جست‌وجوی استاد و پژوهشگر

Wong Kok Sheikمشاهده پروفایل

دانشیار

• Multimedia Signal Processing
• Cybersecurity
• Digital Health
• +۳ مورد دیگر

Associate Professor Wong Kok Sheik is the Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University (Japan) and advanced degrees in Computer Science and Mathematics from Utah State University (USA). His research focuses on multimedia signal processing, cybersecurity, and digital health, with contributions to data hiding, encryption, and smart grid security. He leads a EU-funded WAge project on post-pandemic workplace health interventions. Education: PhD in Engineering, Shinshu University, Japan (2009) Masters in Computer Science & Mathematics, Utah State University, USA (2005-2004) Bachelor of Science, Utah State University, USA (2002) Professional Roles: Associate Editor, IEEE Signal Processing Letters Member, IEEE Signal Processing Society’s IFS Technical Committee Board Member, APSIPA Multimedia Security and Forensics (MSF) Committee His research interests span multimedia forensics, encrypted domain processing, and cybersecurity frameworks. Notable projects include recovery of missing coefficients in compression standards and HDR imaging enhancement. Recent work integrates digital health, addressing workplace mental/physical health in post-pandemic environments. Publications reflect expertise in watermarking, encryption, and biometric security. Key contributions include encryption-resistant data embedding and secure smart grid analytics. Awards include IEEE CES Service Awards (2015, 2016) and Best Paper recognitions. He supervises over 20 PhD/MSc students, focusing on topics like biometric recognition, data hiding, and anomaly detection. Grants include a €2M EU Horizon 2020 project (IDENTITY) and a RM146k smart grid initiative. His teaching emphasizes foundational IT research methods and theoretical computer science.

• Theoretical computer science
• probabilistic programming
• probability theory
• +۲ مورد دیگر

Dr Fredrik Dahlqvist serves as a Lecturer in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he contributes to the Centre for Fundamental Computing and AI. His academic profile reflects a strong commitment to theoretical foundations in computing with direct applications to artificial intelligence research. His research spans theoretical computer science with focused expertise in probabilistic programming, probability theory, category theory, and mathematical logic. Dahlqvist investigates the mathematical frameworks governing probabilistic computation, developing rigorous methodologies for uncertainty quantification in computational systems. His work establishes formal connections between abstract category theory and practical probabilistic programming implementations, creating bridges between pure mathematics and applied AI. Recent publications (2023-2024) demonstrate cohesive thematic development across probabilistic programming semantics, neural network optimization, and formal verification. Key trends include error analysis in probabilistic floating-point systems, neural network pruning techniques based on geometric similarity, and categorical foundations for graded computation. His research consistently merges theoretical depth with practical AI challenges, particularly in verification of probabilistic models and optimization of learning architectures. Dr Dahlqvist has not received any documented scientific awards or fellowships according to available sources. He actively supervises two PhD students: Gregor Meehan researching "Representation Learning For Musical Audio Using Graph Neural Network-Based Recommender Engines" and Niki Omidvari conducting theoretical work in computer science foundations. Dahlqvist currently holds a £15,000 grant from the Academy of Medical Sciences for "Learning from Each Other: Neural Networks and Finite Automata" (March 2025-March 2026), investigating formal connections between neural architectures and automata theory. As an integral member of Queen Mary's Centre for Fundamental Computing and AI, Dahlqvist collaborates within a multidisciplinary research environment focused on advancing theoretical underpinnings of artificial intelligence. His work contributes to the Centre's mission of developing mathematically rigorous frameworks for next-generation AI systems through category-theoretic approaches and probabilistic reasoning.

Albert Qiaochu Jiangمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Theorem Proving
• Artificial Intelligence
• +۳ مورد دیگر

Albert Qiaochu Jiang serves as a Visiting Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His research integrates machine learning with formal theorem proving, focusing on neural theorem provers and mathematical reasoning systems. He leads the reasoning team at Mistral AI while maintaining academic supervision at Cambridge. His research interests center on machine learning for theorem proving , with specific expertise in neural-symbolic integration, autoformalization, and large language models for mathematical reasoning. His work bridges artificial intelligence with formal verification, developing systems that enhance automated reasoning capabilities through neural networks. Current projects involve improving premise selection for theorem provers, multilingual mathematical formalization, and creating efficient architectures for mathematical language models. Analysis of his recent publications reveals a strong focus on advancing neural theorem proving through innovative architectures like Target-Based Automated Conjecturing and Magistral. His research trajectory shows increasing sophistication in integrating language models with formal verification systems, with significant contributions to datasets like Numinamath and frameworks like Llemma. Key trends include optimizing compute efficiency in proof generation, enhancing multilingual mathematical reasoning, and developing interactive human-AI collaboration systems for formal mathematics. While no scientific awards are currently documented in available sources, his research output demonstrates significant impact in the intersection of AI and formal methods. As leader of Mistral AI's reasoning team, Jiang directs research on neural theorem proving systems while contributing to academic supervision at Cambridge. His work involves substantial industrial-academic collaboration, leveraging resources from both institutional contexts to advance mathematical AI. Current projects focus on creating practical systems for mathematical automation with real-world verification applications. His research operates at the intersection of academia and industry through Mistral AI's reasoning team, where he develops neural theorem proving systems with practical applications in formal verification. This dual affiliation enables rapid translation of theoretical advances into deployable tools for mathematical automation.

Sidi Luمشاهده پروفایل

استادیار

• Edge Computing
• Autonomous Vehicles
• Artificial Intelligence
• +۳ مورد دیگر

Sidi Lu is an Assistant Professor in the Department of Computer Science at William & Mary. His research focuses on edge computing, autonomous vehicles, and applied AI, emphasizing reliable and efficient computing systems for transportation and IoT. He joined William & Mary in August 2023 after completing his PhD at Wayne State University, where he specialized in vehicle computing and edge intelligence. Education: PhD in Computer Science, Wayne State University (2023) Exchange Undergrad in Electrical and Computer Engineering, University of California, Riverside B.Eng in Electronic Information Engineering, Xidian University Research Interests: Sidi explores edge computing frameworks for connected vehicles, teleoperation in autonomous systems, and AI-driven solutions for real-world challenges. His work bridges theory and practice, addressing scalability, security, and efficiency in vehicle-edge-cloud ecosystems. Awards & Recognition: Class of 2024 Influencer Recognition (William & Mary) NSF CRII Award (2023) Ralph H. Kummler Award (2022) Dr. Michael E. Conrad Graduate Research Award (2021) Grants & Services: Sidi leads NSF-funded projects and serves on technical committees for top conferences like ACM/IEEE SEC and IEEE MOST. He mentors students in research and organizes events at Grace Hopper Celebrations. Labs & Teams: Director of the Vehicle Computing Lab at William & Mary, focusing on advancing autonomous systems and edge-enabled applications.

Austin Mooreمشاهده پروفایل

مدرس ارشد

• Dynamic Range Compression
• Music Production Techniques
• Distortion in Audio
• +۲ مورد دیگر

Austin Moore is a Senior Lecturer in music production and sound engineering at the University of Huddersfield, where he leads courses in Sound Engineering and Creative Music Production. His research examines distortion, compression, and non-linear signal processing in music production. His work analyzes sonic signatures of audio hardware, perceptual effects of dynamic range compression, and spatial audio techniques. He investigates how distortion shapes musical aesthetics across genres from rock to electronic music. He co-edited Distortion in Music Production: The Soul of Sonics (Routledge 2023) and co-edits the Journal of Music Production Research. He teaches modules on sound engineering, mixing, synthesis, and electronic music production.

Mark Mynettمشاهده پروفایل

مدرس ارشد

• Metal Music Production
• Heavy Metal
• Music Technology
• +۳ مورد دیگر

Mark Mynett is a Senior Lecturer in Music Technology and Production at the University of Huddersfield, affiliated with the School of Computing and Engineering and the Department of Media, Humanities and the Arts. He holds positions in the Centre for Audio and Psychoacoustic Engineering and the Centre for Research in Music and its Technologies. With a PhD in engineering and a BSc in Popular Music Production, his expertise spans music production, heavy metal genres, and audio engineering. His research focuses on 'heaviness' in metal music production, explored through projects like the 2020-2023 'Heaviness in Metal Music Production' initiative. He is a Fellow of the Higher Education Academy and author of Metal Music Manual (2016), Routledge. Mark has a dual career as an academic and professional musician/producer, with six commercial album releases and collaborations with renowned producers like Jens Bogren. His work includes producing, engineering, and mixing for indie, rock, and metal genres, alongside contributions to publications like Sound on Sound and Computer Music . He actively supervises PhD students and presents at international conferences, including the 2025 Audio Engineering Society Convention. His research outputs emphasize genre-specific production techniques, historical analysis of metal music, and listener perception of sonic elements. Key projects include netnographic studies of online metal communities and investigations into mixing conventions. His work bridges artistic practice with academic rigor, addressing topics like dynamic range management, frequency balancing, and the cultural evolution of metal production. Collaborations span academic and industry contexts, reflecting his commitment to advancing both music education and professional production standards.

Emanuele Principiمشاهده پروفایل

دانشیار

• Electrical Engineering
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Emanuele Principi serves as an Associate Professor in the Department of Information Engineering at the College of Engineering, Università Politecnica delle Marche (UNIVPM). His academic appointment falls under the scientific sector IIET-01/A - Elettrotecnica (Electrical Engineering). Based at Via Brecce Bianche 12 in Ancona, Italy, Professor Principi maintains regular office hours on Fridays from 15:00-18:00 via Microsoft Teams, and can be contacted at e.principi@univpm.it or by phone at 0712204626. Professor Principi's research spans electrical engineering with a strong focus on signal processing, machine learning applications, and energy systems. His work particularly emphasizes non-intrusive load monitoring (NILM), acoustic signal analysis, and renewable energy integration. He has developed innovative approaches combining deep learning with electrical engineering principles to address challenges in energy disaggregation, sound event classification, and smart grid technologies. His research bridges theoretical advancements with practical applications in sustainable energy systems and intelligent monitoring solutions. Analysis of Professor Principi's publication trends reveals a consistent research trajectory with increasing focus on machine learning applications for energy systems. His recent work (2023-2025) demonstrates growing sophistication in knowledge distillation techniques for non-intrusive load monitoring, with increasing attention to interpretability and edge computing implementations. He has also expanded his research into photovoltaic-thermal systems with thermoelectric generators, showing diversification into renewable energy conversion while maintaining his core expertise in electrical signal processing. Professor Principi has made significant contributions to the fields of non-intrusive load monitoring and acoustic signal processing through his extensive publication record. His work on knowledge distillation frameworks, weakly supervised learning approaches, and real-time signal processing systems has advanced methodologies in energy monitoring and sound event classification. Professor Principi actively advises students and researchers in electrical engineering, signal processing, and machine learning applications. His research projects likely involve collaborations with industry partners in energy technology and smart grid development, though specific grant details are not provided in the available information. He appears to lead or participate in research teams focused on developing practical implementations of his theoretical work, particularly in edge computing applications for energy monitoring. Professor Principi's laboratory or research group likely focuses on signal processing for energy systems and acoustic analysis, with equipment supporting electrical measurements, audio signal acquisition, and machine learning model development. His work suggests integration of hardware and software systems for real-world implementation of monitoring technologies.

Tim Fingscheidtمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Speech Processing
• +۶ مورد دیگر

Prof. Tim Fingscheidt is a Professor at the Technical University of Braunschweig, heading the Signal Processing and Machine Learning Group within the Institute for Communications Technology. His career spans academia and industry, including roles at AT&T Labs (USA), Siemens, and leadership in research teams. He specializes in speech processing, audio technologies, and communication systems quality assessment. Education : 1987-1993: Dipl.-Ing. in Electrical Engineering (RWTH Aachen University, with distinction) 1998: PhD (RWTH Aachen University, with distinction) Research Interests : His work focuses on signal processing fundamentals, machine learning applications in audio/speech systems, error concealment in mobile transmissions, and automotive hands-free system standards. He actively contributes to ITU-T recommendations and chairs ITG committees on speech communication and audiotechnology. Awards : Received prestigious recognition including the IEEE Fellow (2025), ITG Fellow (2023), and multiple ITG/VDE prizes. Professional Activities : Leadership roles in EURASIP, VDE ITG, and IEEE. Standardization expertise in automotive communication systems (ITU-T P.1100/P.1110). Labs & Teams : Leads the Signal Processing and Machine Learning Group, collaborating on cutting-edge projects in audio/speech technologies and machine learning.

Milan R. Dincicمشاهده پروفایل

دانشیار

• Metrology and Measurement Technology
• Signal Processing
• Data Compression
• +۳ مورد دیگر

Milan R. Dincic is an Associate Professor at the University of Niš, Faculty of Electronic Engineering, Department of Telecommunications. He holds a PhD in Metrology and Measurement Technology (2017) and a Master’s degree in Telecommunications (2012), both from the same institution. His academic career includes research on quantization techniques, signal processing, and metrology applications. Dr. Dincic’s research focuses on optimizing quantizers for measurement signals, including Gaussian and Laplacian distributions. He has contributed to adaptive quantization, lossless coding algorithms, and hybrid systems integrating codecs like ITU-T G.711. His work bridges theoretical signal processing with practical applications in telecommunications and sensor systems. His publications (26 journal articles) emphasize quantizer design, image sampling, and compression techniques. He co-authored the textbook "Sensors in Vehicles" (2014). Current projects include national/international collaborations in metrology and measurement technology. His lab focuses on advancing signal processing methodologies for precision measurement systems.

• Embedded Systems
• AI Algorithms
• FPGA/VHDL Design
• +۵ مورد دیگر

Jürgen Wassner is a Professor of Edge Computing and Co-Head of the Competence Center for Intelligent Sensors and Networks at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a master's degree from Technical University of Dresden and a PhD from ETH Zurich. His career spans industry roles in Silicon Valley Group Inc. and telecommunication R&D before joining academia in 2007. His research focuses on embedded systems, AI at the edge, FPGA/VHDL design, real-time systems, and powerline communication . He leads projects such as 'Visual-Servoing Testbed with AI-Hardware in the Loop' and 'Low-Cost High-Performance Intelligent Camera for Space Debris Mitigation,' collaborating with companies like Diehl Aerospace. Recent publications emphasize wire fault detection using powerline communication, AI-based odor classification systems, and hardware acceleration for CNNs. His work bridges theoretical research with practical applications in avionics, rail freight, and space debris mitigation. Wassner advises Master of Science in Engineering students and has been recognized for his media engagement including a feature in the Luzerner Zeitung. He teaches Digital Design (FPGA, VHDL) and Digital Signal Processing , emphasizing hands-on implementation of cutting-edge systems.

• Sound Synthesis
• Physical Modeling
• Virtual Reality Audio
• +۶ مورد دیگر

Cumhur Erkut is an Associate Professor in the Sound and Music Computing group within the Department of Architecture, Design and Media Technology at Aalborg University, Copenhagen, Denmark. With a publication record spanning over two decades from 2000 to present, his research bridges computer science, audio engineering, and creative arts. His primary research interests include Sound Synthesis, Physical Modeling of musical instruments, Virtual Reality Audio, Sonic Interaction Design, and Embodied Interaction. His work demonstrates a consistent focus on the intersection of technology and human experience, particularly in how sound and movement interact in digital environments. His recent publications show a strong shift toward AI-driven audio processing, voice conversion, and differentiable digital signal processing techniques. Dr. Erkut's publication trends reveal an evolution from traditional physical modeling of musical instruments (particularly string instruments like tanbur, clavichord, and guitar) toward more contemporary applications in virtual reality, embodied interaction, and AI-powered audio processing. His work consistently emphasizes real-time performance considerations and human-centered design principles. He has served in significant academic roles, including organizing the 17th International Conference on New Interfaces for Musical Expression (NIME 2017) at Aalborg University, demonstrating his leadership within the international research community. His collaborative network is extensive, with frequent co-authorship with Stefania Serafin, Vesa Välimäki, Antti Jylhä, Rolf Nordahl, and other prominent researchers in the sound and music computing field. These collaborations span multiple institutions across Europe, reflecting the international nature of his research.

Ammar Belatrecheمشاهده پروفایل

مدرس ارشد

• bio-inspired intelligent systems
• machine learning
• spiking neural networks
• +۵ مورد دیگر

Dr. Ammar Belatreche is a Senior Lecturer in Computer Science and Programme Leader for the MSc Advanced Computer Science at Northumbria University's Department of Computer and Information Sciences. He joined Northumbria University in May 2016 after previous positions as a Research Associate and Lecturer at Ulster University. He is an active member of the Computational Intelligence and Visual Computing (CIVC) research group. Dr. Belatreche earned his PhD in Computer Science from Ulster University in 2007. His professional qualifications include: Member of the Association of Computing Machinery (ACM) since 2012 Fellow of the Higher Education Academy (FHEA) since 2010 Member of the Institute of Electrical & Electronic Engineers (IEEE) since 2009 His research focuses on bio-inspired intelligent systems, machine learning, spiking neural networks, face detection and recognition, structured and unstructured data analytics, capital markets engineering, and image processing. Dr. Belatreche has extensive experience across academic and R&D in these areas, leading numerous research and consultancy projects. His recent work demonstrates a strong emphasis on neuromorphic computing, particularly spiking neural networks and their applications in computer vision, financial analysis, and biometrics. Analysis of his recent publications shows a clear trend toward advancing spiking neural network architectures, with particular focus on quantization, pruning, and binary implementations to improve efficiency. His research spans multiple domains including computer vision (face recognition, palm-vein recognition), financial technology (stock price manipulation detection), and neuromorphic engineering. Many of his recent papers (2024-2025) appear in top-tier conferences like ICLR and journals like IEEE Transactions on Neural Networks and Learning Systems. Dr. Belatreche has received professional recognition including: Fellowship with the Higher Education Academy (FHEA) Role as Associate Editor for the journal Neurocomputing He has successfully supervised or co-supervised 8 PhD students to completion and serves as a Program Committee Member and reviewer for numerous international conferences and journals. As Programme Leader for the MSc Advanced Computer Science, he plays a significant role in shaping postgraduate education in computer science at Northumbria University. His research group work bridges theoretical advances in neural computation with practical applications across multiple domains. Based in CIS 305 at Northumbria University's Newcastle campus, Dr. Belatreche continues to advance research in neuromorphic computing and its applications while contributing to academic leadership through his programme leadership role.

Antonio Servettiمشاهده پروفایل

استادیار

• Speech and Audio Processing
• Multimedia Communications
• Real-Time Multimedia Network Protocols
• +۷ مورد دیگر

Antonio Servetti is an Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy, where he has been a faculty member since 2007. He is affiliated with the Internet Media Group (IMG) and the Interdepartmental Center PIC4SeR for Service Robotics. His work bridges multimedia processing, network communications, and web technologies. MS in Computer Engineering, Politecnico di Torino, 1999 PhD in Computer Engineering, Politecnico di Torino, 2004 Visiting Scholar, University of California, Santa Barbara, 2003 His research focuses on speech and audio processing , multimedia communications over wired and wireless networks , and real-time web-based multimedia applications . Key interests include WebRTC, Web Audio, HTTP adaptive streaming, and perceptual quality assessment. He has contributed to the development of secure multimedia transmission techniques, including selective encryption of speech and audio. The recent publications highlight a strong trend toward AI-driven modeling of subjective quality in multimedia, especially through deep learning for image and video quality prediction, understanding observer behavior, and remote music performance systems. His work often involves collaboration with researchers in the VQEG JEG-Hybrid group and the NEXA Center. Best Paper Award, Web Audio Conference 2021 Dr. Servetti has led and contributed to several research projects, including BRIC-2024 (acoustics in educational settings), PNRR HiFiReM (remote music education), and INAR (artistic research). He teaches courses such as 'Web Applications', 'Machine Learning for Vision and Multimedia', and 'Digital Audio Processing' across various engineering programs. He is also involved in educational governance as a member of academic councils for multiple degree programs. He is a core member of the Internet Media Group (IMG) , which focuses on multimedia processing and transmission, and contributes to the VQEG JEG-Hybrid working group on video quality assessment, where he develops frameworks for reproducible research and modeling of human perception.

Ingrid Daubechiesمشاهده پروفایل

استاد

• Applied Mathematics
• Signal Processing
• Image Compression
• +۳ مورد دیگر

Ingrid Daubechies is a prominent mathematician and physicist known for her foundational work in wavelet theory. Born in Belgium, she earned her B.S. and Ph.D. from the Free University of Brussels. She transitioned from a physics background to applied mathematics, becoming a leading authority on wavelets while at AT&T Bell Laboratories. In 1993, she became the first woman tenured professor of mathematics at Princeton University, later joining Duke University as the James B. Duke Professor of Mathematics. Her research spans signal processing, image compression, and interdisciplinary applications in biology and art restoration. Daubechies' academic journey includes roles at Bell Labs, Princeton, and Duke, alongside extensive collaborations in computational biology and mathematics education. She has pioneered wavelet-based algorithms for data compression (JPEG 2000), developed metrics for biological morphology analysis, and contributed to art restoration techniques using mathematical imaging. Her leadership roles include presidency of the International Mathematical Union (2011–2014) and advocacy for women in mathematics. Recognized with prestigious awards like the National Academy of Sciences Award, John von Neumann Lecture Prize, and Nemmers Prize, her work bridges pure mathematics and applied sciences. She has supervised numerous research projects and authored influential texts, including Ten Lectures on Wavelets . Her legacy includes advancing computational tools for diverse fields, from medical imaging to evolutionary biology.

Stefanos Papadakisمشاهده پروفایل

مدرس

• Software Defined Radio
• Cognitive Radios
• Radio Resource Management
• +۵ مورد دیگر

Stefanos Papadakis serves as a Research Staff Scientist at the Telecommunications and Networks Laboratory (TNL) of the Institute of Computer Science at Foundation for Research and Technology-Hellas (FORTH) and holds an Adjunct Lecturer position in the Department of Computer Science at the University of Crete. Since 2001, he has pioneered hardware and software prototyping at TNL-FORTH, currently leading the Software Defined Radio (SDR) group he established to drive vertical integration from physical layer design to application development. His educational foundation includes a Physics degree (2001) and M.Sc. (2004) and Ph.D. (2009) in Computer Science, all earned at the University of Crete. Teaching responsibilities encompass core courses CS-330: Introduction to Telecommunication Systems Theory and CS-435: Network Technology & Programming. Papadakis' research spans wireless innovation frontiers including software-defined/cognitive radios, spectrum sharing, heterogeneous networking, position location, radio propagation modeling, and emergency communications. His work emphasizes practical implementation, yielding functional prototypes across the entire communications stack. Notable contributions include GPU-accelerated SDR frameworks, robust spectrum virtualization techniques, and emergency response communication systems validated through international competitions. Analysis of his 15 most recent publications (2010-2016) reveals dominant themes in SDR optimization for IoT and critical communications, with significant focus on GPU parallelization, interference management in dense networks, and real-time spectrum sharing mechanisms. His experimental approach consistently bridges theoretical models with hardware validation, particularly in emergency response and heterogeneous network scenarios. Key recognitions include: Ericsson Award of Excellence in Telecommunications for position location research First place in PENED doctoral proposal competition Fourth place in IEEE DySPAN 2015 5G Spectrum Challenge Second place in Virginia Tech ShaRC 2016 with the 'Skynet' cognitive radio system Mentorship spans 16 undergraduate projects (11 completed), 8 M.Sc. students (3 completed theses), and 1 Ph.D. candidate. His research is sustained through major EU and national projects including REDComm (emergency communications), EU-MESH (metropolitan networks), RERUM (IoT security), and Heraklion smart city initiatives. The SDR group maintains critical infrastructure like the Heraklion metropolitan wireless network, FORTH campus network, and specialized mobile emergency nodes equipped with multi-radio SDR platforms, satellite transceivers, and high-performance computing resources.