Find Academic Supervisors

Maria PapaioannouView profile

Researcher

• Cybersecurity
• Machine Learning
• Internet of Things
• +3 more

Maria Papaioannou is a Postdoctoral Researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark, specializing in cybersecurity engineering with contributions to UN Sustainable Development Goals through secure technology development. Her research focuses on Machine Learning applications for Internet of Things security, including intrusion detection systems, honeypot optimization, and user-centric authentication. She investigates technical implementations of tiny machine learning for resource-constrained devices while addressing human factors in security adoption, particularly for emerging technologies like passkeys. Recent publications reveal strong thematic convergence across machine learning-driven security solutions for IoT ecosystems, emphasizing both algorithmic innovation and usability considerations. Her review articles systematically analyze research gaps in intrusion detection scalability, passkey adoption barriers, and adaptive deception technologies, highlighting interdisciplinary connections between cybersecurity, human factors, and embedded systems engineering.

• Artificial intelligence
• Neuromorphic computing
• Edge computing
• +7 more

Luciano Prono serves as a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino, where he is also a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic appointment falls under Scientific Disciplinary Sector IINF-01/A - Electronics within Area 0009 - Industrial and Information Engineering. Dr. Prono's research spans multiple cutting-edge domains in AI and signal processing, with particular expertise in neuromorphic computing, edge AI implementation, biomedical signal processing, and IoT systems. His work bridges theoretical AI concepts with practical hardware implementations, focusing on efficient neural network architectures that can operate effectively on resource-constrained devices. His publication record demonstrates a strong trajectory in developing novel neural network paradigms, particularly centered around Multiply-And-Max/Min (MAM) neurons that enable aggressive pruning while maintaining performance. These publications span top-tier venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, and major IEEE conferences like ISCAS and CVPRW. The research shows a clear progression from theoretical foundations of novel neural architectures to practical implementations in robotics, biomedical applications, and edge computing scenarios. Dr. Prono actively supervises PhD research, currently guiding Lorenzo Nikiforos and Elisabetta Spinazzola in the 40th cycle of the Electrical, Electronics and Communications Engineering PhD program. His teaching responsibilities include Cloud Computing and Data Center Design Lab for the Communications Engineering Master's program and Applied Electronics for the Engineering Physics Bachelor's program. His research aligns with multiple ERC sectors including artificial intelligence systems (PE6_7), machine learning applications (PE6_11), communication networks (PE7_8), and signal processing (PE7_7), demonstrating the interdisciplinary nature of his work that bridges computer science, electrical engineering, and biomedical applications.

Yohannes BekeleView profile

Assistant Professor

• Cyber-physical System Security
• Reliable Computing
• Edge Computing
• +5 more

Dr. Yohannes Bekele is an Assistant Professor of Electrical and Computer Engineering at Hampton University's School of Engineering, Architecture and Aviation. His research focuses on cyber-physical system security, reliable distributed computing, and edge computing. He holds a Ph.D. from North Carolina A&T State University (2023), MSc from Addis Ababa University (2018), and BSc from Arba Minch University (2007). Prior to academia, he worked as a senior engineer/project manager at Ethio Telecom and held technical roles in various organizations. His expertise includes hardware fault analysis, secure embedded systems, and transportation IoT security. He currently teaches courses aligned with his research and leads projects on cyber-physical system reliability. Dr. Bekele is affiliated with professional organizations like NSBE and IEEE. Recent publications highlight work on federated learning-based intrusion detection systems, QEMU fault injection tools, and rowhammer attacks on embedded devices. His research bridges theoretical cybersecurity concepts with practical implementation in edge and embedded systems. Dr. Bekele has no listed scientific awards but demonstrates active participation in conferences such as IEEE HOST and AFRICON. His teaching and research emphasize hands-on projects, reflected in courses taught and industry collaborations.

Tommaso PolonelliView profile

Lecturer

• Embedded systems
• IoT
• Wireless Sensor Networks
• +7 more

Dr. Tommaso Polonelli is a Lecturer and Postdoctoral Researcher at ETH Zürich's Department of Information Technology and Electrical Engineering, affiliated with the Center for Project-Based Learning (PBL). He leads teaching initiatives and research in IoT systems, energy-efficient electronics, and autonomous technologies. His research focuses on energy-efficient systems, smart sensing, and ultra-low power computing, with applications in UAVs, wind turbine monitoring, and wearable devices. He also serves as a Scientific Advisor at RTDT Laboratories AG, an ETH spin-off. Polonelli holds a PhD (2020) and Master’s degree (2017) in Electronic Engineering from the University of Bologna. He has co-authored over 60 publications and received awards such as the Spark Award (2021), KITE Award finalist (2024), and the Certificate of Achievement for National Scientific Qualification (2023). He is an active IEEE member and contributes to both academia and industry through innovations in sensor technology and predictive maintenance. His work emphasizes interdisciplinary collaboration, with projects ranging from structural health monitoring to AI-enhanced systems. Recent efforts include Aerosense—a MEMS-based monitoring system for wind turbines—and ElectraSight, a smart eyewear platform with non-invasive eye tracking.

Mart LubbersView profile

Assistant Professor

• Task-Oriented Programming
• Internet of Things (IoT)
• Embedded Domain-Specific Languages
• +4 more

Mart Lubbers is an Assistant Professor at Radboud University , focusing on Computer Science . He transitioned from a PhD candidate (2018–2023) and Researcher roles to his current faculty position, with a research emphasis on Task-Oriented Programming and IoT systems . Academic affiliations: Radboud University (current), Netherlands Defence Academy (2017), Max Planck Institute for Psycholinguistics (2013–2015) Teaching: Compiler Construction , New Devices Lab , and Sustainable IoT workshops in SusTrainable summer schools His research bridges Embedded Systems , Functional Programming , and Green Computing , particularly through the development of the mTask framework for IoT orchestration. Publications highlight innovations in DSL design , low-power computing , and tierless language architectures . Current supervision includes PhD candidates Niek Janssen and Benedikt Rips , alongside BSc advisees. He actively contributes to conferences as PC member/chair (TFP, IFL, CompSys).

• Embedded Systems
• Neural Networks
• Hardware-Aware Neural Architecture Search
• +7 more

Rodolfo Zunino serves as a Full Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa. His teaching portfolio includes core electronics courses across multiple degree programs including Master's in Electronic Engineering and undergraduate programs in Electronics Engineering and Information Technology. His research focuses on the intersection of embedded systems and neural networks, with particular emphasis on hardware-aware optimization for resource-constrained environments. Key areas include traffic classification for cybersecurity, affordance segmentation for robotics, energy-efficient structural health monitoring, and in-sensor computing architectures. His work consistently addresses the Pareto optimization between computational efficiency and accuracy in embedded AI applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward hardware-software co-design for neural networks, with increasing specialization in wearable robotics and structural monitoring systems. The research demonstrates consistent innovation in neural architecture search techniques specifically tailored for microcontrollers and sensor-embedded systems. Professor Zunino maintains active research supervision with multiple advisees (though specific names aren't listed in available materials) and collaborates on projects involving hardware implementation of neural networks for security and monitoring applications. His work receives consistent publication in relevant engineering venues. His laboratory activities center around embedded electronic systems development, with focus areas in neural network acceleration for microcontrollers, sensor data processing, and real-time system implementation for robotics and infrastructure monitoring applications.

Nakul GargView profile

Assistant Professor

• Ambient Intelligence
• Wireless Sensing
• Healthcare
• +4 more

Nakul Garg is an Assistant Professor in the Department of Electrical & Computer Engineering at Rice University. He leads research in ambient intelligence, wireless sensing, and embedded AI, focusing on energy-efficient and scalable solutions for IoT systems, robotics, and healthcare. His work bridges the gap between sensing, computing, and communication in everyday environments. Education includes a PhD in Computer Science from the University of Maryland, College Park, and a B.Tech in Electronics & Communication Engineering from Indraprastha University, Delhi. His research has been recognized with awards such as the Marconi Society Young Scholar Award (2024) and the Best Paper Award at MobiSys 2022. Research interests span spatial sensing, low-power IoT localization, and integrating AI into wearable devices. Key projects include LiTEfoot (cellular-based asset tracking), FreshSense (food quality monitoring), and SPiDR (acoustic navigation for micro-robots). He actively collaborates with industry (e.g., Microsoft Research, NEC) and has advised multiple students in developing innovative systems. Awards and recognitions include the Cyber-Physical Systems Rising Star (2024), NSF I-Corps support, and multiple best paper/demo awards at top conferences. His work has been featured in ACM GetMobile and highlighted by the ACM SIGMOBILE community.

• Machine Learning
• Embedded Systems
• Neural Architecture Search
• +4 more

Paolo Gastaldo serves as Associate Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa's School of Engineering. His academic appointment focuses on electronic engineering (IINF-01/A classification) with teaching responsibilities spanning undergraduate and graduate programs including Electronic Engineering and Information Technologies. His research centers on machine learning for embedded systems, specializing in neural architecture search techniques optimized for resource-constrained environments. Key investigation areas include hardware-aware AI deployment on microcontrollers, energy-efficient traffic classification, and affordance segmentation for wearable robotics. His work bridges theoretical machine learning with practical hardware constraints, targeting applications in IoT security, edge computing, and smart wearable systems. Analysis of his recent publications reveals a strong trend toward Pareto-optimal solutions balancing computational efficiency and accuracy in neural network design. His research demonstrates particular expertise in adapting deep learning models for microcontroller deployment while maintaining cybersecurity functionality. The publications consistently address the challenge of implementing complex AI tasks within strict memory and power limitations of embedded devices. Gastaldo maintains active research supervision with regular publication output, primarily through the University of Genoa's institutional repository (IRIS). His work shows strong interdisciplinary connections between computer science, electrical engineering, and robotics applications.

Theocharis TheocharidesView profile

Associate Professor

• Embedded Systems
• Hardware Acceleration
• Computer Vision
• +3 more

Theocharis (Theo) Theocharides is an Associate Professor and Department Chair at the University of Cyprus's Department of Electrical and Computer Engineering. He serves as Director of Research at the KIOS Research and Innovation Centre of Excellence. Holding a Ph.D. in Computer Science and Engineering from Pennsylvania State University, his research focuses on designing intelligent embedded systems with specialization in domain-specific architectures, reconfigurable hardware, and low-power embedded processors. He leads the Embedded and Application-Specific System-on-Chip Lab, conducting projects in mobile systems, tinyML, embedded computer vision, pattern recognition architectures, and intelligent system monitoring. His pioneering work includes hardware acceleration for real-time object detection and depth estimation in embedded vision systems for robotics and cyber-physical applications. Current investigations focus on hardware-friendly ML algorithms for tinyML, visual information extraction, and distributed embedded vision systems involving drone swarms and robotic networks. Professor Theocharides has authored over 130 publications and serves as Associate Editor for prestigious journals including ACM's Computing Surveys and IEEE Transactions on Computer-Aided Design. As Technical Programme Committee Chair for DATE 2025, he maintains active involvement in the research community.

Alexey TumanovView profile

Assistant Professor

• Machine Learning
• Distributed Systems
• Resource Management
• +4 more

Alexey Tumanov is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology, part of the College of Computing. His research focuses on systems for machine learning, resource management, and scheduling in distributed environments. He holds a PhD from Carnegie Mellon University and conducted postdoctoral research at UC Berkeley under Ion Stoica. Previously, he worked at the University of Toronto and in industry on cloud computing and datacenter systems. Education: PhD in Computer Science, Carnegie Mellon University (2019) Postdoc, UC Berkeley RISELab (2019) MSc in Computer Science, University of Toronto (2012) BSc in Computer Science, University of Toronto (2010) Research Interests: Tumanov's work addresses challenges in distributed machine learning, including efficient resource management for inference and training pipelines, scheduling algorithms for soft-real-time systems, and federated learning. His lab (SAIL) develops systems like SARATHI-SERVE and RocketKV to optimize LLM inference and co-scheduling. He emphasizes practical deployments in healthcare (HOLMES) and cloud environments. Key Contributions: Ray distributed framework for AI (OSDI 2018) ESCHER scheduler for ephemeral cloud resources (SoCC 2022) InferLine ML pipeline orchestration (SoCC 2020) Awards: NSERC CGS-D3 Scholarship (2016) Best Paper at EuroSys 2016 (TetriSched) Google PhD Fellowship Nominee (2018) Lab & Teaching: Leads the Systems for Artificial Intelligence Lab (SAIL) at Georgia Tech. Teaches advanced courses on operating systems (CS3210) and systems for ML (CS8803-SMR). Supervises 15+ graduate students across PhD and MS programs.

Kakarountas AthanasiosView profile

Associate Professor

• Computer Architecture
• Embedded Systems
• VLSI Circuits & Systems Design
• +7 more

Dr. Athanasios Kakarountas is an Associate Professor at the University of Thessaly, specializing in Embedded Computing Systems. He leads the Intelligent Systems Laboratory (iSL) and manages ParICT, Central Greece's parallel computing node. His roles include Head of the Department of Computer Science and Biomedical Informatics, Deputy Chairman of GRNET (2019–2023), and Chair of the Central Greece Research & Innovation Committee (2018–2022). He holds an MBA from Cyprus Open University and has extensive industry experience in tech policy, innovation, and entrepreneurship. Education: BSc & PhD in Electrical & Computer Engineering (University of Patras, 1998/2004), MBA (Cyprus Open University, 2015). Research focuses on Computer Architecture, VLSI Design, TinyML, IoT Security, and Healthcare Technologies . Key projects include seizure detection systems, blockchain in supply chains, neuromorphic circuits, and cybersecurity solutions for smart cities. He has published 150+ articles, 2 e-books, and 7 book chapters, with 1,600+ citations. Awards include IEEE's Outstanding Branch Counselor Award (2013). Leadership: IEEE Greece Section Secretary (4 years), Vice-Chair (6 years), and co-Chair of the IEEE eGOV Special Committee. Editor-in-Chief of IEEE Potentials magazine, and active in organizing IEEE conferences and hackathons. Founder of A2rTEST creative team and mentor for robotics/tech competitions. Grants & Projects: Over 30 EU/Greek-funded projects. Labs/Teams: iSL, ParICT, and partnerships with Patras Science Park. Future work emphasizes TinyML for healthcare, IoT security, and smart city infrastructure.

Daniele PalossiView profile

Researcher

• Autonomous Systems
• Deep Learning
• Nano Drones
• +4 more

Dr. Daniele Palossi is a Researcher at ETH Zürich's Department of Digital Integrated Circuits and Systems. His work focuses on autonomous robotics, particularly nano-drones and embedded AI systems. He specializes in developing energy-efficient, vision-based navigation frameworks and deep learning architectures for resource-constrained platforms. Research interests include autonomous systems, computer vision for robotics, and hardware-software co-design for UAV applications. His recent work advances swarm robotics, sim-to-real transfer learning, and ultra-low-power neural networks for nano-scale drones. Key contributions include the FünfiBër-Drone modular platform and secure SOC designs for UAV navigation. His research bridges theoretical AI advancements with practical implementations in miniature robotic systems.

Dong MaView profile

Assistant Professor

• Mobile Computing
• Wearable Sensing Systems
• Health Monitoring
• +3 more

Roles & Affiliations: Assistant Professor at Singapore Management University's School of Computing and Information Systems (since 2022). Visitor at University of Cambridge (April 2025–present), with planned move as Associate Professor in Jan 2026. Previously postdoc at University of Cambridge (2020–2022) and PhD from University of New South Wales (2019). Research Interests: Focuses on mobile/wearable computing, health monitoring via earables, and tiny machine learning on embedded devices. Develops systems to analyze human behavior (activity/interaction) and physiological states (e.g., vital signs) in real-world scenarios. Recent work includes respiratory monitoring using earbuds and energy-efficient sensing systems. Awards & Recognition: Google South Asia & Southeast Asia Research Award (2024) Mark Weiser Best Paper Award at IEEE PerCom (2024 & 2025) EPFL Engineering PhD Summit (2019): 1 of 11 worldwide Academic Service: Serves on TPC for major conferences (MobiCom, PerCom, CHI) and editorial boards (IMWUT). Organized workshops like BodySys@MobiSys'24 and co-chair roles in conference committees. Labs/Teams: Leads research groups in wearable sensing and edge AI at SMU. Collaborates with institutions like Nokia Bell Labs, CSIRO, and University of Cambridge on projects funded by ERC and industry grants.

• Human Activity Recognition
• Wireless Sensor Networks
• Internet of Things (IoT)
• +1 more

Emanuele Lattanzi is an Associate Professor in the Department of Pure and Applied Sciences (DiSPeA) at the University of Urbino Carlo Bo, specializing in Information Processing Systems (IINF-05/A). His research focuses on human activity recognition, wireless sensor networks, IoT systems, and artificial intelligence applications. He obtained his Laurea degree in 2001 and PhD in 2003, followed by a visiting scholar position at Pennsylvania State University's Department of Computer Science and Engineering. His educational background forms the foundation for his current research in sensor-based AI systems. Lattanzi's research interests center on developing energy-efficient solutions for human activity recognition using wearable sensors, with particular emphasis on optimizing machine learning models for resource-constrained IoT devices. His work bridges theoretical AI advancements with practical implementation challenges in real-world sensor deployments. Analysis of his 2025 publications reveals a strong thematic focus on human activity recognition, with recurring exploration of energy-accuracy tradeoffs, novel neural network architectures (including transformers and Siamese networks), and self-supervised learning techniques specifically adapted for inertial sensor data. His research demonstrates consistent collaboration with multiple co-authors across international conferences. As an educator, Lattanzi teaches core computer science courses including Operating Systems (12 credits) and IoT Programming (6 credits) across multiple Informatics degree programs. His teaching spans from 2021/2022 through the current 2025/2026 academic year, demonstrating sustained contribution to the department's curriculum in both foundational systems programming and emerging IoT technologies.

• Cyber Security
• Intrusion Detection Systems
• Internet of Things (IoT)
• +5 more

Dr. Ayyaz-Ul-Haq Qureshi is a Lecturer in Cyber Security and Networks at Glasgow Caledonian University, United Kingdom. His academic profile demonstrates active engagement in research and teaching within the field of cyber security, with a particular focus on practical security implementations for IoT environments. Dr. Qureshi's research interests span multiple critical areas in modern cyber security: Intrusion Detection Systems for IoT devices and smart homes Machine learning applications in network security Lightweight security implementations for resource-constrained devices LoRaWAN and wireless network security Neural network approaches to security challenges Malware detection through API call analysis His recent publication record shows consistent research output with publications in reputable journals including IEEE Access, Sensors, and Future Internet. Dr. Qureshi's work demonstrates a strong focus on practical security solutions that bridge theoretical machine learning approaches with real-world implementation challenges in IoT environments. His fingerprint analysis reveals particularly strong concentrations in Random Neural Networks (100%), Intrusion Detection Systems (90%), and LoRaWAN applications (85%). Dr. Qureshi has accumulated a respectable h-index of 7 with 117 citations, indicating growing impact in his research areas. His work shows increasing citation metrics, with several papers accumulating 20+ citations, suggesting growing recognition in the academic community. As a lecturer, Dr. Qureshi contributes to cyber security education at Glasgow Caledonian University, likely teaching courses that align with his research expertise in network security and IoT protection. His research collaborations span multiple institutions, suggesting an active research network focused on addressing contemporary security challenges in connected environments.