جستجوی استاد راهنمای دانشگاهی

Martin Weigelمشاهده پروفایل

استاد

• Human-Computer Interaction
• Virtual Reality
• Wearable Computing
• +۳ مورد دیگر

Prof. Dr. Martin Weigel leads the Interactive Novel Technologies Lab at Technische Hochschule Mittelhessen in Gießen, focusing on Human-Computer Interaction (HCI) with specializations in Virtual Reality (VR), wearable computing, and haptic interfaces. His work explores novel interaction techniques, motion guidance systems, and on-body interfaces. Research interests span VR/AR applications, wearable devices, and tactile feedback systems. Key contributions include VRSketchPen (haptic sketching), Tactile Vectors (arm guidance), and DeformWear (wearable deformation input). Recent projects address telepresence robotics in caregiving and drone-human body interaction. Publications highlight advancements in haptic feedback design, user studies on motion guidance, and multimodal interaction systems. Awards include a Best Paper Award for iSkin (2015), a flexible on-body sensor system. Current lab members include researchers like Kevin Linne and student assistants. Patents include EP4147830B1 (2025) for tele-robotic systems and EP4120223B1 (2024) for drone landing mechanisms. Advised PhD student Dr. Hesham Elsayed (2023) and oversees a team engaged in cutting-edge HCI research.

Riccardo Bertaمشاهده پروفایل

دانشیار

• Tiny Machine Learning
• Reinforcement Learning
• Deep Learning on Microcontrollers
• +۵ مورد دیگر

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.

• Semantic Web
• Knowledge Representation and Reasoning
• Artificial Intelligence
• +۵ مورد دیگر

Floriano Scioscia is an Associate Professor at the Polytechnic University of Bari, Italy, specializing in Information Processing Systems (SSD ING-INF/05). His research bridges Semantic Web technologies, Knowledge Representation, and Artificial Intelligence to develop innovative solutions for Internet of Things, Blockchain, and smart city infrastructures. His primary research domains include Semantic Web, Knowledge Representation and Reasoning, Internet of Things, Blockchain, Edge Computing, and Artificial Intelligence. He applies these to solve critical problems in smart mobility (e.g., blockchain-enhanced ridesharing), last-mile logistics optimization, energy infrastructure management, and healthcare decision support. His work consistently focuses on creating lightweight, scalable frameworks like Tiny-ME for resource-constrained environments and semantic-enhanced platforms for real-world system integration. Analysis of his 2023-2025 publications reveals a dominant trend toward edge-centric semantic technologies. Key themes include OWL reasoning on constrained devices (Tiny-ME Wasm), blockchain integration for transparent service platforms (RideMATCHain), and AI-driven optimization for logistics/smart cities. His work demonstrates exceptional cross-domain applicability, with consistent emphasis on explainability, scalability, and practical implementation in transportation, energy, and healthcare infrastructure. The publications show strong synergy between theoretical knowledge representation advances and deployable system architectures.

Mimi Xieمشاهده پروفایل

استادیار

• IoT Systems
• Non-Volatile Memory Optimization
• Energy Harvesting
• +۳ مورد دیگر

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.

Bradford Campbellمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Smart Buildings/Cities
• Embedded Operating Systems
• +۴ مورد دیگر

Bradford Campbell is an Associate Professor in the Computer Science and Electrical & Computer Engineering Departments at the University of Virginia (UVA), affiliated with the Link Lab. His research focuses on sustainable IoT systems, embedded operating systems, and energy-efficient smart environments. He holds a Ph.D. from the University of Michigan (2017). Education: Ph.D., University of Michigan, 2017. Research Interests: Internet of Things (IoT), Smart Buildings/Cities, Embedded Operating Systems, Energy Harvesting, and Sustainable Computing. His group develops low-power, wireless, and secure IoT systems to enhance sustainability in urban environments. Recent Work: Recent publications emphasize privacy-preserving federated learning (e.g., Atlas), occupancy detection using WiFi (FTM-Sense), and energy-efficient IoT gateways (NexusEdge). His work addresses the environmental impact of IoT scalability and secure edge computing. Awards & Honors: NSF CAREER Award (2022), Raven Society Scholarship (2024), and multiple student awards for teaching and service. Teaching: Courses include CPS1 (Technology and Ethics), CS 4501 (Wireless IoT), and CS 6501 (Smart and Healthy Buildings). He co-teaches IoT courses across universities and emphasizes hands-on, systems-oriented learning. Service: Served on TPCs for SenSys, BuildSys, MobiCom, and other conferences. Advises on IoT security, edge computing, and sustainable systems.