جست‌وجوی استاد و پژوهشگر

Shaji A. Khanمشاهده پروفایل

دانشیار

• Cybersecurity Education
• Information Systems Management
• IT Outsourcing
• +۵ مورد دیگر

Shaji A. Khan is an Associate Professor of Information Systems and Technology and Associate Dean of Undergraduate Programs at the University of Missouri-St. Louis (UMSL)'s College of Business Administration. He holds a Ph.D. in Business Administration from UMSL and has a background in Computer Science and Commerce. His research focuses on cybersecurity education, IT outsourcing, business process offshoring, and entrepreneurial innovation. Khan leads UMSL's Cybersecurity Institute and has developed interdisciplinary cybersecurity programs including graduate/undergraduate certificates and minors. He has secured over $294K in Department of Defense funding for cybersecurity labs. His teaching spans information security, data networks, systems administration, and programming. He has published extensively in top journals like Journal of Business Venturing and Journal of Strategic Information Systems . Notable awards include the Paul Gray Award for thought-provoking IT outsourcing research and multiple teaching accolades. Khan also serves as a media expert on cybersecurity matters and organizes STLCyberCon conferences attracting 700+ attendees annually. Professional service includes roles on the Graduate Studies Committee and leadership in international conferences like ICIS. His industry experience includes founding TechQED Inc. and leading a high school as principal/CTO earlier in his career. Technical expertise spans cybersecurity tools, cloud platforms, and programming languages.

Hari Sundarمشاهده پروفایل

دانشیار

• Parallel Algorithms
• High Performance Computing
• Computational Sciences
• +۳ مورد دیگر

Hari Sundar is an Associate Professor in the Department of Computer Science at Tufts University, holding the Ada Lovelace Associate Professorship. Previously, he served as an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on developing parallel algorithms for computational sciences and high-performance computing, addressing challenges in biosciences, geophysics, computational fluid dynamics, and computational relativity. He leads efforts in adaptive mesh refinement, geometric multigrid methods, and scalable scientific computing frameworks like Dendro-GR for numerical relativity. Education: Ph.D. in Computer Science from the University of Pennsylvania (2009), and a Bachelor of Engineering from the University of Delhi (2000). Postdoctoral work at the Oden Institute, University of Texas at Austin. Research Interests: Parallel algorithms, high-performance computing architectures, computational relativity (binary black hole simulations), multiphase flow modeling, and domain-specific languages for scientific computing. His work emphasizes scalability and efficiency on modern supercomputers. Key Contributions: Development of the Dendro-GR platform for gravitational wave simulations, scalable PDE solvers, and GPU-optimized algorithms for phonon transport and genomic sequence alignment. His recent work includes advancements in gravitational waveform modeling for LISA space missions and thermodynamically consistent two-phase flow simulations. Grants & Collaborations: Active in NSF-funded projects on computational relativity, multiphase flow algorithms, and scalable PDE solvers. Collaborates across disciplines in astrophysics, materials science, and bioinformatics.

Je Hyeong Bahkمشاهده پروفایل

دانشیار

• Thermoelectric materials
• Energy harvesting
• Wearable electronics
• +۴ مورد دیگر

Dr. Je Hyeong Bahk is an Associate Professor jointly affiliated with the Departments of Mechanical and Materials Engineering and Electrical and Computer Engineering at the University of Cincinnati. His research focuses on thermoelectric materials and systems, wearable/flexible electronics, and nano-scale thermal transport phenomena. He holds a Ph.D. in Electrical Engineering from the University of California, Santa Barbara, and has authored over 100 peer-reviewed publications. Education: Ph.D., Electrical Engineering, University of California, Santa Barbara (2010) M.S., Electrical Engineering, Seoul National University (2000) B.S., Electrical Engineering, Seoul National University (1998) Research Interests: Human body-heat energy harvesting Thermoelectric materials and device fabrication Wearable/flexible electronics Nano-scale electron/thermal transport physics Thermoelectric air conditioning systems Recent Contributions: Dr. Bahk's work includes advancements in binder-jet printed ceramic composites, low-dimensional carbon-based energy harvesting, and thermoelectric cooling systems. His lab, the Thermoelectric Energy Conversion Lab, develops next-generation solid-state cooling and energy conversion technologies. Awards: 2023 URC Faculty Scholar Award 2016 UC Faculty Development Award 2010 Editor’s Choice Paper Award Lab & Team: The Thermoelectric Energy Conversion Lab collaborates on projects such as firefighter jacket cooling systems and nanocarbon-based composites. Recent student advisees include Thiraj, Nitin, Isaac, Anirudh, and Ahmad.

Gaofeng Jiaمشاهده پروفایل

دانشیار

• Structural Engineering
• Risk Assessment
• Infrastructure Resilience
• +۴ مورد دیگر

Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award

Kobus Van der Merweمشاهده پروفایل

استاد

• High-Performance Computing
• Scalable Systems
• Networking
• +۱ مورد دیگر

Kobus Van der Merwe is a Professor at the Kahlert School of Computing, University of Utah. His research focuses on High-Performance Computing (Scalable Systems) and networking systems. He is affiliated with the School of Computing, located at 50 Central Campus Dr., Salt Lake City, UT 84112. Contact details include his office MEB 3490 D and phone number (801) 581-3012. Research interests emphasize scalable system architectures and networking innovations. His work continues the legacy of the University of Utah as a founding node of the ARPANET (1969), highlighting its historical contributions to internet infrastructure. No specific scientific awards, grants, or advised students are listed in the provided information. Lab affiliations or ongoing collaborative teams are not explicitly mentioned.

Md Arifuzzamanمشاهده پروفایل

استادیار

• High-Performance Networking
• Quantum Networking
• Distributed Systems
• +۳ مورد دیگر

Dr. Md Arifuzzaman is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology (Missouri S&T). He specializes in High-Performance Systems, Quantum Networking, and Distributed Systems, focusing on optimizing large-scale system performance and scalability. His work addresses challenges in next-generation networks and storage systems, with publications in top venues like IEEE TPDS and ACM Supercomputing. Education: Ph.D. in Computer Science and Engineering, University of Nevada, Reno (2023) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Dr. Arifuzzaman's work spans cutting-edge topics including quantum entanglement routing, reinforcement learning for network optimization, and high-speed file transfer protocols. His research emphasizes practical solutions for emerging technologies like terabit networks and quantum communication systems. Publications: Recent work focuses on quantum network protocols, machine learning-driven network probing, and storage reliability. His articles highlight advancements in both theoretical frameworks and real-world system implementations. Awards: No specific awards mentioned in the provided information. Advising/Grants: Details regarding student advising and grant activities are not explicitly stated in the text.

Sudhakar Gantiمشاهده پروفایل

دانشیار

• Cloud Computing Resource Management
• Software Defined Networking (SDN)
• Traffic Management and Quality-of-Service
• +۳ مورد دیگر

Sudhakar Ganti is an Associate Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Ottawa. His research focuses on cloud computing resource management, software-defined networking (SDN), traffic management, quality-of-service optimization, and performance evaluation through queueing theory. His work bridges theoretical frameworks with practical applications in network efficiency and distributed systems. Dr. Ganti’s expertise includes optimizing resource allocation in fog-cloud systems, enhancing telehealth IoT energy efficiency, and developing dynamic defense frameworks for SDN security. His contributions span network traffic prediction, large file transport protocols, and formal verification of networking systems. He has published extensively in top-tier conferences and journals, addressing challenges in distributed computing, cyber security, and edge computing. His research trends emphasize leveraging reinforcement learning for fog-cloud resource allocation, multi-objective optimization in IoT, and SDN-driven network security. Earlier work includes foundational studies on optical router bypass, cloud workload characterization, and conversational agents for smart environments. Despite his prolific output, no academic awards or grants are explicitly mentioned in his profile.

• Scientific Computing
• GPU Computing
• Signal Processing
• +۵ مورد دیگر

Professor Wes Armour is a Professor of Scientific Computing at the University of Oxford and serves as the Associate Head of Department for Research in the Department of Engineering Science. He previously directed the Oxford e-Research Centre, an interdisciplinary research center within the Engineering Science Department. With over £31 million secured as PI or Co-I, his work spans supercomputing, signal processing, machine learning, computational fluid dynamics, and protein crystallography. Professor Armour's research focuses on extracting science from data through fundamental challenges in modeling, simulation, and data processing. His work draws from numerical analysis, signal processing, and machine learning to develop technologies enabling future scientific discoveries, particularly for the Square Kilometre Array (SKA) telescope. Key interests include GPU computing, high performance computing, and machine learning applications across diverse domains from radio astronomy to finance. As Director and Principal Investigator of JADE and JADE2, a 700-GPU machine, he established the UK's first national High Performance Computer facility dedicated to advancing Artificial Intelligence and Machine Learning. His research group has pioneered GPU applications across multiple fields, including Square Kilometre Array data processing, protein crystallography, and graphene simulations. Current projects span energy-efficient machine learning, stock price prediction in finance, prime number prediction in cryptography, and multi-modal CCTV data analysis. Professor Armour has been instrumental in developing real-time signal processing techniques for astronomical observations, including the ARTEMIS system for millisecond radio transient detection. His publications demonstrate consistent innovation in GPU-accelerated computing dating back to early work in 2008 on accelerating conjugate gradient routines for electron transport in graphene.

• Control Theory
• Digital Signal Processing
• Adaptive Signal Processing and Control
• +۷ مورد دیگر

Dr. Allahyar Montazeri is a Senior Lecturer in Control and Electronics Engineering at Lancaster University's School of Engineering, specializing in advanced control systems and signal processing. His research focuses on adaptive signal processing, robust control, system identification, and applications in robotics, active noise/vibration control, and wave energy conversion. He has over 110 publications and serves on editorial boards such as Frontiers in Robotics and AI, and IFAC Technical Committees. Montazeri holds a Humboldt Research Fellowship (2011) and ERCIM Fellowship (2010). His industrial collaborations include Bosch for automotive noise control and Fraunhofer Institute. He has supervised PhD students in acoustic signal processing and leads projects on autonomous robotics and environmental monitoring. Notable awards include 'Outstanding Associate Editor' (2023) and 'Fellow of The Higher Education Academy.' He actively participates in conferences like IEEE CDC and chairs sessions on mechatronics systems. His work bridges theoretical control advancements with practical applications in extreme environments, including nuclear robotics and underwater systems.

Nicola Calabrettaمشاهده پروفایل

استاد

• Optical Communication Systems
• Photonics
• Telecommunications Engineering
• +۶ مورد دیگر

Nicola Calabretta is a Full Professor in Electro-Optical Communication Systems and Senior Research Fellow at Eindhoven University of Technology (TU/e). His work focuses on smart optical networks, high-speed electronics, FPGA implementations for scheduling algorithms, and photonic integrated circuits. He holds a PhD from TU/e (2004) and previously conducted research at DTU Fotonik and the Sant'Anna School of Advanced Studies. His expertise spans optical signal processing, multi-level modulation formats, and applications in data center and metro networks. Key research areas include optical switching architectures (e.g., SOA-based switches), WDM systems, and low-latency interconnect networks. He has led projects like ADAPTOR (resource optimization), SmartTWO (future telecom technologies), and 5G-MOBIX (cross-border mobility). His courses include 'Optical Fibre Communication Technology' and 'Optical Interconnection Networks.' Collaborations involve institutions globally, with recent work emphasizing photonic integration for neural networks, ultra-fast switching, and edge computing. His contributions align with UN SDGs through sustainable telecom infrastructure advancements.

Duncan Robertsonمشاهده پروفایل

پژوهشگر ارشد

• Millimeter-Wave Radar
• Radiometry
• Imaging Technology
• +۴ مورد دیگر

Prof Duncan Robertson is a Professorial Research Fellow at the School of Physics and Astronomy, University of St Andrews, Scotland. He holds a B.Sc. (Hons.) and Ph.D. in Physics from the same institution. His career has focused on millimeter-wave radar technologies with applications in environmental sensing, security systems, and battlefield systems. He leads the Millimetre Wave Group, specializing in radar imaging, radiometry, electron spin resonance instrumentation, and antenna design. Education: B.Sc. (Hons.) in Physics and Electronics, University of St Andrews (1991) Ph.D. in Millimetre Wave Physics, University of St Andrews (1991) Research Interests: Prof Robertson’s work spans millimeter-wave radar systems, including drone detection, glacier monitoring, sea clutter analysis, and holographic metasurfaces. His group develops technologies for security screening, environmental monitoring, and material characterization. Grants & Projects: Environmental Monitoring: Short Range Interferometric Synthetic Aperture Radar (InSAR) MuWMAS: Snowflake Scattering and Microstructure Analysis Drone Detection Radar Commercialization Labs/Teams: Leads the Millimetre Wave Group, collaborating on radar phenomenology and advanced sensor systems. Active in international radar conferences and experimental field trials.

Ahmed M. Attiaمشاهده پروفایل

پژوهشگر

• Computational Science and Engineering
• Data Assimilation
• Uncertainty Quantification
• +۱۱ مورد دیگر

Ahmed M. Attia is a computational mathematician at the Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA. He is also a member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne. Previously, he was a postdoctoral researcher at Argonne and a research fellow at SAMSI, with affiliation to the Department of Mathematics at North Carolina State University. Education: Ph.D. in Computer Science and Applications, Virginia Tech, 2016 M.S. in Statistics and Computer Science, Mansoura University, 2008 B.S. in Mathematics, Statistics and Computer Science, Mansoura University, 2004 His research spans computational science and engineering, focusing on data assimilation, uncertainty quantification, optimal experimental design, PDE-constrained optimization, Bayesian inference, and high-performance computing . He integrates machine learning and statistical methods into scientific computing frameworks. His work enables robust and scalable solutions for inverse problems in complex physical systems. The primary trend in his recent publications centers on the development of PyOED, an open-source framework that unifies variational and Bayesian data assimilation with optimal experimental design, featuring novel optimization and machine learning solvers. This work bridges applied mathematics, computational science, and software engineering. Scientific Awards: No awards explicitly mentioned. Advising and Grants: Ahmed has mentored and collaborated with researchers such as Abhijit Chowdhary and Shady E. Ahmed on the PyOED project. His research is supported by the U.S. Department of Energy (DOE), particularly through the Office of Science and the Advanced Scientific Computing Research (ASCR) program. Labs and Teams: He is an active member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne National Laboratory, contributing to national efforts in applied mathematics and scientific computing.

Arpan Gujaratiمشاهده پروفایل

عضو هیئت علمی

• Real-Time Systems
• Distributed Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Arpan Gujarati is a Sessional Lecturer in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. He teaches graduate and undergraduate courses such as CPSC 538G (Distributed Systems), CPSC 416 (Operating Systems), and CPEN 432 (Real-Time System Design). He holds a PhD from the Max Planck Institute for Software Systems and TU Kaiserslautern, where he was supervised by Björn B. Brandenburg. PhD: Max Planck Institute for Software Systems & TU Kaiserslautern (2020) Undergraduate: Birla Institute of Technology and Science (BITS Pilani) Postdoctoral Researcher: MPI-SWS Research Associate: UBC Software Development Engineer: Citrix R&D, India His research focuses on real-time and distributed systems, with applications in cyber-physical systems, fault tolerance, and machine learning reliability. He investigates scheduling algorithms, reliability analysis, and the integration of learning-enabled components into safety-critical systems. His work combines theoretical analysis with practical system implementations, often involving real-world testbeds and open-source tools. His recent publications span top-tier venues including RTSS, OSDI, ECRTS, DSN, and Middleware, with a strong emphasis on performance predictability, resilience of ML systems, and real-time communication. His work frequently addresses challenges in timing guarantees, fault tolerance, and system reliability in both cloud and embedded environments. SIGBED Paul Caspi Memorial Dissertation Award Best Paper Award at RTSS 2022 Distinguished Artifact Award at OSDI 2020 Best Student Paper Award at Middleware 2017 Outstanding Paper Award at RTCSA 2025 He advises several PhD students and undergraduate researchers at UBC, including Heng Zhao, Aida Aminian, Zainab Saeed Wattoo, and Philip Schowitz. He has led multiple research projects involving robotic arms, NVIDIA Holoscan, FreeRTOS, and distributed key-value stores. His lab work emphasizes reproducibility, open datasets, and practical system building. He has served on program committees for RTSS, RTAS, ECRTS, and Middleware, and contributes to journals such as Real-Time Systems and JSys.

Charles Raymond Sarimuthuمشاهده پروفایل

مدرس ارشد

• Power Systems
• Renewable Energy
• Smart Grids
• +۳ مورد دیگر

Dr. Charles Raymond Sarimuthu is a Senior Lecturer in the Department of Electrical and Computer Systems Engineering at Monash University Malaysia. He holds a Ph.D. in Electrical Power Engineering from National Energy University, Malaysia. His research focuses on voltage control, power transformers, distribution systems, renewable energy, and smart grids. He actively supervises undergraduate and postgraduate students and has published extensively in high-impact journals such as Electric Power Systems Research and Renewable Energy . Dr. Sarimuthu has collaborated on projects including the Electric Field Imaging (EFI) Sensor for Comprehensive Static Charge Analysis (2024-2025) and the Fundamental study on memristor switching performances (2023-2026). He has also led the Investigation of on-load tap changer transformer operation (2019-2022), demonstrating expertise in grid stability and renewable integration. Teaching commitments include ECE3051 - Electrical Energy Systems , ECE4053 - Electrical Energy-Generation and Supply , and ECE5886/6886 - Smart Grids . He is a member of professional bodies such as the Board of Engineers Malaysia (BEM) and IEEE. His work aligns with UN Sustainable Development Goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Recent research highlights include game theory approaches for voltage control and adaptive synchronverter technologies for solar power systems.

Ameya Jagtapمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Deep Learning
• Computational Physics
• +۸ مورد دیگر

Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.