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Pouyan AhmadiView profile

Associate Professor

• Wireless Networks
• Machine Learning
• IoT Security
• +5 more

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.

Tracy CampView profile

Professor

• Ad Hoc Networks
• Wireless Sensor Networks
• Machine Learning
• +3 more

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 TabeiView profile

Assistant Professor

• Machine Learning
• Smart Sensors
• IoT
• +3 more

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.

Rajendra Prasad SiriginaView profile

Lecturer

• Artificial Intelligence
• Wireless Networks
• Health Diagnostics
• +1 more

Rajendra Prasad Sirigina is a Lecturer in the Department of Computer Science at the National University of Singapore. He holds a Ph.D. from Nanyang Technological University, Singapore (2015), an M.Tech from Indian Institute of Technology Guwahati, India (2007), and a B.Tech from GRIET, JNTU Hyderabad, India (2002). His research focuses on applying artificial intelligence to wireless networks and health diagnostics. Ph.D., Nanyang Technological University, Singapore (2015) M.Tech, Indian Institute of Technology Guwahati, India (2007) B.Tech, GRIET, JNTU Hyderabad, India (2002) His work bridges Artificial Intelligence with Wireless Communication and Health Diagnostics , emphasizing robust feature selection, signal classification, and interference management in UAV and satellite systems. Recent research includes NOMA-aided communications , hybrid-duplex architectures , and deep learning for signal processing . The 15 most recent publications highlight advancements in UAV communication systems , NOMA , hybrid-duplex technologies , and satellite network design . Key trends include optimizing spectrum efficiency, mitigating interference in aerial-ground networks, and enhancing signal reliability through machine learning and outage probability analysis.

Chamith WijenayakeView profile

Senior Lecturer

• multidimensional signal processing
• digital hardware architectures
• FPGA based system design
• +3 more

Dr. Chamith Wijenayake is a Senior Lecturer - Teaching Focused at the School of Electrical Engineering and Computer Science, University of Queensland. He holds a PhD in Electrical and Computer Engineering from the University of Akron (2014) and a BSc (Hons) in Electronic and Telecommunications Engineering from the University of Moratuwa, Sri Lanka (2007). His research focuses on multidimensional signal processing, digital hardware architectures, FPGA-based systems, machine learning accelerators, and engineering education. He has received notable awards, including the 2011 Outstanding Student Research Award and the 2014 IEEE Circuits and Systems Pre-Doctoral Award. Education: BSc (First Class Honours) from University of Moratuwa (2007), PhD from University of Akron (2014). His doctoral work contributed to advancements in signal processing and hardware architectures. Research interests span multidimensional signal processing, FPGA-based system design, and engineering education innovations. He develops low-complexity algorithms for light field processing and multidimensional filters for imaging, sensing, and biomedical applications. His work emphasizes practical implementations in hardware accelerators and educational technologies. Outstanding Student Research Award, University of Akron, 2011 IEEE Circuits and Systems Pre-Doctoral Award, 2014 Teaching and Advising: Focuses on blended learning approaches and project-based instruction in electrical engineering. Prior roles include Lecturer at UNSW Sydney (2015–2019). No explicit student advisee records listed. Grants and collaborations are not detailed in provided texts. Labs/Teams: Involved in multidisciplinary projects integrating signal processing with hardware design, though specific lab affiliations are not specified.

Min SunView profile

Professor

• Educational Policy
• Teacher Education & Research
• AI/ML in Education
• +8 more

Dr. Min Sun is a Professor in the Department of Educational Policy, Organization and Leadership at the University of Washington's College of Education. Her research focuses on teacher learning, AI/ML integration in education, and policy-driven educational reforms. She leads interdisciplinary teams developing AI tools like the NSF-funded Colleague lesson planning platform and the IES-funded AmplifyGAIN Center. Her work addresses inequities in education through policy analysis and partnerships with K-12 schools and EdTech industries. Dr. Sun holds a Ph.D. in Educational Policy and Measurement from Michigan State University. She teaches courses such as EDLPS 302: Intro to Educational Policy and EDLPS 564: Economics of Education. Her research spans four key areas: AI/ML method development, AI-powered educational tools, data science training programs, and policy research with multi-sector collaborations. Notable grants include a $10 million IES grant for the AmplifyGAIN Center and a $1.5 million NSF grant for AI-driven math lesson planning. Her policy work emphasizes equitable education access and data-driven solutions. She directs the Education Policy Analytics Lab (EPAL) and collaborates with stakeholders to translate research into actionable strategies.

Luca SchenatoView profile

Professor

• Control Systems
• Distributed Optimization
• Federated Learning
• +5 more

Luca Schenato is a Full Professor in the Department of Information Engineering at the University of Padova. His research focuses on distributed control systems, federated learning, multi-agent optimization, and wireless communication protocols. He has extensive experience in developing algorithms for cyber-physical systems, with applications in robotics, smart grids, and sensor networks. Education and Appointments section lists his academic journey but lacks explicit details. He has held positions related to control systems and information engineering throughout his career. Research interests include: Design of resilient wireless control systems Federated learning architectures for edge computing Distributed optimization under communication constraints Robotics and multi-agent coordination Smart energy management systems His recent publications (2021–2025) demonstrate a strong focus on: Over-the-air federated learning innovations High-speed wireless control systems (e.g., 1 kHz Wi-Fi control) Resilient distributed optimization algorithms Human-centric building automation He has contributed to numerous projects related to networked control systems and has organized conferences like ECC13. His work emphasizes bridging theoretical control principles with practical industrial applications.

Marwa ChafiiView profile

Associate Professor

• Wireless Communications
• Integrated Sensing and Communication
• Machine Learning for Wireless Communications
• +3 more

Marwa Chafii is an Associate Professor of Electrical Engineering at New York University (NYU) Abu Dhabi and an associated faculty member at the NYU Tandon School of Engineering. She joined NYU Abu Dhabi in 2021 after holding roles as a research group leader at TU Dresden (Germany) and an associate professor at ENSEA (France) with a Chair of Excellence in AI. Her research focuses on advanced waveform design, integrated sensing and communication (ISAC), machine learning for wireless communications, and localization . Education: PhD and Master’s in Electrical Engineering from CentraleSupélec, France. Visiting researcher stints at institutions including Poznan University of Technology, University of York, and University of Oxford. Awards & Recognition: IEEE ComSoc Best Young Researcher Award (EMEA region) Best Editor Award for IEEE Communications Letters (2020) Nominated Top 10 Rising Stars in Computer Networking & Communications (2020) French National Council of Universities (CNU 61) AI Excellence Chair (2018–2022) Best PhD in France (Signal, Image & Vision) Professional Roles: Vice-chair of IEEE ComSoc ETI on Machine Learning for Communications Education Lead for IEEE ComSoc ETI on Integrated Sensing & Communications Associate Editor for IEEE Transactions on Communications and IEEE Communications Letters Research Contributions: Over 50+ publications in top-tier journals/conferences (IEEE Transactions, Globecom, ICC) Active in ISAC, 6G waveform design, and AI-driven communication systems Leading the NYU Wireless Research Lab focused on ISAC and future wireless systems

Cynthia F. MossView profile

Professor

• Sensory coding
• Auditory neuroscience
• Spatial perception
• +6 more

Cynthia F. Moss is a Professor in the Department of Psychological and Brain Sciences at Johns Hopkins University (JHU), with joint appointments in Neuroscience and Mechanical Engineering. She directs the Comparative Neural Systems and Behavior Laboratory (Bat Lab), investigating neural mechanisms of sensory perception and spatial navigation in echolocating bats. Her work combines neurophysiology, behavioral ecology, and engineering to study how bats process auditory information for navigation, communication, and obstacle avoidance. Dr. Moss earned a B.S. (summa cum laude) from the University of Massachusetts, Amherst, and a Ph.D. from Brown University. She held prior faculty positions at Harvard University and the University of Maryland, where she directed the Neuroscience and Cognitive Science graduate program. Her research has received prestigious awards including the Hartmann Award (2017), James McKeen Cattell Award (2018), and Alexander von Humboldt Research Prize (2019). Her research focuses on sensory coding of natural stimuli, spatial perception, attention, and adaptive motor control. Key findings include discoveries about 3D auditory space representation in the midbrain, age-resistant hearing in bats, and the role of wing hairs in flight control. Her lab employs innovative techniques like wireless neural recording, two-photon imaging, and DREADDs-mediated inactivation to study neural circuits. Dr. Moss collaborates across disciplines, bridging neurobiology, robotics, and sensory systems engineering. Her work informs biomimetic technologies for sonar navigation and adaptive control systems. She actively mentors students and postdocs in the Krieger School of Arts and Sciences, Whiting School of Engineering, and School of Medicine at JHU.

Ozgur OzdemirView profile

Research Professor

• Unmanned Aerial Systems (UAS)
• mmWave Technology
• Wireless Communication
• +7 more

Ozgur Ozdemir is an Associate Research Professor in the Department of Electrical and Computer Engineering at North Carolina State University's College of Engineering. He is affiliated with the university's primary faculty and works closely with platforms like AERPAW and AFAR Challenge. His research focuses on advanced wireless systems, including UAV networks, mmWave technology, spectrum analysis, and radar-communication integration. His work spans experimental testbed development, digital twin applications for AI research, and methodologies for improving coverage in rural and urban environments. Ozdemir has contributed to standards like 5G NR and non-standalone 5G systems, emphasizing practical implementation and environmental impact analysis. He leads efforts in signal processing for autonomous systems and has expertise in software-defined radio (SDR) technologies. Research interests include aerial threat detection, RF signal analysis, and robust localization techniques. He explores challenges in NLOS bias correction, antenna radiation patterns, and passive reflector-based coverage enhancement. His work often combines theoretical models with experimental validation using platforms like AERPAW. His articles highlight advancements in drone detection systems, channel modeling for mmWave, and the integration of radar and communication systems. Key trends show a focus on outdoor/indoor spectrum measurements, UAV navigation, and resilient network design. Ozdemir's contributions are characterized by empirical studies and cross-disciplinary approaches to wireless challenges.

• Wearable Computing
• Human-Computer Interaction
• Machine Learning
• +3 more

Silvia Santini is an Associate Professor at the Faculty of Informatics of USI since 2016, leading the People-Centered Computing Lab. Previously, she held roles at TU Dresden (2014–2016) and TU Darmstadt (2011–2014), focusing on embedded systems and wireless sensor networks. She completed her PhD at ETH Zurich in 2009 and earned a Telecommunication Engineering degree from Sapienza University of Rome (2004). Her research centers on wearable computing, human-computer interaction, and sensor networks applied to health monitoring, activity recognition, and physiological signal analysis. Notable projects include BiHeartS (bilateral heart rate monitoring) and studies on sleep quality using wearable devices. Key achievements include being ranked among the World’s top 2% of Scientists (2022) and securing grants for projects like XAI-FinCrime. She advises on PhD and postdoc positions, such as the 2025 Innosuisse-funded project in explainable AI for financial crime detection. Labs/Teams: People-Centered Computing Group (USI), collaborating on cross-disciplinary projects in pervasive computing and healthcare technology.

• Communication Security
• Embedded Systems Security
• Design of Embedded Systems
• +4 more

Alberto Ferrante is a Lecturer and Researcher at the Faculty of Informatics of the Università della Svizzera italiana (USI), affiliated with the IDSIA (Dalle Molle Institute for Artificial Intelligence) USI/SUPSI. His work bridges cybersecurity, embedded systems, and AI applications, particularly in IoT and pest control. He holds a PhD from Università degli Studi di Milano (2006) and an MSc from Politecnico di Milano (2002). Research Interests: Ferrante focuses on Secure communication protocols and embedded systems security Malware detection and resource-optimized hardware solutions Machine learning applications for IoT, including agricultural pest monitoring and healthcare diagnostics Cyber-physical systems design and security-enhanced embedded systems His publications emphasize practical implementations, such as AI-driven UAVs for pest control and low-power drone systems for environmental monitoring. He actively contributes to tech transfer projects with industry partners and participates in major conferences like ICC and Globecom as a TPC member. He teaches the Master’s course Edge Computing in the IoT and collaborates on hardware-software co-design for security-critical systems. His work often integrates real-world constraints like energy efficiency and computational resource limitations. Key contributions include frameworks for dynamic security adaptation in wireless sensor networks and hardware-accelerated security for embedded systems.

Pablo Serrano Yáñez-MingotView profile

Associate Professor

• Wireless Networks
• Network Protocols
• 5G/6G Communication Systems
• +3 more

Pablo Serrano Yáñez-Mingot is an Associate Professor at the Telematics Department of Universidad Carlos III de Madrid (UC3M), where he has been since 2002. He holds a Telecommunication Engineering degree and PhD from UC3M (2002 and 2006, respectively). Currently, he serves as Deputy Director of Infrastructure, Sustainability, and Digitalization at the Higher Polytechnic School (EPS). His research focuses on wireless networks, network protocols, and experimental performance evaluation, with over 100 peer-reviewed publications in top journals/conferences like IEEE/ACM Transactions on Networking and IEEE INFOCOM. He has led major projects such as the Hexa-X 6G flagship and Flex5Gware. He is an IEEE Senior Member and has received awards like the UC3M Excellence Award (2021) and Distinguished Member of the INFOCOM TPC (2022). Education: Telecommunication Engineering (UC3M, 2002), PhD in Telematics (UC3M, 2006). Research Interests: Wireless networks, 5G/6G systems, network protocols, energy efficiency, and experimental evaluation of communication systems. Recent Articles Trends : Focus on 5G/6G architectures, network automation, serverless computing, and energy-efficient solutions. Publications highlight advancements in NFV scaling, network slicing, and AI-driven orchestration. Awards : UC3M Excellence Award 2021 (Young Research Excellence) ACM WiNTECH 2016 Best Paper Award Elevated to IEEE Senior Member (2015) Advising & Grants : Supervised over 30 PhD/MSc students. Principal Investigator of projects like AMAZING-6G and iTrust6G. Actively involved in editorial and organizational roles in IEEE conferences (e.g., INFOCOM, WCNC). Labs & Teams : Leads research groups in network protocols, 5G/6G systems, and experimental evaluation. Collaborates with institutions like the University of Rome Tor Vergata and Trinity College Dublin through visiting positions.

• IoT Security
• Network Measurement
• Privacy Engineering
• +3 more

Dr. Anna Maria Mandalari is an Assistant Professor at University College London (UCL), affiliated with the Electronic and Electrical Engineering Department. She serves as Director of the SafeNetIoT laboratory and the Internet Engineering/Telecommunications MSc/MRes Programme at UCL. She holds an Honorary Research Fellow position at the Institute for Security Science and Technology at Imperial College London. Her research focuses on IoT security, privacy engineering, and network measurement techniques, particularly analyzing information exposure from consumer devices and developing adaptive network security strategies. Her work has led to significant contributions such as the IMC'19 Community Contribution Award-winning study on multidimensional IoT device exposure and the Telekom Challenge-winning IoTrim prototype for blocking non-essential IoT traffic. She has secured funding through InnovateUK’s CyberASAP program and participated in high-impact projects like NATwatcher and the European roaming analysis initiative. Key Awards: Community Contribution Award (IMC'19), Best Paper Award (C2B(I)D 2015) Grants: InnovateUK CyberASAP Phase 2 funding, Telekom Challenge Development Stream Top 5 prize Labs/Teams: Director of SafeNetIoT Lab, Leading M2M and IoT security teams at UCL Her recent work addresses IoT device misactivation risks (e.g., smart speakers), scalable detection of IoT devices in the wild, and protocol design informed by crowdsourced data. She actively collaborates with industry partners like Telekom and academic institutions on network infrastructure security and performance optimization.

• Data fusion
• Machine learning
• Statistical signal processing
• +6 more

Pierluigi Salvo Rossi is a Full Professor and Deputy Director at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), Norway. He leads the Ph.D. Program in Electronics and Telecommunication and serves as Deputy Manager of the Centre for Green Shift in the Built Environment. His roles include leadership in SPIN (Signal Processing for Industry N.0), a research group focusing on industrial digitalization through statistical signal processing and machine learning. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests: His work centers on data fusion, machine learning, sensor networks, and wireless communication systems. He applies these to industrial challenges such as digital twins, IoT, and sensor validation for safety-critical systems. Recent projects include leadership in initiatives like SIGNIFY (sensor validation) and ML4ITS (machine learning for irregular time series). Awards & Roles: Recipient of the Exemplary Senior Editor Award (IEEE Communications Letters, 2018) Editorial roles include Topical Editor for IEEE Sensors Journal and Area Editor for IEEE Open Journal of the Communications Society Coordinator of major projects funded by RCN, ESA, and ERC Grants & Teams: Leads or co-leads projects such as PREFERENCE (CCS primary reference), DARIO (ESA/PRODEX), and PERSEUS (ERC MSCA-COFUND). The SPIN group collaborates across construction, energy, and ocean industries, anchored at NTNU’s Gløshaugen campus.