جست‌وجوی استاد و پژوهشگر

• Mobile and wireless systems
• Internet of Things (IoT)
• Edge computing
• +۷ مورد دیگر

Mahanth Gowda is an Associate Professor in the Department of Computer Science and Engineering, leading a prolific research program at the intersection of mobile systems, wireless sensing, and human-computer interaction. His work has been continuously funded by the U.S. National Science Foundation since 2019, serving as Principal Investigator on four awards and Co-PI on two others, collectively spanning edge computing for XR, sign-language recognition, next-generation wireless networking, and healthcare-oriented wearables. Research Interests Millimetre-wave and ultra-wideband sensing for 3-D finger motion tracking and speech eavesdropping Edge-IoT platforms for real-time sign-language recognition and translation Neural-augmented game streaming and super-resolution on commodity mobile devices Open-source wearable systems for sports analytics and rehabilitative healthcare Security and privacy implications of motion sensors in smartphones and IoT devices Over the past five years his publication trajectory has concentrated on leveraging emerging radio modalities—especially millimetre-wave radar, Wi-Fi, and UWB—to extract fine-grained human-centric information such as finger gestures, facial micro-motions, and spoken content. A complementary thread develops edge-native machine-learning frameworks that push intelligence to resource-constrained devices, enabling immersive AR/VR experiences and assistive technologies for the Deaf and hard-of-hearing communities. Grants & Projects CAREER: Sign-to-Speech – NSF, $500k, 2021-2026; Edge-IoT platform for real-time ASL recognition. SHF: Medium: Next-Gen XR Edge Platform – NSF, $1.2M, 2022-2025; Co-PI with Das, Sivasubramaniam, Kandemir. CNS Core: IoTScope – NSF, $450k, 2020-2025; Sensing physical materials via low-cost IoT radios. CNS Core: Medium: ML-driven Next-G Wireless – NSF, $800k, 2020-2024; Co-PI with Yang and Mahdavi. I-Corps: Smart Ring for Healthcare Analytics – NSF, $50k, 2023-2025; Commercialization of finger-motion wearables. Labs & Teams Gowda directs a research group that operates at the confluence of wireless networking, embedded systems, and applied machine learning. The lab maintains active collaborations with faculty in computer architecture, augmented reality, and accessibility studies, and routinely mentors graduate researchers whose work appears in top-tier venues such as ACM MobiCom, IEEE INFOCOM, ISCA, and ACM IoTDI.

Ramin Karimمشاهده پروفایل

استاد

• Maintenance Engineering
• Cybersecurity
• Industrial Systems
• +۵ مورد دیگر

Ramin Karim is a Professor and Head of Subject in the Department of Civil, Environmental and Natural Resources Engineering at Luleå University of Technology. His research focuses on operation and maintenance technology, with expertise in railway systems, industrial cybersecurity, structural health monitoring, and the application of advanced analytics in asset management. He leads the Operation, Maintenance and Acoustics division, emphasizing interdisciplinary approaches to solving complex engineering challenges. Key research areas include predictive maintenance strategies for railway infrastructure, cybersecurity frameworks for Industry 5.0, and the integration of metaverse technologies in industrial contexts. His work often involves data-driven methodologies such as point-cloud processing, game theory for cyber threat modeling, and digital twin concepts. Recent publications highlight his contributions to railway maintenance policy optimization, health monitoring of ground support systems in mining, and cybersecurity challenges in industrial systems. He has co-authored over 50 peer-reviewed articles, many appearing in high-impact journals like International Journal of Systems Assurance Engineering and Management and Frontiers in Virtual Reality . Ramin Karim’s research also explores emerging technologies like federated learning for digital twins, blockchain applications in railways, and human-centric predictive health management systems. His work aligns with initiatives such as the Reality Lab Digital Railway, aimed at advancing sustainable and digitally enabled transportation solutions.

• Circular Economy
• Business Analytics
• Industrial Ecology
• +۷ مورد دیگر

Dr. Mohammad-Sadegh Taskhiri is a Lecturer in Business Analytics at La Trobe University, Australia. He holds a PhD from Georg August Universität Göttingen, Germany, with a focus on logistics networks for wood flows and cascade utilization. His expertise spans circular economy, industrial ecology, and operations research, with a strong emphasis on integrating business analytics into environmental and supply chain challenges. He has secured over $1.2 million in research grants for projects in precision forestry, transportation, waste management, and energy systems. Education: PhD (Dr. rer. pol) in Economic Sciences, Georg August Universität Göttingen, Germany (2012–2016) Research Interests: Dr. Taskhiri's work focuses on circular economy metrics, sustainable forestry management through AI-driven technologies, and optimizing supply chains for decarbonization. He has published in leading journals such as Annals of Operations Research , Journal of Industrial Ecology , and Remote Sensing , emphasizing interdisciplinary approaches to environmental and operational challenges. Grants & Projects: He has led impactful projects, including modeling biomass utilization for energy plants and developing tools for forest structural complexity analysis. His work bridges academic research with practical applications, such as UAV-based forest canopy sampling and discrete-event simulation for port terminal optimization. Labs/Teams: Collaborates with interdisciplinary teams in circular economy, remote sensing, and precision forestry, fostering innovation in sustainable resource management.

Robyn Choiمشاهده پروفایل

مدرس ارشد

• Audiology
• Middle Ear Assessment
• Auditory Processing Disorder
• +۷ مورد دیگر

Robyn Choi is a Senior Lecturer in Audiology at the School of Human Sciences, The University of Western Australia, and an honorary researcher at the Telethon Kids Institute. She also serves as Senior Audiologist at Fiona Stanley Hospital, demonstrating strong connections between academic and clinical practice. Her educational background includes BSc, MAudSt, MAudA (CCP), and PhD qualifications, with Fellowship of the Higher Education Academy (FHEA). She coordinates and teaches in the Master of Clinical Audiology and Master of Hearing Sciences programs, with specific unit coordination responsibilities across multiple courses. PHYL5502 Basic Clinical Audiology Part 1 PHYL5512 Basic Clinical Audiology Part 2 PHYL5513 Speech, Language and Communication PHYL5515 Hearing Devices and Adult Aural Rehabilitation PHYL5610 Advanced Hearing Aids and Rehabilitation Part 1 PHYL5611 Advanced Clinical Audiology Part 2 PHYL5612 Community and Workplace Audiology PHYL5613 Audiology Practice Management PHYL5615 Advanced Clinical Audiology Part 1 PHYL5617 Advanced Hearing Aids and Rehabilitation Part 2 Dr. Choi's research spans several interconnected areas focused on improving pediatric hearing health outcomes. Her work examines the relationship between hearing loss, auditory processing disorders, and learning difficulties in school-aged children. She investigates novel diagnostic approaches for middle ear disorders using wideband absorbance technology and develops tele-audiology solutions to increase access to services. Her educational research focuses on simulation-based clinical training methods for audiology students. Her recent publications demonstrate consistent focus on pediatric audiology, with emphasis on learning difficulties, middle ear assessment, and telehealth applications. The research shows progression from foundational epidemiological work (like the 2017 systematic review on hearing loss rates) toward more specialized diagnostic and therapeutic interventions. School of Human Sciences Early-Career Research Award (2022) Citation for Outstanding Contribution to Student Learning (2021) Australian Research Training Program Scholarship (2015) QIMR Honours & Coursework Masters Scholarship (2013) Ko Awatea Scholarship (2009) Dr. Choi supervises Higher Degree Research students in the Master of Clinical Audiology program and PhD candidates. Her current research projects include evaluating auditory function in children with learning difficulties, exploring rehabilitative methods for auditory processing disorders, assessing young children's auditory processing abilities, and investigating multisensory signals for safety alerts. She has secured significant funding from sources including Channel 7 Telethon Trust, Medical Research Future Fund, and Defence Science Centre.

• Expressive Speech Synthesis
• Multimodal Interaction
• Prosody Control
• +۴ مورد دیگر

Éva Székely is an Assistant Professor and Researcher at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. Her work focuses on expressive speech synthesis, multimodal interaction, and the application of synthetic speech in human-robot and conversational systems. She teaches the course 'Human Perception for Information Technology (DM2350)'. Her research interests include adapting synthetic speech to situational context, analyzing prosody and pragmatic functions, and investigating ethical implications of synthetic voice design. Notable projects involve developing tools like ConnecTone for modular AAC systems and CreakVC for voice modulation. She has contributed to foundational studies on gender diversity in synthetic voices and the impact of disfluencies on speaker perception. Recent work explores spontaneous speech synthesis, controllable TTS with neural HMMs, and the integration of gesture and speech synthesis using flow matching techniques. Her research often bridges technical innovation with human-centered design principles.

Linus Wunderlichمشاهده پروفایل

مدرس

• Mathematical Finance
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Dr. Linus Wunderlich is a Lecturer in Financial Mathematics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. He holds a PhD in Hybrid Finite Element Methods for Non-linear and Non-smooth Problems in Solid Mechanics from Technical University Munich. His research focuses on integrating machine learning with numerical techniques in mathematical finance, including solving high-dimensional PDEs via neural networks and accelerating risk computations through Chebyshev interpolation. He also explores the theoretical foundations of AI and its applications in finance, engineering, and education. Linus teaches courses such as Mathematical Tools for Asset Management and Masterclass in Business Analytics , emphasizing practical machine learning skills for business contexts. His work spans computational finance, numerical analysis, and interdisciplinary projects including Olympic medal prediction models and violin bridge modeling from CT scans. Research grants include a £10,800 project with Numerical Algorithms Group Ltd on efficient volatility interpolation (2018–2019). He collaborates with institutions like LSE, Birkbeck, and University College Dublin, presenting at conferences such as SIAM Financial Mathematics and the German Probability & Statistics Days. His activities include developing interactive art projects and teaching machine learning courses during the pandemic. No scientific awards are explicitly mentioned in the provided texts. His research interests and publications reflect a blend of mathematical rigor and applied innovation, bridging finance, engineering, and education.

• AI at the Edge
• Low Power Embedded Systems
• Reliable and Secure Computing
• +۸ مورد دیگر

Emanuel Popovici is a Senior Lecturer in Electrical and Electronic Engineering at University College Cork (UCC), Ireland. He holds a Dipl. Ing. in Computer Engineering from the University Politehnica Timisoara, Romania, and a PhD in Microelectronics from UCC. His research focuses on AI at the edge, low-power embedded systems, and secure computing, with applications in healthcare, energy, and IoT. Notable projects include award-winning work in neonatal EEG monitoring, smart beehive systems, and energy-efficient wireless nodes. He has authored over 250 papers and received prestigious awards like the Qualcomm Faculty Award (2024) and three IEEE/IBM Smarter Planet Challenge titles. Education: Dipl. Ing. in Computer Engineering, University Politehnica Timisoara (Hardware Design focus) PhD in Microelectronics, UCC (National Microelectronics Research Centre) Research interests span AI-driven hardware, secure communications, and interdisciplinary collaborations across engineering, medicine, and environmental science. His work emphasizes practical solutions for real-world challenges, such as energy-efficient sensor networks and medical diagnostic tools. Scientific achievements include over 50 awards, including the Reed and Mallick Medal (2020) for urban planning contributions. His lab pioneered innovations like the neonatal EEG sonification system and blockchain-based IoT security frameworks. Collaborative projects span disciplines like anatomy, medicine, physics, and business. His group's work on ultra-low-power wireless nodes and FPGA-based cryptographic processors highlights their focus on energy efficiency and reliability.

Chengyu Caoمشاهده پروفایل

دانشیار

• Dynamics and Control
• Adaptive Systems
• Aerospace Engineering
• +۶ مورد دیگر

Chengyu Cao is an Associate Professor at the University of Connecticut's School of Mechanical, Aerospace, and Manufacturing Engineering since 2008. He holds a Ph.D. in Mechanical Engineering from MIT (2004), an M.S. in Manufacturing Engineering from Boston University (1999), and a B.S. in Electronics and Information Engineering from Xi’an Jiaotong University (China, 1995). His research focuses on dynamics and control, adaptive systems, mechatronics, unmanned systems, and aerospace applications, alongside theoretical work in cosmology and physics, particularly his groundbreaking 'world membranes' theory challenging traditional relativity. Dr. Cao co-authored L1 Adaptive Control: Guaranteed Robustness with Fast Adaptation and authored the Universes are Black Holes series, proposing an axiomatic framework for spacetime dynamics. This theory addresses dark energy/matter, resolves cosmological paradoxes, and predicts novel phenomena awaiting experimental validation. His work bridges gaps between experimental data and theoretical models, emphasizing simplicity and precision. His engineering research spans advanced control strategies for aerospace systems, robotics, and energy infrastructure, with applications in autonomous vehicles, power plants, and combustion monitoring. He has published over 180 peer-reviewed papers and pioneered fiber-optic sensing technologies for high-temperature environments.

Massimo Orazio Spataمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Biomedical Engineering
• Computer Science
• +۳ مورد دیگر

Massimo Orazio Spata is a Research Fellow in Computer Science at the University of Catania's Department of Mathematics and Computer Sciences, specializing in deep learning applications for biomedical, audio, and biometric systems. He has held roles at STMicroelectronics since 1999, focusing on system integration, image processing, and biomedical device R&D. He teaches courses such as Mobile Programming and Computer Architecture at secondary schools and has advised numerous students on grid computing and middleware projects. Education: PhD in Computer Science (University of Catania, 2008), MSc in Computer Science (University of Catania, 2013), and a teaching certification in Computer Science (University of Catania, 1998). Research interests include deep learning algorithms, biomedical device development, grid scheduling, and cybersecurity. He has authored patents on lab-on-chip systems, bio-computer analysis, and scheduling methods, and his work has been recognized with STMicroelectronics Innovation Awards (2016–2014) and a Cisco CCNA certification. Key collaborations include projects with Google (Mediapipe Objectron for robotics), Huawei (video deblurring), and involvement in the PNRR Horizon HiCONNECTS project (2024–present). He serves on conference committees (e.g., ICAETA 2023) and has developed e-learning systems and CAD tools for STMicroelectronics.

Graham Rileyمشاهده پروفایل

مدرس

• High Performance Computing
• Scientific Computing
• Parallel Computing
• +۴ مورد دیگر

Graham Riley is a Lecturer in the School of Computer Science at the University of Manchester and holds a part-time position in the Scientific Computing Department (SCD) at STFC, Daresbury. His research focuses on high performance computing (HPC), software engineering for scientific computing, and performance modeling for parallel machines. Key areas include techniques for developing HPC applications, software architectures for coupled modeling, and performance control in distributed systems. His work emphasizes collaboration with computational scientists in domains such as Earth System Modelling (e.g., UK Met Office), computational chemistry, and biology. He has contributed to projects like the EuroExa architecture for exascale computing and the LFRic weather/climate model porting to FPGAs. Riley's research also explores energy efficiency in HPC systems and FPGA acceleration strategies for scientific workloads. Notable contributions include studies on parallelization strategies, FPGA-based acceleration of climate models, and optimizing OpenCL for heterogeneous architectures. His work aligns with UN Sustainable Development Goals, particularly through contributions to climate modeling and sustainable computing practices. Riley collaborates with institutions like the Met Office and the ESM community in Europe/US. His academic profile reflects a balance between theoretical research and practical application, with a strong focus on bridging computational science and engineering challenges.

Slim Essidمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Slim Essid is a Full Professor at Télécom Paris and coordinator of the Audio Data Analysis and Signal Processing (ADASP) group. He holds a PhD and HDR from Université Pierre et Marie Curie (UPMC). His research focuses on machine learning, artificial intelligence, and signal processing applied to temporal data analysis, including multiview learning, representation learning, and structured prediction. Applications span music content analysis (MIR), multimodal perception (e.g., EEG data analysis), and human behavior analysis. He has advised 15 PhD students and collaborated on over 14 post-doctoral projects. Education: PhD in Signal Processing, Université Pierre et Marie Curie (2005) Habilitation (HDR), Université Pierre et Marie Curie (2015) M.Sc. in Digital Communication Systems, Télécom ParisTech (2002) Engineer Degree, École Nationale d’Ingénieurs de Tunis (2001) Research interests emphasize multimodal learning, self-supervised representation learning, and audio-visual fusion. Key projects include sound-prompted segmentation, zero-shot audio captioning, and EEG-based auditory attention decoding. Over 150 peer-reviewed publications exist across conferences like NeurIPS, ICML, and journals like IEEE Transactions. Active in reviewing for top-tier venues and advising French/EU research projects. Labs/Teams: Member of the Signal, Statistics and Learning (S2A) research team and the Information Processing and Communication Laboratory (LTCI).

Ellen Garlandمشاهده پروفایل

پژوهشگر ارشد

• Animal Culture
• Social Learning
• Bioacoustics
• +۷ مورد دیگر

Dr Ellen Garland is a Principal Research Fellow in the School of Biology at the University of St Andrews , Scotland. She is affiliated with the Centre for Biological Diversity, the Scottish Oceans Institute, the Sea Mammal Research Unit (SMRU), and the Centre for Social Learning & Cognitive Evolution. She leads multiple research projects funded by The Royal Society and is an active Principal Investigator in marine bioacoustics and animal culture. Research Interests: Her primary research focuses on animal culture , social learning , bioacoustics , and behavioural ecology , particularly in cetaceans. She specializes in the cultural transmission, vocal learning, and function of humpback whale song, using vocal sequence analysis to define population structures for conservation. Her work contributes to UN Sustainable Development Goals related to life below water and biodiversity. Recent Research Trends: Her recent publications (2023–2025) explore humpback whale song complexity, cultural evolution, mating systems, and the parallels between whale song and human language. These studies combine bioacoustics, genetics, and network analysis to understand cultural transmission and population dynamics. Scientific Awards: National Academy of Sciences Postdoctoral Fellowship (2012) Academy of Medical Sciences SUSTAIN career programme (2023) ASA Acoustic Expert Multimedia: Honourable mention (2023) Co-lead, UNEP CMS Expert Working Group on Animal Culture (2023) Executive Officer, South Pacific Whale Research Consortium (2019) Advising and Grants: She supervises research and has led multiple funded projects with The Royal Society, including a University Research Fellowship extension (2022–2025) with over £360,000 in funding. She collaborates internationally and mentors early-career researchers. Her lab generates extensive datasets on cetacean vocalizations and genetics. Laboratories and Teams: She is embedded in the Sea Mammal Research Unit and the Centre for Social Learning & Cognitive Evolution, working with interdisciplinary teams on marine mammal cognition, communication, and conservation.

• Signal Processing
• Machine Learning
• Acoustic Engineering
• +۵ مورد دیگر

Manuel Rosa Zurera is a Full Professor at the Universidad de Alcalá, specializing in Signal Theory and Communications. He leads the AES3 research group focused on Acoustic and Electromagnetic Smart Sensor Networks and Signal Processing. His research emphasizes machine learning applications in audio analysis, biomedical signal processing, and passive radar systems. Dr. Rosa holds a PhD from Universidad de Alcalá (1999), supervised by Francisco López Ferreras. Research Interests : Dr. Rosa’s work spans acoustic signal localization, UAV detection, emotion recognition via speech analysis, and evolutionary studies of hominin auditory systems. His innovations include robust detection algorithms for passive radar, energy-efficient anger detection systems, and age/gender classification from speech using neural networks. He has pioneered acoustic sensor networks for urban security and biomedical diagnostics. Key Contributions : Over 150 peer-reviewed articles since 1999 demonstrate his expertise in signal processing for aerospace flutter analysis, real-time violence detection in smart cities, and 3D reconstruction of Neanderthal auditory capacities. His work bridges theoretical signal processing with practical applications in healthcare, surveillance, and evolutionary anthropology. Labs/Teams : Directs the AES3 group and collaborates on EU-funded projects like DVB-T radar systems and acoustic drone detection initiatives. Active in designing microphone arrays for hearing aids and wireless sensor networks.

Mehran Masdariمشاهده پروفایل

مدرس

• Experimental Aerodynamics
• Aeroacoustics
• Heat Transfer Optimization
• +۹ مورد دیگر

Dr. Mehran Masdari is a Lecturer in the Department of Aerospace Engineering at City St George's, University of London, where he has been serving since 2024 after joining as a Research Fellow and Visiting Lecturer in 2022. He previously held the position of Assistant Professor at the University of Tehran from 2012 to 2022, where he founded and led the Experimental Aerodynamic Research Laboratory (EARL). Education: PhD in Aerodynamics, Sharif University of Technology, Iran (2011) Fellow of the Higher Education Academy (FHEA), City, University of London, United Kingdom (2024) Dr. Masdari's research focuses on experimental aerodynamics, measurement techniques, data analysis, and image processing. He specializes in heat transfer optimization using pulsating flows, aeroacoustic analysis of drone propellers, and energy harvesting from flow-induced motions. His work bridges fundamental fluid dynamics with practical engineering applications in aerospace and sustainable technologies. He teaches core courses such as 'Measurements and Data Analysis', 'Advanced Aerodynamics', and 'Aeroelasticity', contributing significantly to aerospace education. The recent publications reflect a strong trend in experimental methods, particularly in image-based diagnostics, unsteady flow control, and energy conversion from fluid-structure interactions. His work spans thermal engineering, aeroacoustics, and smart materials, demonstrating interdisciplinary innovation in aerospace systems. Scientific Awards and Recognitions: Fellow of the Higher Education Academy (FHEA) Fellow of the Royal Aeronautical Society Dr. Masdari has advised numerous graduate students at the University of Tehran and continues to mentor early-career researchers at City St George's. His research has been supported by institutional and national grants related to advanced measurement systems, drone technology, and sustainable energy solutions. He has led projects integrating optical diagnostics with thermal and structural testing, contributing to both academic knowledge and industrial applications. He established and directed the Experimental Aerodynamic Research Laboratory (EARL) at the University of Tehran, a facility dedicated to cutting-edge experiments in flow measurement, heat transfer, and aeroelasticity. His lab work emphasizes innovation in instrumentation and data interpretation, fostering a hands-on research environment for students and collaborators.

• Natural Language Processing
• Graph Neural Networks
• Large Language Models
• +۷ مورد دیگر

Liang Zhao is a Professor in the Department of Computer Science at Harbin Institute of Technology's School of Computer Science and Technology. His research spans multiple areas of natural language processing, artificial intelligence, and machine learning, with a particular focus on large language models, graph neural networks, and explainable AI systems. His research interests include natural language processing, graph neural networks, large language models, model explainability, federated learning, sparse attention mechanisms, hallucination mitigation, transformer length extrapolation, and uncertainty quantification. His work addresses fundamental challenges in modern AI systems, particularly in improving the reliability, efficiency, and interpretability of large language models. Professor Zhao's research output shows a clear trend toward addressing critical limitations in large language models, with recent work focusing on hallucination mitigation, model attribution, uncertainty quantification, and efficient attention mechanisms. His publications demonstrate strong interdisciplinary connections between NLP, machine learning theory, and practical system implementation. Through his extensive collaboration network with researchers including Xiaocheng Feng, Bing Qin, Weihong Zhong, and Yuntong Hu, Professor Zhao has established himself as a leading researcher in the Chinese NLP community. His work appears consistently in top-tier conferences including ACL, EMNLP, and NAACL.