Find Academic Supervisors

• Energy Harvesting
• Embedded Systems
• Internet of Things (IoT)
• +4 more

Sebastian Bader is an Associate Professor at Mid Sweden University 's Department of Computer and Electrical Engineering (DET). His research focuses on energy harvesting for autonomous sensor systems, particularly in Industrial IoT contexts. He leads the Master of Science in Electrical Engineering programs and teaches Embedded Systems Programming , Embedded Machine Learning , and Sensor Networks . Diplom-Ingenieur (Information Technology), University of Applied Sciences, Wilhelmshaven Licentiate of Technology (2011) and Doctor of Technology (2013), Mid Sweden University Research interests include: Self-powered systems utilizing ambient energy sources Tiny Machine Learning (TinyML) on resource-constrained devices Variable Reluctance Energy Harvesting Sustainable IoT architectures Acoustic Emission Analysis for industrial monitoring Scientific roles and honors: Senior Member of IEEE PSMA Energy Harvesting Committee member Associate Editor for Sustainable Computing: Informatics and Systems Topic Editor for MDPI Sensors Journal IEEE Sensors Applications Symposium steering committee His work spans energy-autonomous embedded systems, with applications in rotating machinery, environmental monitoring, and structural health assessment. He supervises PhD students and collaborates internationally with institutions like CSIRO and University of Southampton.

• 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.

• 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.

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.

Riccardo BertaView profile

Associate Professor

• Tiny Machine Learning
• Reinforcement Learning
• Deep Learning on Microcontrollers
• +5 more

Riccardo Berta is an Associate Professor at the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN), University of Genoa. His research focuses on TinyML, deep learning on microcontrollers, and embedded systems for automotive and IoT applications. Current Affiliation: Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN), University of Genoa Academic Role: Associate Professor, Electronics (IINF-01/A) His research interests include: Tiny Machine Learning (TinyML) for resource-constrained devices Deep Reinforcement Learning in automated driving and gaming Edge Computing and Structural Health Monitoring IoT frameworks for data management and serious games Recent work explores the application of neural networks, adversarial training, and synthetic dataset generation in automotive scenarios. He is a member of the Scientific Council of the University Service Center for Simulation and Advanced Training (SIMAV) and the Department Board of DITEN.

Mimi XieView profile

Assistant Professor

• IoT Systems
• Non-Volatile Memory Optimization
• Energy Harvesting
• +3 more

Mimi Xie is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), part of the College of Sciences. She holds a Ph.D. in Electrical and Computer Engineering from the University of Pittsburgh, with prior degrees from Chongqing University. Her research focuses on optimizing energy-efficient, secure, and intelligent IoT systems, particularly in non-volatile memory, energy harvesting, and tiny AI. She leads interdisciplinary efforts addressing challenges in data processing for IoT devices and sustainable computing solutions. Dr. Xie has received the prestigious NSF CAREER Award and co-PI roles in TranSET projects on traffic risk prediction and reinforcement learning. Her work appears in top venues like DAC, ICCAD, DATE, and IEEE journals. She advises multiple Ph.D./Master’s students (Sahidul, Jishnu, Wei, Shyamala) and actively serves on technical program committees for GLSVLSI, IEEE Cloud Summit, and ISVLSI. Key research themes include intermittent systems, energy-efficient AI inference, and secure embedded computing. She has developed frameworks like ELIXIR for self-powered IoT and Autotile for battery-less embedded systems. Over 60+ publications span smart healthcare systems (PPG-based health monitoring), environmental adaptive neural networks, and reinforcement learning for traffic networks. Her academic service includes organizing workshops (e.g., TinyML at DAC 2023), mentoring initiatives like the EDA Job Fair at ICCAD, and outreach efforts such as a virtual summer camp for students with disabilities. She balances technical leadership with educational advocacy, winning the GHC Faculty Award 2020.

Maurício BreternitzView profile

Assistant Professor

• Machine Learning
• High Performance Computing
• Neuromorphic Computing
• +7 more

Maurício Breternitz is an Invited Assistant Professor and Principal Researcher at ISTAR-IUL, ISCTE - University Institute of Lisbon. With a PhD in Computer Engineering from Carnegie-Mellon University and extensive industrial research experience at AMD, Intel, and IBM, he focuses on bridging academia and industry for practical innovation. His academic service includes leadership roles in conferences like IISWC and editorial responsibilities at IEEE Micro. Education: Electronics Engineer (ITA, Brazil), MSc in Computer Science (UNICAMP), PhD in Computer Engineering (Carnegie-Mellon) Research: Machine Learning acceleration, Neuromorphic systems, Cloud workloads optimization, Heterogeneous computing His work spans two decades of patents (56 issued, 55 pending) and projects like the Horizon 2020 DIVIDEND CHIST-ERA and the FCT-funded AIMHealth initiative for AI-based public health solutions. Recent publications emphasize weightless neural networks, edge computing, and federated learning applications. Key contributions include: GPU acceleration for Hadoop MapReduce APU code migration techniques Microcode compression algorithms saving $18M Founding the International Workshop on Architectural Support for Binary Translation He has advised 7 Master's theses and 1 ongoing PhD project at UNICAMP, while serving on editorial boards and program committees for top-tier conferences like ISCA and CGO.

Mohamed Iheb BoussandelView profile

Researcher

• Embedded systems
• TinyML
• Radiation Hardening

Mohamed Iheb Boussandel is a Researcher affiliated with the Laboratory of Electrical and Electronic Engineering of Paris (LEEI-P). His work focuses on critical areas in embedded systems and radiation hardening. Research Interests Embedded systems design and optimization TinyML (Tiny Machine Learning) applications Radiation hardening techniques for electronic components Key Research Themes His recent work explores radiation tolerance of commercial off-the-shelf (COTS) components in nuclear environments, combining materials science with optoelectronic system engineering.