جستجوی استاد راهنمای دانشگاهی

Jeff Albertمشاهده پروفایل

دانشیار

• Improvisation
• Jazz Performance
• Live Computer Music
• +۳ مورد دیگر

Jeff Albert is the Interim Chair and Associate Professor in the School of Music at Georgia Tech’s College of Design. He holds a PhD in Experimental Music and Digital Media from LSU and degrees from Loyola University and the University of New Orleans. His work bridges jazz performance, improvisation, and music technology, with contributions to laptop orchestras and live computer music paradigms. Albert has performed globally, contributed to over 60 recordings, and was part of the Grammy-winning 2017 album *Porcupine Meat*. His research focuses on human-computer interaction in music and audio pedagogy. Education: B.M. (Loyola University New Orleans), M.M. (University of New Orleans), PhD (Experimental Music & Digital Media, LSU). Former roles include faculty positions at Loyola University, Xavier University, and the University of New Orleans. Research emphasizes improvisation frameworks, digital music systems, and performance technologies. Notable projects include founding the Laptop Orchestra of Louisiana (LOLs) and developing interactive musical partner systems. Awards include DownBeat Critics Poll recognition and Boston Globe's Top Jazz Album honor. Grants and collaborations include the Arts @ Tech Catalyst grant for *Sonic Ecology* (2025). Labs/teams include Georgia Tech Laptop Orchestra and involvement in festivals like Instigation Festival. His work spans academic publications, performances, and audio engineering.

Zeljko Ignjatovicمشاهده پروفایل

دانشیار

• Analog VLSI Integrated Circuits
• Sigma-delta A/D Converters
• Ultra-low Power Sensor Interfaces
• +۳ مورد دیگر

Zeljko Ignjatovic is an Associate Professor in the Department of Electrical and Computer Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD in Electrical and Computer Engineering from the University of Rochester (2004). His expertise includes analog VLSI design, high dynamic-range Sigma-delta converters, and ultra-low power sensor interfaces. Education: PhD in ECE, University of Rochester (2004). Research focuses on advancing analog and digital signal processing circuits. Key areas include: High-precision Sigma-delta A/D converters Fully-digital image sensors with pixel-level conversion Focal plane compression reducing computational demands Audio signal data hiding techniques Notable contributions include a 100-fold improvement in dynamic range and 50-fold power reduction in CMOS image sensors through Sigma-delta pixel architectures. Ongoing work explores further optimization of low-power sensor interfaces and compression methodologies. No scientific awards or grants are explicitly listed in the provided text.

Sarah Smithمشاهده پروفایل

دانشیار

• Audio Signal Processing
• Musical Acoustics
• Reverberant Signal Analysis
• +۳ مورد دیگر

Sarah Smith is an Associate Professor of Electrical and Computer Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. She specializes in audio signal processing and musical acoustics, focusing on instrumental vibrato and reverberant signal analysis. Her instructional role includes teaching introductory audio engineering courses and project-based musical acoustics. Education: PhD in Electrical Engineering (University of Rochester, 2019); MA in Music, Science, and Technology (Stanford University’s CCRMA); BA in Physics and Music (Cornell University). Research interests include audio signal processing techniques for reverberant environments, vibrato analysis in musical instruments, and multichannel sound reproduction systems. Her work bridges engineering and music through projects like vibrato integration in artificial reverberation and room impulse response modeling. Publications emphasize practical applications of acoustic theory, including educational tools for audio engineering and advanced measurement techniques for multichannel systems. Her courses incorporate project-based learning strategies to engage students across academic disciplines.

Andrew Gilbertمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Generative Modelling
• +۶ مورد دیگر

Dr. Andrew Gilbert is an Associate Professor in Machine Learning at the University of Surrey, co-leading the Centre for Creative Arts and Technologies (C-CATS). His work bridges computer vision, generative AI, and creative industries, focusing on interpretable and human-centered systems. He leads a multidisciplinary team of 17 PhD students and has published over 100 papers in top venues like CVPR, ECCV, and BMVC. Education & Research: Gilbert earned his PhD in Computer Vision from the University of Surrey (2008) and has pioneered advancements in video understanding, style modeling, and AI-driven media production. His research integrates technical innovation with creative applications, such as AI-generated narratives and blockchain-based content rights. Research Trends: Recent work emphasizes long-term video analysis (DANTE-AD), motion-focused self-supervision (MOFO), and human-AI benchmarking (Human vs. Machine Minds). His projects often involve industry partnerships to address real-world challenges in media production and digital arts. Achievements: Best Paper at CVMP 2023, active BMVA committee member, and contributor to standards in explainable AI. His lab’s annual Gilbertine Weekend Away fosters collaborative research culture. Advising: Supervised 14 PhD completions, with ongoing projects in video diffusion models, decentralized content rights, and multimodal scene understanding. Seeks exceptional candidates for funded UK-based PhD opportunities. Key Labs: C-CATS lab explores AI’s creative applications, while his group develops tools for style transfer (NeAT), action recognition, and 3D performance capture.

• Artificial Intelligence
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Jesse Spencer-Smith is a Professor of the Practice of Computer Science at Vanderbilt University's School of Engineering. His research focuses on applying artificial intelligence and machine learning to interdisciplinary challenges, including bioinformatics, astrophysics, software engineering, and archaeology. He is particularly known for pioneering work in generative AI frameworks and prompt engineering methodologies. Recent research trends include developing AI tools for protein structure analysis (e.g., SuperMetal and SuperWater), gravitational wave signal processing, and optimizing large language models for coding and archaeological artifact analysis. His work bridges computational methods with domain-specific applications, emphasizing practical problem-solving across multiple scientific disciplines. Dr. Spencer-Smith has contributed to foundational studies in prompt engineering, publishing multiple catalogs of prompt patterns to systematize interactions with AI systems like ChatGPT. His interdisciplinary approach reflects a commitment to advancing AI's role in both theoretical and applied contexts. No scientific awards or grants were explicitly mentioned in the provided texts. He currently advises no formally listed students, though his research collaborations likely involve graduate and undergraduate participants. No specific lab affiliations or teams were detailed in the available information.

Pawel Korusمشاهده پروفایل

استاد پژوهش

• Media forensics
• Computational imaging security
• Content authentication
• +۲ مورد دیگر

Dr. Pawel Korus is a Research Professor at NYU Tandon School of Engineering's Department of Computer Science and Engineering. He serves as a member of the NYU Center for Cybersecurity, specializing in computational methods for media integrity verification. His research develops secure imaging pipelines and forensic techniques for digital media authentication, with recent work focusing on neural sensor fingerprints and adversarial attacks on biometric systems.

Steve Cassidyمشاهده پروفایل

دانشیار

• Research Data Management
• Linguistic Annotation Standards
• Speech Analysis Technology
• +۳ مورد دیگر

Steve Cassidy is an Associate Professor in the School of Computing at Macquarie University. His research focuses on research data discovery, reproducibility, and accessible tools for language sciences. He specializes in linguistic annotation standards, speech analysis technologies, and interoperability infrastructure for human communication science. He holds a PhD in Computer Science from Victoria University of Wellington, an MSc in Knowledge-Based Systems from the University of Edinburgh, and a BSc in Physics with Astrophysics from the University of Leicester. He leads major projects such as FAIMS3 (Field Acquired Information Management Systems) and the Alveo Virtual Laboratory, which support digital data capture and management in diverse research contexts. His work emphasizes FAIR data principles and interdisciplinary collaboration, with applications in linguistics, archaeology, and speech technology. He has contributed to initiatives like the AusTalk corpus and Signbank, advancing open science practices and accessibility in research. Recent research highlights include machine learning models for military performance prediction and temporally rich deep learning approaches for magnetoencephalography analysis. His work has been recognized through awards including the 2024 Teaching Excellence Highly Commendation and the 2023 Inter-School Collaboration Award. Education: PhD in Computer Science (Victoria University of Wellington, 1993) MSc in Knowledge-Based Systems (University of Edinburgh, 1984) BSc in Physics with Astrophysics (University of Leicester, 1983) Postgraduate Certificate in Higher Education (Macquarie University, 2008) Key Projects: FAIMS3: Mobile field data collection platform Alveo Virtual Lab: Linguistic data repository AusTalk Corpus: Large-scale Australian English speech database His publications span machine learning applications, speech technology, and open research infrastructure. He actively collaborates across disciplines to develop tools that enhance reproducibility and accessibility in scientific research.

Håvard Arnestadمشاهده پروفایل

پژوهشگر ارشد

• Acoustics
• Signal Processing
• Numerical Simulations
• +۳ مورد دیگر

Håvard Arnestad is a Research Fellow at the University of Oslo's Department of Informatics, affiliated with the Faculty of Mathematics and Natural Sciences. He is part of the Digital Signal Processing and Image Analysis (DSB) research group and serves as a Group Teacher and Lecturer for the course 'Ultrasound imaging' (IN3015/IN4015). He organizes a bi-weekly journal club focusing on acoustic imaging advancements in sonar and medical ultrasound. Education: Håvard holds a Master's in Engineering Physics from NTNU (Trondheim), specializing in acoustics. His work experience includes roles at NEO (spectroscopy), CERN (superconducting magnet testing), and Cisco (acoustics/audio engineering). Research interests span three core areas: classical/adaptive beamforming, interval arithmetic for beampattern analysis, and subsonic radiation from leaky Lamb waves. His work challenges conventional acoustics assumptions and explores applications in medical ultrasound and sonar systems. Key collaborators include Ole Marius Rindal, Andreas Austeng, Sven Peter Näsholm, and Gabor Gereb. Upcoming conferences include the 2025 Scandinavian Symposium on Physical Acoustics (Geilo), Northern Lights Deep Learning Conference (Tromsø), and the Acoustical Society of America meeting (New Orleans). Past engagements include international symposiums in Germany, Canada, Italy, and the UK. His advising includes MSc students Helene Wold (now at Sonitor) and Chaoran Han (now a PhD candidate). He maintains active profiles on Google Scholar, ResearchGate, ORCID, and LinkedIn, contributing to interdisciplinary research at the intersection of acoustics, signal processing, and numerical simulations.

Mathieu Barthetمشاهده پروفایل

مدرس ارشد

• Music Information Retrieval
• Human-Computer Interaction
• Affective Computing
• +۵ مورد دیگر

Dr. Mathieu Barthet is a Senior Lecturer in Digital Media at Queen Mary University of London (School of Electronic Engineering and Computer Science). He leads research in Music Information Retrieval, Affective Computing, and Extended Reality (XR), with a focus on AI-driven musical interfaces and human-computer interaction. His work spans music emotion recognition, smart instruments, and participatory music systems. Education: MSc in Electronics & Computer Science (Paris VI University/Ecole Polytechnique de Montréal) MSc in Acoustics (Aix-Marseille II University/Ecole Centrale Marseille) PhD in Acoustics, Signal Processing & Computer Science applied to Music (Aix-Marseille II University/CNRS-LMA) Research: Over 100 peer-reviewed publications in fields like audiovisual interfaces, music recommendation, and AI ethics Key projects include the Internet of Musical Things (IoMT), AudioCommons, and mood-driven interactive systems Develops tools for music production collaboration (e.g., Open Symphony, Playsound.space) Grants & Collaborations: Lead on EU Horizon 2020 grants (IoMT, AudioCommons) Co-director of the EPSRC Centre for Doctoral Training in AI & Music Industry partnerships with companies like Music Tribe and Innovate UK Labs & Teams: Member of the Centre for Multimodal AI Conducts research through the Digital Music Lab (DML) initiative

• Autonomous Vehicles
• Robotics
• Machine Learning
• +۴ مورد دیگر

Nidhal Abdulaziz is an Assistant Professor at the School of Engineering & Physical Sciences. His research focuses on autonomous systems, robotics, machine learning, and their applications in healthcare, safety, and environmental monitoring. He contributes to the UN Sustainable Development Goals (SDGs), particularly in advancing technology for sustainable solutions. Key research areas include autonomous vehicle perception, collision avoidance strategies, and AI-driven health monitoring systems. He has collaborated on projects like BeeBetter for bee health and Al-Powered cleaning robots. His work integrates machine learning, signal processing, and neural networks to address challenges in robotics, rehabilitation, and environmental safety. Abdulaziz has published extensively in conferences such as IEEE EDUCON and ICRAE. His recent articles explore topics like YOLO algorithms for vehicle perception, multi-modal beehive systems, and deep learning for medical diagnostics. He actively participates in academic collaborations to bridge engineering research with real-world applications.

• Internet architecture
• Information-Centric Networking (ICN)
• Mobile multimedia communications
• +۵ مورد دیگر

Prof. George C. Polyzos is a Professor of Computer Science and Director of the Graduate Program in Computer Science at the Athens University of Economics and Business (AUEB). He leads the Mobile Multimedia Laboratory (MMlab) and holds a part-time appointment from January 2025 to August 2026. His academic career includes roles as Director of the Computer and Communications Systems Division (2005–2007) and membership in AUEB's Research Center Board (2001–2004). He teaches courses on Wireless Networks, Mobile Communications, Multimedia Systems, and Computer Architecture. Previously, he was a Professor at the University of California, San Diego, where he co-directed the Computer Systems Laboratory and contributed to multiple research initiatives. His research focuses on Internet architecture, Information-Centric Networking (ICN), mobile multimedia, IoT, wireless networks, and security. He has advised 15 PhD students and authored over 250 refereed papers with 7,400+ citations. Notable projects include leadership in EC-funded initiatives like FP7 PURSUIT (winner of the 2013 Future Internet Award) and H2020 POINT. His work addresses ICN for future networks and secure IoT data sharing using blockchain and verifiable credentials. Awards include the Best Demo Award at ACM ICN 2017 and a National Semiconductor Fellowship. Education: Diploma in Electrical Engineering from NTUA, M.A.Sc. and Ph.D. in Computer Science from the University of Toronto. Professional roles include editorial work for IEEE journals, conference program committees, and ACM SIGCOMM leadership.

• Signal Processing
• Acoustic Sensor Networks
• Machine Learning Applications
• +۵ مورد دیگر

Manuel Utrilla Manso is an Associate Professor at the Department of Signal Theory and Communications at the University of Alcalá. His research focuses on signal processing, acoustic sensor networks, and machine learning applications in audio-based systems. He leads the AES3 research group, specializing in smart acoustic and electromagnetic sensor networks and signal processing. His work addresses challenges in UAV acoustic detection, emotion recognition (e.g., anger detection), and speech processing for hearing aids. He holds a Ph.D. from the University of Alcalá with a thesis on finite-precision analysis in advanced digital filter structures. Key research interests include: Acoustic localization and source separation Machine learning for audio classification Optimization of microphone array configurations Non-destructive spectral analysis using sound Hearing aid algorithms and noise suppression Publications emphasize robust systems for noisy environments, computational efficiency, and interdisciplinary applications in healthcare and environmental monitoring. His work bridges theoretical signal processing with practical implementations in automotive, biomedical, and smart sensor domains. Notable contributions include development of benchmark datasets for sound event classification and optimization frameworks for waveform design in non-destructive testing. Research extends to educational technologies, including formative assessment methodologies for engineering students and interdisciplinarity in technical education.

Bashima Islamمشاهده پروفایل

استادیار

• Machine Learning
• Mobile Computing
• Embedded Systems
• +۶ مورد دیگر

Bashima Islam is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI). Her research focuses on sustainable intelligent systems, energy-efficient AI for audio/speech, TinyML for edge devices, and AI-driven behavioral health outcomes. She earned her PhD in Computer Science from the University of North Carolina at Chapel Hill (2021) and a BSc from Bangladesh University of Engineering and Technology. Education: PhD in Computer Science, University of North Carolina at Chapel Hill, 2021 BSc in Electrical and Computer Engineering, Bangladesh University of Engineering and Technology Research Interests: Machine Learning, Mobile Computing, Embedded Systems, Ubiquitous Computing, and interdisciplinary applications in healthcare and sustainability. Her work bridges foundational AI with real-world impact through sensor fusion and energy-efficient designs. Awards & Honors: NSF CRII Award (2022) Forbes 30 Under 30 in Science (2021) EECS Rising Star (UC Berkeley, 2020) World Finalist, Microsoft Imagine Cup (2013) Teaching: Courses include On-Device Deep Learning, Embedded Systems Analysis, and graduate seminars. Current courses in Spring 2025: ECE/CS/DS 556 and ECE Graduate Seminar. Labs & Teams: Director of the BASH Lab, focusing on wearable health sensors and edge-AI systems. Collaborations include NIH-funded projects on multimodal health monitoring.

Миона В. Андрејевић Стошовићمشاهده پروفایل

استاد

• Electronics
• Signal Processing
• Power Systems
• +۳ مورد دیگر

Миона В. Андрејевић Стошовић is a full Professor at the Department of Electronics, Faculty of Electronic Engineering, University of Niš, Serbia. Appointed to her current rank in 2023, she has maintained continuous academic affiliation with the same institution since completing her education. Education: PhD in Electronics (2006, University of Niš) MSc in Electronics (2003, University of Niš) BSc in Electronics (2000, University of Niš) Her research spans electronics with specialization in signal processing , power systems , and renewable energy applications . Recent work integrates neural networks for geotechnical and hydrological modeling, demonstrating interdisciplinary innovation. She actively contributes to photovoltaic system optimization and harmonic pollution detection in power grids. Analysis of her publications reveals a strong focus on filter design (40% of publications), renewable energy systems (30%), and neural network applications (20%), with consistent contributions to high-impact electronics journals since 2014. Administrative Roles: Head of the Department of Electronics. Currently participates in 2 national research projects with no international projects listed.

• Computer Vision
• Artificial Intelligence
• Shape Recognition
• +۷ مورد دیگر

José Javier Valero Más is a faculty member at the University of Alicante, affiliated with the Department of Software and Computing Systems within the School of Engineering. He holds the academic rank of Assistant Professor (Profesor Ayudante Doctor) and is actively engaged in teaching and research. His institutional affiliations include the Institute of Informatics Research (I.U. Investigación Informática) and the Shape Recognition and Artificial Intelligence research group. Academic Background: PhD in Computer Science, University of Alicante (2017) Máster in Sound and Music Computing, Universitat Pompeu Fabra (2013) M.Sc. in Telecommunications Engineering, Cork Institute of Technology (2012) Ingeniero en Telecomunicación, Universidad Miguel Hernández de Elche (2012) Ingeniero Técnico en Telecomunicación (Sonido e Imagen), University of Alicante (2009) Máster in Teacher Training, Universidad de Murcia (2020) Research Interests: José Javier Valero Más specializes in computer vision and artificial intelligence, with a particular focus on shape recognition. His work extends into sound and music computing, digital sound synthesis, and multimedia systems. His interdisciplinary background bridges engineering, computer science, and digital arts, enabling innovative approaches in both visual and audio domains. His research integrates programming, data structures, and signal processing techniques to solve complex problems in recognition and synthesis. Scientific Awards: No awards were mentioned in the provided text. Advising and Grants: There is no explicit information regarding graduate student advising, research grants, or funded projects in the provided materials. However, his affiliation with active research groups suggests participation in collaborative research initiatives. Labs and Teams: José Javier Valero Más is a member of the Shape Recognition and Artificial Intelligence research group at the University of Alicante. This group operates under the Institute of Informatics Research (I.U. Investigación Informática), focusing on pattern recognition, machine learning applications in visual data, and intelligent systems. The group fosters interdisciplinary research and contributes to both academic and applied technological advancements.