davis. His primary affiliation is with the College of Engineering. Bishop's research focuses on cybersecurity, including secure programming, insider threat detection, malware analysis, and cybersecurity education. He has contributed extensively to curricular guidelines (e.g., CSEC 2017) and frameworks for cyber defense. His work spans theoretical advancements (e.g., intrusion detection models) and applied systems (e.g., secure voting platforms). Notable research areas include: Cybersecurity Education: Developing curricula and pedagogical frameworks for secure coding and ethical practices. Insider Threat Mitigation: Declarative approaches and behavioral analysis for detecting and preventing attacks. Malware Mitigation: Techniques leveraging uncertainty principles and defensive programming. Election Security: Analyzing vulnerabilities and designing secure voting systems. Bishop has collaborated with institutions like the Department of Homeland Security (DHS) and National Security Agency (NSA) on critical infrastructure protection. His publications span conferences like IEEE Security & Privacy, HICSS, and NSPW, emphasizing real-world applications of cybersecurity principles.

Michael Sammlerمشاهده پروفایل

استادیار

• Formal Verification
• Programming Languages
• Systems Programming
• +۳ مورد دیگر

Michael Sammler is an Assistant Professor leading the Programming Languages and Verification Group at the Institute of Science and Technology Austria (ISTA). He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and was a postdoctoral researcher at ETH Zürich. His research focuses on formal verification of low-level systems code, combining foundational proofs with automation. Key projects include RefinedC (C verification), Islaris (assembly code verification), and DimSum (multi-language interoperability). Education: PhD at MPI-SWS/Saarland Informatics Campus, postdoc at ETH Zürich. Research interests emphasize tool development for safety-critical systems, including Rust verification (RefinedRust), OCaml/C interoperability (Melocoton), and decentralized multi-language semantics (DimSum). Awards: Runner-Up for Informatics Europe 2024 Best Dissertation Award, Dr. Eduard Martin Prize, Distinguished Paper Awards at PLDI/POPL/USENIX, and Google PhD Fellowship. Labs/Teams: Programming Languages and Verification Group at ISTA, collaborations with MPI-SWS and international researchers. His work bridges foundational theory with practical tools for industry-relevant verification challenges.

• Parallel Computing
• Distributed Computing
• Algorithms
• +۴ مورد دیگر

Ramachandran Vaidyanathan (Vaidy Vaidyanathan) is the Elaine T. and Donald C. Delaune Distinguished Professor in the School of Electrical Engineering and Computer Science at Louisiana State University (LSU). His research focuses on parallel and distributed computing, reconfigurable architectures, interconnection networks, and autonomous robot coordination. Developed the Reconfigurable Multiple Bus Machine (RMBM) model Authored the book Dynamic Reconfiguration: Architectures and Algorithms Holds multiple patents for optical networking and reconfigurable hardware Research Interests : Parallel and distributed algorithms Reconfigurable computing models Autonomous robot coordination Optical interconnection networks Scientific Awards : US Patent 6,332,050 US Patent 6,792,175 US Patent 8,862,854 US Patent 9,257,988 US Patent 10,282,347 Advising and Grants : Supervises students in reconfigurable computing and distributed systems. Collaborates with institutions on NSF-funded projects. Labs : Leads the Reconfigurable Computing Group at LSU.

Damir Isovicمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۳ مورد دیگر

Damir Isovic is an Associate Professor and Vice-Chancellor for Internationalization at Mälardalen University's Academy of Innovation, Design and Technology. Previously, he served as Dean of the School of Innovation, Design and Engineering. His roles include leadership in academic administration and participation in national boards. He holds a PhD and has extensive international teaching experience. Research focuses on real-time systems, embedded systems design, and scheduling algorithms. Notable contributions include seminal work in real-time scheduling recognized by the IEEE Technical Community on Real-Time Systems. He has organized major conferences and delivered keynotes globally. His publications emphasize hybrid scheduling approaches, real-time operating systems (RTOS), media processing in resource-constrained systems, and MPEG standards. Recent work integrates memetic algorithms with fuzzy controllers and explores multi-core scheduling fairness. His research bridges theoretical scheduling models with practical embedded system implementations. No scientific awards explicitly listed in the text. Advising activities include supervising PhD students, though specific names are not provided. Lab affiliations include the Division of Networked and Embedded Systems, where he develops frameworks like GENESIS for embedded system engineering. His work emphasizes cross-disciplinary collaboration and industry partnerships in education and technology development.

Pedro Galeanoمشاهده پروفایل

دانشیار

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +۳ مورد دیگر

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

• Belt Conveyor Systems
• Bulk Solids Handling
• Energy Efficiency in Material Transportation
• +۵ مورد دیگر

Professor Craig Wheeler is a distinguished academic in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering with a focus on bulk solids handling and belt conveyor technology. As Associate Director of the Centre for Bulk Solids and Particulate Technologies and Deputy Chairman for the Australian Society for Bulk Solid Handling, he has established the university as a global leader in fundamental and applied research within this field. Wheeler's research interests primarily center on reducing the energy intensity and environmental impact of ore and mineral transportation globally. His work develops novel theoretical approaches to model and optimize belt conveyor and bulk handling systems, with significant contributions in energy-efficient transportation, dust emission control, and innovative conveying technologies like the Rail Conveyor system. His research bridges fundamental computational techniques with practical industrial applications, addressing real-world challenges in bulk material handling. His extensive publication record demonstrates trends toward increasingly sophisticated modeling techniques, combining continuum mechanics, discrete element methods, and computational fluid dynamics to solve complex problems in bulk material flow and energy consumption. Recent work shows particular emphasis on large-diameter idler rollers for energy savings, rail-running conveyor systems, and advanced dust control methodologies. 2023 Engineers Australia - Australian Society for Bulk Solids Handling 2017 Significant Contributions to Engineers Australia's Warman Design and Build Competition (Weir Minerals) 2017 Australian Council of Engineering Deans National Award for Engineering Education Excellence 2016 Innovative Technology Award (Australian Bulk Handling) 2010 Rising Star Award (Newcastle Innovation, The University of Newcastle) 2009 Pro-Vice Chancellor's Award for Research Excellence 2006 Best Research and Development Project (Australian Bulk Handling Review) 2000 A.W. Roberts Award (Australian Society for Bulk Solids Handling) Professor Wheeler has successfully led numerous Linkage Projects with major companies including Rio Tinto, Veyance Technologies, and Laing O'Rourke, securing significant cash and in-kind contributions for research projects. His industrial consulting experience, built on a 10-year engineering career with BHP, provides valuable insights that bridge fundamental research with practical applications. He actively supervises research students and contributes to professional development courses both within Australia and internationally. As a key member of the Centre for Bulk Solids and Particulate Technologies in association with TUNRA Bulk Solids, Wheeler leads research teams focused on developing eco-friendly conveying solutions. His work has resulted in new licensed technologies, internationally recognized testing methods, design guidelines, and Australian Standards that have transformed industry practices worldwide.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

George Bosilcaمشاهده پروفایل

استاد پژوهشی

• Distributed Algorithms
• Parallel Programming Paradigms
• High Performance Computing
• +۳ مورد دیگر

George Bosilca is a Research Professor at the University of Tennessee, Knoxville, affiliated with the Department of Electrical Engineering and Computer Science and the Innovative Computing Laboratory. He holds a PhD in Computer Science (University of Paris XI, 2004) and an MS in Math and Computer Science (University of Paris XI, 1999). His research focuses on distributed algorithms, parallel programming paradigms, performance modeling/optimization, and resilience in programming models. He contributes to exascale computing initiatives through projects like PaRSEC and Open MPI. Key research areas include task-based runtimes, MPI standardization for exascale systems, and fault-tolerant distributed computing. His work emphasizes scalable and portable constructs for high-performance applications. Bosilca is involved with the Innovative Computing Laboratory (ICL) and collaborates on projects like the EPEXA ecosystem and Argobots threading framework. Recent publications highlight advancements in asynchronous many-task systems, GPU-accelerated collective operations, and resilience strategies for HPC platforms. His contributions span theoretical frameworks and practical implementations, bridging algorithmic innovation with real-world HPC challenges.

Graziano Fiorilloمشاهده پروفایل

استادیار

• Civil Engineering
• Structural Analysis
• Risk and Reliability Engineering
• +۴ مورد دیگر

Dr. Graziano Fiorillo is an Assistant Professor in the Department of Civil Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a Ph.D. from the City University of New York and M.Sc./B.Sc. from the University of Naples, Italy. His research focuses on structural reliability, bridge systems analysis, and risk assessment, incorporating machine learning and high-performance computing. He has contributed to probabilistic frameworks for infrastructure resilience, filovirus outbreak modeling, and bridge redundancy evaluation. Education: Ph.D. Civil Engineering, City University of New York, 2016 M.Sc. Building Engineering, University of Naples Federico II, 2003 B.Sc. Building Engineering, University of Naples Federico II Research Interests: Dr. Fiorillo specializes in structural analysis of bridges, risk-based design, and machine learning applications in infrastructure. He develops probabilistic models for bridge network reliability and flood risk assessment, with a focus on Manitoba's infrastructure resilience. His work integrates computational fluid dynamics (CFD) and energy efficiency solutions for buildings. Publications: His recent work emphasizes interdisciplinary approaches to infrastructure challenges, including CFD for sediment transport, EnergyPlus-based building efficiency studies, and MPI parallel computing for reliability analysis. His 2024 studies on flood-overload interactions and additive manufacturing in construction highlight emerging trends in civil engineering. Awards: He received the 2012 New York State Intelligent Transportation Society Award for best student paper. His research has been applied to truck weight regulation strategies and bridge importance factor calibration. Advising & Grants: Offers M.Sc. opportunities in CFD, building energy efficiency, and bridge structures. Positions require expertise in OpenFOAM, EnergyPlus, or structural analysis software. No specific grants mentioned in the text.

• Databases
• Spatial Data Management
• Geospatial Systems
• +۳ مورد دیگر

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

Andrew Ngمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.