جستجوی استاد راهنمای دانشگاهی

Kazimierz Chorośمشاهده پروفایل

استاد

• digital image and video processing
• content-based video indexing and retrieval
• computer animations
• +۴ مورد دیگر

Kazimierz Choroś is a Professor at the Department of Applied Informatics , Faculty of Information and Communication Technology , Wrocław University of Science and Technology. He held leadership roles as Deputy Director of the Institute of Informatics (2008-2014) and Deputy Dean of the Faculty of Computer Science and Management (2016-2020). Research interests: digital image/video processing, content-based video indexing, computer animations, multimedia systems, web systems analysis, and information systems design. Organizer and Chair of the International Conference on Multimedia & Network Information Systems (MISSI 2022) Chair of Special Session WebSys 2020 at ICCCI 2020 Other activities: Since 1993, member and former President (1993-2002) of the SAGE Association (Polish Graduates of French Grandes Ecoles). Since 1982, member of the Polish Numismatic Society, author of dozens of numismatic publications, and Editor-in-Chief of Wrocławskie Zapiski Numizmatyczne (2003-present). Contact: Email: kazimierz.choros@pwr.edu.pl Phone: +48-71.320.3799 Office: Building D2, Room 201/1 Address: Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

• Neural Computation
• Spiking Neural Networks
• Auditory System Processing
• +۵ مورد دیگر

Dr. Dan Goodman is a Senior Lecturer in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. He leads the Neural Reckoning Group, focusing on uncovering principles of neural computation using precisely timed spikes. His work bridges computational neuroscience and artificial intelligence, emphasizing spiking neural networks and sensory processing, particularly in auditory systems. Key tools include the Brian simulator, a widely used spiking neural network framework. Research affiliations include the Artificial Intelligence Network, Centre for Neurotechnology, and Georgina Mace Centre for the Living Planet. His interests span neural computation, neuromorphic engineering, and interdisciplinary applications of neuroscience. Recent work explores multisensory fusion, neural modularity under resource constraints, and adaptive temporal coding. Publications highlight advancements in spiking network models, sound localization, and neural simulation techniques. Collaborations emphasize open science and online conference formats, as seen in initiatives like Neuromatch. No explicit awards or grant details are provided, though his tool development and theoretical contributions suggest significant academic impact.

Jean Le Feuvreمشاهده پروفایل

استادیار

• Multimedia Systems Architecture
• Interactive Media Production and Streaming
• MPEG Standardization
• +۴ مورد دیگر

Jean Le Feuvre is a lecturer and researcher (equivalent to Assistant Professor) in the Image, Data, Signal (IDS) department at Télécom Paris , an active member of the MPEG standardization body since 2000, and the creator of the open-source multimedia platform GPAC . He belongs to the Multimedia (MM) research team within the Information Processing and Communication Laboratory (LTCI). Research Interests: His work spans multimedia systems architecture, focusing on authoring, coding, transmission, composition, and rendering of interactive, animated contents that mix audio, video, text, images, and dynamic metadata. Key areas include: MPEG-DASH adaptive streaming and low-latency delivery ISO Base Media File Format (ISOBMFF) design and extensions HEVC/VVC-based tiled and layered media Media encryption and selective protection (Common Encryption, CENC) Hybrid broadcast–broadband services Open-source multimedia toolchains (GPAC ecosystem) Standards & Publications Trend: Over the past two decades Le Feuvre has continuously contributed to, and edited, core MPEG standards such as ISOBMFF, DASH, Common Encryption, and scene description languages. His recent 2024-2025 patents and papers emphasize low-latency streaming, efficient encapsulation of partitioned (tiled) media, robust indexing/repair mechanisms, and improved random access—demonstrating a clear trajectory toward real-time, adaptive, and secure next-generation media delivery. Scientific Awards & Honors: While no specific named awards are listed, his long-standing role as MPEG standard editor and his continuous leadership of the widely adopted GPAC open-source platform constitute significant recognition within the multimedia research community. Teaching & Supervision: He teaches Master-level courses on Multimedia Networking (M2R AV Transport & Content Security) and mentors multidisciplinary first-year student projects (PACT, PAF, ARTEFACT). He also serves as scientific contact for internships and provides corporate training on Digital TV, Mobile TV, Video over IP, and 3D TV through Télécom Paris. Laboratory & Teams: Le Feuvre is embedded in the LTCI laboratory and leads development within the GPAC open-source community, coordinating contributions to reference software, conformance tools, and end-to-end streaming solutions.

Minje Kimمشاهده پروفایل

دانشیار

• AI for Audio
• Model Compression
• Personalized AI
• +۳ مورد دیگر

Minje Kim is an Associate Professor at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and Department of Computer Science. He also serves as an Amazon Visiting Academic at Amazon Lab126. Previously, he held positions at Indiana University and the Electronics and Telecommunications Research Institute (ETRI) in Korea. His research focuses on efficient machine learning models for audio applications, including model compression, personalized AI, and neural speech/audio coding. He has over 60 patents and has received honors such as the NSF CAREER Award (2021) and IEEE Best Paper Award (2020). Education: Ph.D. in Computer Science, UIUC (2016) M.S. in Computer Science and Engineering, POSTECH (2006) B.E. in Information and Computer Engineering, Ajou University (2004) Research Interests: AI for Audio Model Compression Personalized AI Signal Separation Speech Enhancement Neural Speech and Audio Coding Professional Activities: Editorial roles: ACM/IEEE Transactions in Audio, Speech and Language Processing; EURASIP Journal on Audio, Speech, and Music Processing Conference organization: Co-chair of HSCMA 2024 and General Co-chair of WASPAA 2023 Awards: NSF CAREER Award (2021) IEEE Signal Processing Society Best Paper Award (2020) Indiana University Trustees Teaching Award (2021) Grants & Consulting: NSF CAREER Grant (2021) Amazon Visiting Academic role (2020–present) Labs & Teams: Active collaborations with Amazon Lab126 and contributions to open-source frameworks like the Time-Frequency Domain RF Classification Toolkit.

Luca Abeniمشاهده پروفایل

دانشیار

• Real-Time Systems
• Cloud Computing
• Virtualization
• +۶ مورد دیگر

Luca Abeni is an Associate Professor at the ReTiS Lab (Real-Time Systems) of Scuola Superiore S. Anna , Pisa. He graduated in Computer Engineering from the University of Pisa in 1998 and earned his PhD at Scuola Superiore S. Anna (1999-2002), focusing on real-time operating systems, scheduling algorithms, and QoS management. His career includes a visiting researcher role at Carnegie Mellon University (2000) and Oregon Graduate Institute (2001), followed by a full-time researcher position at the University of Trento's DISI department (2006-2017). Research Interests include real-time systems, cloud computing, virtualization, containerization (e.g., RT-Kubernetes), Linux kernel optimization, fault tolerance, and scheduling algorithms. His work addresses temporal isolation in cloud/edge environments, energy-efficient real-time computing, and adaptive resource management. Scientific Awards 2021 RTSS Influential Paper Award for 1998 paper on real-time scheduling Notable Contributions involve Xen/KVM latency analysis, deadline-based scheduling, ARM big.LITTLE partitioning, and kernel bypass mechanisms. He developed tools like PROSIT for probabilistic real-time analysis and artifacts for reproducing experiments.

Levent Arslanمشاهده پروفایل

استاد

• Speech Recognition
• Speech Synthesis
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Dr. Levent Arslan is a faculty member at Boğaziçi University, where he has served since 1998. He earned his Ph.D. from Duke University in 1996 and worked as a Research Engineer at Entropic Research in the USA before joining academia. He is also the founder and Chairman of the Board at SESTEK, a pioneering company in Turkish speech recognition and synthesis technologies. His research focuses on speech recognition, speech synthesis, assistive technologies, and natural language processing . His innovations have enabled computer access for visually impaired users through synthesized speech. His work bridges academic research and real-world applications in AI-driven voice systems. The analysis of his recent publications reveals a consistent focus on Turkish language speech technologies, including acoustic modeling, speaker adaptation, emotion detection, and robust feature extraction. His work combines deep learning, signal processing, and linguistic analysis to advance voice interfaces in challenging environments. Levent Arslan has made significant contributions to both research and industry. He holds 15 patents and has published extensively in speech technology. His work at SESTEK exemplifies technology transfer from academia to impactful commercial applications. He has advised multiple students and led research projects in speech processing, though specific names are not listed. His lab or research group likely focuses on spoken language systems and AI for accessibility. Future work may involve multilingual voice assistants, low-resource language modeling, and real-time emotion-aware systems.

Paolo Bientinesiمشاهده پروفایل

استاد

• High-Performance Computing
• Numerical Linear Algebra
• Tensor Operations
• +۸ مورد دیگر

Paolo Bientinesi is a Professor at the Department of Computing Science, Umeå University, and Director of the High Performance Computing Center North (HPC2N). His research bridges theoretical and applied computer science, focusing on optimizing computational workflows through domain-specific innovations. Research Interests: Core Areas: Automatic generation of algorithms and code, numerical linear algebra, tensor operations, performance modeling, and computer music. Interdisciplinary Applications: Materials science, molecular dynamics, computational chemistry, computational biology, and computational physics. His work emphasizes leveraging architecture-specific and problem-specific knowledge to develop high-performance solutions. Publication Trends (2022-2026): Recent articles demonstrate a focus on mixed-precision computing, tensor decompositions, linear algebra algorithms (e.g., FLOPs optimization, matrix chains), and music information retrieval (e.g., automatic drum transcription, DJ cue points). Work frequently intersects with parallel computing, performance diagnostics, and machine learning. Leadership: Heads the research group High-Performance and Automatic Computing , driving projects in algorithm automation and computational efficiency.

Yun Pengمشاهده پروفایل

استادیار

• Intelligent Code Analysis
• Automatic Program Repair
• Software Ecosystem
• +۳ مورد دیگر

Dr. Yun Peng is an Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, specializing in software engineering with a focus on intelligent code analysis, automatic program repair, and software ecosystems. With an active research profile, Dr. Peng serves on program committees for major conferences including ASE and ESEC/FSE. Dr. Peng's educational background includes doctoral training focused on programming languages and software engineering, though specific institutions aren't detailed in the available information. Their research primarily addresses challenges in type inference, code analysis, and the application of large language models to software engineering tasks. Research interests center on intelligent code analysis techniques, particularly in type inference systems for dynamic languages like Python. Dr. Peng has pioneered hybrid approaches combining static analysis with deep learning, as demonstrated in their influential ICSE 2022 paper on HiTyper. Recent work has shifted toward leveraging large language models for code review, program repair, and API recommendation, reflecting the field's evolution. Current projects examine LLM applications in secure code review, code efficiency optimization, and vulnerability detection in smart contracts. Dr. Peng's publication record shows a clear progression from foundational type inference research toward cutting-edge LLM applications in software engineering. The work spans multiple dimensions of code quality including security, performance, and maintainability, with tools like HiTyper, TypeGen, and APIBench making significant contributions to the research community. As a program committee member for top-tier conferences, Dr. Peng actively contributes to the software engineering research community. Their work has been recognized through publication in premier venues including ICSE, ASE, and ESEC/FSE, demonstrating consistent high-impact contributions to the field. Dr. Peng maintains an active research group focused on advancing the state of the art in intelligent code analysis, with current projects exploring the intersection of large language models and traditional program analysis techniques. The research has practical applications in developer productivity tools, security analysis systems, and software maintenance automation.

• Audiology
• Hearing Science
• Speech Perception
• +۳ مورد دیگر

Joshua Alexander is Associate Professor in Speech Language and Hearing Sciences at Purdue University. His research focuses on hearing aid technology, speech perception mechanisms, and auditory rehabilitation. Key areas include frequency compression algorithms, music quality enhancement in hearing devices, and neural coding of auditory signals in hearing impairment. Recent work develops novel diagnostic methods like the PHIG technique for feedback quantification and investigates tonotopic distortions in sensorineural hearing loss. His lab also explores open-source hearing aid platforms and ear monitoring systems using speakers as sensors.

Titus Zahariaمشاهده پروفایل

استاد

• Computer Vision
• 3D Point Cloud Compression
• Neural Networks
• +۵ مورد دیگر

Titus Zaharia is a Professor at Telecom SudParis, affiliated with the SAMOVAR research department. His work focuses on computer vision, 3D data compression, neural networks, and augmented reality applications. He has contributed extensively to standards like MPEG-4 and MPEG-7, particularly in 3D mesh compression and multimedia indexing. Zaharia leads research in assistive technologies for visually and hearing-impaired individuals, developing systems like DEEP-HEAR and wearable devices for navigation assistance. His work bridges theoretical advancements with practical industrial applications, such as optimizing AR systems for manufacturing and improving public transportation prediction through machine learning. Zaharia has collaborated on projects like the INVENIO platform for content reuse in multimedia production, and his contributions span over 20 years of academic and applied research. Key research areas include dynamic point cloud compression, lightweight neural network libraries (e.g., FasterAI), and multimodal fusion for video advertising and accessibility. He has published over 100 peer-reviewed papers in journals like IEEE Access and Image and Vision Computing, and presented at conferences including CVPR, ICCV, and ISMAR. His research emphasizes real-world impact, addressing challenges in efficient data representation, human-centric technology, and industrial automation.

Renzheng Shiمشاهده پروفایل

پژوهشگر

• Speech Coding
• Machine Learning
• Audio Signal Processing
• +۳ مورد دیگر

Renzheng Shi is a Researcher at the Institute of Communications Engineering within the Faculty of Electrical Engineering, Information Technology, Physics at Braunschweig University of Technology, specializing in signal processing and machine learning applications for speech and audio systems. His academic background includes: Master of Science in Signal and Information Processing from Xidian University, China (2016-2019) Bachelor of Science in Internet of Things from Jiangnan University, China (2012-2016) Shi's research centers on neural network-based speech coding, with emphasis on high-fidelity audio reconstruction using generative models. His work bridges traditional signal processing with deep learning techniques, particularly exploring generative adversarial networks (GANs) and latent space representations for speech enhancement. Current projects focus on developing efficient neural codecs that improve transmission quality while reducing computational complexity, directly addressing limitations in modern communication technologies like video conferencing and streaming services. As part of the Signal Processing and Machine Learning department, Shi collaborates on cutting-edge research that integrates machine learning methodologies into audio coding frameworks. His supervisory role in master's theses demonstrates active contribution to academic training, with recent work targeting publishable advancements in neural audio codec architectures.

• Computer Graphics
• VR/AR/MR
• Physics-Based Animation
• +۷ مورد دیگر

Oh-Young Song is a Professor in the Department of Software at Sejong University's Ocean Humanities College, where he has been a faculty member since 2006. He also serves as a Researcher at the eXtended Reality Research Center and directs the Sejong University Graphics Lab, which focuses on natural phenomena simulation and related applications in special effects and animation. Sejong University, Department of Software (2006-present) Adobe Systems, Visiting Professor (2013-2014) Seoul National University, Automation and Systems Research Institute (2004-2006) Song earned his educational credentials from Seoul National University: B.S. (1998), M.S. (2000), and Ph.D. (2004), all in Electrical Engineering and Computer Science. His research spans computer graphics, VR/AR/MR, physics-based animation, fluid simulation, and deep learning . His work bridges theoretical computer graphics with practical applications in healthcare, IoT, and special effects. Song has developed physics-based animation techniques used in movies and games, with particular expertise in fluid simulation where he holds a US patent for simulating detailed fluid movements using derivative particles. His recent research increasingly integrates AI and deep learning approaches with traditional computer graphics techniques. Analysis of his 15 most recent publications (2021-2024) reveals a strategic expansion of his research into healthcare applications, with significant work in medical image analysis, gastrointestinal disease detection, and secure patient data transmission. His research shows strong interdisciplinary collaboration, particularly between computer graphics and medical informatics, with consistent publication in high-impact journals across computer science and healthcare domains. Song's scholarly achievements include 49 research outputs with 1922 citations and an h-index of 20. His most notable scientific achievement is the US-patented technique for simulating detailed fluid movements using derivative particles, with related papers published in ACM Transactions on Graphics. US Patent for Method of Simulating detailed movements of fluids using derivative particles 49 research publications across computer graphics, VR/AR, and medical AI domains 1922 total citations with h-index of 20 As an educator, Song teaches courses including Advanced Computer Graphics, Algorithms and Practice, and Physics Coding for General Public. He advises graduate students through Master's thesis research and Graduation Research and Career courses. His lab, the Sejong University Graphics Lab, actively collaborates with industry on physics-based animation techniques for special effects and animation in movies and games. Current research directions indicate growing emphasis on integrating extended reality technologies with healthcare applications, particularly in medical simulation and diagnostic support systems.

• Artificial Intelligence
• Machine Learning
• Data Analysis
• +۱۲ مورد دیگر

Dmitry Vladimirovich Alexandrov serves as Professor at the Faculty of Computer Science, Department of Software Engineering at the National Research University Higher School of Economics (HSE) and heads the Research and Educational Laboratory of Cloud and Mobile Technologies. With 24 years of scientific and teaching experience, he joined HSE in 2014 and has established himself as a leading expert in multiple computer science domains. His academic credentials include Doctor of Technical Sciences (2010) and the academic title of Professor (2012). His educational foundation was built at Vladimir State University, where he graduated in 1997 with specialty in "Computers, complex systems and networks" and earned his Candidate of Technical Sciences degree in 2000. Dr. Alexandrov's research spans artificial intelligence, machine learning, mobile application development (particularly iOS), software engineering, and database technologies. His work bridges theoretical research with practical applications in cloud computing, mobile technologies, and intelligent systems development. His recent publications reveal a strong focus on practical AI applications in software engineering, including code generation using large language models, service mesh optimization, face recognition algorithms, and database technologies. This research demonstrates interdisciplinary collaboration across computer science subfields with real-world applications. HSE University Honorary Diploma (February 2025) Gratitude from the Faculty of Computer Science, National Research University Higher School of Economics (July 2023) Dr. Alexandrov actively supervises students at all academic levels, from bachelor's theses to doctoral research. He has secured significant research funding including the Grant of the President of the Russian Federation for state support of scientific research by young Russian scientists-doctors of sciences. As laboratory head, he directs research on cloud and mobile technologies, oversees scientific seminars, and leads collaborative projects with industry partners. His laboratory recently developed and registered with Rospatent software for visualization and markup of digital medical images in DICOM format, developed in partnership with the N.V. Sklifosovsky Research Institute of Emergency Medicine.

François-Raymond Boyerمشاهده پروفایل

دانشیار

• Computer Architecture
• VLSI Systems
• Microelectronics
• +۴ مورد دیگر

François-Raymond Boyer is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds a B.Sc. and Ph.D. from Université de Montréal and teaches courses in programming and digital audio. His research spans: Computer architecture and VLSI systems Network processing and traffic management Digital audio signal processing Hardware acceleration and optimization He is affiliated with ReSMiQ (Strategic Cluster for Microsystems) and OICRM (Music Research Observatory). Recent publications focus on 5G network processing, modular programming frameworks, and high-speed traffic management architectures, primarily in IEEE Access and IEEE Transactions on VLSI Systems. Professor Boyer has supervised 3 PhD and 6 Master's students, with research topics including network architecture, audio processing, and hardware design. No scientific awards are mentioned in the available records.