Find Academic Supervisors

• Smartphone Addiction
• Cyberloafing
• Cyberbullying
• +9 more

Dr. Deniz Mertkan GEZGİN serves as an Associate Professor in the Department of Computer Education and Instructional Technology within the Faculty of Education at Trakya University. His academic journey includes post-doctoral studies at Middle East Technical University (2016) following doctoral and master's degrees in Computer Engineering from Trakya University (2011, 2006), and a bachelor's degree from Çanakkale Onsekiz Mart University (1999). Doctoral Degree: Trakya University - Computer Engineering (2011) Master's Degree: Trakya University - Computer Engineering (2006) Bachelor's Degree: Çanakkale Onsekiz Mart University - Computer Engineering (1999) High School: İzmir Selçuk High School - Science and Mathematics (1995) His research centers on digital behavior pathologies, with primary focus on smartphone addiction, nomophobia, and Fear of Missing Out (FoMO) among students. Secondary interests include cyberloafing, cyberbullying, mobile learning, and AI applications in education. His work demonstrates strong interdisciplinary connections between computer science, psychology, and educational technology, particularly examining how technology use impacts academic performance and mental health. Publications reveal consistent productivity in high-impact journals like Computers in Human Behavior and British Journal of Educational Technology, with recent emphasis on AI-driven addiction prediction (2025) and AIlessphobia scale development (2024). His research methodology frequently employs structural equation modeling and latent profile analysis to explore complex behavioral relationships. Guest Editor for Frontiers in Public Health (2023) Science Board member for International Conferences (2014, 2016) As an active academic contributor, he serves as referee for 40+ journals including Computers in Human Behavior, PLOS One, and Cyberpsychology, demonstrating recognition in both computer science and psychological research communities. His editorial work focuses on digital behavior pathologies and educational technology applications.

Tamás TettamantiView profile

Professor

• Road Traffic Control
• Intelligent Transportation Systems
• Traffic Simulation
• +3 more

Tamás Tettamanti is a Professor at Budapest University of Technology and Economics, serving as Deputy Head of the Department of Control for Transportation and Vehicle Systems. He holds a PhD (2013), Habilitation (2023), and DSc (2023) in Transportation Engineering, with expertise in road traffic modeling and control. 2007–2010: PhD Student 2010–2013: Assistant Lecturer 2014–2018: Senior Lecturer 2019–2024: Associate Professor 2025–present: Full Professor Education: High School Graduation (2001), DEUG (2004), MSc in Transportation Engineering (2007), Jazz Trumpet Graduate (2008) PhD (2013), Habilitation (2023), DSc (2023) Research focuses on road traffic modeling, intelligent transportation systems (ITS), autonomous vehicle integration, and emission-aware traffic control. His work bridges theoretical developments with real-world applications, including wireless traffic light systems and deep learning for urban mobility peaks. Scientific Awards: BME PhD Research Prize (2012) Literary Awards from Hungarian Scientific Association for Transport (2013, 2017, 2021, 2023) Bolyai János Research Scholarship (2017–2020) Michelberger Master Prize (2022) BME Jubilee Medal (2023) He has led major projects such as Dynamic Adaptive Traffic Control Services (2020–2024) and Deep Learning Anticipated Urban Mobility Peaks (DARUMA) (2021–2024), with industry collaborations including Google-BME Traffic Lab.

Balázs VargaView profile

Research Fellow

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +3 more

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.

Ramy H. GoharyView profile

Assistant Professor

• Machine-to-machine communications
• Internet-of-Things (IoT)
• MIMO wireless communication systems
• +9 more

Ramy H. Gohary is an Assistant Professor in the Department of Systems and Computer Engineering at Carleton University. His research focuses on advanced wireless communication systems, including machine-to-machine communications, Internet-of-Things (IoT), MIMO systems, convex optimization, differential geometry applications in signal processing, and information-theoretic aspects of multiuser systems. Principal Investigator for Ericsson-Carleton Strategic Partnership projects on 5G+ wireless networks Research emphasizes fairness, cross-layer design, cooperative communications, and jamming-resistant detection techniques His recent work explores direction-of-arrival estimation, non-coherent communication in jamming environments, and distributed MIMO architectures. Publications highlight applications of differential geometry, optimization techniques, and sparse signal recovery in modern wireless systems. He actively contributes to advancements in channel modeling, beamforming, and energy-efficient network design.

Kwame Porter RobinsonView profile

Assistant Professor

• Community-Based Economies
• Digital Sustainability
• Generative Justice
• +5 more

Kwame Porter Robinson serves as Assistant Professor of Technology, Information Systems and Analytics at Wayne State University, where he develops community-based economic systems through co-designed AI and automation. His work bridges academic research with practical applications in digital sustainability and generative justice, informed by prior experience as a Department of Defense data scientist and NGO consultant. His educational background spans technical and creative disciplines: Ph.D. in Information, University of Michigan (2024) M.S. in Computer Science, University of Maryland (2012) B.S. in Electrical Engineering (honors), New Mexico State University (2008) B.F.A. in Graphic Design (magna cum laude), Boston University (2002) Research focuses on decolonial computing and community economies , examining how human-AI collaboration can create unalienated labor systems in artisanal contexts. His methodology emphasizes bottom-up technology co-design with communities, particularly evident in Detroit-based artisan studies and blockchain implementations for economic empowerment. Publications since 2019 reveal accelerating focus on computational reparations and generative justice, with recent work exploring fractal scaling in AI governance and worker-owned AI ecosystems. Key themes include decolonial transitions, circular value flow, and environmental sustainability in crafting economies. Scientific recognition includes: Rackham Merit Fellowship (2019-2024) with multiple research grants AWS Cloud Credits for Research (2019) Best Student Talk award for ethical AI (2023) National Intelligence Meritorious Unit Citation (2012) Teaching includes Business Information Systems and Blockchain & CyberSecurity courses, reflecting his industry-academia synthesis. While specific grant details beyond Rackham awards aren't provided, his DoD background and AWS credits indicate strong industry partnerships. Collaborative work with scholars like Ron Eglash demonstrates interdisciplinary team engagement in justice-oriented computing initiatives.

Hao YeView profile

Assistant Professor

• Machine Learning
• Wireless Communications
• AI Applications
• +4 more

Hao Ye is an Assistant Professor in Electrical and Computer Engineering at the University of California, Santa Cruz (UCSC). Previously, he worked as a Machine Learning Researcher at Qualcomm AI Research before transitioning to academia. His research focuses on integrating machine learning methodology into communication networks, particularly for applications involving edge devices such as autonomous vehicles. PhD in Electrical and Computer Engineering, Georgia Institute of Technology His research interests center on wireless communications, AI-driven network optimization, and signal processing for vehicular networks. He explores deep learning applications in channel estimation, resource allocation, and end-to-end communication systems, emphasizing adaptability in unreliable environments. Key trends in his publications include: Advancing Massive MIMO systems with data-driven approaches Developing graph neural networks for wireless resource management Integrating reinforcement learning into vehicular network spectrum sharing Proposing pilot-free end-to-end communication frameworks using deep learning Investigating federated learning in decentralized wireless architectures Applying meta-learning to networked AI systems Scientific contributions include being invited as an Editor for IEEE Transactions on Wireless Communications in 2024. He actively mentors PhD students and collaborates with researchers like Le Liang, Geoffrey Ye Li, and Shi Jin. Recent team announcements include welcoming Jing Qiao in September 2024, reflecting ongoing growth in his research group focused on machine learning and wireless systems.

Xiaoyan HongView profile

Associate Professor

• Distributed Systems
• Internet of Things (IoT)
• Wireless Communications
• +4 more

Dr. Xiaoyan Hong is an Associate Professor in the Department of Computer Science at The University of Alabama's College of Engineering. Her research focuses on mobile/wireless networks, vehicular networks, and delay-tolerant systems. Ph.D., Computer Science, University of California-Los Angeles (2003) M.S., Computer Science, Zhejiang University (2000) Research spans Internet of Things (IoT) , Connected Vehicles , and Underwater Wireless Networks . Key projects include NSF-funded underwater robot communication infrastructure and smart traffic light systems. Recent work explores Named Data Networking (NDN) in vehicular environments, Task Synchronization for autonomous vehicles, and V2I Communication for traffic optimization. NSF Research Experience for Undergraduates (REU) grant recipient $1.5M NSF grant for underwater robotics networking Her research integrates with multiple engineering centers, including the Center for Advanced Vehicle Technologies and Center for Transportation Operations .

Stephan PreihsView profile

Researcher

• Acoustics
• Digital Signal Processing
• Audio Coding
• +6 more

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'

Chen PanView profile

Assistant Professor

• Energy-Harvesting Embedded Systems
• Internet of Things (IoT)
• Low-Power Computing
• +5 more

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.

Konstantinos KantelisView profile

Assistant Professor

• Biological Nanonetworks
• Electromagnetic Nanogrids
• Hybrid Nanonetworks
• +5 more

Konstantinos Kantelis is an Assistant Professor at the Department of Informatics, Aristotle University of Thessaloniki, specializing in Biological Nanonetworks. He holds a PhD in Nanonetworks and has extensive professional experience as a Software Engineer/Manager at Piraeus Bank (2007-2023), preceded by system administration roles at Marfin-Egnatia Bank (2007-2014). Education: PhD in Nanonetworks (2018), Department of Informatics, Aristotle University of Thessaloniki MSc in Nanotechnology and Nanosciences (2012), Department of Physics, Aristotle University of Thessaloniki MSc in Computer Systems Technology (2006), Department of Informatics and Telecommunications, National University of Athens BSc in Mathematics (2003), Department of Mathematics, Aristotle University of Thessaloniki His research focuses on Biological Nanonetworks, Electromagnetic Nanogrids, and energy-efficient wireless communication protocols for IoT systems. He has contributed to projects like Maestro, IntelTriage, and AYGEIAS, developing protocols for LoRaWAN networks and WDM backbone resilience. Recent publications address spatial scheduling in LoRaWAN, earthquake-tolerant routing, and reinforcement learning-assisted protocols. His work bridges nanoscale communication theory with practical IoT implementations, emphasizing energy efficiency and reliability. As an educator, he teaches graduate courses in Communication Networks, Advanced Topics in Biological Nanonetworks, and Applied Mathematics, alongside postgraduate courses in Networks and Internet (2019-2021).

Ayush BhartiView profile

Research Fellow

• Probabilistic Machine Learning
• Bayesian Statistics
• Simulation-based Inference
• +3 more

Ayush Bharti is an Academy Research Fellow (Research Fellow) in the Department of Computer Science at Aalto University, Finland. His primary affiliations include the Probabilistic Machine Learning research group and the Academy Professorship led by Samuel Kaski . His research centers on probabilistic machine learning with dual focus areas: advancing simulation-based inference methodologies and applying statistical techniques to stochastic radio channel modeling. Key interests include Bayesian statistics, robustness under model misspecification, handling missing data, and developing efficient approximate Bayesian computation frameworks. His work bridges theoretical machine learning with practical wireless communication challenges. Analysis of his 15 most recent publications (2021-2025) reveals a consistent trajectory in simulation-based inference innovation, featuring neural processes, diffusion models, and multilevel architectures. Approximately 60% of his work targets general statistical challenges (e.g., robustness, cost-awareness), while 40% addresses radio channel modeling applications. Notable trends include integration of domain expertise in inference loops and development of context-aware optimization techniques. No information is available regarding student supervision or research grants. Bharti operates within Aalto University's Probabilistic Machine Learning ecosystem, contributing to the Academy Professorship project under Samuel Kaski. This collaborative environment focuses on developing scalable inference algorithms for complex real-world systems, with particular emphasis on uncertainty quantification in simulation-driven scientific discovery.

• Geochemistry
• Environmental Science
• Air Pollution
• +4 more

Jesús Damián de la Rosa Díaz serves as a Professor in the Department of Earth Sciences within the Faculty of Experimental Sciences at the University of Huelva, Spain. He maintains active affiliations with the Sustainable Chemistry Research Center and was previously associated with the RNM347 research group focused on Geology and Analytical Chemistry. His research expertise spans: Environmental geochemistry with emphasis on trace element behavior in sediments and air particulate matter Advanced source apportionment techniques for PM10 in urban and industrial settings Ecotoxicological assessment of mining and industrial pollution impacts Volcanic emission characterization and atmospheric dispersion modeling Development of low-cost sensor networks for real-time air quality monitoring Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on Southern European and Latin American environmental challenges. Key trends include quantification of industrial and mining emissions, volcanic plume impacts, North African dust interactions, and innovative monitoring approaches using machine learning and low-cost sensors. His work consistently addresses regulatory needs through source-specific pollution characterization. Scientific Awards: No awards were documented in the provided materials Advising and Grants: Student advising information was not disclosed Research grant details were absent from the source data Labs and Teams: Dr. de la Rosa Díaz operates within the Sustainable Chemistry Research Center infrastructure at the University of Huelva and previously contributed to the RNM347 Geology and Analytical Chemistry research group, focusing on collaborative environmental geochemistry projects across European and Latin American networks.

Christos XenakisView profile

Professor

• Systems Security
• Network Security
• Mobile and Wireless Communications Security
• +6 more

Prof. Christos Xenakis is a Professor at the Department of Digital Systems of the University of Piraeus, where he has been a faculty member since 2007. He is also the director of the Postgraduate Degree Programme on "Digital Systems Security" and a member of the Systems Security Laboratory at the University of Piraeus. Prof. Xenakis received his B.Sc. in Computer Science in 1993, M.Sc. in Telecommunication and Computer Networks in 1996, and Ph.D. in 2004, all from the Department of Informatics and Telecommunications at the University of Athens. His research interests focus on systems, networks and application security . He has participated in numerous European (ACTS, ESPRIT, IST, AAL, DGHOME, Marie Curie, Horizon2020) and National research programs, coordinating several Horizon2020 projects including CUREX, SealedGRID, INCOGNITO and SECONDO. He has also served as Principal Investigator for Horizon Europe projects like ENTRUST and ERATOSTHENES. Prof. Xenakis has authored over 90 publications in peer-reviewed journals and international conferences, focusing on cybersecurity topics such as blockchain security, smart contracts, self-sovereign identity, adversarial machine learning, smart grid security, cyber insurance, phishing detection, wireless security, and authentication protocols. He has served on editorial boards of several scientific journals including Computers & Security , Computer Communications , and IET Information Security . He is also a member of the steering committee of the European Cyber Security Challenge and leader of the Hellenic Cyber Security Team.

Jean MuhammadView profile

Associate Professor

• Software Engineering
• Computer Science Education
• Cyber Security Education
• +2 more

Jean Muhammad serves as Chair and Associate Professor in the Department of Computer Science at Hampton University's School of Science, specializing in software engineering, cybersecurity education, and leadership management. Her academic journey spans Florida State University (Ph.D., 2005), Illinois Institute of Technology (M.S., 1991), and Chicago State University (B.S., 1984), complemented by industry roles at AT&T Bell Labs and Nokia. Howard University: Business Administration (1968-1970) District of Columbia Teachers College: Business Education (1973-1975) Chicago State University: Computer Science B.S. (1984) Illinois Institute of Technology: Computer Science M.S. (1991) Florida State University: Computer Science M.S. (1999) Florida State University: Computer Science Ph.D. (2005) Her research bridges software engineering pedagogy with practical cybersecurity applications, focusing on risk management frameworks and inclusive technology education. Key initiatives include developing curricula for underrepresented students and analyzing ethical implications of autonomous systems through interdisciplinary collaborations. Recent publications reveal strong trends in applied AI ethics (autonomous vehicles), healthcare technology integration, and cybersecurity awareness across demographics. Her work consistently addresses societal impacts of technology, with 70% of recent publications involving student co-authors in undergraduate research symposia. NPSC Fellowship Award (1996) As primary advisor for 15+ undergraduate/graduate researchers, she has secured $8.4M in grants including NSF S-STEM scholarships ($622K), ONR cybersecurity training ($250K), and Amazon Robotics funding ($500K). Her leadership extends to the HU GETS-IA CyberCorps program and Apple/HBCU C2 partnerships. She co-advises the CyberCorps Scholarship for Service cohort and leads the Chair's Graduate School Club, fostering industry placements through partnerships with Lockheed Martin, IBM, and Deloitte while directing Hampton's cybersecurity research infrastructure.

Tianshi GaoView profile

Researcher

• Machine Learning
• Computer Vision
• Large-scale Systems
• +3 more

Tianshi Gao is a research scientist at Facebook specializing in large-scale machine learning systems. He earned his Ph.D. in Electrical Engineering from Stanford University (2012) under Professor Daphne Koller, who co-founded Coursera. His bachelor's degree with honors comes from Tsinghua University (2007) in Electrical Engineering. Research Interests : Machine learning algorithms for computer vision and big data Hierarchical classification and few-shot learning Object detection with occlusion handling Cooperative systems in transportation and wireless communication Active learning and discriminative modeling Applications of output coding in multiclass boosting Publication Trends : His work from 2007-2012 spans computer vision, machine learning, and transportation systems. Key contributions include structured priors for efficient detection, hierarchical learning frameworks, and novel boosting algorithms. Scientific Awards : Best Poster Runner-up Award at CVPR 2011 Workshop Professional Service : He has served as a reviewer for top-tier journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and Neural Information Processing Systems , plus organizing the Neural Information Processing Systems Workshop on Probabilistic Models for Big Data in 2013.