جست‌وجوی استاد و پژوهشگر

Xiaowen Gongمشاهده پروفایل

دانشیار

• Wireless Networks
• Machine Learning in Wireless Systems
• Edge Computing
• +۴ مورد دیگر

Xiaowen Gong is currently the Godbold Associate Professor in the Department of Electrical and Computer Engineering (ECE) at Auburn University. They hold a PhD from Arizona State University and completed postdoctoral work at Ohio State University. Their research focuses on wireless networks , federated learning , and edge computing with an emphasis on heterogeneous environments and quality-aware systems . BEng, Huazhong University of Science and Technology (2008) MSc, University of Alberta (2010) PhD, Arizona State University (2015) Postdoc, Ohio State University (2015-2016) Assistant Professor, Auburn University (2017-2023) Godbold Associate Professor, Auburn University (2023-present) Research spans ML/AI in wireless networks , distributed computation , and socially-aware networking . Recent work includes anarchic federated learning , delay-optimal edge computing , and privacy-preserving crowdsensing . Publications address challenges in client heterogeneity , channel-aware scheduling , and secure data aggregation . Key contributions include NSF CAREER Award (2022) for efficient federated learning , IEEE Internet of Things Journal Best Paper Runner-Up , and organizing RET Site for rural STEM teacher education in ML/robotics. Teaching covers Wireless Communication Systems , Neural Networks , and Information Security , with outreach activities involving robotics demonstrations for K-12 students.

Matteo Rinaldiمشاهده پروفایل

استاد

• Micro/Nanoelectromechanical Systems (M/NEMS)
• Piezoelectric and Ferroelectric Materials
• Nanoscale Sensors
• +۵ مورد دیگر

Matteo Rinaldi is a Professor in the Department of Electrical and Computer Engineering at Northeastern University's College of Engineering and serves as Director of the Institute for NanoSystems Innovation. His work focuses on advanced micro/nanoelectromechanical systems (M/NEMS) for sensing and communication applications. Ph.D. in Electrical and Systems Engineering, University of Pennsylvania (2010) M.Sc. and B.S. in Electronic Engineering, University of Rome Tor Vergata (2007, 2004) Research Interests: Rinaldi's research spans fundamental and applied aspects of piezoelectric nanomaterials, with emphasis on Aluminum Nitride (AlN) and Scandium Aluminum Nitride (ScAlN) based NEMS Plasmonically enhanced infrared and chemical sensors Low-power reconfigurable radio communication systems Integration of MEMS/NEMS with electronics Nanomaterials for harsh environments Publications & Research Trends: Rinaldi's recent work explores high-frequency resonators, infrared detection systems, and zero-power sensing technologies. Key trends include plasmonics for energy-efficient devices, ScAlN integration for improved performance, and machine learning applications in nanoscale device design. Scientific Recognition: 2025 IEEE IFCS Walter G. Cady Award 2024 Northeastern Global Network Accelerator Award Optica Fellow IEEE Sensors Council Early Career Award DARPA Young Faculty Award NSF CAREER Award Advising & Funding: Rinaldi advises graduate researchers like Antea Risso. His lab secures major grants from DARPA, NSF, DOE, and the Bill & Melinda Gates Foundation, focusing on next-generation sensing technologies and 6G communication systems. Research Infrastructure: Leads the Northeastern Sensors & Nano Systems Laboratory (NS&NS Lab) and co-directs the bicoastal Institute for NanoSystems Innovation with Boston and Oakland campuses, advancing nanoscale semiconductor technologies.

• Computer Science
• Wireless Sensor Networks
• Internet of Things (IoT)
• +۳ مورد دیگر

Muhammad Khalil Afzal is a faculty member at COMSATS University Islamabad within the Department of Computer Science . His career focuses on Internet of Things (IoT) , Wireless Sensor Networks , and Machine Learning applications in network optimization. Key collaborations with researchers like Byung-Seo Kim and Sung Won Kim Published extensively in IEEE Access , Sensors , and Future Generation Computer Systems Research spans data-driven intelligence , QoS in IoT , and smart transportation systems : Developed graph convolutional GRU frameworks for traffic prediction Explored NOMA for low-latency communications in 5G Innovated blockchain-based privacy preservation in vehicular networks

Andreas Andreouمشاهده پروفایل

استاد

• Neuromorphic Engineering
• CMOS Microsystems
• Sensory Information Processing
• +۶ مورد دیگر

Andreas Andreou is a Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the JHU Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab, focusing on brain-inspired microsystems, neuromorphic engineering, and biomedical sensors. His research spans CMOS-based neuromorphic processors, event-based vision systems, and wearable health monitoring devices like the StethoVest. Key contributions include silicon retinas, polarization-sensitive imagers, and algorithms for pattern analysis. Research Interests: Neuromorphic Computing: Designing energy-efficient brain-inspired chips using 3D CMOS, FETs, and memristive technologies. Biomedical Microsystems: Wearable acoustic sensors for cardiac monitoring and vestibular prosthetics. AI Hardware: Neuromorphic accelerators for edge computing, leveraging LLMs for automated circuit design. Notable Achievements: IEEE Fellow (since 2020) Recipient of the 3rd Best Paper Award at IEEE BioCAS 2018 $2M DARPA grant for bio-inspired event cameras Labs/Teams: The Andreou Lab collaborates with the Kavli Neuroscience Discovery Institute and NSF-funded neuromorphic projects. Ongoing work includes neuromorphic Ising machines, LLM-driven chip design, and quantum sensing for medical applications.

Adnan Siraj Rakinمشاهده پروفایل

استادیار

• AI Security
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Adnan Siraj Rakin is an Assistant Professor at Binghamton University's School of Computing. He holds a PhD and MS in Computer Engineering from Arizona State University (2022 and 2021) and a BS in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2016). His research focuses on AI security, including adversarial attacks on deep learning systems, model stealing, and hardware vulnerabilities. Notable contributions include defenses against bit-flip attacks, weight duplication frameworks, and RowHammer exploits. Research Interests: Adversarial Attacks (Input/Weight/Model Stealing) Deep Learning Security Hardware Vulnerabilities (e.g., FPGA/DRAM) Robust Neural Network Design Publications highlight advancements in detecting and mitigating adversarial perturbations, with work featured in CVPR, ICCV, and IEEE Security & Privacy. His recent efforts address LLM vulnerabilities and edge computing efficiency. He received the 2022-2023 Educator of the Year award from Binghamton's CS department. Current projects include developing full-stack obfuscation frameworks, secure domain adaptation, and exploring adversarial impacts on robotics systems.

Ian Craddockمشاهده پروفایل

استاد

• Digital Health
• Internet of Things (IoT)
• Biomedical Engineering
• +۳ مورد دیگر

Professor Ian Craddock holds dual roles as a Professor at the School of Engineering Mathematics and Technology and Head of the School of Civil, Aerospace and Design Engineering at the University of Bristol. His research focuses on digital health technologies, IoT systems, and wearable sensors for healthcare applications. He leads the SPHERE initiative, a sensor platform for residential healthcare monitoring, and has pioneered work in activity recognition, indoor localization, and disease management (e.g., Parkinson’s, dementia). Education: B.Eng and Ph.D. in Engineering from the University of Bristol, Chartered Engineer (C.Eng). Key projects include the HEmiSPHERE study assessing post-surgery recovery and the CUBOId study on early dementia detection through behavioral analysis. His work integrates machine learning, computer vision, and wireless communication to address clinical challenges in real-world settings. Research interests span smart home technologies, assistive devices, and ethical AI deployment in healthcare. He collaborates with institutions like the Bristol Medical School and the School of Electrical, Electronic and Mechanical Engineering on interdisciplinary projects. Publications highlight advancements in multimodal data fusion, wearable sensor optimization, and pandemic response systems. His contributions bridge engineering innovation with clinical needs, emphasizing user-centric design and privacy-aware technologies.

• Computer Networking
• Machine-to-Machine Communications
• IoT (Internet of Things)
• +۴ مورد دیگر

Gianluca Rizzo is an Adjunct Professor at HES-SO Valais-Wallis, affiliated with the Higher School of Management (Haute Ecole de Gestion) and the Internet of Things (IoT) department linked to EPFL. He holds a Computer Science Bachelor's degree from UC3M University in Madrid. His research focuses on IoT, vehicular communications, AI-driven network optimization, and disaster-resilient systems. Education: Computer Science BSc (UC3M University, Madrid) Affiliations: HES-SO Valais-Wallis, EPFL IoT Group, RECODIS (Post-Disaster Communications Lab) His work spans energy-efficient networking, opportunistic content dissemination (Floating Content), and distributed learning techniques. Key contributions include optimizing multi-agent systems in dynamic environments, developing gossip learning frameworks for urban trajectory prediction, and analyzing SWIPT (Simultaneous Wireless Information and Power Transfer) in vehicular networks. He also explores emergency networks for post-disaster scenarios, leveraging technologies like UAVs and floating content for situational awareness. Recent publications emphasize AI-native vehicular communications, edge computing orchestration, and infrastructure savings via moving base stations. Collaborative projects include V-Edge (virtual edge computing) and the NOSE nomadic sensing ecosystem. His work bridges theoretical models (e.g., stochastic geometry) with practical implementations, addressing challenges in 5G/6G, smart cities, and industrial IoT.

Muhammad Hamad Alizaiمشاهده پروفایل

دانشیار

• Internet of Things
• Cyber-Physical Systems
• Embedded AI
• +۷ مورد دیگر

Muhammad Hamad Alizai is an Associate Professor in the Department of Computer Science at the School of Science and Engineering (SBASSE), Lahore University of Management Sciences (LUMS), Pakistan. His research spans the Internet of Things (IoT), Cyber-Physical Systems, Embedded AI, and intermittent computing, with a focus on sustainable and accessible technologies for developing regions. His research interests include energy harvesting, batteryless computing, GenAI for IoT, wireless sensor networks, and distributed systems . He explores how generative AI can democratize IoT access in low- and middle-income countries and addresses societal challenges through computational solutions. His work is supported by grants from HEC, LUMS, DAAD, and NCBC. Recent publications reveal a strong trend in intermittent computing, energy-efficient IoT, and AI-driven system optimization , with high-impact papers in ACM SenSys, IPSN, BuildSys, and CACM. His team develops innovative systems like CheckMate, Glitch in Time, and Guardian Angel, tackling reliability, security, and accessibility in transiently powered devices. Scientific recognitions include: Best Paper Candidate at ACM SenSys 2019 Audience Choice Award at ACM BuildSys 2017 Best Abstract Award at ACM SenSys 2010 He has advised numerous graduate students, including PhDs like Saad Ahmed and Samar Abbas, and leads pedagogical innovation as head of the LUMS Learning Institute , promoting teaching excellence and AI integration in education. He has served on technical committees for ACM CoNEXT, IPSN, MobiSys, and BuildSys, and organized key workshops in transient computing. His lab focuses on building privacy-preserving, energy-neutral, and AI-augmented embedded systems , particularly for applications in water, energy, and transportation in developing regions.

Antoine Grenierمشاهده پروفایل

پژوهشگر

• Global Navigation Satellite Systems (GNSS)
• Approximate Computing
• Edge AI
• +۵ مورد دیگر

Antoine Grenier is a researcher in the Department of Electrical Engineering at Tampere University, focusing on Global Navigation Satellite Systems (GNSS), Approximate Computing, and Edge AI. His work spans signal processing, sensor technology, and low-power system design for positioning applications. Developing energy-efficient GNSS receivers using approximate computing Creating benchmarking environments for GNSS algorithms Analyzing ionospheric errors for LEO-PNT solutions Exploring consumer-grade sensors in Android wearables for robust positioning His recent publications (2022-2025) emphasize interdisciplinary applications of GNSS in IoT, edge computing, and satellite-based navigation systems. Collaborations with researchers like Elena-Simona Lohan and Aleksei Ometov highlight his contributions to open-access datasets and algorithm optimization frameworks.

Faycal Bouhafsمشاهده پروفایل

مدرس ارشد

• Programmable and software defined wireless networks
• Beyond 5G and 6G communication networks
• Security in Internet of Things
• +۴ مورد دیگر

Dr. Faycal Bouhafs is a Senior Lecturer at the School of Systems & Computing , UNSW Canberra . His research focuses on performance and reliability in wireless communication networks, with expertise in programmable networks, 5G/6G architectures, and IoT security. He leads initiatives in radio resource optimization and physical-layer security using software-defined approaches. Research Interests: Dr. Bouhafs investigates: Programmable wireless networks for dynamic resource allocation Beyond 5G/6G infrastructures supporting massive IoT deployments Security frameworks for cyber-physical systems and IoT ecosystems AI-driven optimization of radio access and network management Publication Focus: Recent works (2020-2025) demonstrate strong emphasis on: Software-defined wireless networking (SDWN) for spectrum sharing Physical-layer security via jamming and deep learning 6G resource optimization and IoT scalability Practical implementations using off-the-shelf equipment

Tughrul Arslanمشاهده پروفایل

استاد

• Integrated Electronic Systems
• Reconfigurable Hardware and Software
• Low Power Design
• +۶ مورد دیگر

Professor Tughrul Arslan holds the Chair of Integrated Electronic Systems at the School of Engineering, University of Edinburgh . He leads the Embedded Wireless and Wearable Sensor Systems (EWireless) Group and co-founded sensewhere Ltd. and Sofant Technologies . His research spans reconfigurable architectures, low-power wireless systems, and biomedical RF sensing. Academic Background: BEng and PhD in Electronics Professional Affiliations: Senior Member IEEE, Fellow IET, Chartered Engineer His work focuses on smart wearable devices , indoor positioning systems , and AI-driven healthcare monitoring . Recent publications emphasize microwave imaging for dementia detection , edge AI accelerators , and non-invasive tremor monitoring . Key contributions include patented technologies like the Reconfigurable Instruction Cell Architecture (RICA) . Award-winning academic, he has supervised over 40 PhD students and authored 400+ peer-reviewed papers. His external roles include Chief Technology Officer at sensewhere , driving commercialization of indoor navigation solutions.

Sreenivasa Reddy Yeduriمشاهده پروفایل

پژوهشگر ارشد

• Wireless Communications
• Machine Learning
• 5G Networks
• +۹ مورد دیگر

Sreenivasa Reddy Yeduri is a Postdoctoral Fellow at the University of Agder , Department of Information and Communication Technology . He holds a Ph.D. from National Institute of Technology Goa and Indian Institute of Technology Hyderabad (2016-2021) and a Master of Technology from ABV-Indian Institute of Information Technology Gwalior (2014-2016). His research spans Wireless Communications , 5G Networks , UAV-Assisted Communications , Machine Learning , and Cyber-Physical Systems . Key areas include Hybrid Communication Systems , Physical Layer Security , Smart Sensing , and Computer Vision . Recent publications focus on Q-Learning for energy-efficient UAV networks, Deep CNN for hand gesture recognition, Spectrum Cartography , and Small World Network optimization in IoT contexts. Collaborations include IEEE/ACM Transactions, IEEE Sensors Journal, and Pervasive and Mobile Computing. He contributes to the Autonomous and Cyber-Physical Systems (ACPS) research group, integrating UAVs , Edge Computing , and Signal Processing in wireless communication frameworks.

• Brain-Computer Interfaces
• Neural Engineering
• Electroencephalography (EEG)
• +۷ مورد دیگر

Prof. Dr. Gernot R. Müller-Putz is a leading academic in neural engineering and brain-computer interfaces at the Institute of Neural Engineering , Graz University of Technology , within the Faculty of Computer Science and Biomedical Engineering . He leads both the institute and its associated Laboratory of Brain-Computer Interfaces , driving cutting-edge research in non-invasive neurotechnology and assistive systems. Research Interests: His work spans Brain-Computer Interfaces , EEG signal processing , neurorehabilitation , neuroprosthetics , and cognitive neuroscience . He focuses on decoding motor intentions, sensory feedback restoration, and developing interpretable AI models for neural data. His research bridges engineering, neuroscience, and clinical applications to enhance human-machine interaction and improve quality of life for individuals with disabilities. Recent Research Trends: Analysis of his latest publications (2024–2025) reveals a strong emphasis on EEG-based decoding of gait and reaching movements , cross-subject generalization using deep learning, sensory and proprioceptive restoration in amputees, and cognitive processing in augmented reality . These reflect a cohesive trajectory toward real-world, clinically viable BCI systems with robust and interpretable performance. Scientific Leadership and Editorial Roles: Specialty Chief Editor, Brain-Computer Interfaces , Frontiers in Human Neuroscience Associate Editor, Neuroprosthetics , Frontiers in Neuroscience Review Editor, Health Informatics , Frontiers in Digital Health Topic Editor, Neurophysiological foundations of videoconference fatigue He plays a significant role in shaping discourse in BCI and neural engineering through editorial leadership. Advising and Research Leadership: As head of the Institute of Neural Engineering and the BCI Laboratory, he mentors students and researchers, oversees multiple funded projects, and collaborates internationally. While specific students are not listed, his high publication output and editorial roles suggest active supervision and team leadership. His research is supported by ongoing grants in neural engineering and assistive technologies, though specific funding sources are not detailed here. Laboratories and Teams: Institute of Neural Engineering , Graz University of Technology Laboratory of Brain-Computer Interfaces (affiliated with the institute) These units form a hub for interdisciplinary research in neural signal processing, BCI development, and clinical translation.

Vincent Leungمشاهده پروفایل

دانشیار

• RF/mixed-signal ICs
• Ultra-low-power IoT circuits and systems
• ICs for neurotechnology and biomedical applications
• +۷ مورد دیگر

Dr. Vincent Leung is an Associate Professor in the Department of Electrical and Computer Engineering at Baylor University, College of Engineering. He leads the Bio-Electronics and RFIC (BEaR) Lab and maintains active research in neurotechnology and RF/mm-Wave ICs. He also holds concurrent appointments as a Faculty Research Engineer at the Naval Surface Warfare Center (Crane, IN) and as a Visiting Associate Professor at Brown University (Providence, RI). PhD, Electrical and Computer Engineering, University of California, San Diego (2004) M.Eng., Electrical Engineering, McGill University (1997) B.Eng. (Honors), Electrical Engineering, McGill University (1995) Dr. Leung's research focuses on cutting-edge integrated circuit design for biomedical and wireless applications. His work spans RF/mixed-signal ICs , ultra-low-power IoT systems , neural interfaces , and wireless communication electronics for both commercial and military use. He has extensive industry experience from Analog Devices, IBM, Silicon Labs, and Qualcomm, where he led RFIC development for 3G/4G smartphones. His recent publications show a strong trend toward distributed implantable systems and wireless neural interfaces , with high-impact work in Nature Communications and Nature Electronics . These studies emphasize asynchronous wireless networks , energy-efficient power transfer , and machine learning-enhanced circuit design , reflecting a convergence of biomedical engineering, low-power electronics, and AI-driven hardware innovation. Dr. Leung has taught graduate courses in Analog VLSI and RFICs at Columbia University and UCSD. He has co-authored over 50 technical papers and holds 13 U.S. patents. His lab is actively engaged in federally relevant research, particularly through collaboration with the Naval Surface Warfare Center, indicating significant grant and defense-related funding. He mentors students through the BEaR Lab and contributes to interdisciplinary neurotechnology initiatives. The BEaR Lab (Bio-Electronics and RFIC Lab) at Baylor University, led by Dr. Leung, focuses on developing advanced integrated circuits for biomedical and wireless applications. The lab specializes in low-power, high-performance RF and mixed-signal ICs for neurotechnology, IoT, and defense systems, fostering innovation in implantable and distributed sensor networks.

Yanliang Zhangمشاهده پروفایل

استاد

• Thermal Science
• Thermal Energy Conversion
• Clean and Sustainable Energy Systems
• +۹ مورد دیگر

Yanliang Zhang is a Professor and the Advanced Materials and Manufacturing Collegiate Chair in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame's College of Engineering. He directs the Advanced Manufacturing for Energy and Health (AMEH) Lab, where he leads cutting-edge research at the intersection of advanced manufacturing, energy conversion, and healthcare sensing technologies. His educational background includes: Ph.D. in Mechanical Engineering from Rensselaer Polytechnic University (2011) M.S. in Mechanical Engineering from Southeast University, China (2008) B.S. in Mechanical Engineering from Southeast University, China (2005) Professor Zhang's research spans multiple domains with a focus on thermal science and energy conversion . His work centers on developing innovative manufacturing techniques for functional materials and devices, particularly in the areas of thermoelectrics, flexible electronics, and advanced sensing systems. The lab employs an "Atomic to System Engineering" approach to bridge fundamental research with practical applications. Key research thrusts include: Additive manufacturing and scalable nanomanufacturing of functional materials Thermal and thermoelectric energy conversion, harvesting, and storage Advanced sensors for extreme environments and healthcare monitoring Autonomous materials discovery through high-throughput combinatorial methods Analysis of Professor Zhang's recent publications (2024-2025) reveals a strong emphasis on aerosol jet printing , thermoelectric devices , and machine learning-assisted manufacturing . His work demonstrates a progression from fundamental materials research toward integrated systems for energy conversion and healthcare applications. A notable trend is the increasing integration of AI/ML techniques with advanced manufacturing processes to optimize device performance and enable autonomous fabrication. Professor Zhang has received significant recognition for his work, including: International Thermoelectric Society 2020 Young Investigator Award His research program is supported by prestigious funding from the U.S. Department of Energy, National Science Foundation, and industry partners. Professor Zhang actively mentors the next generation of engineers through his laboratory, which includes postdoctoral researchers, PhD students, and undergraduate researchers working on diverse projects spanning from fundamental materials science to applied device engineering. The Advanced Manufacturing for Energy and Health Lab maintains strong collaborative ties with industry and national laboratories, facilitating the translation of research discoveries into practical applications. Current research directions include bioprinting, autonomous manufacturing systems, and next-generation thermoelectric technologies for energy harvesting and cooling applications.