جست‌وجوی استاد و پژوهشگر

• Internet of Things
• Neural Networks
• Smart Manufacturing
• +۳ مورد دیگر

Kyle Madden serves as a Research Fellow at Ulster University within the School of Computing, Engineering and Intelligent Systems at the Derry~Londonderry campus. His work bridges theoretical computer science with industrial applications, focusing on intelligent systems development for manufacturing and IoT environments. Education: PhD in Computer Science (awarded June 2025) from Ulster University with thesis: “Spiking neural networks for detecting denial-of-service attacks in networks-on-chip” supervised by Dr. Jim Harkin and Dr. Liam Mc Daid. Dr. Madden’s research spans cutting-edge domains in neuromorphic engineering and industrial IoT. He pioneers frameworks for translating spiking neural networks to FPGA hardware while optimizing power efficiency, develops cloud-based IoT systems for industrial decision-making, and applies computational intelligence to 3D printing quality control. His work integrates user experience considerations in smart manufacturing interfaces and advances event-based sensor data processing through neuromorphic event alarm systems. Recent publications (2024-2025) reveal strong thematic focus on deploying AI solutions in real-world industrial contexts. Key trends include hardware acceleration of neural networks for manufacturing, cloud-IoT integration using AWS infrastructure, and human-centered design for industrial IoT applications. His research consistently addresses practical implementation challenges in sensor data processing and neural network deployment. Scientific Awards: No awards documented in available information Dr. Madden has no listed advisees in the provided materials. His research outputs indicate collaborative project involvement, particularly in EU-funded industrial IoT initiatives and neuromorphic computing consortia, though specific grant details aren’t disclosed. Current work appears integrated within Ulster University’s Computer Science research group focusing on intelligent systems. He actively contributes to research teams developing neuromorphic event-based processing systems and industrial IoT frameworks. Recent work on FrostRune demonstrates leadership in creating asymmetric translational pipelines from high-level neural models to FPGA deployment, positioning him within hardware-aware AI research communities.

• Computational Neuroscience
• Neural Networks
• Computer Hardware
• +۳ مورد دیگر

Professor Liam McDaid serves as Interim Research Director at Ulster University's School of Computing, Engineering and Intelligent Systems (Magee Campus, Derry~Londonderry). His leadership spans computational neuroscience and neuromorphic engineering projects addressing global challenges through the UN Sustainable Development Goals framework. Research focuses on computational neuroscience and neural engineering , with expertise in spiking neural networks, astrocyte modeling, hardware implementations (FPGAs/Networks-on-Chip), and biomedical applications. His work integrates computer hardware with biological systems for fault-tolerant computing and medical diagnostics. Key publications reveal trends in neuromorphic hardware for real-time fault detection (RISC-V systems), medical AI for cardiac/adrenal diagnostics, and event-driven sensing . Research bridges computer engineering with neuroscience to solve healthcare infrastructure challenges. Prize 'Biotechnology' category in Northern Ireland Science Park 25K Award (2011) IgniteNI's global Accelerator Programme (2021) Life and Health Startup Company of the Year 2019 (InventNI) Prize Asthma (2019) Leads major projects including AI-EPOCMON (cardiac diagnostics), Nervous Systems (neural interfaces), and microwave thermal therapy for hypertension. Supervises PhD researchers in neural engineering while directing industry collaborations with healthcare technology focus. Maintains active partnerships across Europe in neuromorphic computing and medical device development. Directs research teams advancing spiking neural networks for hardware security and medical applications, with recent work on adrenal segmentation pipelines and RISC-V watchdog mechanisms. Future initiatives focus on expanding AI-driven point-of-care diagnostics and neuromorphic infrastructure monitoring systems.

• Computer Architecture
• Distributed Systems
• Machine Learning
• +۵ مورد دیگر

Chitaranjan Das is a Professor in the Department of Computer Science and Engineering at Penn State University . His work aligns with the United Nations Sustainable Development Goals , particularly contributing to advanced computing infrastructure and global technological innovation. Das actively collaborates with researchers like Anand Sivasubramaniam , Mahmut Kandemir , and Vijay Narayanan on cutting-edge projects. Key Research Areas: Computer Architecture, Network-on-Chip (NoC) systems, Quality of Service (QoS), Spiking Neural Networks (SNNs), Cloud Computing, and Bioinformatics. Das leads projects funded by the National Science Foundation (NSF), including SHF: Medium: Exploring an Edge Platform Design Trajectory for Next Generation XR Applications and CNS Core: Small: Embracing cross stack heterogeneity in next-generation cloud platforms . His research outputs span over 300 publications, with recent works focusing on Edge Computing, Serverless Computing, and hardware acceleration for genomic data analysis. Recent Publications (2024-2025) highlight his interdisciplinary approach, integrating Machine Learning with FPGA-based acceleration for nanopore sequencing, optimizing VR streaming on edge devices, and enhancing serverless computing on heterogeneous hardware. His projects often bridge the gap between system design and algorithmic innovation , with a strong emphasis on real-world applications in cloud platforms and neural networks.

• Particle Physics
• Dark Matter Detection
• Neutrino Physics
• +۳ مورد دیگر

Dr Jim Brooke is an Associate Professor in Particle Physics at the School of Physics, University of Bristol. He holds a PhD and M.Sci. from the University of Bristol and is active in experimental physics collaborations like CMS and DUNE. Education: M.Sci. and PhD from University of Bristol His research focuses on developing fast online data processing systems for high-energy physics experiments, particularly using programmable logic devices (FPGAs) to identify physics signatures in data. Key projects include: Searches for dark matter via Higgs boson decays Investigations into exotic long-lived particles beyond the Standard Model DUNE experiment contributions for neutrino detection from supernovae and astrophysical sources Recent research trends highlight his work on machine learning acceleration for fast triggers (2021-2022) and medical imaging applications. His expertise spans detector design, data acquisition, and algorithm optimization for particle physics. Jim Brooke leads the DUNE Data Acquisition Project Stage 2 (2025-2027) and has collaborated on interdisciplinary projects involving FPGA-based systems and medical imaging analysis.

Sufyan Ali Memonمشاهده پروفایل

استاد

• Target Tracking
• Unmanned Aerial Vehicles
• Data Association
• +۶ مورد دیگر

Sufyan Ali Memon serves as a Professor in the Department of Defense Systems Engineering at Sejong University, Republic of Korea. His academic journey includes Assistant Professor positions at Isra University (2016-2017), UNIST as Postdoc Research Associate (2017-2018), Indus University (2018-2019), and Mehran University of Engineering & Technology (2019-2021) before joining Sejong University in 2021. His research focuses on Tracking, Estimation, Guidance, Navigation, and Control systems, with particular expertise in multi-target tracking in cluttered environments, UAV trajectory estimation, and smoothing algorithms. His fingerprint analysis reveals strong activity in Target Tracking (97%) , Multi-Target Tracking (83%) , Unmanned Aerial Vehicles (37%) , and State Estimation (27%) . Analysis of his 42 research outputs (2015-2025) shows consistent growth in publication output, with 8 papers in 2025 alone. His work spans journals like Expert Systems with Applications , IEEE Access , and Sensors , demonstrating interdisciplinary applications from defense systems to social media analysis and pipeline inspection robotics. The publications reveal a clear evolution from fundamental tracking algorithms toward practical implementations in UAVs, industrial systems, and security applications. h-index 12 330 Citations Web of Science ResearcherID: AAD-9239-2019 His professional background includes significant contributions to smoothing algorithms for target tracking in cluttered environments, with major achievements including dynamic trajectory estimation for UAVs and multi-UAV tracking systems. Current research appears focused on integrating machine learning with traditional tracking systems and expanding applications to industrial inspection and security domains.

Mojtaba Haghiمشاهده پروفایل

پژوهشگر ارشد

• Embedded Control Systems
• Model Predictive Control
• Multi-core Platforms
• +۴ مورد دیگر

Mojtaba Haghi is a post-doctoral researcher at Eindhoven University of Technology (TU Eindhoven), affiliated with the Department of Electrical Engineering . He is actively involved in the Embedded Control Systems Lab , CompSOC Lab - Predictable & Composable Embedded Systems , and the High Tech Systems Center . His research focuses on co-designing control applications for resource-constrained embedded platforms, particularly targeting multi-core hardware architectures. PhD in Electrical Engineering (2025) from TU Eindhoven MSc in Electrical Engineering from AmirKabir University of Technology His work addresses critical challenges in: Resource utilization optimization in embedded control systems Multi-rate controller design for predictable platforms Hardware/software co-design for industrial applications Parallel computing in model predictive control algorithms Recent publications demonstrate expertise in: Water grid model predictive control frameworks GPU-accelerated optimization algorithms Embedded system performance verification Industrial motion control applications He collaborates with research groups including: EAISI (Eindhoven AI Systems Institute) MedTech Innovation Center

• Computer Science
• Telecommunications Engineering
• Mobile and Wireless Networks
• +۳ مورد دیگر

Dr Shahedur Rahman is a Senior Lecturer in Computer Science at Middlesex University , with extensive contributions to telecommunications engineering, image processing, and bioinformatics. His research focuses on wireless network optimization, perceptual distortion metrics for video coding, and molecular biology database integration. Research Interests : Interference management in LTE/5G networks Perceptual quality assessment in multimedia systems Computer vision for accessibility and mobility aids Integration of biological and medical databases Wavelet-based image compression Genomic data analysis Selected Scientific Publications highlight trends in: Dynamic channel allocation techniques for LTE networks SSIM/SATD hybrid distortion metrics Gene mutation data modeling Image processing for visual impairment assistance Database interoperability in medical systems Optimization algorithms for hardware systems

Hong Yuمشاهده پروفایل

دانشیار

• EEG-Based BCI Systems
• Big Data Analysis
• Internet of Things (IoT)
• +۵ مورد دیگر

Dr. Hong Yu is an Associate Professor in the Engineering Technology department at Fitchburg State University's School of Business and Technology. Specializing in wireless communication protocols, neural networks, and embedded systems, their research spans earthquake nowcasting, IoT applications, and industrial sensor development. Current projects include 5G network optimization and FPGA-based measurement systems. PhD in Electrical & Electronics Engineering from The Catholic University of America Active in NSF-funded initiatives like the Microcontroller Training System project Professional affiliations include IEEE Senior Membership and leadership roles in multiple IEEE chapters Teaches courses in microprocessor systems, digital electronics, and mobile application development Research focuses on merging biological neural network principles with technological applications, while maintaining expertise in analog electronics and environmental monitoring. Recent publications emphasize wireless protocol design (2021), color sensing automation (2021), and cross-platform smart home development (2020), showing consistent innovation across 5G networking, semiconductor design, and seismic analysis. Professional contributions include serving as Vice Chairperson in IEEE ROBOT Chapter (Worcester County) and IEEE AESS Chapter (Baltimore), plus judging robotics competitions like FIRST LEGO League.

Claudio Talaricoمشاهده پروفایل

استاد

• VLSI Integrated Circuits
• Embedded Systems
• Hardware/Software Co-design
• +۳ مورد دیگر

Claudio Talarico is a Professor in the Department of Electrical and Computer Engineering at Gonzaga University's School of Engineering & Applied Science. He holds a Ph.D. in Electrical Engineering from the University of Hawaii and an M.S. from the University of Genoa, Italy. With industry experience at Infineon Technologies, IKOS Systems, and Marconi Communications, his expertise spans VLSI design, embedded systems, and hardware/software co-design. His research focuses on: Low-power/high-performance VLSI circuits Embedded system-on-chip design Computer-aided design methodologies Wireless communication systems Hardware/software co-design Recent publications (2020-2024) demonstrate strong emphasis on: Beam steering architectures for 5G/wireless systems Angle-of-arrival estimation techniques Time synchronization in body area networks Health monitoring platforms FPGA/digital implementations He teaches core courses including VLSI Circuits & Systems, Computer Hardware Design, and Digital Systems. No awards or current students are documented in available materials.

Vikram Adveمشاهده پروفایل

استاد

• Compiler Infrastructure
• Computer Systems and Architecture
• Edge Computing
• +۶ مورد دیگر

Vikram Adve is the Donald B. Gillies Professor of Computer Science at the University of Illinois at Urbana-Champaign, with appointments in both the Computer Science Department and the Center for Digital Agriculture. He co-founded and co-leads the Center for Digital Agriculture and serves as the director of AIFARMS, a $20M National Artificial Intelligence Research Institute funded by USDA NIFA and NSF. Adve has been a professor at UIUC since August 2011 and previously served as Interim Head of the Computer Science Department from 2017 to 2019. Adve received his Ph.D. in Computer Science from the University of Wisconsin-Madison in 1993. His academic journey has been marked by significant contributions to compiler infrastructure and computer systems research, culminating in his current distinguished professorship at one of the world's leading computer science departments. Adve's research spans multiple cutting-edge domains in computer systems. His work on the LLVM Compiler Infrastructure has revolutionized how software is compiled and optimized across diverse hardware platforms. Currently, his research focuses on three primary thrusts: Digital Agriculture and AI : Through the Center for Digital Agriculture and AIFARMS Institute, he's developing AI solutions for agricultural challenges, including the CropWizard system for generative AI in farming Edge Computing : His HPVM, ApproxHPVM, and ApproxTuner projects address the programming challenges of heterogeneous computing at the network edge Compiler Innovation : His Hydride and MISAAL projects use program synthesis to automatically build compilers for complex hardware architectures His work bridges theoretical compiler research with practical applications in agriculture, autonomous systems, and distributed computing. Adve's publication record demonstrates a consistent trajectory from foundational compiler research to applied AI systems. Early work focused on memory safety (SAFECode), deterministic parallel programming (DPJ), and the LLVM infrastructure. More recently, his publications reflect a strategic pivot toward agricultural AI and edge computing, with significant contributions to generative AI applications, compiler techniques for heterogeneous systems, and multimodal data processing for precision agriculture. His work maintains strong theoretical foundations while addressing real-world challenges in resource-constrained environments. Adve's scientific recognition includes numerous prestigious awards: ACM Software System Award (2012) for LLVM ACM Fellowship (2014) NSF CAREER Award (2001) Multiple best paper awards at top conferences including PLDI 2005, SOSP 2007, and CGO 2004 (retrospective) University Scholar designation at UIUC (2015) Donald B. Gillies Professorship (2018) Distinguished Alumnus Award from IIT Bombay (2023) As an advisor, Adve has mentored numerous successful students, including Chris Lattner (co-creator of LLVM), Robert Bocchino (ACM SIGPLAN Outstanding Dissertation Award winner), and John Criswell (ACM Doctoral Dissertation Award Honorable Mention). His research group has secured significant funding from diverse sources including USDA NIFA, NSF, Intel Corporation, Amazon-Illinois AICE Center, and the state of Illinois through the Discovery Partners Institute. Current projects include the $20M AIFARMS institute and multiple edge computing initiatives focused on agricultural robotics and distributed AR/VR systems. Adve leads the Programming Languages, Systems, and Networking research group at UIUC, which maintains strong connections with industry partners. His group's work on LLVM has had widespread industry impact, with applications in Apple's iOS ecosystem, Android, NVIDIA GPUs, and numerous other commercial products. The group's current focus on agricultural AI through the Center for Digital Agriculture represents a strategic expansion into domain-specific applications of systems research.

Mohammadreza Mehrabianمشاهده پروفایل

استادیار

• Autonomous Systems
• Connected Autonomous Vehicles
• Cyber-Physical Systems
• +۴ مورد دیگر

Dr. Mohammadreza Mehrabian is an Assistant Professor in the Department of Electrical Engineering and Computer Science at South Dakota School of Mines and Technology. He holds a Ph.D. in Computer Engineering from Arizona State University and directs the CPS lab focusing on cyber-physical systems, robotics, and autonomous vehicles. His research specializes in autonomous systems design, particularly cooperative driving algorithms for connected autonomous vehicles, robust intersection management, and run-time verification methods for safety-critical systems. Current projects apply these technologies to smart city infrastructure and agricultural robotics. His work integrates formal methods with practical implementations using FPGA acceleration and sensor fusion techniques. Recent publications develop responsibility-sensitive safety frameworks for autonomous vehicles and invariant extended Kalman filters for IMU-UWB sensor fusion. His lab collaborates with industry partners to advance reliable autonomous systems.

Ignacio Bravo Muñozمشاهده پروفایل

استاد

• Electronics Engineering
• Signal Processing
• FPGA Applications
• +۳ مورد دیگر

Ignacio Bravo Muñoz is a Professor at the Department of Electronics, Universidad de Alcalá (Spain), affiliated with the GEINTRA research group focusing on Electronic Engineering applications in Intelligent Spaces and Transport. He holds a PhD from Universidad de Alcalá (2007) with a thesis on FPGA-based object detection using computational vision and PCA techniques. His research spans indoor positioning systems (using LED/PSD sensors), sustainable energy frameworks for smart communities, FPGA-based hardware design , and remote laboratory platforms . Key contributions include real-time metrology for ESA's PLATO mission, cooperative demand response algorithms, and innovative pedagogical approaches integrating sustainability into digital electronics education. Recent work emphasizes edge computing for video surveillance , machine learning in human action recognition , and non-cooperative target identification using radar signatures. His interdisciplinary projects bridge electronics engineering with energy systems, biomedical applications, and educational technology. He actively collaborates with industry and academic institutions on EU-funded projects, contributing to advancements in aerospace instrumentation (PLATO FPA qualification), smart grid technologies, and STEM education innovation.

• Reservoir Computing
• Machine Learning
• Embedded Systems Design
• +۴ مورد دیگر

Jonas Kantic is a Ph.D. student and Researcher at the Chair of Integrated Systems within the Faculty of Electrical Engineering and Information Technology at Technical University of Munich . Holding a Master of Science in Technical Informatics from Leibniz University Hannover, his work focuses on AI acceleration architectures and embedded systems design. Education Master's in Technical Informatics (2017-2020), Leibniz University Hannover Chinese Language Studies (2018-2019), Beijing Foreign Studies University Bachelor's in Technical Informatics (2013-2017), Leibniz University Hannover Research Focus Specializes in reservoir computing architectures Expertise in FPGA-based AI acceleration Investigates temporal/spatial compression techniques Develops efficient edge AI inference systems Applies machine learning to motorcycle control systems Works on hyperdimensional computing models Supervision Mentored 5+ students in RNN accelerators, CNN optimization, and stochastic computing Collaborates with industry partners (BMW Motorrad, NXP Semiconductors) Publications 2024 - Complex & Intelligent Systems: Cellular Automata for Reservoir Computing 2024 - IEEE NorCAS: FPGA Implementation for High-Speed Reservoir Models 2021 - Current Directions in Biomedical Engineering: Hearing Aid CNN Optimization

Dermot Kerrمشاهده پروفایل

مدرس ارشد

• Robotics
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Dermot Kerr is a Senior Lecturer at Ulster University's School of Computing, Engineering and Intelligent Systems , specializing in robotics, machine learning, and computer vision. He leads research in biologically inspired image processing and user experience, teaching both undergraduate and postgraduate students. Education: B.Sc. (Hons) in Computing Science (2005), Ph.D. in Computing and Engineering (2008) from Ulster University. His research focuses on visual neuroscience applications in robotics, spiking neural networks, and industrial visual inspection. Recent work includes medical image segmentation with DENSE-INception U-nets and neuromorphic computing frameworks like FrostRune. Key projects address 3D printing defect detection, energy consumption forecasting, and human-robot interaction via pose estimation. Article trends show expertise in computer vision (87 conference contributions, 46 articles), deep learning (272 citations for medical segmentation), and robotics (101 citations for predator robot control). Scientific Awards: MVA Best Poster Award 2021 The Ireland Funds Business Plan Competition Runner-up 2021 Silver Level Innovator - Innovate NI 2021 Carnegie Mellon New Venture Competition Winner 2019 Research Excellence Award Finalist 2019 Grants & Projects: Smart Manufacturing Data Hub (Co-I) Hartree Northern Ireland (Co-I) NeuroEye_POC (Co-I) Digital Twin Process Monitoring (Co-I) He collaborates with institutions across 6 countries, contributing to 169 research outputs and 32 projects. His work aligns with UN Sustainable Development Goals for industrial innovation and environmental protection.

• Wireless Communications
• Radio Frequency (RF) Engineering
• Signal Processing
• +۴ مورد دیگر

Ronan Farrell is a Professor in the Department of Electronic Engineering at Maynooth University’s Faculty of Science & Engineering and currently serves as Vice President Academic and Registrar. He earned a BE and PhD from University College Dublin (1993, 1998) and previously worked at ICI/Zeneca Chemicals (1993–1995) and Parthus Technologies (1998–2001) as a mixed-signal ASIC designer. His academic career at Maynooth spans from Lecturer to Professor (2016), with leadership roles as Head of Department (2012–2019) and Director of the Callan Institute (2008–2015). He leads SFI research initiatives in radio frequency electronics and sensor networks. Education: BE (1993), PhD (1998) – University College Dublin Leadership: Head of Electronic Engineering (2012–2019), Director of Callan Institute (2008–2015) Research Focus: Wireless system design, RF/mixed-signal electronics, technology transfer, and innovation. His work bridges theoretical advancements (e.g., MIMO capacity optimization) with practical applications (e.g., 5G transmitters, digital predistortion techniques). Publication Trends: Recent articles emphasize 5G wireless systems, power amplifier linearization, OFDM signal processing, and behavioral modeling. Collaborations span institutions in Ireland, Europe, and Asia, with a focus on hardware implementation and system optimization. Students & Collaborations: Mentions co-authors in publications but no explicit student list provided. Collaborates with researchers in Ireland, Germany, and China.