جست‌وجوی استاد و پژوهشگر

• In-Memory Computing
• AI Hardware Acceleration
• Edge Computing
• +۵ مورد دیگر

Shaahin Angizi is an Assistant Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on next-generation computing systems, emphasizing energy-efficient architectures, in-memory computing, and secure AI hardware. He leads multiple National Science Foundation (NSF)-funded projects, including initiatives on robotic vision systems, in-cache AI acceleration, and edge computing. His work spans hardware security, emerging memory technologies, and interdisciplinary applications of AI in healthcare and IoT. Notable projects include developing secure imaging sensors (SenGuard/iSEW), adversarial attack defenses for neural networks, and AI-driven accelerator design automation using large language models (LLM-IMC/TPU-Gen). Angizi has authored over 136 publications and secured 4 active NSF grants between 2022-2027. His research bridges device-level innovations (e.g., magnetoelectric FETs) with system-level designs, aiming to reduce computational carbon footprint while enhancing reliability. Key Projects: Infrared Retinomorphic Vision (2024-2027) Toward Opportunistic In-Cache AI Acceleration (2023-2025) Integrated Sensing & Normally-off Computing (2022-2026) Research Themes: Processing-in-Memory Architectures Edge Intelligence Hardware Hardware Security & Trust Non-Volatile Memory Systems Angizi’s contributions include pioneering sensor-embedded watermarking (iSEW), adversarial attack analysis on LLMs, and energy-efficient in-sensor processing frameworks (PISA/HyperSense). His work frequently explores synergies between photonics, analog computing, and AI to create sustainable high-performance systems.

• Artificial Intelligence
• Cyber-physical systems
• Machine Learning
• +۳ مورد دیگر

Berend Jan van der Zwaag is an Assistant Professor at Universiteit Twente, affiliated with the Sensors and Smart Systems Research Centre. His work contributes to UN Sustainable Development Goals related to sustainable industry and innovation. He focuses on Artificial Intelligence, Cyber-physical systems, and Machine Learning applications in sensor networks and inertial measurement systems. Research interests include equine gait analysis using IMU sensors, machine health monitoring through ontology-based frameworks, and wireless sensor network design. Notable projects include the EquiMoves system for objective horse gait examination and an ontology framework for smart product-service systems in industrial IoT. Recent work emphasizes terrain classification for equine systems, gait event estimation, and speed estimation using body-mounted sensors. His publications span conferences like IEEE DCOSS-IoT and journals like Sensors and IEEE Internet of Things Journal. Accepting PhD Students Key collaborator: Prof. Paul Havinga (Twente) Research partnerships in Netherlands and international institutions

Lei Yangمشاهده پروفایل

استادیار

• Hardware/Software Co-Exploration for Neural Network Architectures
• Embedded Systems
• Automated Machine Learning
• +۴ مورد دیگر

Dr. Lei Yang is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University, focusing on Hardware/Software Co-Exploration for Neural Network Architectures, Embedded Systems, and High-Performance Computing. Previously, she served as an Assistant Professor at the University of New Mexico and held post-doctoral and research scholar positions at Notre Dame, UC Irvine, and the University of Pittsburgh. Research Interests: System-Level Optimization for Applied Machine Learning Automated Machine Learning (AutoML) Federated Learning for Medical AI and Drug Discovery Quantum Neural Networks and Dark Silicon Many-Core Systems Thermal-Aware and Energy-Efficient Computing Award Trends: 50+ publications in premier venues like DAC, ICCAD, and IEEE Transactions, with multiple Best Paper Awards and Nominations. Notable recognitions include the 2021 IEEE TCAD Donald O. Pederson Best Paper Award and the 2017 ICCD Best Paper Award. Honors & Service: General Chair, SIGDA Student Research Forum at ASP-DAC 2023 Organizer, E2ML Workshop at GLVLSI 2021 Registration Chair, ICCD 2021 TPC Member, DAC 2022, ICCAD 2021, and IEEE SOCC 2020

David Doermannمشاهده پروفایل

استاد

• Document Image Understanding
• Video Analysis
• Pattern Recognition
• +۹ مورد دیگر

David Doermann is a Professor and Department Chair of the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on document image understanding, video analysis, pattern recognition, computer vision, media forensics, and artificial intelligence. Education: PhD in Computer Science, University of Maryland, College Park (1993) MS in Computer Science, University of Maryland, College Park (1989) BS in Computer Science/Mathematics, Bloomsburg University of PA (1986) Research Interests: Dr. Doermann's work spans computer vision, document analysis, and AI, with recent emphasis on histopathology image synthesis, motion prediction, and personalized vision-language models. His publications highlight advancements in neural architecture search, federated learning, and binary neural networks. Scientific Awards: Award for Excellence from the Under Secretary of Defense (2017) DARPA Results Matter Award (2016) IEEE Fellow (2014) IAPR Fellow (2014) Royal Academy of Engineering Fellowship (2011-2013) Honorary Doctorate from University of Oulu (2002) Future Work: He is exploring the integration of generative AI in software development and advancing techniques for human-centric video-depth generation.

Chahat Deep Singhمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Chahat Deep Singh is an Assistant Professor in the Paul M. Rady Department of Mechanical Engineering at the University of Colorado. His research focuses on minimal perception frameworks to enable autonomy in small robots, combining robotics, AI, and computational imaging. He leads the PRAISe Lab, pioneering bio-inspired perception algorithms and deploying them on robots for real-world applications. Education: PhD in Computer Science and Robotics, University of Maryland Master's in Computer Science and Robotics, University of Maryland Research Interests: Autonomous drones, neuromorphic perception, and computational imaging. His work emphasizes onboard autonomy for resource-constrained systems, with a focus on active perception and bio-inspired algorithms. Recent breakthroughs include the Minimal Perception framework, enabling credit-card-sized robots to navigate autonomously. Awards: Ann G. Wylie Fellowship (2022-2023) Future Faculty Fellowship (2022-2023) University of Maryland Dean Fellowship (2018-2020) Advising & Grants: Postdoctoral research funded by the Army Research Laboratory and Maryland Robotics Center (2023-2024). Currently advises students in robotics and AI at the University of Colorado. Labs & Teams: Director of the PRAISe Lab, collaborating with interdisciplinary teams to advance robotics solutions for environmental and societal challenges.

Gergely Vakulyaمشاهده پروفایل

دانشیار

• Cybersecurity
• Agricultural Technology
• Sensor Networks
• +۳ مورد دیگر

Dr. Gergely Vakulya is an Associate Professor and Research Fellow at Óbuda University. He specializes in interdisciplinary research spanning cybersecurity, agricultural technology, sensor networks, and image processing. His work integrates hardware design, algorithm development, and real-world applications. Dr. Vakulya’s recent focus includes developing rumen bolus sensors for dairy cattle health monitoring, gamification of cybersecurity training, and innovative methods in camera exposure time measurement. He is affiliated with Óbuda University’s Budai Road and Pirosalma Street campuses, with an office at building F room 316. His research frequently addresses challenges in data collection, sensor fusion, and embedded systems. Research Interests: Dr. Vakulya’s expertise includes cybersecurity frameworks (e.g., CTF challenges), agricultural IoT systems (e.g., rumen bolus sensors for livestock monitoring), and image processing techniques (e.g., genetic algorithms for shape approximation). His work on visible light communication (VLC) and wireless sensor networks highlights his contributions to communication protocols and energy-efficient systems. Recent trends in his publications emphasize cross-disciplinary approaches, such as applying AI methods to agricultural sensor data and optimizing sensor networks for real-time applications. Advising & Grants: No specific advising relationships or grants are listed in available materials. His research infrastructure is likely supported through institutional and collaborative projects. Labs/Teams: While not explicitly stated, his research likely involves collaborations within Óbuda University’s engineering and computer science departments. His work on rumen sensors and VLC systems suggests potential affiliations with robotics, biomedical engineering, or smart agriculture research groups.

Amila Akagicمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۱۵ مورد دیگر

Prof. Amila Akagic is an Associate Professor at the Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo. Her expertise spans Computer Architecture , Artificial Intelligence , Computer Vision , and High-Performance Computing . She holds a Ph.D. from Keio University (Amano Lab) and has conducted research at UC Riverside and the University of Ljubljana under prestigious scholarships including the Fulbright and MEXT awards. Her work focuses on FPGA-accelerated algorithms, reconfigurable architectures, and deep learning applications in medical imaging and environmental monitoring. Education: Bachelor's/Master's (Electrical Engineering) – University of Sarajevo (2006/2009) Ph.D. (Computer Science) – Keio University, Japan (2013) Research Interests: Combines image segmentation , embedded systems , and energy-efficient computing . Recent work includes semantic tumor segmentation (MRI), blockchain-based UXO tracking, and FPGA-based CRC acceleration. She explores interdisciplinary topics like AI-driven plant phenotyping and wildfire detection using computer vision. Recent Trends in Publications: Over 40% of her 2023-2025 articles address medical imaging and fire detection , with growing emphasis on explainable AI and humanitarian technology . Her work frequently uses frameworks like OpenVINO and TensorFlow to optimize computational efficiency. Awards: Fulbright Visiting Student Award (2007/2008) MEXT Scholarship (Japan, 2010) Grants & Labs: Active in the HiPEAC project (EU Horizon Europe), focusing on edge computing and smart grid resilience. Collaborates with the Embedded Systems and Architectures Lab (UC Riverside) and Amano Lab (Keio University). Labs/Teams: Leads research groups in FPGA-accelerated architectures and AI-driven environmental sensing. Engages in humanitarian demining initiatives via data observatories and blockchain systems.

• Continual Learning
• Few-Shot Learning
• Domain Adaptation
• +۷ مورد دیگر

Mahardhika Pratama is a Professor in the Department of Computer Science at Universitas Indonesia's Faculty of Computer Science, where he leads research in machine learning and artificial intelligence. His work bridges theoretical advancements with practical applications across various domains including time series analysis, autonomous systems, and resource-constrained environments. Pratama's research focuses on overcoming fundamental challenges in machine learning systems, particularly in the areas of continual learning and few-shot learning. His work addresses the critical problem of catastrophic forgetting in neural networks while developing efficient learning systems that can adapt to new tasks with minimal data. His publications reveal a strong emphasis on developing practical solutions for real-world applications where data is scarce or constantly evolving. Analysis of his recent publications (2023-2025) shows a clear research trajectory toward more sophisticated approaches for continual learning under data scarcity, with increasing focus on transformer architectures, multi-agent reinforcement learning, and cross-domain adaptation techniques. His work demonstrates a consistent pattern of addressing both theoretical foundations and practical implementations of adaptive learning systems. His research has been supported through numerous collaborative projects with international institutions including UNSW Canberra, Swinburne University of Technology, and University of Alberta, reflecting the global impact of his work in the machine learning community.

Raúl Ordóñezمشاهده پروفایل

استاد

• Nonlinear Control
• Adaptive Control
• Extremum-Seeking Control
• +۴ مورد دیگر

Raúl Ordóñez is a full professor and Director of Graduate Programs in the Department of Electrical and Computer Engineering at the University of Dayton’s School of Engineering. He holds a Ph.D. and M.S. in Electrical Engineering from The Ohio State University (2001 and 1996) and a B.E. from Monterrey Institute of Technology. His research focuses on nonlinear control, adaptive systems, extremum-seeking control, and applications in aerospace, robotics, and power systems. Professional highlights include serving as Associate Editor for Automatica since 2006, co-authoring textbooks on adaptive control and extremum-seeking techniques, and holding editorial roles for IEEE conferences. Awards include the Boeing Welliver Faculty Fellowship (2008) and AFRL Summer Faculty Fellowship (2014). He has conducted research collaborations at institutions like Université de Picardie Jules Verne (France) and TU Wien (Austria). Key research areas include control of hypersonic vehicles, industrial robots, scramjets, and power generators. His work integrates advanced control methodologies with practical systems, emphasizing real-time optimization and robustness. Courses taught span control systems, nonlinear dynamics, and adaptive control. Education: Ph.D./M.S. (Ohio State), B.E. (Monterrey Tech) Professional Activities: IEEE Control Systems Society, CCCS Collaborative Center Recent Awards: AFRL (2014), Boeing (2008) Labs/Teams: Collaborates with robotics and aerospace teams at UD and international institutions

• Soft Robotics
• Mechanical Metamaterials
• Nonlinear Dynamics
• +۳ مورد دیگر

Bas Overvelde is the Group Leader of the Soft Robotic Matter Group at AMOLF in Amsterdam and an Associate Professor (part-time) at Eindhoven University of Technology (TU/e) . His research focuses on embodied intelligence in soft robotic systems, combining mechanics, metamaterials, and nonlinear dynamics to create autonomous, adaptive machines. He leads a multidisciplinary team working on applications like soft robotic hearts, grippers, and smart architectural facades. Education: BSc and MSc in Mechanical Engineering (cum laude) from TU Delft (2004–2012) PhD in Applied Mathematics from Harvard University (2016), under Prof. Katia Bertoldi Research Interests: Soft robotics and bio-inspired design Mechanical metamaterials and nonlinear dynamics Autonomous systems and environmental feedback Applications in biomedical engineering and sustainable architecture Key Achievements: Developed programmable fluidic robots using pneumatic coding blocks Designed a soft robotic artificial heart prototype Explored synchronization in modular soft robotic limbs Labs/Teams: Leading the Soft Robotic Matter Group at AMOLF Collaborates with TU/e’s Institute for Complex Molecular Systems

Pedro Henriquesمشاهده پروفایل

استاد

• Programming Languages
• Compiler Design
• Software Analysis
• +۲ مورد دیگر

Pedro Henriques is Professor of Computer Science at University of Minho, where he coordinates the Language Processing group at Algoritmi Research Center. With a PhD in Formal Languages and Attribute Grammars, he teaches compiler design and programming language engineering. His research develops: Formal methods for software analysis Educational tools for programming pedagogy Ontology-driven computational thinking frameworks AI applications in agriculture and healthcare Recent work includes neuroeducation-informed frameworks (OntoCnE) and Programming Cocktails methodology for optimizing cognitive load in code instruction. He has supervised 14 PhD dissertations and authored the foundational text "XML & XSL: da teoria a prática". Current EU projects explore VR cognitive rehabilitation and olive cultivar identification using deep learning.

• Agricultural Engineering
• Precision Agriculture
• Remote Sensing
• +۷ مورد دیگر

Troy Jensen is an Associate Professor at the School of Agriculture and Environmental Science , University of Southern Queensland. He is affiliated with the Centre for Agricultural Engineering and held a Fulbright Scholar position funded by the Kinghorn Foundation in 2023, alongside a Visiting Professorship at the University of Florida during the same year. Education: He holds a BEng (1983), MEng (1991), and PhD (2009) from the University of Southern Queensland. Research Interests: His expertise spans agricultural machinery, precision agriculture, remote sensing, controlled traffic farming, UAVs, and technology innovation . Specific focuses include sugarcane yield monitoring, soil tillage mechanics, and energy-efficient farming practices. He has pioneered work in UAV-based crop monitoring, discrete element modeling for soil analysis, and sustainable machinery design. Grants & Projects: Notable projects include: Australian Government’s Site-Specific Tools for Herbicide Management (2023) Cotton Seed Distributors’ Cotton Picker Extensions (2023) New Columbo Mobility Plan for Thailand-Australia engineering collaboration (2022) Supervision: He currently supervises doctoral students on topics like sugarcane replanting automation and pest monitoring technologies. Completed projects include studies on nutrient management in Nicaraguan sugarcane and soil compaction from cotton harvesting. Labs/Teams: His work is embedded within interdisciplinary teams at the Centre for Agricultural Engineering, focusing on mechanization, precision agriculture, and sustainable farming systems.

Christian Vogtمشاهده پروفایل

مدرس

• IoT and Embedded Systems
• Wireless Sensor Networks
• Medical Electronics
• +۳ مورد دیگر

Dr. Christian Vogt Dr. Christian Vogt is a Lecturer at the Department of Information Technology and Electrical Engineering within the Center for Project-Based Learning at ETH Zürich. His research focuses on IoT systems, embedded electronics, and wearable technology with applications in agriculture, healthcare, and urban computing. He specializes in energy-efficient sensor networks, flexible electronics, and real-time data transmission systems. Research Interests Vogt's work bridges hardware innovation and software optimization, emphasizing practical implementations through project-based learning. Key areas include: IoT and Wireless Sensor Networks Low-power embedded machine learning Wearable biomedical devices Flexible/stretchable electronics Ultra-wideband and radar-based sensing Publications Recent work highlights energy-efficient livestock monitoring systems using LoRa, battery-free sensor nodes powered by human body energy, and real-time gesture recognition with novel sensors. His research often involves interdisciplinary collaborations between computer science, electrical engineering, and biomedical applications. Affiliations ETH Zürich - Center for Project-Based Learning Department of Information Technology and Electrical Engineering Contact: christian.vogt@pbl.ee.ethz.ch

Shuminoski Tomislavمشاهده پروفایل

پژوهشگر

• Telecommunications
• Wireless Networks
• Cybersecurity
• +۶ مورد دیگر

Shuminoski Tomislav is a researcher at the Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, North Macedonia. He holds a PhD in Electrical Engineering and Information Technologies (2016) with a dissertation on 5G QoS mechanisms and vertical multi-homing. His academic background includes a Master’s (2010) and Bachelor’s (2008) in Telecommunication, both with perfect grades. He specializes in telecommunications, cybersecurity, and next-generation mobile networks, focusing on QoS optimization, 5G infrastructure, and secure communication frameworks. Education: PhD, 2016: Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies (Dissertation: “QoS Mechanisms and Vertical multi-homing for 5G Mobile and Wireless Networks”) M.Sc., 2010: Telecommunication, Module: Communication and Information Technologies B.Sc., 2008: Telecommunication Research Interests: His work spans 5G networks, mobile edge computing, cybersecurity, IoT security, QoS provisioning, and blockchain applications in cloud computing. He emphasizes practical implementations of theoretical models in real-world scenarios, such as emergency response systems and ultra-reliable low-latency communication. Publications: Over 30 peer-reviewed articles since 2009, including innovative papers on 5G frameworks, secure communication platforms, and steganographic techniques. Recent work focuses on AI-driven resource allocation and quantum-resistant cryptography for IoT. Awards/Grants: No specific awards mentioned, but active in collaborative industry projects on network security and cloud infrastructure. Labs/Teams: Associated with the Telecommunications Institute at FEIT, contributing to research on wireless networks and critical infrastructure systems.

António Casimiroمشاهده پروفایل

دانشیار

• Dependable adaptive systems
• Middleware for distributed embedded real-time systems
• Cyber-Physical Systems
• +۴ مورد دیگر

António Casimiro is an Associate Professor at the Department of Informatics of the Faculty of Sciences, University of Lisbon. He holds a PhD in Computer Science (2003) from the same institution. His research focuses on dependable adaptive systems, middleware for distributed real-time applications, and cyber-physical systems. He leads the Cyber-Physical Systems research line at the LASIGE research unit and coordinates EU projects like ADMORPH and VEDLIoT, as well as the national AQUAMON project for water monitoring. Education: PhD in Computer Science, University of Lisbon (2003) Research Interests: Middleware for embedded real-time systems Fault-tolerant distributed systems IoT and environmental monitoring Cybersecurity in autonomous systems Key Projects: KARYON (FP7): Safe cooperative automotive/avionics systems VEDLIoT (H2020): Accelerated AIoT systems AQUAMON: Wireless sensor networks for water monitoring Awards: None explicitly listed, but active in editorial roles like Editor-in-Chief of the Ada User Journal. Teaching & Leadership: Coordinates the Master in Information Security program Former Vice-Director of LASIGE and member of Faculty Scientific Council Labs/Teams: LASIGE research unit, Navigators group, involvement in GULLIVER testbed for vehicular systems.