جست‌وجوی استاد و پژوهشگر

Nathan Dahlinمشاهده پروفایل

استادیار

• Machine Learning
• Stochastic Control
• Optimization
• +۲ مورد دیگر

Nathan Dahlin is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany's College of Nanotechnology, Science, and Engineering. He holds a BS, MS, and PhD in Electrical Engineering and an MA in Applied Mathematics from the University of Southern California. Prior to joining UAlbany, he was a Postdoctoral Research Associate at the University of Illinois Urbana-Champaign and a senior audio DSP research engineer at Audyssey Laboratories. Dr. Dahlin's research focuses on fundamental problems in machine learning, stochastic control, optimization, and microeconomics, with applications in developing computationally efficient decision-making approaches for smart energy systems. His work emphasizes reliability in uncertain environments and risk management. His recent publications demonstrate strong focus on machine learning applications in control systems, energy management, and algorithm design. Articles frequently address topics like imitation learning, economic dispatch optimization, neural network transformation, and kernel-based learning methods, often with practical implementations in energy systems and smart grids. Dr. Dahlin is active in professional organizations including the Institute of Electrical and Electronics Engineers (IEEE) and the Association for the Advancement of Artificial Intelligence (AAAI). He serves as a reviewer for leading conferences and journals including AAAI Conference on Artificial Intelligence, IEEE Transactions on Control of Network Systems, IEEE Transactions on Power Systems, and IEEE Transactions on Smart Grid.

• Traffic Engineering
• Optimization
• Heuristic Methods
• +۵ مورد دیگر

Stuart Berry is an academic researcher affiliated with the College of Science and Engineering , contributing to interdisciplinary studies at the intersection of optimization, traffic engineering, and network environments. His role as a lecturer involves both teaching and research, focusing on practical applications of heuristic methods in complex systems. Research Institutes : College of Science and Engineering Academic Rank : Lecturer Research Interests Traffic assignment and optimization Heuristic and computational modeling Opportunistic network environments Green transport planning Audio engineering and production systems Equilibrium problem analysis Publication Trends His work spans transportation engineering , network optimization , and heuristic algorithms applied to diverse systems, from small manufacturing firms to ad-hoc networks. Recent papers focus on integrating mathematical modeling with simulation for solving real-world problems in green logistics and service matching .

• Creativity Studies
• Educational Technology
• Music Education
• +۳ مورد دیگر

Michael Brown is a Lecturer in Childcare affiliated with the University of Derby's Provost Learning and Teaching research institute. His work focuses on creativity development in education, interdisciplinary artistic processes, and the intersection of technology with music and human experience. He has contributed to over 20 publications since 2012, exploring topics ranging from creative pedagogy in the Fourth Industrial Revolution to innovative soundscapes using tissue-conducted audio interfaces. His research emphasizes the application of artistic methodologies to project management and education, with notable projects including 'Feel it in my bones' (2017) and 'The business of invention' (2013). Brown collaborates extensively with institutions like the West East Institute and KIE Conference Publications, focusing on creativity dynamics across domains. Despite no listed awards, his work has been cited in multiple academic platforms. His research outputs demonstrate a consistent exploration of creative constraints, multimodal composition, and technology-enhanced learning environments, with over 2,000 total views across platforms.

• Computer Vision
• Deep Learning
• Cybersecurity
• +۳ مورد دیگر

Marcella ASTRID is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the CVI2 department. Her research focuses on advanced machine learning techniques applied to cybersecurity and computer vision challenges, particularly in the detection of synthetic media (deepfakes) and anomaly detection systems. She specializes in developing robust models that address vulnerabilities in adversarial scenarios and improve generalizability across diverse datasets. Key research areas include deepfake detection through spatio-temporal analysis, anomaly detection via novel training paradigms, and efficient neural network compression for resource-constrained environments. Her work bridges theoretical advancements in machine learning with practical applications in surveillance systems, autonomous robotics, and cybersecurity infrastructure. Recent contributions emphasize leveraging autoencoder weaknesses, pseudo anomaly generation, and localized attention mechanisms to enhance detection accuracy and model robustness. Marcella’s research also explores cross-modal learning (audio-visual synchronization) and semi-supervised methodologies to tackle data scarcity issues in critical domains like battery thermal imaging and multi-camera traffic classification. She actively publishes in top-tier venues and collaborates on interdisciplinary projects addressing real-world security and reliability challenges.

Atau Tanakaمشاهده پروفایل

استاد

• Embodied Interaction
• Human-Computer Interaction (HCI)
• Musical Performance Technology
• +۳ مورد دیگر

Prof Atau Tanaka is a Professor of Media Computing at Goldsmiths, University of London. His research focuses on embodied musical interaction, integrating physiological sensing, machine learning, and participatory design methodologies. He leads the EAVI (Embodied AudioVisual Interaction) group, exploring gestural interfaces, bio-interfaces, and audiovisual systems. Tanaka has held roles at institutions worldwide, including Sony CSL and Apple, and his work spans academic research and artistic performance. His educational background includes a Doctor of Musical Arts from Stanford University and degrees from Harvard and Peabody Conservatory. Research Interests: Embodied interaction, gestural computing, EMG-based musical interfaces, HCI, machine learning for live performance, and interdisciplinary art-science collaborations. Awards include recognition from Ars Electronica and Fraunhofer Institute. Publications highlight his work on telematic performance, EMG-based instruments, and audiovisual user interfaces. He explores topics like latency in networked performance and the entanglement of body signals with artistic expression. Tanaka’s projects include installations like Sonic Womb and collaborations with institutions such as the ZKM and SFMOMA.

Armando Barretoمشاهده پروفایل

استاد

• Digital Signal Processing
• Biomedical Signal Processing
• Adaptive Digital Signal Processing
• +۵ مورد دیگر

Armando Barreto is a Professor in the Department of Electrical & Computer Engineering at Florida International University (FIU). He holds a Ph.D. in Electrical Engineering from the University of Florida (1993), an M.S.E.E. from FIU (1989), and a Mechanical-Electrical Engineering degree from the National Autonomous University of Mexico (1987). His research focuses on digital signal processing with applications in biomedical systems, including EEG analysis, adaptive algorithms, neural networks, and real-time embedded systems. He also explores human-computer interaction through sensor technologies like MEMS, inertial measurement units, and multi-touch interfaces. His work addresses accessibility challenges for visually impaired users through dynamic image precompensation and assistive technologies. Key contributions include innovations in signal denoising, affective computing via pupil dynamics analysis, and multimodal data fusion for early detection of neurodegenerative diseases like Alzheimer’s. His research bridges theoretical signal processing with practical applications in healthcare, virtual reality, and wearable devices. Barreto’s publications span over three decades, demonstrating sustained leadership in sensor-based systems, medical signal processing, and computational neuroscience. He collaborates with interdisciplinary teams to advance technologies addressing clinical and accessibility challenges.

CARLOS BUSSOمشاهده پروفایل

استاد

• Affective Computing
• Multimodal Human-Machine Interfaces
• In-Vehicle Safety Systems
• +۴ مورد دیگر

Carlos Busso is a Professor in the Department of Electrical and Computer Engineering at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering & Computer Science. He leads the Multimodal Signal Processing (MSP) Laboratory and holds IEEE Fellow status. His research focuses on affective computing, multimodal human-machine interfaces, and machine learning applications in healthcare, transportation, and education. Education: PhD in Electrical Engineering (University of Southern California, 2008), MS/BS in Electrical Engineering (University of Chile, 2000-2003). Awards include the NSF CAREER Award, ICMI Ten-Year Technical Impact Award, and Best Paper recognitions at IEEE ICME and AAAC ACII. Research interests span human-centered multimodal intelligence, in-vehicle safety systems, and speech-based emotion recognition. He has contributed to landmark datasets like the MSP-GEO Corpus and pioneered label-free metabolic imaging techniques for oral cancer detection. Professional activities include leadership roles as General Chair of ACII 2017 and ICMI 2021, and editorial positions with IEEE Transactions on Affective Computing and IEEE Signal Processing Letters.

Eduardo Mirandaمشاهده پروفایل

استاد

• Computer Music
• Artificial Intelligence
• Brain-Computer Music Interface
• +۵ مورد دیگر

Professor Eduardo Miranda is a visionary academic and composer specializing in computer music, AI, and neuroscience. As Head of the Interdisciplinary Centre for Computer Music Research (ICCMR) at the University of Plymouth, he pioneers innovations like brain-computer music interfaces (BCMI) and quantum computing applications in music. His work bridges art and science, focusing on assistive technologies for people with disabilities and dementia through projects like RadioMe. He holds a PhD from the University of Edinburgh (1995), specializing in AI for music, and has supervised over 20 doctoral students globally. His research spans biocomputing, quantum music systems, and neurotechnology, with grants exceeding £5M. Notable collaborations include work with the BBC Concert Orchestra and Jarvis Cocker, and his opera Lampedusa uses particle physics data from the Large Hadron Collider. Research Interests: - AI and Machine Learning in Music - Quantum Computing for Creativity - Biocomputing and Living Organisms as Processors - Music Neurotechnology for Health - Ethical AI in Creative Industries - Historical Computing (e.g., Charles Babbage’s legacy) - Music-Based Palliative Care Projects & Impact: - RadioMe : AI-driven radio personalization for dementia patients. - QuTune : Quantum computing tools for musicians. - Music and the Brain research group at the Brain Research & Imaging Centre (BRIC). - Development of bio-inspired algorithms for composition and performance. Teaching: Courses in AI, quantum computing, unconventional computing, and music technology. Advising & Grants: Supervised 22+ PhD completions. Secured £5M+ in research funding. Doctoral alumni work globally in academia (e.g., McGill University) and industry (e.g., Arm microprocessors). Recognition: Featured in Forbes for quantum music innovation. Keynote speaker at Royal Institution and EG Conference. First classical composer to perform in the Metaverse.

Phillip De Leonمشاهده پروفایل

استاد

• Digital Signal Processing (DSP)
• Machine Learning
• Speech Processing
• +۳ مورد دیگر

Phillip De Leon is an accomplished academic and administrator serving as Associate Vice Chancellor for Research and Chief Research Officer at the University of Colorado Denver. He is also a Professor in the Department of Electrical Engineering within the College of Engineering, Design and Computing. His career includes prior roles as a Professor at New Mexico State University, where he held distinguished professorships for teaching excellence and research leadership. Dr. De Leon leads CU Denver’s strategic research initiatives, emphasizing interdisciplinary collaboration and innovation across diverse academic programs. His research focuses on Digital Signal Processing (DSP), with specializations in machine learning, speech processing (including speaker recognition and speech enhancement), time-frequency analysis, and embedded systems. His work has been supported by agencies such as the Air Force Research Laboratory, Army Research Laboratory, NSF, NASA, and Sandia National Laboratories. He has authored numerous publications in DSP, addressing topics like spoofing detection, spectral analysis, and biomedical signal applications. Education: PhD in Electrical Engineering (prior academic details not specified) Dr. De Leon’s research trends reflect a strong emphasis on security applications of signal processing, such as anti-spoofing for voice authentication systems, and practical implementations of advanced DSP techniques. His recent work explores instantaneous frequency analysis, embedded system optimizations, and machine learning for healthcare monitoring (e.g., falls risk prediction in elderly populations). His professional service includes roles such as Associate Dean of Research and Chief Science Officer, demonstrating leadership in both academic governance and scientific innovation. He maintains active collaborations with industry and government agencies to advance applied research outcomes.

Arashdeep Kaurمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Audio Watermarking
• Deep Learning
• +۳ مورد دیگر

Arashdeep Kaur is a Senior Lecturer in the Department of Computer Science at New Jersey Institute of Technology (NJIT). She holds a Ph.D. in Computer Science and Engineering from Amity University (2017), an M.Tech. from Punjab Technical University (2008), and a B.Tech. (2006) in the same field. Her research focuses on artificial intelligence, audio watermarking, deep learning applications, environmental science, and IoT-based healthcare solutions. She has contributed to crop freshness assessment using deep learning, ethanol production optimization from food waste, and heavy metal adsorption using nanotechnology. Dr. Kaur’s work spans over 20 years, with notable contributions in audio watermarking algorithms for security and robustness, including methods leveraging multi-resolution decomposition and neural networks. Her environmental projects address waste valorization and sustainable energy solutions. She actively teaches courses in AI and computer science fundamentals at NJIT. Her publications highlight interdisciplinary applications of CS in agriculture, cybersecurity, and environmental engineering. While no scientific awards are explicitly mentioned, her prolific research output indicates impactful contributions to multiple fields. She has advised no listed students, but her courses mentor future computer science professionals. Labs or collaborative teams are not detailed in the provided information, but her work intersects with NJIT’s strategic research areas in technology and sustainability.

• Accessibility in Computing
• Digital Forensics
• Artificial Intelligence
• +۳ مورد دیگر

Ramzi Djemai is a Lecturer in Computer Science and Applied Computing at the School of Computing and Digital Media, London Metropolitan University. His work focuses on accessibility technologies, digital forensics, and artificial intelligence applications. He contributes to inclusive computing through assistive tools for visually impaired users and explores cutting-edge methods in steganography detection and emergency path planning. Research interests include: Design of accessible computing solutions Steganography analysis in digital evidence Autonomous systems for evacuation routing Human-centered AI applications Recent articles highlight advancements in: Image recognition systems for visual impairment support Hybrid methods for steganographic evidence recovery Dynamic path planning frameworks No scientific awards or grants are listed in the provided information. Advising records are unavailable. No specific lab affiliations are mentioned.

Tanguy Rissetمشاهده پروفایل

استاد

• Embedded Systems
• Compilation
• High Level Synthesis
• +۳ مورد دیگر

Tanguy Risset is Professor in the Telecommunication Department at Insa-Lyon, specializing in embedded systems, compilation, and software-defined radio. As head of Inria's Socrate project, he coordinates research on cognitive radio networks integrating signal processing, communications, and computer science. His research develops FPGA-based solutions for real-time audio processing and wireless systems, emphasizing compilation techniques for hardware acceleration. Recent publications focus on low-latency audio transmission, spatial sound implementation, and open-source toolchains for FPGA audio DSP.

• Geoinformatics
• Intelligence Informatics
• Geospatial Data Mining
• +۷ مورد دیگر

Ickjai Lee is an Associate Professor at the School of IT, James Cook University, specializing in geoinformatics and intelligence informatics. He leads the Information Technology department and has held roles including Senior Lecturer and Lecturer since 2003. He obtained his PhD in 2002 from the University of Newcastle, Australia. Research Interests: Geospatial data mining, trajectory analysis, Voronoi tessellations, mobile AR, and IoT applications. Projects: Includes VR healthcare trials, AI-driven vehicle damage assessment, and environmental monitoring systems. His work focuses on applying machine learning and geospatial techniques to solve real-world problems in health, environmental, and urban domains. Notable achievements include best paper awards at international conferences and faculty citations for teaching excellence. He collaborates on interdisciplinary projects, such as using AR for marine growth monitoring and developing tools for crime pattern analysis. His research also involves large-scale ecoacoustic data analysis and VR-based education platforms. Lee has supervised numerous PhD students exploring topics like differential privacy in biokinetics, spatio-temporal anomaly detection, and VR in cultural heritage preservation.

Xiaokun Yangمشاهده پروفایل

دانشیار

• FPGA Acceleration
• AI/ML Hardware
• System-on-Chip Design
• +۲ مورد دیگر

Dr. Xiaokun Yang is an Associate Professor of Computer Engineering in the College of Science and Engineering at the University of Houston-Clear Lake. With extensive industry experience at AMD and CEC, his research bridges hardware design and AI acceleration. Research focuses on FPGA acceleration for AI/ML, hardware/software co-design, and IoT system architectures. Publications demonstrate innovations in neural network accelerators, hardware-efficient AI, secure SoC design, and edge computing solutions. Recent work explores ReRAM-based accelerators, blockchain-secured hardware, and federated learning systems. Awards include Best Ph.D Forum Paper at ISVLSI 2014. Education includes a Ph.D. in Electrical and Computer Engineering from Florida International University (2016) and dual M.S. degrees from FIU and Beihang University. Courses taught include Advanced Digital Systems Design, Electronics, and Senior Project. Current research projects investigate FPGA-based AI acceleration and IoT architectures.

Longfei Shangguanمشاهده پروفایل

استادیار

• Mobile and wireless systems
• IoT systems
• Edge computing
• +۵ مورد دیگر

Longfei Shangguan is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. He leads the Pittsburgh Experimental Research Group (PERG), focusing on hardware/software systems to advance computing, sensing, and communication in sensor/wireless technologies. Previously, he was a postdoc at Princeton and a senior researcher at Microsoft. He holds a PhD from HKUST (2015) under Professors Yunhao Liu and Ke Yi. Research Interests: Mobile/wireless systems, IoT, edge computing, and hardware/software co-design for sensor networks. Key areas include novel IoT applications, low-power hardware, and wireless protocol optimization. Key Projects Sensor-in-the-loop AI agents leveraging multimodal sensors Human-centric sensing using acoustic/vision modalities Wireless Digital Twin via neural ray tracing Professional Contributions Editor: IEEE Pervasive Computing, ACM GetMobile PC Member for top conferences including MobiCom, NSDI, SenSys Organized Dagstuhl Seminar on IoT Wireless Scenarios Awards NSF CAREER Award (2024) Google Research Scholar Award (2023) Best Paper Awards at MobiCom 2024 and MobiCom 2021 Advising & Grants Supervises 5 PhD students, 3 MS students, and undergraduate researchers Recipient of MassAITC Pilot Award (2025), NSF CAREER (2024) Labs/Teams: PERG lab focuses on experimental systems research with hands-on prototyping.