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Li YangView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Deep Learning
• +12 more

Li Yang is an Assistant Professor in the Department of Information Technology , part of the Faculty of Business and Information Technology at Ontario Tech University. His research focuses on applying AI and machine learning to cybersecurity, particularly in intrusion detection and anomaly detection for 5G/6G networks and IoT systems. He holds a PhD in Electrical and Computer Engineering from Western University (2022), and has held roles such as Vice Chair of IEEE Computer Society, London Section (2022–2023). Education: PhD in Electrical and Computer Engineering, Western University (2022) Master of Science, University of Guelph (2018) Bachelor of Engineering, Wuhan University of Science and Technology (2016) Research Interests: His work spans cybersecurity, machine learning, deep learning, AutoML, model optimization, network automation, IoT security, intrusion detection, and adversarial machine learning. He develops frameworks for concept drift adaptation and online learning to enhance cybersecurity measures, with a focus on trustworthy AI and defense strategies against adversarial attacks. Awards: Graduate Student Award for Excellence in Research (2022) Graduate Symposium Award for Best Presentation (2022) Mitacs Accelerate Fellowship (2021) OC2 Lab Industrial Research Excellence Award (2020) Ranked in Stanford/Elsevier's Top 2% Scientists (2024) Grants & Involvement: Li Yang has contributed to conferences like IEEE GlobeCom and IEEE CCECE, and authored patents such as 'Convenient primary-secondary barrels' (2009). His work has garnered thousands of citations and GitHub stars, emphasizing practical applications of AI in cybersecurity.

Pietro ManzoniView profile

Professor

• Internet of Things (IoT)
• Mobile Wireless Networks
• LPWAN Technologies
• +7 more

Pietro Manzoni is a Professor of Computer Engineering at the Polytechnic University of Valencia (UPV), Spain. He holds a Master's from the University of Milan (1989) and a Ph.D. from Politecnico di Milano (1995). His research focuses on IoT, edge computing, and wireless networks, with emphasis on TinyML, LPWAN, and edge-cloud systems. He coordinates the Computer Networks Research Group (GRC) and is active in IEEE committees. Education includes a Master's in Computer Science (Università degli Studi di Milano, 1989) and a Ph.D. in Computer Science (Politecnico di Milano, 1995). He interned at Bellcore Labs (USA, 1992–1993) and ICSI (USA, 1994). Research interests span IoT applications, resource-constrained devices, and distributed systems. His work prioritizes empirical validation through prototypes. Teaching includes courses on Networks and Security, Intelligent IoT Systems, and IoT fundamentals in Spanish programs. Publications emphasize IoT protocols, UAV swarms, and TinyML. No scientific awards listed, but over 130 theses advised. Coordinates GRC projects and contributes to editorial boards and conferences.

• Pervasive Sensing
• Tiny Machine Learning (TinyML)
• Edge AI
• +7 more

Prof. Eiman Kanjo is a Professor of Pervasive Sensing & TinyML and Head of the Smart Sensing Lab at Nottingham Trent University. She leads the Smart Nottingham and Smart Campus initiatives, focusing on impactful applications of mobile sensing, wearable tech, and edge AI. Her work bridges academia, industry, and community, emphasizing privacy-preserving AI for mental health, environmental monitoring, and social prescribing. Education: PhD in Computer Science (2005), University of Abertay Dundee. Formerly a Research Associate at the University of Cambridge and researcher at the University of Nottingham’s Mixed Reality Lab. Collaborates with organizations like the tinyML Foundation, Health Data Research UK (HDRUK), and the Alan Turing Institute. Research Interests : Pervasive computing, TinyML, Edge AI, wearable devices, environmental sensing, mental health technologies, and community-driven solutions for urban wellbeing. She pioneered early mobile sensing systems like NoiseSPY (2010) and MobSens (2009). Key Projects : 5G Connected Forest (£10m DCMS grant) : Smart environmental monitoring in Sherwood Forest. TagWithMe : AI-driven proximity-based games for community wellbeing. EPSRC Green+ Network : Tech for healthier environments. Awards & Recognition : Top 50 Women in Engineering (2023). Outstanding Educator of the Year Award (EDGE AI Foundation, 2023). 2021 Vice-Chancellor’s Outstanding Research Team Award (Smart Sensing Lab). Grants & Collaborations : Over £10m in funding from EPSRC, InnovateUK, MOD, and DEFRA. Partners include Nottingham City Council, tinyML Foundation, Health Data Research UK, and industry stakeholders. Labs & Initiatives : Leads the Smart Sensing Lab , developing technologies for Green Social Prescribing at Rufford Country Park and Highbury Mental Health Hospital.

Hui HanView profile

Senior Lecturer

• Edge AI
• TinyML
• Sustainable Supply Chain Management
• +7 more

Dr. Hui Han is a Senior Lecturer at Luleå University of Technology (LTU) in Sweden, working within the Machine Learning Group with a Wallenberg AI, Autonomous Systems and Software Program (WASP) professorship. She is affiliated with the Department of Computer Science, Electrical and Space Engineering, specifically within the Embedded Intelligent Systems LAB. Her office is located in Luleå at room A3424. Dr. Han earned her Ph.D. in Industrial Engineering from the Technical University of Madrid (UPM), Spain. Following her doctoral studies, she completed postdoctoral research at the Data Science department of Fraunhofer IESE in Germany and later served as a postdoctoral fellow in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), Norway. Dr. Han's research focuses on data science with specific expertise in Edge AI and TinyML (Tiny Machine Learning). She leads research efforts in developing sustainable modeling for Sustainable Supply Chain Management, particularly in reverse logistics and circular economy. Her work also encompasses social commerce and Industry 4.0 applications. As the first author, she has published 17 papers including 10 journal papers, 4 conference papers, and 3 magazine papers, accumulating over 1,000 citations according to Google Scholar. Her publications appear in high-impact journals such as Technological Forecasting & Social Change (IF 13.3), Expert Systems with Applications (IF 8.5), and Education and Information Technologies (IF 5.5). Dr. Han's teaching portfolio includes Management Information Systems, Project in Industrial AI, and TinyML. She has created an introductory TinyML course available on her YouTube channel. Her research spans multiple interdisciplinary areas including Industry 4.0, IoT, Cloud Computing, Big Data Analytics, Cyber-Physical Systems, Multicriteria decision support systems (particularly Fuzzy TOPSIS), Complex Relationships Analysis (PLS-SEM), Data Science, TinyML, Edge AI, Random Forest techniques, Information Systems, Social commerce, E-commerce, Sustainable Supply Chain Management, Reverse logistics, Closed-loop Supply Chains, Circle Economy, and Healthcare applications. Her Embedded ML for Health Project demonstrates practical applications of TinyML in healthcare, using Arduino Nano 33 BLE Sense and OV7675 Camera for melanoma detection through image processing and machine learning algorithms. This project showcases her ability to apply theoretical research to real-world healthcare challenges.

• IoT Security
• Digital Forensics
• Web Application Security
• +3 more

George Li is an Associate Professor in the Department of Computer Science at Southeast Missouri State University, based in Dempster Hall 214. He can be contacted at (573) 651-2653 or zli2@semo.edu, with mailing address One University Plaza, MS 5950, Cape Girardeau, MO 63701. His research spans critical cybersecurity domains including: IoT Security and device forensics Digital evidence network construction Web application security pedagogy Reverse engineering for vulnerability analysis Tiny Machine Learning (TinyML) education platforms Face-recognition IoT security applications Dr. Li's 2021-2023 publications reveal a consistent focus on practical security solutions, with 50% dedicated to IoT forensics methodologies and 30% to educational platforms. His work bridges academic research and real-world security challenges, particularly in evidence retrieval from constrained IoT devices and developing hands-on cybersecurity curricula. He actively presents findings at major conferences including IEEE GLOBECOM, EDSIG, and ISCAP, with recent talks covering forensic evidence networks, IoT device analysis, and security lab exercises.

Qasim AhmedView profile

Professor

• Wireless Communications
• Millimeter Wave Communications
• Ultra-Wideband Systems
• +7 more

Qasim Ahmed is a Professor in the Department of Engineering at the University of Huddersfield, UK. He holds affiliations with the School of Computing and Engineering and the Centre for Efficiency and Performance Engineering. His academic journey includes a BEng from NUST (Pakistan), MSc from USC (USA), and PhD from the University of Southampton (UK). He has held roles at NUCES (Pakistan), KAUST (Saudi Arabia), and the University of Kent before joining Huddersfield in 2017. His research focuses on wireless communications, millimeter wave systems, ultra-wideband (UWB) technologies, and 5G/6G networks. Key interests include antenna design, cooperative communication, and blockchain applications in telecommunications. He leads EU-funded projects like MOTOR5G (€3.9M) and RECOMBINE (€0.5M), and has secured grants for innovations like the 'Smart Mattress' (£175k) and EVOLVE (€0.7M). His work contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Recent publications emphasize indoor positioning systems, deep learning for spectrum sensing, and hybrid blockchain solutions for network resource sharing. Research grants and projects include EU H2020 funding for beyond 5G ecosystems and smart healthcare systems. He actively collaborates on antenna measurements, vehicular communications, and tiny machine learning (TinyML) applications. His team’s work spans theoretical advancements and practical implementations in next-generation wireless infrastructure.

• Machine Learning
• Data Systems
• Resource-Aware Computing
• +5 more

Pinar Tözün is an Associate Professor and Head of the Data, Systems, and Robotics department at the IT University of Copenhagen. She leads multiple research groups, including the Resource-Aware Data Systems and Data-intensive Systems and Applications. Her roles also include academic responsibility for DASYA and involvement in the Center for Climate IT. Her research focuses on resource-aware computing, machine learning systems, heterogeneous hardware optimization, and sustainable data management. Key areas include workload characterization, GPU utilization, and benchmarking frameworks for edge devices and cloud systems. Dr. Tözün has led significant projects such as MOTH (Machine Learning on Tiny Hardware), RAD+ (Resource-Aware Data Science), and DAPHNE (Integrated Data Analysis Pipelines), funded by institutions like the Novo Nordisk Foundation and the European Commission. She has published extensively in top venues, including Proc. ACM Manag. Data and Dagstuhl Reports, with a focus on efficient machine learning pipelines and hardware-aware systems. Her work has been highlighted in media discussions on sustainable hardware and software practices. Tözün also actively participates in academic governance, serving on hiring committees and shaping future faculty recruitment in data-intensive systems.

Morten Opprud JakobsenView profile

Associate Professor

• Embedded Systems
• Tiny Machine Learning
• Condition Monitoring
• +2 more

Morten Opprud Jakobsen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University. His research focuses on condition monitoring of rotating machinery , with special emphasis on lubrication condition detection using acoustic signals and implementation of Embedded Machine Learning for predictive maintenance. Primary Research Areas: Embedded Systems, Tiny Machine Learning, Machine Diagnostics Key Techniques: MEMS sensors, Acoustic Emission Analysis, LoRa/Bluetooth Low Energy communication Recent work demonstrates technical expertise in developing low-cost condition monitoring systems that combine vibration and acoustic analysis for early failure detection in mechanical components. Publications in Mechanical Systems and Signal Processing and Tribology International highlight interdisciplinary applications of machine learning in mechanical engineering. Contact: morten@ece.au.dk | +45 21 36 70 53

Vijay Janapa ReddiView profile

Professor

• Machine Learning
• Robotics
• Computer Engineering
• +3 more

Vijay Janapa Reddi is the Gordon McKay Professor of Electrical Engineering at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). His research focuses on machine learning systems, embedded AI, and autonomous systems. He leads initiatives in TinyML (Tiny Machine Learning) and edge computing, emphasizing energy-efficient hardware-software co-design. His work bridges computer architecture, robotics, and data-centric AI, with applications in healthcare, robotics, and sustainable technology. Research interests include TinyML education, fault-tolerant autonomous systems, and benchmarking frameworks like MLPerf. He co-founded the TinyML open education initiative and collaborates with industry partners such as Google and Edge Impulse. His lab develops hardware accelerators for edge AI, focusing on low-power embedded devices and safety-critical systems. Key projects include the Edge SEAS Lab and TinyML SEAS Lab, advancing real-time robotics computing and sensor-driven AI. Publications emphasize system-level challenges in AI deployment, from neuromorphic computing to federated learning in healthcare. His work addresses societal impacts, including AI for developing regions and ethical considerations in sensor technologies.

• Electronic Design Automation (EDA)
• RISC-V Architecture
• TinyML (Tiny Machine Learning)
• +3 more

Philipp van Kempen is a researcher at the Chair of Design Automation (Prof. Schlichtmann) at the Technical University of Munich. His work focuses on electronic design automation, specifically targeting RISC-V architecture, TinyML optimization, and LLVM compiler infrastructure for custom hardware accelerators. Key Research Areas: RISC-V ISA extensions, autovectorization, neural network accelerators, and compiler toolchain automation. Collaborations: Works with Daniel Mueller-Gritschneder, Ulf Schlichtmann, and Jefferson Parker Jones. His recent publications highlight contributions to benchmarking TinyML CNN kernels on RISC-V vector hardware, developing semi-automated LLVM support for ISA extensions, and optimizing TinyML inference through frameworks like MLonMCU and muRISCV-NN.

• 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.

Brian PlancherView profile

Assistant Professor

• Robotics
• Embedded Systems
• Machine Learning
• +3 more

Brian Plancher is an Assistant Professor of Computer Science at Barnard College, Columbia University, and will transition to Dartmouth College in Fall 2025. He leads the Accessible and Accelerated Robotics Lab (A²R Lab), focusing on optimizing robotic systems through algorithm-hardware-software co-design. His research spans Robotics, Embedded Systems, and Machine Learning, with an emphasis on accessibility in STEM education and global TinyML initiatives. He co-chairs the TinyMLedu Open Education Initiative and serves on the IEEE RAS Technical Committee. Education: PhD (2022), MEng (2018), and BA (2013) in Computer Science from Harvard University. Research Interests: Developing open-source algorithms for dynamic motion planning, GPU-accelerated optimization, and education initiatives to lower barriers in robotics and embedded ML. Recent efforts include TinyMPC (microcontroller optimization) and MPCGPU (real-time NMPC on GPUs). Grants & Awards: NSF CSSI Grant (2024), Toyota Research Institute Grant (2025), and best paper/poster awards at IEEE ICRA and robotics conferences. His work emphasizes sustainability, ethics, and global accessibility in AI and robotics. Lab & Teaching: Teaches Parallel Optimization for Robotics at Barnard and organizes the Optimization for Robotics Summer School. Collaborates internationally on TinyML4D to scale education in developing countries.

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.

Jonatan LangletView profile

Research Fellow

• Computer Networking
• Network Telemetry
• Programmable Hardware
• +4 more

Jonatan Langlet is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Division of Software and Computer Systems (SCS) and the Network Systems Lab (NSLab). Holding a Digital Futures fellowship, his work bridges programmable hardware, network telemetry, and machine learning, with a focus on high-speed monitoring and security technologies embedded in network switches and cards. Education: PhD in Computer Science (2024) from Queen Mary University of London, supervised by Prof. Gianni Antichi Teaching: Distributed Systems (QMUL 2021-2022), Data Structures and Algorithms (Karlstad 2019) Research spans three main areas: In-Network Intelligence : Developing machine learning inference capabilities within programmable switches (P4), optimizing neural networks for hardware constraints Telemetry Systems : Creating RDMA-based telemetry pipelines for zero-CPU, line-rate data collection (Direct Telemetry Access, ACM SIGCOMM 2023) 5G Network Innovation : Designing programmable pipelines for 5G user plane functions (IEEE TMC 2022) His publications in top venues like ACM SIGCOMM, SIGMETRICS, and HotNets demonstrate technical depth in programmable hardware, while community roles (TPC member, reviewer) highlight academic engagement. The Digital Futures fellowship supports his ongoing work on large-scale monitoring technologies.

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).