جستجوی استاد راهنمای دانشگاهی

George C. Alexandropoulosمشاهده پروفایل

دانشیار

• Wireless Communication Systems
• Signal Processing
• Algorithmic Design
• +۹ مورد دیگر

George C. Alexandropoulos George C. Alexandropoulos is an Associate Professor at the Department of Informatics and Telecommunications, School of Sciences, National and Kapodistrian University of Athens (NKUA). He also holds an adjunct professorship at the University of Illinois Chicago. His research focuses on wireless communication systems, signal processing, and reconfigurable intelligent surfaces (RIS), with contributions to integrated sensing and communications (ISAC), millimeter-wave/THz systems, and distributed machine learning. Education Ph.D. in Computer Engineering and Informatics, University of Patras, Greece (2010) M.A.Sc. in Signal and Communications Processing Systems, University of Patras, Greece (2005) Engineering Diploma (5 years), Computer Engineering and Informatics, University of Patras, Greece (2003) Research Interests Alexandropoulos' work spans algorithmic design for wireless networks, optimization of multi-antenna systems, and full-duplex radios. He is particularly known for pioneering contributions to reconfigurable metasurfaces, including their application in ISAC and 6G networks. His research emphasizes practical implementations, such as prototypes for AoA estimation and field trials for RIS deployment. Articles His publications highlight advancements in secure ISAC, fluid RIS design, and edge-intelligence networks. Recent work explores challenges in THz RIS hardware and OTA edge inference using metasurface-integrated neural networks. Scientific Awards IEEE ComSoc Leonard G. Abraham Prize (2024) IEEE Marconi Prize Paper Award (2021) NKUA Research Excellence Award (2023-2024) Advising & Grants Alexandropoulos has led several EU and industry projects, including contributions to Huawei's 5G standards and TII's AI research. His work often involves interdisciplinary teams and practical prototyping. Labs & Teams He is affiliated with the Communications and Signal Processing division at NKUA and collaborates with institutions like ATHENA Research Center and the Technology Innovation Institute (Abu Dhabi).

• High Performance Computing
• Embedded Systems
• Medical Imaging
• +۴ مورد دیگر

Elisa Marenzi is an Assistant Professor at the University of Pavia's Department of Industrial and Information Engineering. She holds a PhD in Bioengineering and Bioinformatics (2014) and master's/bachelor's degrees in Biomedical Engineering from the same institution. Her research focuses on High Performance Computing (HPC), embedded systems for biomedical monitoring, signal processing in rehabilitation/automotive fields, and cerebellar neural circuit modeling. Education: PhD in Bioengineering (2014): Thesis on pressure ulcer monitoring systems Master's in Biomedical Engineering (2010): Thesis on capacitive sensor systems Bachelor's in Biomedical Engineering (2007): Thesis on medical guideline formalization Research Highlights: Her work spans HPC optimization for hyperspectral imaging, wearable medical devices, and neurocomputational modeling of cerebellar circuits. Notable contributions include: STRATUM project: AI-driven 3D decision support tools for neurosurgery HBP (Human Brain Project) simulations of hippocampal dynamics Hyperspectral imaging applications in skin cancer diagnosis FPGA-based real-time medical signal processing systems Awards: ETIC Award (2014) for PhD thesis Lifebility Award (2012) for social tech innovation NVIDIA GPU Research Center accreditation (2016) Professional Activities: She has held roles including Systems Administrator (2020-2023) and post-doctoral research in neurocomputation (2017-2019). Active in editorial roles for Microprocessors and Microsystems and Frontiers in Computational Neuroscience. Co-chair for ASHWPA special session at Euromicro DSD 2024. Labs/Teams: Key affiliations include the Neurocomputation Laboratory (DBBS) and collaboration with NVIDIA GPU Research Center.

TOORAJ NIKOUBINمشاهده پروفایل

استاد

• NanoElectronics Device/Circuit/System
• Approximate Computing
• Reconfigurable Computing
• +۳ مورد دیگر

Dr. Tooraj Nikoubin is a Professor of Instruction in the Department of Electrical and Computer Engineering at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. in Computer Engineering from Shahid Beheshti University (2009), and M.Sc./B.Sc. degrees in Electrical Engineering-Electronics from K.N.T. University of Technology (Iran). His research focuses on nano-electronics, approximate computing, reconfigurable systems, and wearable electronics, emphasizing device-circuit co-design in emerging technologies. With over 35 journal/conference publications and one book chapter over 13+ years in academia, Dr. Nikoubin teaches both graduate and undergraduate courses across computer and electrical engineering domains. Research interests include nano-scale device optimization, energy-efficient circuits, and hardware acceleration for edge computing applications. His work spans from biometric authentication systems using PPG signals to drone-based precision agriculture and molecular property prediction. Recent efforts prioritize low-power, area-efficient designs in cryptography (e.g., AES S-box optimization) and health monitoring wearables. Dr. Nikoubin has contributed to innovative hardware solutions such as fiber antennas for wireless body area networks and low-cost radar systems for structural health monitoring. His lab explores intersections between emerging technologies like CNTFETs and FinFETs with system-level power management. Current openings exist for students/researchers to contribute to this dynamic research environment.

Jiawei Yuanمشاهده پروفایل

دانشیار

• Cybersecurity
• UAV (Drone) Security
• Artificial Intelligence
• +۵ مورد دیگر

Jiawei Yuan is an Associate Professor in the Department of Computer & Information Science at the University of Massachusetts Dartmouth, part of the College of Engineering. His research focuses on cybersecurity, UAV security, and AI-driven systems, with emphasis on privacy preservation, adversarial defense mechanisms, and practical educational frameworks. He holds a PhD from the University of Arkansas Little Rock (2015) and a BS from the University of Electronic Science and Technology of China (2011). Research Grants: NSF: $299,999 for UAV Security Platform (2023) NSF: $240,000 for Cybersecurity Education (2023) CyberCorps Scholarship: $1,218,640 for Accelerating Cybersecurity Education (2023) Teaching: Courses include Database Systems, Cybersecurity, and Data Science Thesis Supervision. His research interests span UAV cybersecurity, AI anomaly detection, privacy in 6G systems, and blockchain applications. He has developed novel platforms like UCLP for cybersecurity education and contributed to robust UAV tracking solutions in adversarial environments. His work emphasizes cross-disciplinary approaches, integrating machine learning with cybersecurity for real-world systems. Notable projects include a hybrid delay-aware UAV anomaly detection system and the Virtual Curtain privacy framework for augmented reality. He has also pioneered adversarial attack defenses for AI-assisted infrastructure inspection drones. Awards/Grants: Multiple NSF grants, including a $240K award in 2023 for cybersecurity mindset training. Labs/Teams: Leads the UMass Dartmouth UAV Cybersecurity Lab and collaborates on CyberCorps initiatives.

• Software Defined Radio
• AI/ML for Wireless Communications
• Cyber Security
• +۳ مورد دیگر

Ruolin Zhou is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Massachusetts Dartmouth, part of the College of Engineering. She joined UMass Dartmouth in 2018 after serving as a tenured Associate Professor at Western New England University from 2012 to 2018. Her research focuses on software-defined radio (SDR), AI/ML-driven wireless communications, spectrum sensing, cyber & electromagnetic spectrum security, and hardware security. Her work has been supported by grants from NSF, ONR, AFRL, ARL, and industry partners like Lockheed Martin. Dr. Zhou holds a PhD in Engineering from Wright State University (2012), an MS in Electrical Engineering (2007), and a BS in Electrical Engineering from Dalian Jiaotong University (2003). She teaches courses including Foundations of Cybersecurity, Digital Logic Design, and Software-Defined Radio. Her research has led to innovations in FPGA-accelerated deep learning architectures, real-time spectrum monitoring, and secure communication systems. Her contributions have been recognized with awards including the 2024 IEEE Region 1 Outstanding Teaching Award, ONR Summer Faculty Research Fellowships (2022-2023), and Best Demo Awards at GLOBECOM 2010. She actively serves in IEEE roles, including Vice President for Technical Activities within the IEEE Reliability Society (2025) and steering committee memberships in IEEE Future Networks and IoT Technical Communities.

Dongsheng Luoمشاهده پروفایل

استادیار

• Data Mining
• Deep Learning
• Natural Language Processing
• +۵ مورد دیگر

Dr. Dongsheng Luo is an Assistant Professor at the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU). He holds a Ph.D. in Information Science and Technology from Pennsylvania State University (2022) and a B.Eng. in Computer Science and Technology from Beihang University (2017). His research focuses on developing interpretable AI tools for complex data analysis, particularly in graph mining, deep learning, and natural language processing, with applications in neuroscience, healthcare, and environmental systems. Education: 2022: Ph.D., Penn State University 2017: B.Eng., Beihang University His research interests emphasize explainable AI, robust graph neural networks, and multimodal integration. Recent work includes frameworks for medical plan generation, causal discovery, and flood forecasting. He investigates methods to enhance model reliability and transparency, such as confidence-aware explanations and fidelity evaluation. Collaborations span biomedical informatics, climate science, and IoT systems. Publications highlight contributions to graph explainability, time series analysis, and molecular modeling. His work bridges theoretical advances with practical applications in healthcare, environmental monitoring, and communication systems.

• Big Data Management
• Scalable Data Science
• Spatial and Spatio-Temporal Data Processing
• +۵ مورد دیگر

Suprio Ray is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick, Canada, leading the Big Data Systems and Analytics Lab. He holds a PhD from the University of Toronto, an M.Sc. from the University of British Columbia, and a B.E. from NIT Trichy. Previously, he worked in industry roles at Oracle, Bell Labs, and Webtech Wireless, and completed a PhD internship at SAP. His research focuses on scalable data systems, spatial/spatio-temporal data management, and modern hardware utilization. Key projects include the Jackpine spatial database benchmark , DaskDB (a scalable data science system), and NUMA-aware query processing . He collaborates across disciplines with ECE and GGE departments, supported by grants from NBIF, NSERC, and industry partners. Research interests span Big Data systems, privacy/security in databases, blockchain analytics, and parallel/distributed computing. He has pioneered techniques like STILT multi-dimensional indexes , privacy-preserving spatial queries (Pystin) , and learned spatial indexes . Over 60 publications appear in top venues like SIGMOD, ICDE, and IEEE BigData. Education: PhD (Computer Science), University of Toronto (2015) M.Sc. (Computer Science), University of British Columbia (2003) B.E. (Computer Science & Engineering), NIT Trichy (2000) Teaching includes courses on big data systems (CS4545/6545), database foundations (CS6585), and data science (CS2545). He advises graduate students in scalable analytics and spatial systems, emphasizing industry-relevant software skills. Key awards include the 2024 ACM SIGSPATIAL best poster award , IBM Best Student Paper (2014) , and Harrison McCain Foundation award (2016) . His work has been recognized with 7 best paper awards and 3 patents. Current projects explore FPGA-accelerated joins , serverless data analytics , and privacy-preserving blockchain queries . Past contributions include the GEMM mobility model (2003) and foundational spatial benchmarks.

Percy Shuo Liangمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +۵ مورد دیگر

Percy Shuo Liang is an Associate Professor of Computer Science and Courtesy Associate Professor of Statistics at Stanford University. He directs the Center for Research on Foundation Models (CRFM) and is affiliated with Human-Centered Artificial Intelligence (HAI), the Artificial Intelligence Lab, and the Natural Language Processing Group. His research focuses on foundational aspects of machine learning, natural language processing, and reproducible research methodologies. He co-developed CodaLab Worksheets, a platform for experiment reproducibility, and leads the Marin community for open foundation model development. Education: B.S. (2004) and MEng (2005) in Electrical Engineering and Computer Science from MIT, advised by Michael Collins. Ph.D. (2011) in Computer Science from Berkeley under Michael Jordan and Dan Klein. Postdoctoral researcher at Google (2012). Research interests include foundation models, copyright challenges in AI, data weighting strategies, model interpretability, and ethical AI. Over 30 students and postdocs have been advised, many now holding prestigious academic and industry roles. Awards include the Presidential Early Career Award (2019), Sloan Fellowship (2015), and ACM ICPC World Finals 2nd place (2002). Labs/Teams: CRFM, HAI, AI Lab, NLP Group, Machine Learning Group. Notable contributions include CodaLab Worksheets and Marin platform. Active in programming contests and piano competitions (KDFC Classical Star Search winner, 2008).

• Ecology
• Artificial Intelligence
• Biodiversity Monitoring
• +۵ مورد دیگر

Sarab Sethi is an Assistant Professor in the Department of Life Sciences at Imperial College London, leading the Ecosystem Sensing research group under the Imperial-X AI initiative. His work focuses on developing novel acoustic monitoring systems and AI-driven approaches to assess biodiversity across global ecosystems. Current affiliations include honorary roles at University College London (Honorary Research Fellow, Genetics, Evolution & Environment) and non-academic advisory roles with the Norwegian Institute for Nature Research. Academic career highlights include Herchel Smith Postdoctoral Fellowships at the University of Cambridge (2022-2023) and a PhD in interdisciplinary Mathematics/Engineering/Life Sciences from Imperial College (2016-2020). Technical expertise spans ecoacoustic sensor networks, machine learning applications, and automated data analysis frameworks. Notable projects include large-scale biodiversity monitoring networks in Borneo and Norway's 'Sound of Norway' initiative. Research interests emphasize translating acoustic data into actionable conservation insights, with applications in pest control, supply chain transparency, and ecological restoration. Technical innovations include the MAARU multichannel recording system and AI-driven sound analysis tools. Active in both academic and applied sectors, advising public/private organizations on machine learning integration in ecological monitoring. Awards and recognitions include grants supporting Imperial-X AI initiatives and international collaborations. Ongoing work focuses on advancing low-power sensor technologies and creating scalable biodiversity monitoring solutions. Supervises PhD candidates through externally funded fellowships requiring applicants to align research interests with lab priorities in ecoacoustic systems.

Kevin Leachمشاهده پروفایل

استادیار

• Cybersecurity
• Software Engineering
• Dependable Systems
• +۳ مورد دیگر

Kevin Leach is an Assistant Professor of Computer Science at Vanderbilt University's School of Engineering. His research focuses on designing dependable cybersecurity systems and software engineering practices that withstand sophisticated adversarial attacks. He holds a PhD from the University of Virginia (2016), where he received the Louis T Rader Outstanding Research Award. Prior to Vanderbilt, he was a research scientist and lecturer at the University of Michigan. Education: Ph.D. in Computer Engineering, University of Virginia MS in Computer Science, George Mason University BS with Distinction in Computer Engineering and Computer Science, University of Virginia His research investigates two core areas: leveraging hardware capabilities like Trusted Execution Environments for low-overhead security instrumentation, and understanding how developers and operators make decisions in adversarial environments. He has published over 30 refereed articles, 3 of which have received awards. Recent work includes studies on cognitive biases in cybersecurity competitions, AI-driven code assessment, and malware detection techniques. Awards: Louis T Rader Outstanding Research Award (2016) 3 award-winning papers (specific awards unspecified) His research bridges technical security mechanisms with human decision-making processes, emphasizing both system dependability and developer/operator awareness. Current projects include work on federated learning defenses, runtime security optimization for embedded systems, and ethical considerations in crowdsourced data labeling.

Sotiris Moschoyiannisمشاهده پروفایل

دانشیار

• Complex Networks
• Control Theory
• Reinforcement Learning
• +۵ مورد دیگر

Sotiris Moschoyiannis is a Reader in Complex Systems at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering. He holds dual roles as Postgraduate Research Director in his School and the Computer Science Research Centre. His expertise spans mathematics, theoretical computer science, and interdisciplinary research in complex systems, cybersecurity, and artificial intelligence. Dr. Moschoyiannis earned his PhD in Theoretical Computer Science from the University of Surrey and a Mathematics degree from the University of Patras. His research focuses on control theory, reinforcement learning, and computational methods applied to complex networks, aiming to design interventions for steering systems toward desirable outcomes. Key areas include cybersecurity in 5G networks, healthcare data analysis, and systems biology modeling. Teaching responsibilities include Machine Learning and Data Mining (MSc in Data Science) and Database Systems across MSc programs in Data Science and Information Security. His work bridges formal methods with machine learning, addressing challenges in dynamic systems, network controllability, and secure communication protocols. Notable contributions include studies on prostate cancer diagnosis data quality, anomaly detection in 5G networks, and AI-driven healthcare solutions. He actively collaborates with institutions like the Surrey Institute for People-Centred AI and the Surrey Centre for Cyber Security, advancing interdisciplinary research and innovation.

Andrea Carenaمشاهده پروفایل

استاد

• Optical Communication Systems
• Digital Signal Processing
• Fiber Optics
• +۳ مورد دیگر

Andrea Carena is a Full Professor in the Optical Communication Group at the Department of Electronics and Telecommunications, Politecnico di Torino, Italy. He holds a M.Sc. and Ph.D. in Electronic Engineering from the same institution (1995 and 1998). His career includes a Visiting Researcher role at UC Santa Barbara (1998) and a decade as an Assistant Professor at Politecnico di Torino before becoming a Full Professor. His research focuses on optical communication systems, particularly digital signal processing, coherent detection, fiber nonlinearities, and advanced modulation formats. He has authored over 200 publications and received the IEEE/OSA Best Paper Award (2014, 2015). Education: M.Sc. in Electronic Engineering, Politecnico di Torino (1995) Ph.D. in Electronic Engineering, Politecnico di Torino (1998) Research Interests: Physical layer design of optical systems Coherent detection and digital signal processing Fiber nonlinearity modeling and mitigation Multi-core and few-mode fiber applications Machine learning for optical system optimization Publications Trends: Recent work emphasizes machine learning-driven optimization of optical amplifiers (Raman, bismuth-doped), SWDM/VCSEL systems for data centers, and reconfigurable photonic integrated circuits. His 2020s research bridges lab-to-field trials, including SDM transmission over deployed fibers and probabilistic shaping in multi-core environments. Awards: 2014 IEEE/OSA Journal of Lightwave Technology Best Paper Award 2015 IEEE/OSA Journal of Lightwave Technology Best Paper Award Advising & Grants: While no student names are listed, his leadership in the OptCom group suggests extensive PhD/Master’s supervision. His work on OptSim and modular WSS designs reflects major research grants in optical networking and photonics. Labs/Teams: Leads the Optical Communications Group (OptCom) at Politecnico di Torino, specializing in experimental and theoretical studies of advanced optical communication systems. Collaborates internationally on projects like OPERA (optical label swapping networks) and multi-core fiber field trials.

Estela Suarezمشاهده پروفایل

استاد

• High Performance Computing (HPC)
• Heterogeneous HPC Architectures
• Modular Supercomputing Architecture
• +۴ مورد دیگر

Estela Suarez is a Professor of High Performance Computing at the University of Bonn's Computer Science Department and holds leadership roles at the Jülich Supercomputing Centre (JSC) in Forschungszentrum Jülich. Her work focuses on advancing modular supercomputing architectures, heterogeneous systems, and application optimization for exascale computing. She currently serves as Chair of the EuroHPC Joint Undertaking's Research and Innovation Advisory Group (RIAG) and leads the Next Generation Architectures and Prototypes research group at JSC. Education includes a PhD in Physics from the University of Geneva (2010) and a Master's in Physics (Astrophysics specialization) from Universidad Complutense de Madrid (2004). She has held senior research positions at JSC since 2010 and has led major EU-funded projects like DEEP, DEEP-ER, and DEEP-SEA. Research interests include HPC system design, hardware-software co-design, and energy-efficient supercomputing architectures. She was awarded the University of Bonn's 2023/2024 teaching award and actively contributes to initiatives like the European Processor Initiative (EPI) and the NUMERIQS consortium. Current projects include optimizing exascale systems for Earth system modeling (IFCES2), AI-driven data analytics (AIDAS), and modular supercomputing software (DEEP-SEA). During her 2024/2025 sabbatical, she is not accepting new students. Her work is published in journals like Geoscientific Model Development and conferences such as ISC and EuroHPC.

• Machine Learning
• Cybersecurity
• Mechanical Engineering
• +۵ مورد دیگر

Peter Volgyesi is a Research Scientist and Lecturer in the Department of Computer Science at Vanderbilt University School of Engineering. His work focuses on integrating artificial intelligence, machine learning, and advanced engineering methodologies into practical applications such as UAV design optimization, wearable health monitoring systems, and cybersecurity for cyber-physical systems. He has contributed to frameworks like MoSDeF and SURE, advancing reproducible simulations and resilient CPS evaluation. His research interests span multiple domains including: Machine learning in engineering design and optimization Wearable sensor systems for musculoskeletal monitoring Cybersecurity and resilience in smart grids AI-driven CAD-CFD integration for aerospace applications Key trends in his recent work (2020–2024) emphasize: Combining ML with physics-based models for efficient design Development of deployable sensor networks for health and security applications Advances in UAV and underwater vehicle design automation No scientific awards were explicitly mentioned in the provided text. He collaborates on projects involving the Resilient Information Architecture Platform (RIAPS) and Molecular Simulation & Design Framework (MoSDeF), contributing to both academic research and industry-relevant solutions.

• Quantum Information
• Quantum Cryptography
• Post-Quantum Cryptography
• +۷ مورد دیگر

Carlo Ottaviani is a Senior Lecturer in the Department of Computer Science at the University of York and a member of the York Centre for Quantum Technologies. His academic qualifications include a PhD in Physics & Computer Science, and Fellowships from the Advance HE (HEA) in 2023 and 2013. He has held roles such as Senior Research Fellow and Research Associate within the department, contributing to quantum information, post-quantum cryptography, and quantum networks. Education: PhD in Physics & Computer Science PGCAP & Fellow of Advance HE (2023) YLTA & Associate Fellow of Advance HE (2013) Research Interests: Quantum Information and Quantum Technologies Quantum Cryptography (QKD), Post-Quantum Cryptography Quantum Optics and Quantum Atom-Optics Security, Continuous-Variable Quantum Systems Dr. Ottaviani has contributed to major projects, including the UK Quantum Technology Hub for Quantum Communications Technologies. His work focuses on developing secure quantum communication protocols and advancing theoretical frameworks for quantum systems. He actively participates in academic collaborations and has authored over 45 publications in peer-reviewed journals and conferences. Grants and Projects: Principal Investigator for projects on Coherent Control of Quantum Optical Systems and Quantum Non-Linear Optics Contributor to the EPSRC-funded UK Quantum Communications Hub Labs/Teams: York Centre for Quantum Technologies (YCQT) Quantum Information and Cyber Security & Privacy Research Groups