جست‌وجوی استاد و پژوهشگر

Jerry Gibsonمشاهده پروفایل

استاد

• Data Compression
• Speech Coding
• Image and Video Compression
• +۵ مورد دیگر

Dr. Jerry D. Gibson is a Distinguished Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB). He holds a PhD and MS from Southern Methodist University and a BS from the University of Texas. His research focuses on data/speech/image/video compression, wireless communications, information theory, and digital signal processing. He leads the ViVoNets Lab in Harold Frank Hall. Education: PhD in Electrical Engineering, Southern Methodist University MS in Electrical Engineering, Southern Methodist University BS in Electrical Engineering, University of Texas Research Interests: Dr. Gibson’s work spans foundational and applied topics in communications and signal processing, including predictive coding, rate distortion theory, and multimedia transmission over networks. His research emphasizes practical implementations for emergency communication systems and high-efficiency codecs. Awards and Recognition: IEEE Signal Processing Society Paper Award IEEE Medal of Honor Selection Committee Member of the National Academy of Engineering Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Advising & Grants: While specific student names are not listed, Dr. Gibson has advised numerous graduate students in communications and signal processing. His work has been supported by grants from the NSF Science and Technology Center for Science of Information. Labs & Teams: The ViVoNets Lab focuses on video and voice networking, developing low-complexity coding schemes for UAVs and first responders. Collaborations include cross-disciplinary projects in 3D video communication and emergency communication systems.

Anton Arapinمشاهده پروفایل

پژوهشگر

• Computer Vision
• Decision Making
• Machine Learning
• +۳ مورد دیگر

Anton Arapin is a Software Engineer and Researcher affiliated with the Computer Science Department at the University of Chicago . He works on machine learning systems at Google, focusing on user behavior modeling and optimizing inference/training efficiency. His research bridges academia and industry, with collaborations spanning the GRASP Lab at University of Pennsylvania, Dr. Bernadette Bucher at University of Michigan, and Dr. Junchen Jiang's group at University of Chicago. Core research areas: Computer Vision , Decision Making , and Generative AI Key applications: Autonomous Navigation , Real-Time Video Systems , and Uncertainty Modeling Recent publications highlight advancements in neural video codecs ( GRACE , 2024) and uncertainty-aware navigation systems. His work has been featured in top venues including NSDI, ICRA, and MLSys. Anton maintains active collaborations across multiple institutions while balancing industry and academic research roles. Non-academic interests include studying languages (English, Ukrainian, Russian, Japanese, Spanish), Japanese woodblock prints, ancient Chinese bronze artifacts, and martial arts (Taekwondo, Boxing, Brazilian Jiu Jitsu).

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.

• Speech Communication Technology
• Voice Pathology Detection
• Machine Learning in Audio Analysis
• +۳ مورد دیگر

Saska Valtteri Tirronen is a Doctoral Researcher at the Department of Information and Communications Engineering at Aalto University , specializing in Speech Communication Technology . Their research focuses on applying advanced machine learning techniques to address complex challenges in voice pathology and health diagnostics. Key Research Areas: Speech signal processing for medical applications Transformer-based and WAV2VEC models in disorder detection Glottal feature analysis and acoustic modeling Robust detection in telephony environments Severity classification of speech disorders Publication Trends: Recent work emphasizes database-independent voice disorder detection, leveraging self-supervised models to improve generalizability. Collaborations span interdisciplinary fields including biomedical engineering, acoustics, and clinical diagnostics. Research Groups: Speech Communication Technology

• Speech Processing
• Signal Enhancement
• Bandwidth Extension
• +۳ مورد دیگر

Esteban Andrés Gómez Mellado is a Visiting Doctoral Researcher at the Department of Information and Communications Engineering at Aalto University. His work focuses on advanced signal processing techniques for speech enhancement and bandwidth extension. Academic affiliation: Aalto University, Department of Information and Communications Engineering Research focus: Speech processing, neural networks, and real-time audio systems Recent publications analyze noise suppression in reverberant environments and low-complexity neural network implementations. He also contributes to interdisciplinary studies in Turkish music theory. Collaborators include Tom Bäckström and Benedikt Wimmer. Current research trends involve deep learning applications for audio signal enhancement and cultural evolution of musical systems.

Paul Kennedyمشاهده پروفایل

استاد

• Biomedical Data Analytics
• Bioinformatics
• Machine Learning
• +۴ مورد دیگر

Professor Paul Kennedy is the Head of the School of Computer Science at the University of Technology Sydney (UTS) Faculty of Engineering and Information Technology. He has made significant contributions to data analytics education, receiving a 2013 OLT Citation for Outstanding Contributions to Student Learning and multiple UTS teaching awards. His research focuses on biomedical data analytics, particularly in pediatric cancer treatment outcomes, bioinformatics pipelines for vaccine discovery, and visual analytics for explainable AI in healthcare. His recent publications highlight advancements in multi-criteria decision-making for vaccine candidates, edge-cloud frameworks for omics data, and immersive VR environments for genomic data visualization. He leads the Biomedical Data Science Laboratory at UTS Australian Artificial Intelligence Institute and co-directs the UTS-Queens University AI-driven clinical tools initiative. A key collaborator with the Children’s Hospital at Westmead, he has secured over $2.4 million in research funding and served on international data mining conference committees since 2006. Scientific Awards: 2013 OLT Citation for Outstanding Contributions to Student Learning 2012 UTS Learning and Teaching Award 2020 Team Teaching Award 2019 Team Teaching Citation His methodological innovations span topological data analysis for stress detection, feature-ranking in RNA sequencing, and domain-adaptation techniques in machine learning. He also contributes to global AI ethics standards through ISO/IEC SC42 committee membership.

Sanja Fidlerمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Sanja Fidler is an Associate Professor at the University of Toronto (Department of Computer Science) within the Mathematical and Computational Sciences School. She is also a co-founder and affiliated faculty at the Vector Institute and serves as VP of AI Research at NVIDIA. Her research focuses on the intersection of Computer Vision, Machine Learning, and Graphics, with emphasis on 3D reconstruction, generative models, and interactive methods. She previously held a Research Assistant Professor position at the Toyota Technological Institute at Chicago. Research Interests: Dr. Fidler specializes in Computer Vision and Machine Learning, particularly in areas such as 3D vision, 3D reconstruction/synthesis, object detection, and interactive scene understanding. Her work bridges graphics and vision, addressing challenges in geometric representations, generative models, and real-world applications like medical imaging and autonomous systems. Awards: She has received notable awards including the Canada CIFAR AI Chair (2018), Connaught Innovation Award (2020), NVIDIA Pioneer of AI Award (2016), and multiple outstanding reviewer distinctions at top conferences. Her teaching excellence was recognized with the Professor of the Year award by UofT's CS Student Union in 2014/2015. Collaborations & Labs: Leading the NVIDIA Toronto AI Research Lab and contributing to initiatives at the Vector Institute, her work integrates academic and industrial research. She co-organized workshops on topics like 'Geometry Meets Deep Learning' and '3D Indoor Scene Understanding' at major conferences.

Nilesh Madhuمشاهده پروفایل

استاد

• Machine Learning
• Audio & Speech Enhancement
• Automated Audio Scene Analysis
• +۴ مورد دیگر

Nilesh Madhu is a Professor for Audio and Speech Processing at Ghent University and imec, Belgium, where he leads research in the Internet Technology and Data Science Lab (IDLab). His academic journey began with a Dr.-Ing. degree (summa cum laude) from Ruhr-Universität Bochum, Germany in 2009, followed by a Marie-Curie postdoctoral fellowship at KU Leuven. Prior to his academic appointment, he served as Principal Scientist and team lead at NXP Semiconductors from 2011-2017, developing advanced audio algorithms for mobile devices. His research spans multiple domains within signal processing, with a particular focus on machine learning applications for audio and speech enhancement, automated audio scene analysis, and hearing prostheses. Professor Madhu's work bridges theoretical signal processing with practical implementations in communications, healthcare, and assistive technologies. Analysis of his recent publication record reveals a strong emphasis on deep learning approaches for speech enhancement, with particular attention to phase reconstruction, multichannel processing, and efficient model architectures. His research increasingly incorporates biomedical applications, particularly in cardiovascular monitoring using advanced sensing techniques like laser Doppler vibrometry. His laboratory work centers around the Internet Technology and Data Science Lab (IDLab) at Ghent University, where his team develops innovative signal processing algorithms with applications spanning mobile communications, hearing aids, and biomedical monitoring systems. The group maintains strong industry connections, evidenced by their focus on practical implementation constraints and real-world performance metrics.

• Computer Architecture
• Memory Systems
• Reconfigurable Computing
• +۶ مورد دیگر

Ronald Dreslinski is a Professor in the Department of Computer Science and Engineering at the University of Michigan, College of Engineering. His work focuses on computer architecture, memory systems, and reconfigurable computing, with applications in machine learning, wireless communication, and autonomous systems. Research spans hybrid memory systems, fault-tolerant networks, and low-power processors Key technologies include PIM (Processing-in-Memory), systolic arrays, and Galois field accelerators His publications highlight trends in adapting reconfigurable architectures for sparse data, optimizing wireless communication decoders, and integrating neural networks with traditional circuit design. He mentors PhD students through weekly 1-on-1 meetings, project-specific sessions, and group presentations. The department emphasizes broadening participation in computing and student support through community initiatives.

Stuart Cunninghamمشاهده پروفایل

مدرس ارشد

• Affective Technologies
• User Experience (UX)
• Human-Computer Interaction
• +۶ مورد دیگر

Dr. Stuart Cunningham is a Senior Lecturer in Computer Science at the University of Chester, where he also serves as Programme Leader for the MSc Advanced Computer Science, REF Unit of Assessment 11 co-ordinator, and Senior Postgraduate Research Tutor. Previously, he held roles at Manchester Metropolitan University (Visiting Research Fellow and Senior Lecturer) and Glyndwr University (Reader in Audio and Affective Computing, Head of Department, and Lecturer/Senior Lecturer). With 20+ years in higher education, his expertise spans academic leadership, teaching, and research in computing and creative technology hybrids. He holds a PhD in Computer Science from the University of Wales (2009), an MSc in Multimedia Communications from the University of Paisley (2003), and a BSc in Computer Networks (2001). Cunningham’s research focuses on affective technologies, audio compression (e.g., ACER codec), and interdisciplinary applications of sound design in fields like healthcare, gaming, and film. He chairs the Audio Mostly steering committee and contributed to the MPEG-SMR working group developing ISO/IEC 14496-23:2008. His work bridges technical and creative domains, exploring biofeedback sensors for emotional state analysis, Foley artistry in 3D animation, and auditory alerts in safety systems. He advocates for practice-led research methodologies that combine creative outputs with empirical validation. Cunningham has authored numerous publications, including research articles, book chapters, and creative works, and actively participates in peer reviewing for journals like IEEE Transactions and Personal and Ubiquitous Computing . In teaching, he emphasizes interactive/collaborative learning and portfolio-based assessment across undergraduate and postgraduate courses. He has designed and delivered degree apprenticeship programs, particularly during the pandemic, and teaches courses like Web Systems, Software Development, and User Experience Design. His research also extends to UX education, esports analytics, and assistive technologies for dementia care.

Olivier Fercoqمشاهده پروفایل

استاد

• Optimization Algorithms
• Primal-Dual Methods
• Coordinate Descent Methods
• +۴ مورد دیگر

Olivier Fercoq is a Professor at Télécom Paris, affiliated with the Laboratoire Traitement et Communication de l’Information (LTCI). He holds a PhD in Applied Mathematics from École Polytechnique (2012) and has held academic roles including Assistant Professor (2015–2021) and Postdoctoral positions at Télécom ParisTech and the University of Edinburgh. Education: PhD in Applied Mathematics, École Polytechnique (2009–2012) Master of Research in Optimization, Paris 6 University (2008–2009) Engineer's Degree in Mathematical Engineering, ENSTA-ParisTech (2006–2009) Research Interests: First-order optimization algorithms, primal-dual methods, and coordinate descent Applications in machine learning, web ranking, and chemotherapy optimization Key Achievements: Second Prize of the IMA Leslie Fox Prize (2015) PhD Thesis Award from the Gaspard Monge Programme (2013) Developed software tools like APPROX and contributions to Scikit-learn Teaching: Advanced courses on optimization, machine learning, and signal processing at Télécom Paris and IPP Master’s programs

• Systems
• Networking
• Security
• +۳ مورد دیگر

Matthew Lentz is an Assistant Professor in the Department of Computer Science at Duke University, where he joined in Fall 2021. Prior to his faculty position at Duke, he was a Postdoctoral Researcher at VMware Research Group and continues to collaborate with them as an Affiliated Researcher. His academic journey includes a PhD in Computer Science from the University of Maryland, where he was advised by Bobby Bhattacharjee. Dr. Lentz's research focuses broadly at the intersection of systems, networking, and security. His current research directions include: (1) introducing new abstractions and tools for building secure, trustworthy software systems, and (2) building systems that improve performance for modern networking and machine learning applications. His work spans diverse areas including network performance measurement, security threat analytics, and privacy-preserving systems. His research often addresses practical challenges in real-world systems while maintaining strong theoretical foundations. As an educator, Dr. Lentz teaches courses including Operating Systems (CPS 510), Introduction to Operating Systems (CPS 310), and Secure Software Systems (CPS 585). He is actively involved in mentoring graduate students, currently advising three PhD candidates: Alexander Du (with Danyang Zhuo), Chenyang Liu (with Kartik Nayak), and Luka Duranovic. His recently graduated students include Yongji Wu (now a postdoc at UC Berkeley), Yalu Cai (at CertiK), Nathan Ostrowski (at Octane Security), and Andres Montoya-Aristizabal (at Two Sigma). Dr. Lentz has served on numerous program committees for top-tier conferences including IEEE S&P (2024-2026), MobiSys (2023-2024), MobiCom (2023-2024), CCS (2021-2022), EuroSys (2021), and HotMobile (2021). He also served as Proceedings Chair for SOSP 2023, one of the most prestigious conferences in systems research.

• Video Coding
• Energy Efficiency
• HEVC
• +۴ مورد دیگر

Christian Herglotz is a researcher affiliated with the University of Erlangen-Nuremberg , Germany. His work focuses on energy efficiency in video coding and decoding systems, with a particular emphasis on HEVC and VVC standards. He has published extensively in IEEE journals and conferences like ICIP, ICASSP, and QoMEX, often collaborating with André Kaup and Matthias Kränzler. Key research themes: energy-aware video compression, decoding power optimization, rate-energy-distortion modeling. Co-edited special sections on deep learning-based video coding. Recent Publications (2022-2025): Explored power reduction in HDR video encoding, motion prediction for 360-degree video, and heterogeneous quantization for DNN accelerators. His studies integrate machine learning with traditional codec design to improve energy efficiency. Technical Contributions: Developed models for decoding energy estimation, analyzed carbon impact of streaming devices, and proposed methods for viewport-adaptive motion compensation. Collaborative work spans thermal imaging for power analysis and reliability-aware DNN hardware optimization.

• Video Streaming
• Virtual Reality
• Multi-Access Edge Computing
• +۳ مورد دیگر

Matti Siekkinen is a Lecturer in the Department of Computer Science at Aalto University. He is also affiliated with the Helsinki Institute for Information Technology (HIIT), the Professorship Di Francesco Mario, and the Computer Science Lecturers group. His research focuses on latency-critical mobile multimedia services, cloud-based virtual reality (VR), and edge computing solutions to optimize network performance and user experience. Education: Doctoral degree in Engineering and Technology (2007) from Université Côte d'Azur Master's degree in Engineering and Technology (2003) from Helsinki University of Technology Research Interests: His work emphasizes improving Quality-of-Experience (QoE) in mobile and cloud gaming, optimizing video streaming for VR applications, and deploying edge computing to reduce latency. Key topics include foveated rendering, dynamic resource allocation, and energy-efficient mobile service delivery. He has led projects such as CloudVR (2019–2020) and Latency-Critical Mobile Multimedia Services (2016–2019), which investigate network architecture and latency reduction strategies. Recent Research Trends: Recent publications highlight advancements in foveated compression techniques, head-pose prediction for remote VR, and neural network-based depth map packing. His work often bridges theoretical models with practical implementations in edge and cloud environments. Grants & Projects: Principal Investigator for CloudVR: High-Quality Mobile VR Using Cloud Acceleration (Business Finland) Principal Investigator for Latency-Critical Mobile Multimedia Services (Academy of Finland) Labs & Groups: He leads the Siekkinen Matti group, focusing on innovative solutions for mobile and cloud-based multimedia systems. Collaborations include Tsinghua University and industry partners like Elisa.

• Biomedical Image Analysis
• Medical Imaging
• Computer-Aided Diagnosis
• +۴ مورد دیگر

Marios Pattichis is a Professor of Electrical and Computer Engineering at the University of New Mexico (UNM), serving as Director of Online Programs since 1999. He leads the Image and Video Processing and Communications Lab (ivPCL) and co-founded Cosmiac. His work focuses on biomedical image analysis, video analytics for clinical decision support, and educational technology initiatives like the AOLME project targeting underrepresented middle-school students. Pattichis holds 10 patents, over 80 journal papers, and $15.9M in research funding from NSF, NIH, and AFRL. Education: Ph.D. and M.S. in Computer/Electrical Engineering from UT Austin (1998, 1993), with dual B.A./B.Sc. in Mathematics and Computer Sciences (1991). Research Interests: Biomedical CAD systems, explainable AI in imaging, dynamically reconfigurable architectures, and bilingual STEM education. His lab develops tools for stroke risk assessment via carotid ultrasound, solar coronal hole segmentation, and classroom activity analysis to measure student participation. Awards: 2022 EAMBES Fellow for contributions to biomedical image analysis. Editorships include IEEE Transactions on Image Processing and IEEE Signal Processing Letters. Grants & Teams: Directed over $15.9M in grants, advised numerous students (though none listed explicitly), and collaborates with educators on AOLME's integrated math/CS curricula. Active in codec comparisons for healthcare video streaming (VVC vs. AV1) and FPGA-based image processing acceleration. Labs/Initiatives: ivPCL (medical imaging/video systems); DRASTIC (adaptive video processing); ESTRELLA (bilingual STEM outreach). Involved in IEEE journal special issues on video analytics and multilingual education.