جست‌وجوی استاد و پژوهشگر

Christopher Monroeمشاهده پروفایل

استاد

• Quantum Physics
• Quantum Information Science
• Quantum Computing
• +۳ مورد دیگر

Christopher Monroe is the Gilhuly Family Presidential Distinguished Professor at Duke University, with joint appointments in the Pratt School of Engineering (Department of Electrical and Computer Engineering) and the Department of Physics. He serves as Director of the Duke Quantum Center , leading cutting-edge research in quantum information science and trapped ion systems. Education: B.Sc. from MIT (1987), Ph.D. from University of Colorado, Boulder (1992) Key Research Areas: Quantum computing, quantum networking, quantum simulation, and multi-body entanglement Monroe's work focuses on trapped ion quantum systems , advancing remote entanglement, mid-circuit measurement, and N-body interactions. His team has demonstrated high-fidelity quantum gates , efficient cooling methods, and photonic interconnects for quantum networks. Recent projects include simulating quantum field theories and studying discrete time crystals with programmable ion chains. Scientific accolades include election to the National Academy of Sciences (2016). He has pioneered multi-species ion trapping for scalable quantum computers and machine learning integration with quantum circuits. Monroe's leadership extends to national policy through the U.S. National Quantum Initiative , where he testified before Congress on quantum computing's strategic importance. His lab develops hybrid quantum-classical architectures and error-corrected qubit memories , with applications in quantum gravity-inspired algorithms like thermofield double states . Monroe's group also explores quantum scrambling and verified quantum information processing , pushing boundaries in both fundamental physics and practical quantum technology.

• Cybersecurity
• Cryptography
• Wireless Networks
• +۲ مورد دیگر

Olga León Abarca is a faculty member at Polytechnic University of Catalonia (UPC), affiliated with the Department of Telematic Engineering within the School of Telecommunications and Aerospace Engineering of Castelldefels (EETAC). She co-leads the Information Security Group - Mathematics Applied to Cryptography (ISG-MAK) research group, focusing on advanced cybersecurity solutions. Her research explores: IoT and medical device security architectures Wireless positioning vulnerabilities (Wi-Fi RTT) Privacy-preserving mechanisms for vehicular networks Cryptographic applications in cognitive radio systems Intrusion detection frameworks for critical infrastructure Publications (2010-2025) demonstrate consistent focus on practical security solutions, evolving from foundational cognitive radio protection to contemporary IoT/medical security challenges. Recent work emphasizes machine learning applications and domain-specific threat modeling. Awards: Mejor paper RECSI (2018) Leads multiple competitive R&D projects including CARISMATICA (2023 cybersecurity chair) and privacy-focused blockchain initiatives. Collaborates extensively within UPC's security research network and European partnerships.

• Electronics
• Analog Design
• Circuits and Systems Theory
• +۵ مورد دیگر

Professor Ismail Serdar Özoğuz is a faculty member at the Department of Electronics and Communication Engineering, Istanbul Technical University. He has held administrative roles including Department Head (2020–present) and Vice Dean (2017–2020). His research focuses on analog design, circuits and systems theory, neural networks for engineering optimization, and emerging technologies like memristor emulators and stochastic computing. PhD in Electronics and Communications Engineering (Istanbul Technical University, 1995) Master’s and Licence in Electronics and Communication Engineering (Istanbul Technical University, 1991 and 1986) Research interests span filter design , wireless power transfer systems , and AI-driven optimization for RF and antenna modeling. Recent projects include hardware-software co-optimization for stochastic computing and GaN amplifier development. TUBITAK Incentive Award (2004) Mustafa Parlar Foundation Research Incentive Award (2003) GEBIP Award, Turkish Academy of Sciences (2002) As principal investigator, he has led 7 major projects between 2010–2025, including spintronic devices for wireless communication and fractional-order neural models. His work has been cited 3023 times with an h-index of 28 (Scopus).

• Software Defined Networking
• Network Functions Virtualisation
• Network Security
• +۶ مورد دیگر

Nicholas Race is a Professor of Networked Systems at Lancaster University's School of Computing and Communications, where he also serves as Associate Dean for Research for the Faculty of Science & Technology since 2020. His academic career at Lancaster spans over two decades, progressing from Research Assistant (2000-2001) to his current professorship (2017-present), with previous roles including Reader in Networking (2015-2017) and Senior Lecturer (2008-2015). His educational background includes a Doctor of Philosophy from Lancaster University (2000) and a 1st Class B.Sc. Hons in Computer Science from the same institution (1996). Race leads significant research initiatives including NG-CDI (an EPSRC/BT funded Prosperity Partnership), AI4ME (an EPSRC/BBC funded project), and MANY, building on previous leadership roles in projects such as 5GRIT, INITIATE, MPAT, and several EU-funded initiatives. Race's research focuses on Software Defined Networking (SDN) and Network Functions Virtualisation (NFV) for developing next-generation networking services. His work spans network security (including the TENNISON framework for distributed SDN security), media distribution with Quality of Experience optimization, and autonomic network management. His recent publications reveal a strong emphasis on intent-based networking, security automation, quality of experience in adaptive streaming, and the integration of 5G with other networking technologies. His scholarly output shows consistent contributions across multiple venues, with recent work addressing challenges in low-latency streaming, network security evaluation frameworks, and the practical implementation of software-defined networking concepts. The publications demonstrate an interdisciplinary approach bridging theoretical networking concepts with practical implementations. Race serves on the editorial board of Computer Communications and has held leadership roles in major conferences including as Programme Co-Chair of DE2013: Open Digital and as PC Member for ACM Multimedia, Multimedia Systems, and EWSDN. As a research supervisor, he mentors multiple PhD students working on topics related to SDN, network security, and media delivery. His projects attract significant funding from EPSRC, BT, BBC, and EU sources, demonstrating the applied relevance of his work to both industry and academic communities. Race is affiliated with several research groups including the Cyber Security Research Centre (Networking), Data Science Institute Foundations, Security Lancaster (Networks), and other specialized centers focusing on network security and systems.

Jon Michael Granمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Lifetime Analysis
• +۴ مورد دیگر

Jon Michael Gran is a Professor at the Department of Biostatistics , Institute of Basic Medical Sciences (IMB), University of Oslo, since 2022. He also holds a 20% senior researcher position at the Norwegian Institute of Public Health since 2024. PhD in Biostatistics (University of Oslo, 2011) MSc in Statistics (NTNU, 2005) His research focuses on causal inference , lifetime analysis , multistate models , and epidemiological methods , with applications in infectious disease modeling, clinical data analysis, and public health policy evaluation. Recent work examines opioid use in trauma patients, prenatal drug exposure effects, and interventions for inclusive working life. Publications (2025-2023) span journals like Acta Anaesthesiologica Scandinavica , Biometrika , and JAMA Network Open , emphasizing translational biostatistical methods applied to clinical registries and public health datasets. Notable collaborations include the Causal Inference Research Group and IPOT project on trauma outcomes. He serves as Associate Editor for Biometrika (since 2018) and Statistics in Medicine (since 2025), with teaching roles in medical statistics and machine learning at the University of Oslo.

• Particle Physics
• High Energy Physics
• Quantum Field Theory
• +۳ مورد دیگر

Professor Daniel Maitre is a Professor in the Department of Physics at Durham University and a Lecturer in the Institute for Particle Physics Phenomenology (IPPP). His research focuses on advancing computational methods for high-energy physics, particularly at the intersection of particle physics phenomenology and machine learning. His primary research interests include particle physics phenomenology , quantum field theory computations , and machine learning applications in collider physics. Maitre specializes in developing novel algorithms for matrix element emulation, event generation, and negative-weight elimination in Monte Carlo simulations. His work addresses critical computational bottlenecks in predicting multi-jet processes at the Large Hadron Collider (LHC), with emphasis on factorisation-aware neural architectures and equivariant network designs that preserve physical symmetries. Analysis of his 15 most recent publications (2014-2025) reveals a dominant trend toward machine learning integration in high-energy physics. Key focus areas include neural network-based unweighting of events, precision matrix element calculations, and efficient integration methods for complex phase spaces. These innovations significantly enhance simulation accuracy for LHC data analysis, particularly in high-multiplicity jet environments where traditional methods face computational limitations. Scientific Awards: No awards mentioned in available documentation. Maitre currently supervises at least one postgraduate research student (Wendy Gray). His sustained publication record in top-tier journals like Journal of High Energy Physics and Physical Review D indicates active research funding, though specific grants are not detailed. He is a core member of the IPPP, collaborating on cutting-edge projects involving LHC event generation and precision QCD calculations. The Institute for Particle Physics Phenomenology serves as Maitre's primary research hub, facilitating collaborations across global particle physics initiatives focused on next-generation collider data analysis and theoretical advancements.

Shih-Hao Hungمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Processing
• Performance Evaluation
• +۷ مورد دیگر

Shih-Hao Hung is a Full Professor in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served since 2005. He established the Performance, Applications and Security Lab (PAS Lab) and previously served as Section Director in the Computer and Information Network Center of NTU from 2012 to 2016. Before joining NTU, he worked for 5 years at Sun Microsystems in the San Francisco Bay Area. Professor Hung's research focuses on computer architecture, parallel processing, performance evaluation, and optimization. His work spans embedded systems, mobile applications, cloud computing, business applications, and information security. Current research interests include heterogeneous and multicore system architecture, active utilization of open source system software, and high performance deep learning systems. He has published numerous papers on GPU acceleration, heterogeneous computing, malware detection, and system performance optimization. His research has resulted in several notable publications and two Best Paper Awards (RACS 2014 and RACS 2017). Professor Hung maintains strong industry collaborations with leading technology companies including IBM, Intel, AMD, NVIDIA, ARM, MediaTek, and Foxconn, focusing on practical applications of advanced computing technologies. Best Paper Award at RACS 2017 for Program Analysis research Best Paper Award at RACS 2014 for Android malware detection framework Extensive publications in performance evaluation and optimization Significant industry collaborations across multiple technology sectors Professor Hung is known for his practical approach to computer science education and research, emphasizing the importance of bridging academic knowledge with real-world applications. His blog frequently discusses issues in computer science education, system architecture, and the development of skilled professionals in Taiwan's technology sector.

JoAnn M. Paulمشاهده پروفایل

دانشیار

• Bio-inspired Computer Architecture
• Computer Architecture
• Heterogeneous Multiprocessing
• +۳ مورد دیگر

JoAnn M. Paul is an Associate Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. She is affiliated with the Virginia Tech Research Center - Arlington, where she conducts research on bio-inspired computer architecture, heterogeneous multiprocessing, and computational neuroscience. Her work explores how principles from neurobiology can inform the design of parallel computing systems, particularly in contexts like computational dreaming and chip-level performance optimization. Education: Ph.D., University of Pittsburgh, 1994 M.S.E.C.E., Carnegie Mellon University, 1987 B.S.E.E., University of Pittsburgh, 1983 Research Interests: Her research focuses on advancing heterogeneous computing architectures through interdisciplinary approaches. Key areas include bio-inspired system design, computational models of dreaming, and performance evaluation metrics for parallel systems. She investigates how structural intelligence and paradox resolution can enhance system efficiency, with applications in embedded systems and real-time processing. Publications Trends: Her work spans over three decades, with notable contributions to heterogeneous multiprocessor design, capacity metrics, and stochastic simulation. Recent publications emphasize integrating cognitive-inspired algorithms into hardware architectures to address challenges in parallel computing scalability and energy efficiency. Labs/Teams: As part of the Virginia Tech Research Center, she collaborates on projects blending academic and industrial needs, particularly in embedded and high-performance computing domains.

Prabha Sundaravadivelمشاهده پروفایل

استادیار

• Internet of Things (IoT)
• Smart Healthcare
• IoT Architectures
• +۴ مورد دیگر

Dr. Prabha Sundaravadivel is an Assistant Professor in the Department of Electrical Engineering at the University of Texas at Tyler (UT Tyler). She joined UT Tyler in Fall 2018 and holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2018), an M.Tech in VLSI Design from Vellore Institute of Technology (2015), and a B.Tech in Electronics and Communication from SRM Institute of Science and Technology (2011). Her research focuses on IoT-driven smart healthcare systems, low-power VLSI design, and embedded systems for IoT applications. As Director of the Computer Engineering Research Laboratory (CERL), her team explores smart healthcare and smart cities initiatives, leveraging IoT technologies to enhance quality of life. Current projects include UAV-based agricultural monitoring, bio-inspired robotics, and edge-intelligent wearables for health monitoring. Dr. Sundaravadivel is an IEEE member and Faculty Advisor for the IoT Student Club. Her work emphasizes interdisciplinary innovation, integrating machine learning, hardware design, and environmental intelligence into practical solutions. Recent research highlights include autonomous UAV navigation systems, low-power memristor emulators, and FPGA-based intrusion detection for aerial robots. Her teaching philosophy incorporates project-based learning (PBL) to integrate sustainability and entrepreneurial skills into engineering education. Notable educational initiatives include video creation for developing entrepreneurial mindsets and blending entrepreneurship into senior-level courses.

Adrián Pérez-Suayمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Dependence Estimation
• +۸ مورد دیگر

Adrián Pérez-Suay is an Assistant Professor in the Department of Computer Science at Universitat de València. He holds a B.Sc. in Mathematics (2007), Master in Advanced Computing and Intelligent Systems (2010), and Ph.D. in Computational Mathematics and Computer Science (2015), all from Universitat de València. He has served as 'profesor asociado' (2015-2019) and is currently a full-time Assistant Professor in Mathematics Education. His research focuses on Machine Learning applications in Remote Sensing, Causal Inference, and Education Technology. He leads projects like DEEPCLOUD (cloud detection in satellite imagery) and SCALE (causal inference in Earth systems), and collaborates with the Image Processing Laboratory (IPL). Education: B.Sc. Mathematics, Universitat de València (2007) Master Advanced Computing and Intelligent Systems, Universitat de València (2010) Ph.D. Computational Mathematics and Computer Science, Universitat de València (2015) Research Interests: Machine Learning for Remote Sensing (e.g., Gaussian processes, kernel methods), Causal Inference, Algorithmic Fairness, and Educational Data Analytics (student performance modeling via LMS data). He has published over 20 JCR-indexed papers and 50 conference articles, with an h-index of 10 (Google Scholar). Grants/Projects: DEEPCLOUD (Spanish Ministry, 2020-2024) SCALE (Fundación BBVA, 2020-2022) SEDAL (ERC Consolidator Grant, 2015-2020) Labs: Image Processing Laboratory (IPL) at Universitat de València, focusing on Earth observation and AI applications.

• Turbulent Flow
• Large Eddy Simulation (LES)
• Renewable Energy Systems
• +۳ مورد دیگر

Guillaume Balarac is a Professor at ENSE3 (Grenoble Institute of Technology) within the University of Grenoble Alpes. He is affiliated with the Laboratory of Geophysical and Industrial Flows (LEGI) and leads research in the MOST team, focusing on turbulence modeling and simulation. Current Role: Professor since 2021 Prior Role: Maître de conférences (2008-2021) Award: IMT Espoir Prize - Academy of Sciences (2019) Recognitions: Junior Member, Institut Universitaire de France (2017-2022) His research explores turbulent flows through numerical simulations, emphasizing Large Eddy Simulation (LES) development, subgrid-scale modeling, and industrial applications in renewable energy systems. Key contributions include turbulence prediction methods, analysis of swirling flows, and machine learning integration for boundary condition reconstruction. Recent publications focus on LES optimization for complex geometries, turbulence in microencapsulation processes, and fluid-structure interaction solvers. His work spans fundamental turbulence studies and applied projects for hydraulic machinery, wind turbines, and additive manufacturing flows. Scientific Awards: IMT Espoir Prize - Academy of Sciences (2019) Institut Universitaire de France (IUF) Junior Membership (2017-2022) Balarac's collaborative efforts include partnerships with Stanford's Center for Turbulence Research and contributions to patents related to floating vertical-axis wind turbines.

Jim W. Portegiesمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Applied Mathematics
• +۵ مورد دیگر

Jim W. Portegies is an Assistant Professor in the Applied Analysis group at the Eindhoven University of Technology (TU/e). He holds additional roles as EAISI Foundational Assistant Professor and EAISI Mobility Assistant Professor within the TU/e's interdisciplinary initiatives. His research focuses on applying mathematical techniques from analysis, geometry, and measure theory to problems in machine learning and artificial intelligence, with a particular interest in designing algorithms that emulate human learning mechanisms. He obtained his MSc in Industrial and Applied Mathematics and Applied Physics from TU/e before completing his PhD in Mathematics at the Courant Institute of Mathematical Sciences, New York. After postdoctoral work at the Max Planck Institute in Leipzig, he returned to TU/e in 2016. Education: MSc in Industrial and Applied Mathematics & Applied Physics (TU/e, 2009) PhD in Mathematics (Courant Institute, NYU, 2014) Research Interests: Jim's work bridges pure mathematics and applied AI. Key areas include: Geometric and spectral methods for machine learning Universal learning algorithms inspired by human cognition Analysis of dimensionality reduction techniques Stochastic processes and control systems His recent work explores neural stochastic differential equations and interpretable AI mechanisms. Awards: Member of TU/e Young Academy of Engineering (2016–present) Collaborations & Grants: Jim collaborates extensively in interdisciplinary projects, including EU-funded initiatives on AI mobility systems. His research has been supported by grants from NWO (Netherlands Organization for Scientific Research) and industry partnerships with semiconductor and control systems companies. Labs & Teams: He contributes to the TU/e's Center for Analysis, Scientific Computing and Applications (CASA) and leads the Applied Analysis & AI research group, focusing on theoretical foundations of AI and their practical implementations.

Rajeev Gangulaمشاهده پروفایل

عضو هیئت علمی

• Signal Processing
• Wireless Communications
• Software-Defined Radio
• +۳ مورد دیگر

Rajeev Gangula is a Research Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University's College of Engineering. His research focuses on signal processing, physical layer protocol design, and prototyping with software-defined radios and connected robotics systems. Education background includes a PhD in Mobile Communications from Télécom ParisTech (EURECOM, 2015), MSc in Mobile Communications from Télécom ParisTech (EURECOM, 2012), and Master of Technology in Signal Processing from Indian Institute of Technology Guwahati (2010). Research interests span wireless communication systems, UAV-aided networking, 5G/6G technologies, and real-time prototyping. Current work explores AI-enabled open RAN control, bistatic sensing in 5G NR, and UAV-assisted localization using hybrid radio models. Recent publications demonstrate strong focus on O-RAN implementation, spectrum sharing techniques, and advanced positioning methodologies using OpenAirInterface platforms.

• Plasma Processing
• Surface Engineering
• Superhydrophobic Surfaces
• +۳ مورد دیگر

Vivek Pachchigar is a Postdoctoral Research Associate at the University of Illinois. His work focuses on advanced materials science and surface engineering, particularly in plasma processing, nanotechnology, and functional surface modifications. He explores applications ranging from corrosion protection to energy-efficient cooling systems and sensor technologies. Key research areas include: Superhydrophobic and anisotropic surface treatments using ion beams and plasmas Development of high-performance SERS (Surface-Enhanced Raman Scattering) substrates Material characterization of polymers and semiconductors under plasma and ion irradiation Applications in fusion energy materials through the INFUSE program collaboration Recent studies highlight innovations in nanostructuring for electronic cooling, graded oxide layers for neuromorphic devices, and plasma-based synthesis of functional materials. His contributions bridge fundamental material science with applied engineering solutions.

Francis Yanمشاهده پروفایل

استادیار

• Networked Systems
• Computer Networks
• Machine Learning for Systems
• +۳ مورد دیگر

Francis Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and the Electrical & Computer Engineering department. He leads the Illinois Networked Systems and AI (NSAI) Lab, focusing on integrating practical machine learning into networked systems. Previously, he was a Senior Researcher at Microsoft Research Redmond, working on systems and networking research. His work emphasizes deployable ML solutions, validated through large-scale platforms like Puffer and Pantheon. Education: Ph.D. in Computer Science (Stanford University, 2020); B.S. in Computer Science (Tsinghua University, 2015) and B.A. in Economics (Tsinghua University, 2015). Research Interests: Networked systems, machine learning for systems and networking, intelligent video systems, and cloud resource management. His lab develops frameworks like DeDe, Mowgli, and Autothrottle to enhance system performance and robustness. Awards: USENIX NSDI Outstanding Paper Award (2024), Applied Networking Research Prize (2021), and USENIX ATC Best Paper Award (2018). He serves on program committees for NSDI, SIGCOMM, and ACM workshops. Grants & Service: Co-chair of SIGCOMM 2024 Artifact Evaluation. Active in open-source projects and research platforms supporting reproducible networking experiments. Labs/Teams: Directs the NSAI Lab, collaborating on projects such as Puffer (400k-user video service), Teal (WAN optimization), and Genet (curriculum learning for networking).