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Shahriar Nirjon is an Associate Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. His research focuses on Embedded Intelligence, developing end-to-end systems that make resource-constrained real-time and embedded sensing systems capable of learning, adapting, and evolving. Dr. Nirjon received his Ph.D. from the University of Virginia in 2014. Before joining UNC Chapel Hill in 2015, he worked as a Research Scientist at HP Labs (2014-2015) and as a Research Intern at Microsoft Research (Summer 2013) and Deutsche Telekom Lab (Summer 2010). His primary research interest is Embedded Intelligence, with recent works broadly categorized into embedded deep learning and multi-modal sensing techniques. Applications of his research span wearables and implantables, long-term monitoring and control systems, smart home environments, and mobile health solutions. Dr. Nirjon's research bridges theoretical foundations with practical implementations, resulting in systems that have real-world impact in healthcare, safety, and everyday computing. His work has been highlighted in prominent media outlets including IEEE Spectrum, The Economist, New Scientist, and BBC. Dr. Nirjon's publication record demonstrates a strong focus on mobile computing systems, embedded sensor networks, and wireless technologies. His recent work shows increasing integration of machine learning and artificial intelligence with embedded systems, particularly in healthcare applications. There's a clear trajectory toward more sophisticated, energy-efficient systems capable of on-device intelligence, with growing emphasis on privacy-preserving techniques and real-world deployments in healthcare settings. Best Paper Award, Challenges in AI and Machine Learning for IoT (AIChallengeIoT '20) Best Presentation Award, Pervasive and Ubiquitous Computing (Ubicomp '20) Best Paper Award, Distributed Computing in Sensor Systems (DCOSS '19) Best Presentation Award, Vehicular Networking Conference App Contest (VNC '18) Best Demo Runner Up, Vehicular Networking Conference App Contest (VNC '18) Best Paper Nomination, Embedded Wireless Systems and Networks (EWSN '17) Best Demo Runner Up Award, Embedded Networked Sensor Systems (SenSys '16) Best Paper Award, Mobile Systems, Applications, and Services (MOBISYS '14) Best Paper Award, Real-Time and Embedded Technology and Applications Symposium (RTAS '12) Dr. Nirjon has advised numerous PhD students including Chong Shao (Google), Shiwei Fang (Assistant Professor at Augusta University), Tamzeed Islam (Research Staff at Amazon), Bashima Islam (Assistant Professor at Worcester Polytechnic Institute), Seulki Lee (Assistant Professor at UNIST, Korea), and Yubo Luo (Black Sesame Technologies Inc.). He currently advises Mahathir Monjur, Zhenyu Wang, and Louie Lu who are in various stages of their PhD programs. His research is supported by significant grants including an NSF CAREER award ($561K), an NSF SCH grant ($941K), and multiple other NSF-funded projects totaling over $2 million. His active projects include Audio Privacy, Pedestrian Safety, IoT Data Privacy, and HVAC Acoustic Fingerprinting. Dr. Nirjon leads research in the Embedded Intelligence Lab at UNC Chapel Hill, where his team develops cutting-edge technologies in mobile computing, embedded systems, and wireless networks. His work spans multiple domains including healthcare (mobile health systems), safety (pedestrian safety applications), and smart environments (smart homes). He collaborates with researchers across disciplines, particularly in healthcare through the Carolina Health Informatics Program (CHIP), and is actively involved in the Be-A-Maker (BeAM) network of makerspaces at UNC.

• Embedded Intelligence
• Embedded Deep Learning
• Multi-Modal Sensing Techniques
• +7 more

Donald Adjeroh is a Professor and Associate Chair of Computer Science at the Lane Department of Computer Science and Electrical Engineering, within the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University. He also serves as the Program Director for Computer Science Graduate Studies and is the Project Lead PI for the WVAR-CRESH (West Virginia Arkansas Research Collaboration in Engineering, Science, and Health) initiative. Dr. Adjeroh received his Ph.D. from the Chinese University of Hong Kong in 1997. His academic journey has led him to become a prominent researcher in multiple interdisciplinary fields, with a focus on developing computational methods for biomedical applications. Dr. Adjeroh's research spans several interconnected domains: Machine Learning approaches for biomedical data analysis Computer Vision techniques for medical image processing Data Compression algorithms with applications in bioinformatics Search Algorithms & Data Structures for large-scale biological data Bioinformatics and Computational Biology methods for genomic analysis Digital Health applications for healthcare cost reduction and improved outcomes His publication record demonstrates consistent contributions across these fields, with recent work focusing on parameterized string algorithms, probabilistic suffix arrays for protein family prediction, and robust texture analysis techniques. His research shows a clear trajectory toward increasingly sophisticated applications of computational methods to biomedical problems. Dr. Adjeroh has received significant research funding from various sources including the National Science Foundation (NSF), Department of Energy (DOE), NASA, Department of Defense (DOD), Department of Homeland Security (DHS), and others. His current projects include "NSF NRT: Bridges in Digital Health" and "RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health," among others. He has mentored numerous students through research projects and serves as an advisor for graduate students interested in pursuing PhD research in areas related to search data structures, computational biology, bioinformatics, biometrics, machine learning, and digital health. Dr. Adjeroh is actively involved in organizing academic events, including the Summer School on AI & Smart Health, and serves as a key collaborator in multi-institutional research efforts focused on leveraging artificial intelligence to improve healthcare outcomes.

• Machine Learning
• Computer Vision
• Data Compression
• +4 more

Jari Lietzen serves as a Postdoctoral Researcher within the Department of Information and Communications Engineering at Aalto University, Finland, actively contributing to the Communication Engineering research group. His work focuses on pioneering ultra-low-power communication solutions for next-generation wireless networks, particularly through backscatter technologies that enable battery-free device operation by harvesting ambient energy. His research spans critical domains including Backscatter Communications for Ambient IoT, Physical Layer Security mechanisms like secret key generation, Visible Light Communication integration, and Quantum-Enhanced Wireless Systems. He investigates thin-film device fabrication using additive manufacturing, polarization conversion techniques, and reconfigurable intelligent surfaces for harmonic beam steering, addressing fundamental challenges in energy efficiency and security for constrained IoT environments. Analysis of his 13 publications (2018-2024) reveals a cohesive research trajectory centered on backscatter communications evolution. Key trends include the shift from foundational quantum backscatter paradigms (2018) toward practical hardware implementations like light-controlled thin-film devices (2024) and multi-antenna integrated systems. His work consistently bridges theoretical advances in physical layer security with experimental validation, demonstrating expertise in Sub-1GHz radio systems, satellite communications security, and hybrid VLC-backscatter architectures for ambient IoT. Within Aalto University's Communication Engineering group, Lietzen collaborates extensively on experimental projects involving prototype development and link budget validation, with strong partnerships including Boxuan Xie, Kalle Ruttik, and Riku Jäntti. His research directly supports emerging 6G technologies through innovations in passive wireless infrastructure and quantum-inspired communication protocols.

• Backscatter Communications
• Wireless Communications
• Internet of Things
• +4 more

Dr. Mihai Teodor Lazarescu is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino , where he contributes to research and teaching activities. He is also a member of the PolitoBIOMed Lab (Biomedical Engineering Lab) and the Ambient Sensing and Processing research group. Scientific Affiliation: IEEE Member (2019-present) Editorial Roles: Guest Editor for SENSORS, ELECTRONICS, and ACM Transactions on Embedded Computing Systems His research interests focus on hardware acceleration for machine learning algorithms, particularly using FPGAs for data center and embedded applications. He works on high-level synthesis optimization flows, capacitive sensor design for indoor monitoring, and low-power embedded systems . His work intersects Internet of Things , Wireless Sensor Networks , and Machine Learning with applications in human localization, environmental monitoring, and industrial automation. Recent publications demonstrate expertise in neural network optimization , DSP resource sharing , and multi-FPGA allocation . His teaching spans Applied Electronics , Digital Electronic Design , and Embedded Systems Optimization across bachelor's and master's programs in Electronic Engineering and Computer Engineering . Patents: Noise cancellation for single-plate capacitive sensors Capacitive sensor for space change detection Projects: Scientific Manager for Horizon 2020-S2RJU project (2018)

• Artificial intelligence
• Capacitive sensors
• Embedded software
• +10 more

Ali Hassan Sodhro is a Senior Lecturer in Computer Science at Kristianstad University's Faculty of Natural Science, Department of Computer Science. With over 80 publications in top-tier journals and conferences, Dr. Sodhro has established himself as a prominent researcher in healthcare technology and networking systems. His educational background includes a PhD in Computer Applications Technology from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences (2016), an M.E in Communication Systems and Networks (2011), and a B.E in Telecommunication (2008) from Mehran University of Engineering and Technology in Pakistan. Dr. Sodhro's research focuses on the intersection of AI, healthcare technology, and networking systems. His primary areas include AI/ML for Edge/Cloud Computing, Smart Healthcare IoT, Physical Layer Security in 5G applications, and Energy Harvesting for Healthcare Systems. His work bridges theoretical computer science with practical healthcare applications, particularly in wireless sensor networks and medical data transmission. Analysis of his recent publications reveals a strong trend toward healthcare-focused IoT systems, particularly Internet of Medical Things (IoMT) applications. His research spans from foundational networking technologies like 5G and digital twins to practical implementations in medical equipment and patient monitoring systems, with increasing emphasis on AI integration for cybersecurity and efficiency. IEEE Senior Member Dr. Sodhro serves as Associate Editor for several prestigious journals including IEEE Transactions on Intelligent Transportation Systems (T-ITS) and IEEE Transactions on Industrial Informatics (TII). His research has secured multiple projects including the EU COST Action Project on Physical layer security for 6G systems (6G-PHYSEC), Smart and Pervasive Healthcare for Elderly Patients, and Energy-efficient Video Transmission in Wireless Body Sensor Networks. His collaborative network spans multiple institutions including Luleå University of Technology, Mid Sweden University, Linköping University, Chinese Academy of Sciences, and University of Glasgow, reflecting the international scope of his research in healthcare technology and networking systems.

• AI/ML for Edge/Cloud Computing
• Smart and Healthcare Internet of Things (IoT)
• Internet of Medical Things (IoMT)
• +6 more

Dr. Petros Karadimas is an Associate Professor at the School of Computing, Engineering and the Built Environment of Edinburgh Napier University , UK. Prior to this, he held academic positions at the University of Glasgow (2016-2022) and the University of Bedfordshire (2009-2011). He earned his M.Eng. and Ph.D. in Electrical and Computer Engineering from University of Patras , Greece. Radio propagation and wireless channel modeling Antenna arrays and MIMO antenna optimization Reconfigurable intelligent surfaces for 6G Communication and information theory Physical layer wireless security His research output includes 15+ publications on antenna design , vehicular communications , and physical layer security . Current projects focus on 6G wireless systems with funding from EPSRC and CDE/DSTL . He serves as: IEEE Vehicular Technology Society Propagation Committee Member Senior Member IEEE (SMIEEE) Fellow Higher Education Academy (FHEA) Grant reviewer for FONDECYT and UKRI

• Radio Propagation
• Wireless Channel Modelling
• Antenna Arrays
• +5 more

Professor Christian Ritz serves as Director of SMART and Professor in the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong. He has held his professorship since 2017 and served as Associate Dean (Research) in 2022 and Associate Dean (International) from 2016-2021. Professor Ritz's research focuses on signal processing for speech, audio, acoustics and visual information. His current projects include microphone array signal processing for enhancing sound recorded in classrooms, sound scene classification for dementia care environments, spatial audio for Virtual and Augmented Reality, and undersea surveillance. His work spans multiple technical domains including signal processing, audio engineering, computer vision, and communications engineering. His recent publication trends show a strong focus on neural network applications for audio processing, particularly for classroom monitoring and spatial audio reproduction. His work integrates deep learning with traditional signal processing techniques to address challenges in spatial audio synthesis, room impulse response generation, and directional audio reproduction. He has also contributed to applications in mining automation, public safety surveillance, and positioning systems. Professor Ritz is a member of the Centre for Signal and Information Processing (CSIP), part of the Signals, Information and Communications Research Institute (SICOM) at the University of Wollongong. His research is funded by the ARC, overseas research institutes, and defense organizations. His grant portfolio is extensive, including projects such as AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods, Multi-Modal Satellite-based Vessel Surveillance, Net Zero Industry and Innovation Program, Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane, and numerous projects related to spatial audio and underwater surveillance. He has secured funding from diverse sources including the Australian Research Council, defense organizations, and international collaborations. Professor Ritz maintains an active research laboratory environment focused on signal and information processing. His work spans multiple application domains from classroom monitoring to underwater mine detection, with strong connections to both academic and industry partners. His research group appears to focus on practical applications of signal processing techniques to solve real-world problems across various domains.

• Signal processing
• Audio processing
• Spatial audio
• +7 more

Andrey I. Lyakhov is a Professor and Doctor of Computer Science at the Institute for Information Transmission Problems of the Russian Academy of Sciences. He serves as the Head of Laboratory №18 and has been active since 1959. His research focuses on wireless networks, particularly IEEE 802.11 and 802.16 protocols. Position: Laboratory Chief Affiliation: Institute for Information Transmission Problems, Russian Academy of Sciences Research Interests: Wireless Networks, MAC Protocol Analysis, Network Performance, Distributed Control, Multicast QoS, Channel Assignment His work includes analytical modeling of data transmission, beaconing in mesh networks, and studies on network unfairness and congestion. He has contributed to patents in wireless sensor networks and piconet beacon management. Notable publications span wireless LANs, WiMAX, and sensor network optimization. Lyakhov's research has involved collaborations with international conferences and journals, focusing on throughput estimation, channel contention, and quality of service in wireless systems. Recent publications include studies on bandwidth piggybacking (2010), intra-flow interference in mesh networks (2010), and multicast QoS support in WLANs (2007). Earlier works from 1983-2008 cover foundational topics in queueing theory, cache efficiency, and distributed control systems.

• Wireless Networks
• IEEE 802.11 Protocol
• Network Performance Analysis
• +3 more

Firas Al-Doghman is a Lecturer at the School of Computer Science, University of Technology Sydney. With expertise in Machine Learning, Cybersecurity, and Internet of Things (IoT), he teaches subjects like iOS Applications, Networking, and Data Engineering while conducting research on smart algorithms and their cybersecurity applications. PhD in Computer Engineering and Data Management (UTS, 2020) Teaching since 2017 at UTS Former Associate Researcher/Lecturer at UNSW ADFA Research focuses on: Machine Learning integration with cybersecurity Blockchain applications in real estate Edge computing security frameworks Consensus-based data aggregation Publications highlight advancements in: Cybersecurity for supply chains Fractional NFTs in property ownership Carbon credit price prediction models Secure microservices orchestration Key collaborations include researchers from UNSW and industry partners.

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +3 more

Prof. Dr. Christoph Wildfeuer is a Professor for Sensor Technology at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW) School of Engineering and Environment. He leads the Quantum Technologies working group and module, focusing on quantum sensors, cryptography, and secure communication systems. Doctorate in Quantum Technologies from University of Siegen (2003) Diploma in Physics from University of Siegen (1999) Postdoctoral research at Louisiana State University, NIST, and Duke University His research spans Quantum Cryptography , Post-Quantum Cryptography , Optical Sensors , and Quantum Communication in aerospace contexts. Projects include: Developing quantum-safe encryption for corporate networks Simulating quantum communication via drones and stratospheric balloons Demonstrating entanglement and post-quantum key exchange on nano-satellites He contributes to secure satellite communications and integrates quantum technologies into embedded systems and wireless security .

• Quantum Technologies
• Quantum Information
• Quantum Cryptography
• +6 more

Younghyun Kim is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University. His research spans interdisciplinary domains including machine learning, edge computing, IoT security, and wearable medical devices, with a notable emphasis on agricultural technology via dairy cattle monitoring systems. Institution: Purdue University Department: Electrical and Computer Engineering Academic Rank: Associate Professor Kim's work focuses on energy-efficient systems, hardware-software co-design, and security mechanisms for IoT and medical devices. His recent publications highlight applications in dairy cattle health monitoring, virtual reality authentication, and distributed edge AI architectures. His 15 most recent articles reflect trends in agricultural IoT (e.g., MooBot, MmCows), secure device pairing (e.g., VoltKey, AeroKey), edge computing (e.g., Content-aware input scaling), and energy-efficient hardware (e.g., AxFTL, SAADI). Notable subfields include precision livestock farming, federated learning, sensor fusion, homomorphic encryption, hardware reverse engineering, and low-power design. Email: younghyun@purdue.edu

• Machine Learning
• Edge Computing
• IoT Security
• +5 more

Professor Vasilios Tsaousidis is affiliated with the Department of Electrical and Computer Engineering at Democritus University of Thrace since 2003. He was elected Professor in 2007 and has held visiting positions at MIT (2009) and Technical University of Berlin (2005). His work is conducted within the Programming and Information Processing Laboratory and focuses on DTN , Information-Centric Networking , and Space Internetworking . Education : Applied Mathematics (Aristotle University, Greece), Computer Science PhD (Humboldt University, Berlin) Postdoctoral Experience : Rutgers University, SUNY Stony Brook, Northeastern University His research spans transport/network layers with emphasis on energy-efficient and delay-tolerant protocols. Key areas include congestion control , QoS mechanisms , and reliable data dissemination in challenged environments. His publications from 2023-2025 focus on Kubernetes networking , edge cloud , and blockchain-integrated trust systems , demonstrating adaptability to emerging paradigms like IoT and space networking . Current projects like UMOBILE and SENSKIN explore mobile-centric architectures and structural health monitoring systems. Earlier work with SPICE (2010-2014) pioneered space internetworking protocols for Mars data transmission and space weather applications .

• Delay and Interruption Tolerant Networks (DTN)
• Information-Centric Networking
• Space Internetworking
• +5 more

Hamid Mehmood serves as an Adjunct Assistant Professor at McMaster University's School of Earth, Environment & Society, contributing to interdisciplinary environmental and societal research through computational methods. His scholarly focus spans multiple domains: Environmental Science (flood/reservoir mapping, carbon cycle analysis) Geospatial Informatics (GNSS/WLAN positioning, Landsat/GIS platforms) Machine Learning (neural networks for urban/environmental applications) Public Health (AMR, cardiovascular coping, clinical trials) ICT for Development (Urdu systems for farmers, mobile scaling) Recent publications demonstrate a shift toward urgent global challenges: 2024 machine learning-driven urban slum mapping in Indonesia, 2022 environmental health reviews on antimicrobial resistance, and 2021 geospatial platforms for flood management. His work consistently bridges technical innovation (e.g., Google Earth Engine, IVIG trials) with socio-economic contexts in Pakistan, Saudi Arabia, and Southeast Asia, while earlier research (2013-2015) established foundations in neural network-optimized positioning systems.

• Environmental Science
• Geospatial Informatics
• Machine Learning
• +3 more

Ijaz Ahmad is a Doctoral Researcher at the University of Oulu 's Faculty of Information Technology and Electrical Engineering , affiliated with the Centre for Wireless Communications (Networks & Systems) unit. He holds qualifications including a BCS (Hons), MS in Information Security, and certifications as a Certified Ethical Hacker (C|EH) and Senior Fellow of the Higher Education Academy (SFHEA) . Education: BCS (Hons), MS Information Security Certified Ethical Hacker (C|EH) Senior Fellow of the Higher Education Academy (SFHEA) Research interests focus on cybersecurity in emerging technologies, including 6G networks , AI/ML , Internet of Things , and Edge-Cloud Continuum . His recent work explores AI-driven adaptive security , decentralized architectures , and energy-efficient protocols for healthcare applications. Scientific awards : Certified Ethical Hacker (C|EH) Senior Fellow of the Higher Education Academy (SFHEA) Projects : Involved in TOMOHEAD , Eware-6G , and Hola 5G , focusing on next-generation network security and healthcare applications.

• Adaptive Cybersecurity
• 6G Networks
• AI/ML
• +7 more

Nagham Saeed serves as Associate Professor in Electrical and Electronic Engineering at the School of Computing and Engineering, University of West London, a position she has held since April 2023. Previously, she was Senior Lecturer in Electrical Engineering at the same institution from November 2017 to April 2023, following roles as Electronic Lecturer at Uxbridge College (2012-2017) and Electronic/Control Lecturer at Brunel University (2007-2012). Her academic credentials include: Ph.D. in Optimization in Wireless Networks and Communications (Brunel University, 2007-2011) M.Sc. in Mechatronics (University of Technology, Baghdad, 1997-1999) B.Eng. in Computer and Control (University of Technology, Baghdad, 1988-1992) DTLLS Diploma in Teaching (University of Westminster, 2012-2014) BAPP in Learning and Teaching (Brunel University, 2009-2010) Assessing Competence Certification (Uxbridge College, 2014) Dr. Saeed's research spans wireless communications, IoT, electric vehicles, and AI-driven energy optimization. Her work focuses on Bluetooth Low Energy applications for healthcare monitoring, battery management systems for EVs, sustainable ICT practices, and GIS-based land use analysis. She pioneers solutions for energy-efficient radio access networks and develops AI models for vehicle classification and low-resource language processing. Analysis of her 2022-2025 publications reveals three dominant trends: (1) AI integration for sustainable energy systems in telecommunications and transportation, (2) blockchain-secured vehicular networks for 6G infrastructure, and (3) cross-disciplinary applications of IoT in healthcare and environmental monitoring. Her research consistently bridges electrical engineering with computer science to solve real-world sustainability challenges. Professional recognition includes: MDPI Reviewer Acknowledgements (2020) for Algorithms, Applied Sciences, and Information journals While her 2022 educational research on feedforward teaching approaches demonstrates pedagogical engagement, no student supervision or grant management details are documented. Current institutional affiliations show no dedicated laboratory facilities or research teams specified in available records.

• Wireless Communications
• Internet of Things (IoT)
• Electric Vehicles
• +6 more