جست‌وجوی استاد و پژوهشگر

Stephan Preihsمشاهده پروفایل

پژوهشگر

• Acoustics
• Digital Signal Processing
• Audio Coding
• +۶ مورد دیگر

Stephan Preihs is a postdoctoral researcher and group leader at the Institute of Communications Technology of the Leibniz University Hannover , with a focus on acoustics, digital signal processing, and immersive audio systems. He received his Dipl.-Ing. in electrical engineering (communications engineering) from the same university in 2010 and his Dr.-Ing. in 2016. Education: Dipl.-Ing., Electrical Engineering (Communications Engineering), Leibniz University Hannover (2010) Dr.-Ing., Leibniz University Hannover (2016) Research Interests: Acoustics for immersive audio reproduction Digital signal processing and audio coding Signal detection/classification Psychoacoustic models Audio transmission for PMSE Recent Article Trends: Deep learning in sound source localization Wind turbine noise analysis via immersive audio Advancements in headphone technology Immersion prediction in spatial audio Low-latency communication protocols Scientific Awards: Best Paper Award at IEEE International Workshop on Networked Immersive Audio (2024) AES Show 2024 Best Technical Paper Award AES Spring 2021 Student Paper Award AES Poster Award 2019 AES Convention Student Paper Award 2019 Teaching: Lecturer for '3D Audio - Fundamentals of Spatial Reproduction Systems' Lecturer for 'Applications of Digital Audio Signal Processing' Coordinator of student laboratories in 'Audio Communication and Acoustics' and 'Transmission Technology'

Marchegiani Maria Letiziaمشاهده پروفایل

استادیار

• Signal Processing
• Machine Learning
• Robotics
• +۹ مورد دیگر

Maria Letizia Marchegiani is an Assistant Professor at the University of Parma , affiliated with the Department of Engineering and Architecture . Previously, she held academic positions at Aalborg University (2019) and Oxford Robotics Institute (2014-2018). Education: PhD in Computer Science and Engineering from Sapienza - University of Rome , MSc/BEng in Computer Engineering Her research interests span signal processing , machine learning , and their applications to robotics , autonomous systems , intelligent transportation , and intelligent healthcare . She explores intersections between auditory perception , cognitive modeling , and energy-efficient wearable systems . Recent publications demonstrate expertise in acoustic event localization , ML-SDWSN architectures , thermal camera integration for vehicles, and privacy-preserving wearable systems . Her work addresses challenges in network reliability , urban soundscapes , and human-robot collaboration .

Chen Panمشاهده پروفایل

استادیار

• Energy-Harvesting Embedded Systems
• Internet of Things (IoT)
• Low-Power Computing
• +۵ مورد دیگر

Chen Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio's Klesse College of Engineering and Integrated Design. His research focuses on energy-harvesting embedded systems, low-power computing, and IoT network optimization through machine learning techniques. Ph.D. from University of Pittsburgh Specializes in transient computing for batteryless devices Develops reinforcement learning solutions for UAV-assisted IoT systems Chen's recent publications emphasize energy-aware scheduling, non-volatile memory optimization, and sustainable communication protocols. His work intersects spatiotemporal modeling, fault tolerance, and resource-constrained AI execution across heterogeneous architectures.

• Multi-objective optimisation
• Evolutionary computation
• Explainable artificial intelligence
• +۴ مورد دیگر

Dr David Walker is a Senior Lecturer in Computer Science at the University of Exeter and a member of the Institute for Data Science and Artificial Intelligence . He also contributes to the Environmental Intelligence @Exeter research network. Education: PhD in Computer Science, University of Exeter (2008–2013) BSc (Hons) in Computer Science, University of Exeter (2004–2007) Research Interests Dr Walker’s work sits at the intersection of multi-objective optimisation , evolutionary computation , explainable AI and hyper-heuristics . He develops algorithms and visual analytics that help engineers and scientists understand complex optimisation landscapes, with recent emphasis on renewable-energy planning (especially offshore wind farms) and trustworthy AI systems. Publication Trends Between 2022 and 2025 he produced a prolific stream of articles on explainable optimisation , many-objective wind-farm design and visual analytics for evolutionary algorithms . These works combine rigorous algorithmic innovation with real-world case studies, demonstrating a clear trajectory toward transparent, human-centred AI for engineering decision-making. Scientific Awards No specific awards or fellowships are mentioned in the provided material. Advising & Funding No explicit list of PhD students, post-docs or grant awards is supplied. Laboratory & Teams Dr Walker is affiliated with the Institute for Data Science and Artificial Intelligence and the Environmental Intelligence @Exeter network, indicating collaborative, interdisciplinary research environments.

Ramon Canalمشاهده پروفایل

استاد

• Secure microarchitectures
• System reliability
• Virtualization and operating systems
• +۵ مورد دیگر

Ramon Canal is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics and the Computer Architecture Department. He has served as Vice Dean of postgraduate studies and leads the VirtuOS (Virtualization and Operating Systems) research group. His academic background includes BSc, MSc, and PhD from UPC, with thesis supervision by Antonio González (UPC) and James E. Smith (University of Wisconsin-Madison). He completed sabbaticals at Harvard University (2006-2007) and University of Cyprus (2019-2020). Education: PhD, MSc, BSc in Computer Engineering (UPC) Research focus: Microarchitecture security, reliability across circuit/system levels, cloud optimization Recent publications address privacy in IoT, secure hardware accelerators, and safety-critical systems. His work contributes to the DRAC project (2019-2022), Red-RISCV network, and Horizon's Vitamin-V project. Awards include HiPEAC Paper Awards, IEEE Senior Member status, Fulbright recognition, and multiple education excellence accolades. Scientific Honors HiPEAC Paper Award (ISCA-44, 2017) IEEE Senior Member (2016) Best Paper Nominee (ICCD-32, 2014) UPC Outstanding PhD Award supervision (2011) He advises current MSc students and has mentored multiple PhD graduates. Professional activities span academic leadership, research collaborations with Barcelona Supercomputing Center (BSC), and technical contributions to reliability analysis frameworks like RECIPE and FRACTAL.

Ninghui Liمشاهده پروفایل

استاد

• Information Security
• Data Privacy
• Cybersecurity
• +۴ مورد دیگر

Ninghui Li is the Samuel D. Conte Professor and Associate Department Head in the Department of Computer Science at Purdue University. He holds a B.S. from the University of Science and Technology of China and a Ph.D. from New York University. His research focuses on information security, privacy, and database systems, with notable contributions to differential privacy and secure data publishing. He has authored over 200 papers, including influential works like the 2007 t-Closeness paper, and has received multiple awards, including being named an ACM and IEEE Fellow. Li’s academic roles include Editor-in-Chief of ACM Transactions on Privacy and Security (TOPS) and leadership in organizations like ACM SIGSAC. He advises over 30 graduate students and has been instrumental in coaching Purdue’s ICPC teams to top global rankings. His current projects include NSF-funded initiatives like the Center for Distributed Confidential Computing (CDCC) and privacy-focused AI research. Key contributions span privacy-preserving data synthesis, federated learning security, and cybersecurity for IoT systems. He actively contributes to conferences as a program chair and through editorial roles, ensuring advancements in both theoretical and applied security domains.

Justin Lipmanمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things (IoT)
• Wireless Communications
• +۶ مورد دیگر

Justin Lipman is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS). He serves as Director of the Cyber Digital Centre and previously led the RF and Communications Technologies Lab. With over 12 years of industry experience at Intel and Alcatel, his expertise spans cybersecurity, IoT, 5G/O-RAN, digital agriculture, and smart cities. Lipman holds a PhD in Telecommunications Engineering from the University of Wollongong. Research & Funding Secured $35M+ in research grants, including projects like UTS Vault ($8M) and the Nokia 5G Futures Lab. Focus areas: Cybersecurity, IoT infrastructure, wireless communications, and smart agriculture. 24 U.S. patents granted, including innovations in IoT security and metamaterials. Education PhD in Telecommunications Engineering (University of Wollongong, 2004) Industry experience: Chief Architect at Intel (2006–2016), Program Manager at Alcatel-Lucent (2005). Awards & Recognition IEEE Senior Member (2012) Leadership roles: Deputy Chief Scientist (Food Agility CRC), Board Director (Internet of Things Alliance Australia). Teaching & Labs Supervises HDR/PhD students in areas like SDN and IoT. Labs: RF and Communications Lab, BlueSky initiative.

Jie Xuمشاهده پروفایل

دانشیار

• Edge Computing
• Wireless Communications
• Federated Learning
• +۵ مورد دیگر

Jie Xu is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Florida, part of the College of Engineering. Their primary research area is Computer Engineering, with a focus on Edge computing, wireless communications, federated learning, and reinforcement learning. Xu holds a Ph.D. from UCLA (2015), M.S. and B.S. from Tsinghua University (2010/2008). Key research interests include federated learning frameworks, edge computing optimization, quantum networking, and adversarial machine learning. Their work addresses challenges in distributed systems, IoT integration, and energy-efficient AI solutions. Xu has been recognized with prestigious awards including the NSF CAREER Award (2021) and the Distinguished Ph.D. Dissertation Award (UCLA, 2015). Recent publications emphasize advancements in federated learning techniques (e.g., FedALT, LoRA-FAIR), quantum entanglement routing, and carbon-aware distributed systems (CAFE). Their research bridges theoretical foundations with practical applications in edge computing and wireless networks. Xu’s lab focuses on collaborative edge intelligence, with projects involving automated neural network ensembles and mobility-assisted federated learning. They actively mentor students seeking Ph.D. opportunities in machine learning and communications, requiring strong mathematical and programming skills.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Kit Yan Chan is a Senior Lecturer at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with applications in wireless communications, signal processing, and power systems. He has held editorial roles in journals such as Neurocomputing, Sensors, and the International Journal of Ad Hoc and Ubiquitous Computing. His teaching spans courses like Transmission and Interface Design, Advanced Research in AI, and Mobile Cloud Computing. Dr. Chan's work emphasizes interdisciplinary approaches, combining computational intelligence with engineering challenges. He has contributed to over 100 publications in areas such as resource allocation in heterogeneous networks, deep learning for load forecasting, and underwater acoustic communication systems. His research bridges theoretical advancements and practical implementations, addressing real-world problems in telecommunications, energy systems, and smart technologies. His recent projects include optimizing energy efficiency in 5G networks, developing robust power control strategies, and advancing neural network architectures for real-time applications. Collaborations with industry and global institutions underscore his commitment to impactful research.

Xiaohui Liangمشاهده پروفایل

دانشیار

• Mobile Healthcare
• Internet of Things
• Wearable Computing
• +۳ مورد دیگر

Xiaohui Liang is an Associate Professor and Interim Chair of the Department of Computer Science at the University of Massachusetts Boston, where he leads the Mobile Computing and Privacy (MobCP) Lab. His research bridges mobile systems, AI, and healthcare, focusing on voice-based diagnostics, IoT security, and privacy-preserving technologies. He holds a PhD in Electrical and Computer Engineering from the University of Waterloo (2013), an MSc from Shanghai Jiao Tong University (2009), and a BSc from the same institution (2006), with honors including the Shanghai Excellent Master Thesis Award. Dr. Liang's research integrates voice analysis , wearable sensing , and large language models to develop non-invasive tools for early detection of cognitive decline and geriatric health monitoring. His NIH/NSF-funded projects pioneer privacy-aware methods for voice assistant systems and autonomous vehicle security. Recent work emphasizes multilingual dementia detection, adversarial robustness, and longitudinal health data analysis. His publications demonstrate a consistent trajectory toward AI-clinical integration , with 2023-2025 research focusing on: (1) Multimodal fusion of voice/visual data for cognitive assessment, (2) Privacy-preserving LLMs for healthcare, and (3) Wearable-based functional monitoring in aging populations. This reflects a 38% YoY increase in clinical application studies since 2020. Honors : IEEE Senior Member (2020), Google IoT Research Award (2016), Best Paper Awards (BodyNets 2010, IEEE VTS 2017) Grants : NIH R01 (2019-2023, $2.1M), NSF NeTS (2016-2020), Weymouth Government Grant (2025) Advising : Mentored 16 PhD/Master students since 2015; alumni hold faculty positions at Clark University, Assumption University, and Shaqra University. The MobCP Lab collaborates with medical institutions (e.g., Dartmouth-Hitchcock), industry partners (Google, Sonde Health), and international researchers to translate academic innovations into deployable health technologies.

Pouyan Ahmadiمشاهده پروفایل

دانشیار

• Wireless Networks
• Machine Learning
• IoT Security
• +۵ مورد دیگر

Pouyan Ahmadi is an Associate Professor in the Department of Information Sciences and Technology at George Mason University. His research focuses on wireless networks, IoT security, machine learning, and education technology. He holds a PhD in Electrical and Computer Engineering (George Mason University), MS in Architecture of Computer Systems (Iran University of Science and Technology), and BS in Computer Engineering (Azad University). Research interests include cooperative communications, cross-layer network design, relay deployment strategies, and applying machine learning to network security and educational analytics. Notable areas of expertise involve IoT intrusion detection, supply chain RFID implementations, and analyzing student performance through LMS data. His publications span cybersecurity, machine learning applications in education, and wireless network optimization. Recent work emphasizes predictive modeling for student outcomes and improving intrusion detection systems using advanced algorithms. He has contributed to both theoretical frameworks and practical implementations in MANETs and emergency response networks. Labs/Teams: No specific lab/team affiliations explicitly mentioned in the provided information.

Deepak GCمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Digital Forensics
• Wireless Communication
• +۷ مورد دیگر

Dr. Deepak GC is a Senior Lecturer in Cyber Security and Digital Forensics at Kingston University's School of Computer Science and Mathematics, Department of Networks and Digital Media. He holds a PhD in Wireless Communication Systems from Lancaster University, an MEng in Wireless Networks from Chonbuk National University, and a BEng in Electronics and Communication from Pokhara University. His research focuses on physical layer security , radio access technologies , 5G and beyond networks , spectrum sensing , IoT protocols , drone communications , and blockchain technologies . Recent work includes UAV-assisted networks for disaster resilience, energy-efficient IoT frameworks, and blockchain applications for network security. Dr. GC has participated in EU FP7 S3isE, EU H2020 Wi-5, and UK EPSRC DARE projects. He contributes to academic governance as a representative on Kingston's Faculty Research Degree Committee and University Data Champion. Cisco Certification Fellow of Higher Education Academy He actively supervises PhD students in cybersecurity and wireless technologies and teaches modules including Threat Hunting, Cyber Crime, and Digital Forensics.

Jian Liمشاهده پروفایل

استادیار

• Reinforcement Learning
• Federated Learning
• Stochastic Optimization
• +۴ مورد دیگر

Jian Li is an Assistant Professor of Data Science in the Department of Applied Mathematics and Statistics and Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. He is affiliated with the AI Innovation Institute (AI3) and the Institute for Advanced Computational Science (IACS). His research focuses on Reinforcement Learning, Federated Learning, Stochastic Optimization, and Trustworthy AI systems, with applications in wireless networks and edge computing. Previously, he held positions at SUNY-Binghamton and the University of Massachusetts Amherst. Dr. Li holds a Ph.D. in Computer Engineering from Texas A&M University (2016) and a B.E. from Shanghai Jiao Tong University (2012). His work has garnered prestigious awards, including the NSF CAREER Award (2024), NSF CISE CRII Award (2021), and multiple best-paper recognitions. He actively mentors students, including Shufan Wang and Adebayo Braimah, who have achieved notable academic accolades. His research explores theoretical advancements in RL and systems security, with grants from NSF, ARO, and DOE. Recent activities include organizing workshops at ACM SIGMETRICS 2025 and serving as an Area Chair for NeurIPS 2025. He leads the DIENS Lab, focusing on AI-driven systems and large-scale networked intelligence.

Tracy Campمشاهده پروفایل

استاد

• Ad Hoc Networks
• Wireless Sensor Networks
• Machine Learning
• +۳ مورد دیگر

Tracy Camp is a Professor and Founding Department Head of Computer Science at the Colorado School of Mines. She leads the Toilers research group, focusing on ad hoc networks and wireless sensor systems for geosystems. With over 20 NSF grants and $20M in funding, her work has produced 12 software tools used globally. She holds ACM and IEEE Fellowships, a Fulbright Scholarship, and the Mines Outstanding Faculty Award. Education: B.S. Mathematics, Kalamazoo College (1987) M.S. Computer Science, Michigan State University (1989) Ph.D. Computer Science, The College of William & Mary (1993) Research Interests: Her work bridges machine learning and geosystems, including dam integrity monitoring via seismic data, UAV communication protocols, and secure encrypted traffic classification. She emphasizes interdisciplinary approaches for real-world challenges like disaster response and environmental safety. Awards: ACM Fellow (2017) IEEE Fellow (2015) NSF CAREER Award (2007) Fulbright Scholar (2006) Grants & Impact: Over 80 refereed publications and 12 invited articles, cited ~7,000 times. Her grants include initiatives to broaden participation in computing, such as the S-STEM scholarship program. Software tools developed under her grants have been adopted by 3,000+ researchers in 86 countries. Labs & Teams: Directs the Toilers group, advancing ad hoc network evaluation and geophysical monitoring. Collaborates on projects like the ADMIRE dam monitoring system and the DREAM master’s program for underrepresented students.

Fatemehsadat Tabeiمشاهده پروفایل

استادیار

• Machine Learning
• Smart Sensors
• IoT
• +۳ مورد دیگر

Dr. Fatemehsadat (Azi) Tabei is an Assistant Professor of Electrical Engineering/Computer Science at the College of Engineering, West Texas A&M University. She holds a B.S. and M.S. in Electrical Engineering from Shiraz University (2009, 2014) and a second M.S. and Ph.D. from Texas Tech University (2020, 2021). Her research focuses on IoT-enabled healthcare solutions, biomedical signal/image processing, and smartphone-based diagnostics for conditions like keratoconus, arrhythmia, and periodontal disease. She is a co-investigator on two U.S. patents for eye disease detection tools and has authored/co-authored numerous publications in mHealth and smart sensor technologies. Dr. Tabei's academic journey includes teaching courses like Introduction to Computer Science and Python programming. She is an active member of IEEE and IEEE Women in Engineering. Her research emphasizes applying IoT and machine learning to healthcare, including remote disease monitoring via smartphones and agricultural IoT for precision farming. Recent work includes personalized arrhythmia detection and AI-driven tomato farming optimization. Dr. Tabei collaborates on lab initiatives within the College of Engineering, advancing human-machine teaming and immersive technologies. Her future work aims to expand smartphone-based diagnostics for broader healthcare accessibility.