جست‌وجوی استاد و پژوهشگر

• Wireless Sensor Networks
• Internet of Things
• Mobile Networks
• +۷ مورد دیگر

Sudip Misra is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur, West Bengal, India. With an extensive publication record of 168 publications and over 1,142 citations, he has established himself as a leading researcher in networking and sensor systems. His work spans multiple domains within computer science and engineering, with a particular focus on practical applications of theoretical concepts. Dr. Misra's research interests encompass Wireless Sensor Networks, Internet of Things (IoT), Mobile Networks, Routing Protocols, Network Security, Energy Efficiency, Underwater Sensor Networks, UAV Networks, and Machine Learning applications for networking. His work demonstrates a consistent focus on solving real-world problems through innovative networking solutions, particularly in resource-constrained environments. He has made significant contributions to security protocols, energy management, and connectivity solutions across various network paradigms. Analysis of his recent publications reveals a strong trend toward practical applications of networking technologies, with increasing focus on IoT security, vehicular networks, underwater communication systems, and UAV-enabled networks. His work shows evolution from fundamental networking protocols to more complex, application-specific solutions addressing contemporary challenges in smart transportation, precision agriculture, and secure cloud-based IoT services. Dr. Misra has mentored numerous students and researchers, as evidenced by his extensive co-authorship record. His collaborative work spans multiple institutions across India and internationally, demonstrating strong research leadership and networking capabilities. His research has been supported by various funding mechanisms, though specific grants are not detailed in the available information. His laboratory or research group appears to focus on sensor networks, IoT systems, and related communication technologies, with particular emphasis on security, energy efficiency, and practical deployment scenarios. The research output suggests a well-established team working on cutting-edge networking challenges with real-world applicability.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

• Mathematical Physics
• Quantum Information
• Semiclassical Analysis
• +۲ مورد دیگر

Tommaso Aschieri serves as a Postdoctoral Researcher (academic rank: Researcher) in the Department of Mathematical Sciences at the University of Copenhagen, Denmark. His institutional address is Universitetsparken 5, 2100 København Ø, with contact details including phone +4535329648 and email toas@math.ku.dk. His research spans Mathematical Physics, Quantum Information, and Semiclassical Analysis, with emphasis on symmetry-constrained quantum systems. He investigates SU(2)-equivariant structures in quantum channels using semiclassical frameworks to bridge quantum-classical descriptions, contributing to quantum computing foundations and information theory. His work integrates advanced mathematical techniques with physical applications in quantum error correction and symmetry preservation. His sole publication to date (2024) in Communications in Mathematical Physics analyzes SU(2)-equivariant quantum channels through semiclassical lenses, revealing connections between representation theory and quantum information processing. This research establishes foundational principles for symmetry-invariant quantum operations with implications for quantum hardware design. No scientific awards or honors were documented in the provided materials. There is no available information regarding graduate student mentorship, research grants, or collaborative funding initiatives. Details about laboratory infrastructure, research teams, or institutional affiliations beyond the Department of Mathematical Sciences remain unspecified in the current data.

• Cyber Security
• Wireless Sensor Networks
• Internet-of-Things
• +۵ مورد دیگر

Professor Hadi Larijani serves as a full Professor in Cyber Security and Networks within the School of Computing, Engineering and Built Environment at Glasgow Caledonian University. His research portfolio spans secure wireless communications, Internet-of-Things security, and neural network applications in networking systems, contributing significantly to UN Sustainable Development Goals through technological innovation. His primary research interests include: Wireless Sensor Network Security Intrusion Detection Systems Long Range Wide Area Networks (LoRaWAN) 6G Communication Security EEG Sensing and Medical Device Validation Digital Twin Frameworks for Logistics Professor Larijani's recent publication trends (2024-2025) show strong focus on practical security implementations for emerging technologies, with significant work in EEG validation systems, logistics optimization, antenna design, and VPN protocol analysis. His research bridges theoretical security frameworks with real-world applications across healthcare, agriculture, and transportation sectors. Notable research activities include: Principal Investigator for Market Assessment of Wearable Smart Wireless EEG Headset Co-Investigator for Secure 6G Enabled Realtime Joint Communication and Sensing Testbed Co-Investigator for Ghanaian tomato irrigation improvement project Co-Principal Investigator for British Academy Researcher at Risk - Ukraine Professor Larijani supervises research students with three documented supervised works, and maintains active collaborations across multiple institutions as evidenced by multi-author publications and international projects. His research group appears focused on practical security implementations for next-generation communication systems and IoT applications.

• Embedded Systems
• Internet of Things
• Machine Learning
• +۴ مورد دیگر

Dr. Mairo Leier is a Senior Research Fellow at Tallinn University of Technology's School of Information Technologies, Department of Computer Systems, where he leads the Internet of Things Development Centre. His career spans industry roles at Ericsson Estonia and Elisa Estonia, alongside academic positions since 2010. He holds a PhD in Computer Systems (2016) and MSc (2010) from Tallinn Tech. Research interests center on embedded systems , IoT , and edge AI , with applications in smart infrastructure, autonomous vehicles, and biomedical sensing. Key focus areas include: Machine learning optimization for resource-constrained devices Real-time sensor data processing Hardware-software co-design Industrial IoT solutions His publications emphasize embedded AI efficiency, with recent work exploring model compression, hardware-aware neural networks, and edge deployment for computer vision. Awards and recognitions include: Tallinn Entrepreneurship Award (2017, 2018) 1st Prize in TTÜ Applied Research Competition (2017) Industry collaboration awards with Bosch Sensortec He actively advises students, with 14+ bachelor supervisees in embedded systems/IoT projects. Grants include applied research funding for industrial sensor algorithms and smart wearables. He directs the IoT Development Centre, fostering industry-academia partnerships and embedded systems innovation.

Vincent Weaverمشاهده پروفایل

دانشیار

• Hardware Performance Counters
• Computer Architecture
• High Performance Computing
• +۸ مورد دیگر

Vincent Weaver is an Associate Professor in the Electrical and Computer Engineering Department at the University of Maine's College of Engineering. He leads the VMW Research Group, focusing on low-level systems research including hardware performance counters, computer architecture, and operating systems. Weaver received his BS in Electrical Engineering from the University of Maryland College Park in December 2000, followed by MS (January 2009) and PhD (May 2010) degrees in Electrical and Computer Engineering from Cornell University. He joined the University of Maine faculty in July 2012 as an Assistant Professor and earned tenure and promotion to Associate Professor in September 2018. His research centers on hardware performance analysis, architectural simulation, and systems programming with emphasis on Linux kernel development and embedded systems. Weaver's work bridges theoretical computer architecture with practical systems implementation, often resulting in open-source tools that advance the field. His publications reveal a consistent focus on performance analysis techniques, code optimization, and security through low-level system understanding. Weaver maintains an active teaching schedule including courses in embedded systems, operating systems, and network engineering. He values students with strong programming skills and encourages open source contributions as part of the learning process. His research group provides hands-on experience with cutting-edge processor architectures and performance analysis tools.

• Digital Communications
• Signal Processing
• Specific Emitter Identification
• +۷ مورد دیگر

Donald R. Reising serves as a Guerry and UC Foundation Professor of Electrical Engineering in the College of Engineering and Computer Science at the University of Tennessee at Chattanooga (UTC). He holds appointments in both the Electrical Engineering department and the Computational Science program, with active roles in university governance as Faculty Senate Chair (2022-2025) and member of multiple strategic committees including the PhD Computational Science Committee. B.S. in Electrical Engineering, University of Cincinnati (2006) M.S. in Electrical Engineering, Air Force Institute of Technology (2009) Ph.D. in Electrical Engineering, Air Force Institute of Technology (2012) Dr. Reising's research spans three interconnected domains: wireless security through Specific Emitter Identification (SEI) and RF fingerprinting, smart grid intelligence for real-time disturbance analysis, and radiation effects characterization in microelectronics. His work integrates machine learning with signal processing to solve critical infrastructure protection challenges, particularly in adversarial environments where traditional security measures fail. Recent publications demonstrate increasing focus on deep learning countermeasures against SEI spoofing attacks and edge-computing solutions for power grid resilience. His 15 most recent publications reveal strong emphasis on adversarial machine learning (6 articles), SEI robustness under environmental stressors (5 articles), and smart grid data analytics (3 articles), with growing interdisciplinary work in quantum correlations and transportation safety. This trajectory shows evolution from foundational RF fingerprinting techniques toward comprehensive security frameworks for critical infrastructure. IEEE Chattanooga Chapter's Outstanding Engineer Award (2022) UTC’s Outstanding University Service Award (2018) AFRL Sensors Directorate Dr. Samuel M. Burka Award (2013) Association of Old Crows Research Excellence Award (2009) MASINT Committee Academic Excellence Award (2009) Dr. Reising leads multiple research initiatives including the RF Security Lab and Smart Grid Analytics Group, securing consistent funding from DoD agencies (AFRL, ARL), NSF, and industry partners like EPRI. His service extends to advising graduate students through the Computational Science PhD program and directing undergraduate research in radiation effects characterization. Current projects focus on SEI resilience against deep learning attacks, quantum-enhanced signal processing, and edge-computing solutions for grid cybersecurity.

Bernardi Pranggonoمشاهده پروفایل

دانشیار

• Cybersecurity
• Internet of Things
• Computer Networks
• +۳ مورد دیگر

Dr. Bernardi Pranggono serves as Associate Professor in Cyber Security and Computer Networks at Anglia Ruskin University's School of Computing and Information Science, Cambridge. With over 20 years of combined academic and industry experience, he previously held positions at Sheffield Hallam University, Glasgow Caledonian University, Queen's University Belfast, and the University of Leeds, alongside industry roles at Oracle, PricewaterhouseCoopers, Accenture, and Telstra. Education: PhD in Electronics and Electrical Engineering, University of Leeds, UK Master in Digital Communications, Monash University, Australia BEng in Electronics and Telecommunications Engineering, Waseda University, Japan PGCert in Learning and Teaching in Higher Education, Glasgow Caledonian University, UK Research Focus: His work centers on cybersecurity for resource-constrained IoT ecosystems, lightweight cryptographic protocols, and sustainable ICT solutions. Current investigations span IoMT security frameworks, AI-driven intrusion detection systems, green networking for smart grids, and Industry 4.0 security architectures. His approach integrates theoretical rigor with practical industry applications, particularly in healthcare and critical infrastructure protection. Publication Trends: Recent works (2021-2023) demonstrate concentrated efforts in IoT security mechanisms (35%), healthcare cybersecurity (25%), and energy-efficient computing (20%). Key patterns include development of lightweight cryptographic primitives for LoRaWAN, anomaly detection in medical IoT, and AI-enhanced security for smart grid infrastructure, reflecting strong interdisciplinary collaboration with engineering and medical researchers. Scientific Recognition: Fellow of the Higher Education Academy (FHEA) Senior Member of IEEE (SMIEEE) Research Leadership: As Principal Investigator for British Council's Going Global Partnerships project (2024) on AI data science skills for women's employability and Engineering/Mathematics Research Grant on IoMT Security (2022), he directs internationally funded initiatives. His EPSRC/FP7 project participation (INSTANT, HIPNet, PRECYSE) demonstrates sustained grant acquisition capability across cybersecurity and IoT domains. Professional Engagement: Chairs IEEE's SIG-Industrial IoT Networks, serves as Associate Editor for Frontiers journals, and provides extensive peer review services for IEEE, Elsevier, and Springer publications while maintaining active industry consultation roles.

• Robotics
• Artificial Intelligence
• Electronics
• +۶ مورد دیگر

Dr. Matt Felicetti is a dedicated Lecturer in Engineering at La Trobe University's Bendigo campus, specializing in robotics, electronics, and artificial intelligence. He holds a PhD in randomized artificial intelligence for industrial applications and a bachelor's degree in computer systems engineering with top honors. Matt plays a pivotal role in enhancing the engineering capstone program and teaches subjects including Robotic System Design, Advanced Research, and Advanced Engineering Innovation. His educational background includes: PhD in Randomized Artificial Intelligence for Industrial Applications, La Trobe University (2019-2022) Bachelor of Engineering (Computer Systems) with First Class Honors, La Trobe University (2013-2016) Advanced Diploma of Electronics, Swinburne University of Technology (2011-2012) Matt's research primarily focuses on collaborative industry partnerships through the RAMPS R&D group, implementing innovative engineering solutions using electronics, sensors, embedded systems, robotics, AI, and algorithm design. He has a particular interest in field robotics and machine vision in agriculture. Beyond industry applications, Matt specializes in randomized artificial intelligence algorithms, particularly Stochastic Configuration Networks, with a focus on optimizing these algorithms for industrial environments. His research extends to low-level computing aspects including binary operations, data encoding, and hardware implementation on FPGAs or small embedded devices. His recent publications demonstrate a strong progression from theoretical algorithm development to practical implementations in specific industry contexts, with increasing attention to hardware implementation and agricultural robotics applications. The research shows a clear trajectory toward real-world industrial problem solving. Matt has received numerous scientific awards and recognitions: David Myers Medal Nancy Millis Medal SEMS Teaching Award for Industry Relevance and Student Engagement David Myers Research Scholarship D.M. Myers University Medal Fellow of the Higher Education Academy (FHEA) In terms of academic service, Matt serves as an Associate Editor for the journal Industrial Artificial Intelligence and has conducted peer reviews for Neural Computing and Applications, IEEE Transactions on Industrial Informatics, and Information Sciences. He currently leads the funded research project "Robotic Based Sewer Pipe Condition Assessment" through SmartCrete CRC (2024-2027). His teaching portfolio includes coordinating Robotic System Design, Advanced Research, and Ideas for Innovation courses. Matt is an active member of the RAMPS R&D group, which focuses on crafting innovative solutions for industry-specific problems. His work bridges academic research with practical industry applications, particularly in agricultural robotics and industrial AI implementation, demonstrating strong collaboration with researchers like Ross R, Wang D, and Putland S.