جست‌وجوی استاد و پژوهشگر

Daniel Prattمشاهده پروفایل

مدرس ارشد

• Music Production
• Audio Engineering
• Spatial Audio
• +۵ مورد دیگر

Dr Daniel Pratt is a Senior Lecturer and Course Leader at the University of West London 's London College of Music , directing programs in BMus (Hons) Performance and Recording and BA (Hons) Recording, Mixing and Production . He co-directs London Noise Records , a student-run label distributed through AWAL. With over two decades in the music industry, Dr Pratt has worked as a record producer , performer , and educator , releasing music through Sony , Universal , and independent labels. His engineering collaborations include Bruce Woolley ( Video Killed the Radio Star ), Christopher Elliot (Amy Winehouse), and Ralph Salmins (Aretha Franklin, Elton John). As a performer, he has toured Australia, Hong Kong, The UK, and USA, playing venues like the Whisky a Gogo (LA) and Bowery Ballroom (NY) . Dr Pratt's research focuses on innovative recording concepts , Dolby Atmos applications, and transnational music production , with publications in the UWL Repository . His work explores the studio as an interactive instrument , analyzing how engineers mediate creative processes through spatial audio technologies. Recent publications examine sonification of radiological data and flow-state optimization in music production. He teaches across multiple programs including MA Advanced Music Technology , BA (Hons) Music Technology , and supervises PhD Music and MRes Creative Practice candidates.

Anders Riddersholm Bargumمشاهده پروفایل

پژوهشگر

• Virtual Reality
• Artificial Intelligence
• Voice Conversion
• +۳ مورد دیگر

Anders Riddersholm Bargum is a Researcher at Aalborg University's Technical Faculty of IT and Design , affiliated with the Department of Architecture and Media Technology . His work spans virtual reality, artificial intelligence, and advanced audio signal processing. Research Focus: Virtual Reality immersion, AI-driven voice conversion, and spatial audio mixing. Collaborations: Active in international networks for digital audio effects and sound computing. Key Publications (2025-2022): Explored AI speech synthesis in VR (2025), non-parallel voice conversion frameworks (2024), and spatial audio design principles (2022). Research trends emphasize deep learning integration, signal alignment techniques, and immersive audio environments. Technical Contributions: Developed differentiable all-pass filters for phase response estimation and optimized voice conversion systems at high sampling rates. His Virtual Reality research includes studies on avatar realism and social presence through synthetic speech.

• Radiation detector design
• Microelectronics for scientific instrumentation
• X-ray Free-electron Laser applications
• +۷ مورد دیگر

Angelo Dragone serves as a Distinguished Staff Engineer at SLAC National Accelerator Laboratory, operated by Stanford University. He currently holds the dual leadership roles of Deputy Associate Lab Director for the Technology Innovation Directorate and Program Director for Detector R&D and Applied Microelectronics. Dr. Dragone also contributes to academic instruction as an instructor for Advanced Integrated Circuit Design (EE 214B) at Stanford University during the Winter quarter. Dr. Dragone earned his Ph.D. in Microelectronics from the Polytechnic University of Bari, Italy, with research conducted at Brookhaven National Laboratory on mixed-signal readout architecture for radiation detectors. His professional journey includes working at Brookhaven National Laboratory from 2004 before transitioning to SLAC in 2008, where he has since established himself as a leader in detector technology. With over two decades of experience in scientific instrumentation, Dr. Dragone's research focuses on innovative radiation detector systems with applications spanning multiple scientific domains. His work encompasses: Design of ultra-fast X-ray detector architectures for X-ray Free-Electron Lasers Development of high frame rate, large dynamic range detector systems Creation of efficient, scalable systems with real-time processing capabilities Exploration of fundamental performance limits in radiation detection systems Applications in photon science, particle physics, medical imaging, and national security Dr. Dragone's publication record reveals a strategic evolution toward increasingly sophisticated detector technologies. His recent work demonstrates strong integration of advanced electronics with machine learning techniques for particle detection, while maintaining focus on practical implementation challenges in high-energy physics environments. The publications span fundamental physics measurements to cutting-edge circuit design, reflecting his dual expertise in both theoretical understanding and practical engineering solutions. As head of the Integrated Circuits Department within SLAC's Instrumentation Division, Dr. Dragone leads strategic R&D planning for the SLAC X-ray detectors Initiative. His leadership extends to the Applied Microelectronics program, where he guides research directions that bridge academic inquiry with real-world scientific applications. Under his direction, these programs have made significant contributions to advancing detector technology for major scientific facilities.

Shaolan Liمشاهده پروفایل

استادیار

• Analog Integrated Circuits
• Mixed-Signal Integrated Circuits
• RF Integrated Circuits
• +۳ مورد دیگر

Shaolan Li is an Assistant Professor at the Georgia Institute of Technology's College of Engineering, Department of Electrical and Computer Engineering. He specializes in analog, mixed-signal, and RF integrated circuits with applications in sensors, bioelectronics, and miniature medical imaging systems. B.Eng. (Highest Honor), Hong Kong University of Science and Technology (HKUST), 2012 Ph.D., University of Texas at Austin, 2018 Postdoctoral Fellow, UT Austin Department of Electrical and Computer Engineering, 2018-2019 His research focuses on high-performance data converters , ultra-low-power sensor interfaces , integrated medical imaging systems , and analog design automation . Recent publications emphasize advancements in noise-shaping ADCs, compute-in-memory architectures, and medical imaging SoC integration. Selected awards include: NSF CAREER Award (2023) NIH Trailblazer Award (2023) IEEE Solid-State Circuit Society Predoctoral Award (2018) HKUST Academic Achievement Medal (2012)

David V Andersonمشاهده پروفایل

استاد

• Audio and Psycho-Acoustics
• Machine Learning
• Digital Signal Processing
• +۷ مورد دیگر

David V. Anderson is a Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology's College of Engineering. He holds appointments in the Bioengineering, Computer Engineering, and Machine Learning research focus areas, with technical interests spanning Digital Signal Processing, Bio-Devices, and Low-Power Integrated Circuits. Dr. Anderson earned his B.S. and M.S. from Brigham Young University and his Ph.D. from Georgia Tech in 1993, 1994, and 1999 respectively. His research focuses on audio and psycho-acoustics, machine learning for human auditory characteristics, and real-time signal processing applications. Key projects include the development of digital hearing aid algorithms that became commercial products, ultra-low power signal processing systems, and bio-inspired signal processing architectures. His recent publications (2022-2024) demonstrate a strong focus on tensor methods for robust data analysis, lightweight neural networks for audio classification, and real-time audio processing systems. Research trends show increasing integration of machine learning with traditional signal processing techniques, particularly for hearing assistance applications and hyperspectral data analysis. National Science Foundation CAREER Award (2004) Presidential Early Career Award for Scientists and Engineers (2004) As a Senior Member of IEEE and member of the Acoustical Society of America and Tau Beta Pi, Dr. Anderson has secured significant research funding resulting in over 150 technical publications and 8 patents/patents pending. His work bridges theoretical signal processing with practical applications in hearing assistance and low-power systems. He has been actively involved in computer-enhanced education initiatives and development of real-time audio processing frameworks like Audiosockets.

PerMagnus Lindborgمشاهده پروفایل

دانشیار

• Sound Art
• Composition
• Sonification
• +۱۲ مورد دیگر

PerMagnus Lindborg is a composer, sound artist, and researcher in sound perception with over 150 scholarly publications, compositions, and media artworks. Currently serving as Associate Professor at City University of Hong Kong's School of Creative Media, he teaches sound, music, research skills, and perception while coordinating research degrees. PhD: KTH Stockholm (2015) DEA: Paris (2003) IRCAM: Paris (1999) BMus: Oslo His research focuses on sound perception , sonification , spatial audio , multi-sensory environments , and data-driven art . He pioneered low-cost spatial audio solutions through the Open Ambisonics Toolkit and explores sensory heritage documentation via soundscapes and smellscape analysis in Hong Kong. Recent publications demonstrate trends in climate data sonification , neural audio processing , and immersive sound design . His work intersects computer music , psychoacoustics , and environmental art . Scientific Awards : Best New Director (World Film Carnival, Cannes Short Film Festival, ISA Awards - 2020) First Prize (Stavanger Symphony Orchestra Prize - 2002) Fellowships: The Arctic Circle (2023), SCM Team Research (2020-25), TBA The Current (2016) As principal investigator for Multi-Modal Hong Kong (GRF 2023-25) and Data Art for Climate Action (2020-23), he supervises PhD students in areas like multi-sensory cultural heritage , deep learning audio processing , and virtual reality sound design . He co-founded the SoundLab and DACA Conference .

• Nonlinear Optimization
• Numerical Optimization
• Optimization and Control with PDEs
• +۸ مورد دیگر

Prof. Dr. Nicolas R. Gauger is a Full Professor and Chairholder for Scientific Computing at the University of Kaiserslautern-Landau (RPTU), holding dual appointments in the Department of Mathematics and Department of Computer Science. Since February 2015, he has also served as Director of the Computing Center (RHRZ) at RPTU. His academic career includes positions as Assistant Professor at Humboldt University Berlin (2005-2010), Associate Professor at RWTH Aachen University (2010-2014), and a Visiting Professorship at MIT (March-August 2014). Prof. Gauger earned his Master in Mathematics from Leibniz University of Hanover in 1998 and his Ph.D. in Applied Mathematics from Braunschweig University of Technology in 2003. Prior to his professorial positions, he worked as a Research Scientist in Numerical Methods for Aerodynamics at the German Aerospace Center (DLR) in Braunschweig from 1998 to 2010, while also being a Member of the DFG Research Center MATHEON in Berlin from 2006 to 2010. His research spans multiple disciplines within computational science and engineering, with primary interests in Nonlinear Optimization, Numerical Optimization, Optimization and Control with PDEs, Aerodynamic Shape Optimization, Computational Fluid Dynamics (CFD), Computational Aeroacoustics (CAA), Algorithmic Differentiation (AD), Machine Learning (ML), and High-Performance Computing (HPC). His work bridges theoretical mathematics with practical engineering applications, particularly in aerospace and medical physics. Recent publications show a strong trend toward integrating differentiable programming techniques with traditional computational methods, especially in optimizing experimental setups in fundamental physics and medical applications like proton therapy. Among his notable recognitions are being named an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) in August 2018, receiving a Teaching Award (June 26, 2025), and a Best Student Paper Award at AIAA Aviation 2020. He serves on the Managing Board of ERCOFTAC (European Research Community on Flow, Turbulence and Combustion) and the Steering Committee of the ERCOFTAC Special Interest Group on Design Optimization. Prof. Gauger has been actively involved in multiple research initiatives including the Research and Development Lab 'Data Analysis and Artificial Intelligence' of the Fraunhofer Performance Center (vice spokesperson since March 2016), AICES (2010-2019), (CM)^2 (2014-2019), MathApp (2019-2024), and currently MSO (Modelling, Simulation and Optimisation) since 2024. He leads a research team of approximately 15 members working on projects related to algorithmic differentiation, computational fluid dynamics, and optimization methods. His laboratory, the Scientific Computing research group at RPTU, is involved in multiple interdisciplinary projects including SIVERT (pCT) for fighting cancer with AI and the 'AI Care' project. The team has developed several important software tools including CoDiPack, OpDiLib, and SU2, which are widely used in the computational science community for algorithmic differentiation and aerodynamic optimization.

Qing-Long Hanمشاهده پروفایل

استاد

• Control Theory
• Control Engineering
• Networked Control Systems
• +۱۲ مورد دیگر

Professor Qing-Long Han is Pro Vice-Chancellor (Research Quality) and Distinguished Professor at Swinburne University of Technology, Australia. He has previously held academic and leadership roles at Griffith University and Central Queensland University. He is a globally recognized expert in control theory and engineering, particularly in networked and time-delay systems. Pro Vice-Chancellor (Research Quality), Swinburne University of Technology Distinguished Professor, Swinburne University of Technology Editor-in-Chief, IEEE/CAA Journal of Automatica Sinica President-Elect, Asian Control Association His research interests span Control Engineering, Networked Control Systems, Multi-Agent Systems, Smart Grids, Unmanned Surface Vehicles, Neural Networks, and Secure Control . He is deeply involved in resilient and secure control under cyber threats, optimization, and industrial metaverse applications. His work integrates control theory with AI, IoT, and industrial automation. The recent publications highlight a strong trend toward secure, resilient, and distributed control in cyber-physical systems, particularly under attacks (replay, falsification), communication constraints, and delays. There is a clear focus on real-world applications in autonomous vehicles, trains, drilling systems, and chiller networks, using advanced methods such as reinforcement learning, event-triggering, and neurodynamic optimization. Award highlights include: IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2024) Norbert Wiener Award (2021) M. A. Sargent Medal (2021) IEEE Andrew P. Sage Best Paper Award (2019, 2020, 2022) Highly Cited Researcher (Clarivate, multiple years) Professor Han actively supervises PhD students in cutting-edge control topics and leads major research grants from the Australian Research Council and National Natural Science Foundation of China. He is a Fellow of IEEE, IFAC, Engineers Australia (Honorary), and the Chinese Association of Automation. He also contributes to editorial leadership in top journals and professional societies. He is associated with research teams and editorial boards in control systems, automation, and intelligent systems, fostering international collaboration and innovation in engineering.

• Biological robots
• Modular robotics
• Marine automation
• +۵ مورد دیگر

Houxiang Zhang is a Professor and Deputy Head of Department (Research) at the Department of Ocean Operations and Civil Engineering , Faculty of Engineering , Norwegian University of Science and Technology (NTNU) in Ålesund, Norway. He joined NTNU in 2011 and holds an industry-supported gift professorship (2011-2016). His research focuses on biological robots/modular robotics and virtual prototyping/marine mechatronics , with over 300 publications and 8 patents. Ph.D. in Mechanical and Electronic Engineering (2003) Habilitation in Informatics (2011) Research Interests include: Bio-inspired robotics and system design Marine automation and ship intelligence Digital twin technology and co-simulation Hybrid modeling approaches Artificial intelligence applications in maritime systems Scientific Awards and recognitions: Multiple IEEE best paper awards (2008, 2014, 2015, 2023, 2024) Finalist in IEEE Robotics and Automation conferences Dimitris N. Chorafas Award for outstanding PhD work Academy memberships: IEEE Senior (2012), NTVA (2019), DKNVS (2024) Advising and Research Leadership : Supervised 21 PhD/Master's students WP Leader for EU Horizon project RoboSAPIENS (2024-2026, 7.5M Euros) Key member in Norwegian Digital Ocean Space-Møre Ocean Lab (2023-2030, 30.7M NOK) Coordinated major SFI projects (2015-2022, 190M NOK each)

Tjerand Sildeمشاهده پروفایل

دانشیار

• Lattice-Based Cryptography
• Zero-Knowledge Protocols
• Post-Quantum Cryptography
• +۹ مورد دیگر

Tjerand Silde is an Associate Professor in Cryptology at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU). He serves as Research Group Leader of the NTNU Applied Cryptology Lab and as Security and Cryptography Expert at Pone Biometrics . His academic journey includes a PhD thesis titled Privacy-Preserving Cryptography from Zero-Knowledge Proofs (2022). Primary affiliation: NTNU (Department of Information Security and Communication Technology) Secondary affiliation: NTNU Department of Mathematical Sciences Industry role: Security and Cryptography Expert at Pone Biometrics His research focuses on lattice-based cryptography and zero-knowledge protocols , with broader interests in post-quantum cryptography , anonymous communication , multiparty computation , homomorphic encryption , electronic voting , and secure implementation . The research demonstrates trends in advancing lattice-based cryptographic solutions for real-world applications, particularly in authentication, e-voting, and secure communication. Notable scientific achievements include receiving a 765K NOK equipment grant from the IE Faculty - Research (2025) to establish the CRYPTO-LAB for cryptographic research and education. He has held leadership roles as General Chair for IACR PKC 2025 and coordinates the Cryptographic Engineering profile in NTNU's master's program. His teaching portfolio includes courses like TTM4205 Secure Cryptographic Implementations , IIK8105 Quantum-Secure Cryptography , and TM8107 Cryptographic Protocols and Applications . He actively supports Electronic Frontier Foundation , Tor Project , and Signal Foundation .

Evan Morganمشاهده پروفایل

پژوهشگر

• Design Informatics
• Human-Computer Interaction
• Affective Computing
• +۵ مورد دیگر

Evan Morgan is a Researcher at the University of Edinburgh 's Edinburgh College of Art , specializing in Design Informatics . His work bridges technical expertise with behavioral research, focusing on data-driven technologies, affective computing, and participatory design approaches. PhD in Media and Arts Technology (2016), Queen Mary University of London MEng in Biomedical Engineering (2008), Imperial College London Current research interests include social and behavioral design informatics , with specific focus areas in mixed methods behavioral measurement , human behavior understanding , and design for behavior change . His projects often intersect with climate change mitigation and digital economy through blockchain applications. Recent publications demonstrate expertise in climate change technology (2024), blockchain-based attribution (2024), and human-centered energy systems . Notable grant funding includes AHRC support for Creative Informatics (2018-2024) and University of Edinburgh's EFI Research Award for data education tools. Scientific contributions span multiple domains: Climate Change: Participatory design for climate action Blockchain: Non-transferable NFTs and decentralized platforms Energy Systems: Behavior-based consumption reduction Active in public engagement, Morgan has contributed to major exhibitions including ARS Electronica and Edinburgh Science Festival . He applies sensor technologies and digital systems in both creative industries and social care contexts.

• Analog and Mixed-Signal Integrated Circuit Design
• Time-Domain Signal Processing
• Sensing Interfaces
• +۳ مورد دیگر

Ahmed Mohey is a Doctoral Researcher at Aalto University's Department of Electronics and Nanoengineering. His research focuses on analog and mixed-signal integrated circuit design with applications in time-domain signal processing, power management, and in-sensor/memory computing. Education: B.Sc. and M.Sc. in Electrical Engineering from Ain Shams University (2012, 2018) His research interests emphasize time-domain signal processing , sensing interfaces , and in-sensor computing for enhanced embedded systems performance. Recent publications highlight advancements in all-digital neural network accelerators and precision in-sensor processing techniques using cutting-edge 22-nm technology. He is affiliated with the Jussi Ryynänen Group , focusing on next-generation integrated circuit solutions for low-power and high-efficiency applications.

Gregor Oelsnerمشاهده پروفایل

پژوهشگر

• Superconducting Quantum Circuits
• Quantum Sensing
• Circuit Quantum Electrodynamics
• +۵ مورد دیگر

Dr. Gregor Oelsner is a researcher at Friedrich Schiller University Jena, affiliated with the Quantum Circuits work group under the Research Department Quantum Systems. He specializes in superconducting quantum circuits, Josephson junctions, and quantum sensing technologies. Education: Diplom in Physics (2008), PhD in superconducting qubits (2016) from Friedrich Schiller University Jena. His research spans circuit quantum electrodynamics, quantum metamaterials, and hybrid quantum systems. Recent work focuses on scalable fabrication of Josephson junctions, microwave photon detection for axion searches, and cryogenic sensor development. Key article trends include advancements in quantum circuit integration , Josephson junction reliability , and magnetic field sensing . He collaborates on projects involving alkali vapor cells and superconducting resonators . His team develops wafer-scale fabrication processes for quantum devices, optimizing critical current control and low-inductance interconnects . Applications include quantum computing and ultrasensitive magnetic field detection .

Jacob Rosensteinمشاهده پروفایل

دانشیار

• Bioelectronics
• Integrated Circuit Design
• Biosensors
• +۳ مورد دیگر

Jacob Karl Rosenstein is an Associate Professor of Engineering at Brown University , specializing in mixed-signal electronic design and bioelectronics . His research focuses on developing high-resolution, low-power interfaces to biological systems using integrated circuits and materials. Education : PhD (2012), MS (2009) in Electrical Engineering from Columbia University; ScB (2005) from Brown University. Research Interests : Bioelectronics, biosensor arrays, nanopore technology, electrochemical imaging, and molecular computation. Teaching : Courses include Digital Computing Systems (ENGN 0500), Electrical Circuits and Signals (ENGN 0520), Mixed-Signal Electronic Design (ENGN 2912K), and Topics in Bioelectronics (ENGN 2912L). Scientific Contributions include advancements in CMOS-integrated nanopore sensors, high-speed bioelectronic interfaces, and electrochemical imaging systems. His recent work involves machine olfaction, capacitance tomography, and ultrafast thermal cameras. Awards : Best Paper Award at BioCAS 2021, Best Paper Nomination at ISCAS 2025, Halpin Capstone Prize 2023. Students advised include Kangping Hu (PhD 2022), Steven (PhD candidate), Manar Incandela (extended paper 2024), and Yutong (Master’s thesis 2024). The Rosenstein Laboratory for Embedded Bioelectronics fosters interdisciplinary research between electronic systems and biological applications.

Timothy D Johnsonمشاهده پروفایل

استاد

• Bayesian Methods
• Statistical Image Analysis
• High-Dimensional Data
• +۵ مورد دیگر

Timothy D Johnson is a Professor in the Biostatistics department at the University of Michigan School of Public Health . His research focuses on Bayesian statistical methods, neuroimaging analysis, and biomedical data modeling. Education: PhD, University of California, Los Angeles (1997) MS, University of California, Riverside (1986) BS, University of California, Riverside (1984) Research Interests: Dr. Johnson develops advanced Bayesian methodologies for high-dimensional biomedical data, particularly in neuroimaging applications. His work addresses challenges in spatial statistics, mixture models, and variable parameter spaces, with applications spanning neuroscience, cancer, endocrinology, and radiology. Publications Trends: His recent work emphasizes scalable Bayesian frameworks for neuroimaging, applications of stimulated Raman histology in oncology, and statistical solutions for clinical radiology. Many articles focus on improving diagnostic accuracy in gliomas and understanding social cognition through neuroimaging.