جست‌وجوی استاد و پژوهشگر

• Child and Adolescent Psychiatry
• Mental Health Stigma Reduction
• Digital Mental Health Interventions
• +۷ مورد دیگر

Andrés S Martin is the Riva Ariella Ritvo Professor at the Child Study Center and Professor of Psychiatry at Yale School of Medicine. He serves as Medical Director of the Children's Psychiatric Inpatient Service at Yale-New Haven Children's Hospital, Director of the Standardized Patient Program at the Teaching and Learning Center, and Director of Medical Studies at the Child Study Center. He is also a faculty affiliate at the Center for Medical Education. Education: PhD in Medical Education, University of Groningen (2022) MPH in Chronic Disease Epidemiology, Yale School of Public Health (2002) Fellowship in Child and Adolescent Psychiatry, Harvard Medical School / Massachusetts General & McLean Hospitals (1995) Residency in Psychiatry, Harvard Medical School / Massachusetts Mental Health Center (1993) Residency in Internal Medicine, University of Miami / Jackson Memorial Hospital (1991) MD, Anahuac University (1990) Dr. Martin’s research centers on child and adolescent mental health, with a strong emphasis on reducing stigma—particularly surrounding transgender youth and survivors of childhood maltreatment—through innovative digital interventions such as animated videos and social media campaigns. He explores climate change anxiety in adolescents and the reintegration of youth into school after psychiatric hospitalization. His work integrates clinical practice with educational innovation, especially in standardized patient training and medical education curriculum development. His recent publications span themes of stigma reduction, digital mental health, and psychosocial interventions, appearing in journals such as JAACAP , SSM - Mental Health , and Child and Adolescent Psychiatry and Mental Health . These works reflect a consistent focus on accessible, scalable, and evidence-based mental health promotion strategies. Professional Roles and Leadership: Editor-in-Chief, Journal of the American Academy of Child and Adolescent Psychiatry (2008–2017) Co-Editor, IACAPAP e-Textbook of Child and Adolescent Mental Health Director, Standardized Patient Program, Teaching and Learning Center Director of Medical Studies, Child Study Center Dr. Martin is actively engaged in mentorship and medical education, contributing to national and global child mental health resources. He has no listed scientific awards in the provided text, but his leadership in editorial and training roles underscores his academic influence. He collaborates extensively with colleagues such as Laelia Benoit, Dorothy Stubbe, and James Leckman, reflecting a strong interdisciplinary research network.

Detlev Marpeمشاهده پروفایل

پژوهشگر

• Video Coding
• Image Compression
• Signal Processing
• +۳ مورد دیگر

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.

• Wireless Sensor Networks
• Next-Generation Computer Networks
• Wireless Mesh Networks
• +۵ مورد دیگر

Professor Cormac J. Sreenan is a full professor in Computer Science at University College Cork (UCC), leading the Mobile & Internet Systems Lab (MISL) since 1999. He previously served as Head of School (2019-2021) and Head of Department (2015-2018 and 2000-2004). His research focuses on wireless sensor networks, multimedia networking, IoT, and adaptive video streaming. He has published over 200 peer-reviewed papers and holds 9 patents. He is a Science Foundation Ireland Principal Investigator and a Fellow of both the British Computer Society (2005) and the Irish Academy of Engineering (2022). Education: PhD from the University of Cambridge Computer Laboratory Member of Christ's College, Cambridge Research Interests: Wireless sensor networks and fault-tolerant designs Next-generation computer networks and IoT infrastructure Adaptive video streaming and QoE optimization Network security and technology transfer Grants & Collaborations: Principal Investigator on SFI grants including the €1M ENABLE project Collaborations with Irish companies and international agencies Experience in technical due diligence and expert witness roles Labs & Teams: Directs the Mobile & Internet Systems Lab (MISL), a multidisciplinary research group focused on mobile and multimedia network systems.

Andy Yeمشاهده پروفایل

دانشیار

• Field-programmable gate array (FPGA) architectures
• Computer-Aided Design (CAD) tools for FPGAs
• Logic synthesis
• +۵ مورد دیگر

Dr. Andy Ye is an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering at Ryerson University, where he conducts research and teaches courses in advanced digital systems. Education : PhD (2004), MASc (1999), and BASc (1996) from the University of Toronto Research Interests focus on: Field-programmable gate array (FPGA) architectures Computer-Aided Design (CAD) tools for FPGAs and VLSI Logic synthesis and hardware implementation Digital communication algorithms and computer vision systems Publication Trends demonstrate expertise in FPGA area modeling, motion estimation architectures, and VLSI design optimization across multiple IEEE and ACM venues. Scientific Achievements : Best Paper Award (2016) at International Conference on Field Programmable Logic and Applications Teaching Contributions include graduate-level FPGA design (EE 8219), low-power digital circuits (ELE 734), and fundamental network theory (ELE 302). He maintains active supervision availability for students.

Igor Molybogمشاهده پروفایل

استادیار

• Artificial Intelligence
• Large Language Models
• Multimodal Modeling
• +۵ مورد دیگر

Igor Molybog is an Assistant Professor at the University of Hawai'i at Manoa, holding joint appointments in the Departments of Electrical and Computer Engineering and Information and Computer Sciences. His research focuses on advancing artificial intelligence, particularly through large language models (LLMs), multimodal modeling, and core machine learning optimization. He leads the HawAII research group, exploring applications like LLM alignment, efficient inference systems, and scaling properties of foundation models. Education: Ph.D. in Engineering from UC Berkeley (2022), specializing in optimization algorithms for complex systems. Previously worked at Meta AI on LLaMa model development. Research Interests: Efficient LLM development and evaluation frameworks Multimodal AI integration (video/audio + text) Scalable optimization for large models Computational efficiency in training/ inference Recent Work: Presented REAL alignment method (2024), developed long-context scaling techniques (2023), contributed to Llama 2 chat models (2023). Collaborates with organizations like Epoch AI on scaling challenges. Teaching: Offers courses in AI, machine learning, and optimization across ECE and ICS departments. Labs/Teams: Leads HawAII Initiative fostering AI collaboration at UH Manoa, organizes paper reading seminars, and hosts technical talks with industry experts.

• Computer-Supported Collaborative Learning
• Virtual Reality in Education
• Science Education
• +۵ مورد دیگر

Rolf Steier serves as Professor at Oslo Metropolitan University within the Faculty of Education and International Studies, Department of Primary and Secondary Teacher Education, holding the position of Head of Studies for Area of Responsibility 5. His office is located at Pilestredet 52, 0167 Oslo (B534), with contact details including mobile +47 906 92 626, office +47 672 36 014, and email rolf.steier@oslomet.no. He actively contributes to the Digital Learning Arenas research group, focusing on technology-enhanced educational environments. Steier's research centers on computer-supported collaborative learning (CSCL) and virtual reality applications in education, with specific expertise in embodied cognition, science education, museum learning, and narrative structures in STEM. His work examines how learners co-construct knowledge through digital interactions, particularly investigating the role of physical embodiment in virtual environments and the integration of narrative techniques to facilitate engagement in informal learning contexts. Recent projects explore immersive VR experiences for collaborative meaning-making and the adaptation of interaction analysis methodologies for technology-mediated educational research. Analysis of Steier's 2021-2025 publications reveals a clear trajectory toward increasingly sophisticated integration of virtual reality in educational research, with growing emphasis on interdisciplinary applications in science education. Key thematic developments include the refinement of interaction analysis frameworks for digital contexts, the exploration of perspective-taking across blended realities, and the strategic use of narrative to enhance STEM engagement in informal settings. His work consistently bridges theoretical learning science with practical educational technology implementation. The Digital Learning Arenas research group serves as the primary platform for Steier's collaborative work, functioning as an interdisciplinary hub for developing and studying innovative digital learning solutions. This group facilitates partnerships across educational sectors to transform teaching practices through emerging technologies, with recent focus on VR-based collaborative learning environments and cross-contextual knowledge transfer.

• Video Coding
• Machine Learning in Video Compression
• Energy-Efficient Encoding
• +۳ مورد دیگر

Alexandre Mercat is an Assistant Professor in the Department of Computer Engineering at Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on video coding, energy-efficient encoding, and real-time multimedia systems. He leads projects on open-source video encoders and standards, including contributions to HEVC, VVC, and V-PCC technologies. His work emphasizes machine learning integration, low-power hardware optimizations, and scalable distributed encoding frameworks. Key technical interests include improving video compression efficiency through algorithmic innovations, developing open-source tools like the UVG dataset and Kvazaar encoder, and addressing challenges in volumetric video communication and 3D point cloud encoding. His research spans theoretical algorithm design to practical implementations, with applications in virtual reality, live streaming, and edge computing. Recent projects include real-time saliency-guided video coding frameworks, energy reduction techniques for HDR streaming, and multi-layer VVC coding schemes for hybrid machine-human consumption. He also explores FPGA acceleration and parallelization strategies for distributed video encoding systems. No scientific awards are explicitly mentioned in the provided texts. While no formal advisees are listed, his research group likely involves students through open-source development and collaborative projects. His work integrates closely with industry standards bodies and open-source communities, emphasizing reproducible evaluation frameworks and end-to-end software tools.

• Mathematical Physics
• Differential Geometry
• Topology
• +۴ مورد دیگر

Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.

• Signal processing for communications
• MIMO communications
• Wireless, ad hoc and sensor networks
• +۱۱ مورد دیگر

Nuno Manuel Branco Souto is an Associate Professor (with Habilitation) at the Department of Information Science and Technology, School of Technologies and Architecture, ISCTE - University Institute of Lisbon. He also holds leadership roles as Deputy Director of both the School of Technologies and Architecture and the Institute of Telecommunications - IUL, where he is an Integrated Researcher in the Radio Systems Group. Aggregation in Information Sciences and Technologies, ISCTE-University Institute of Lisbon, 2021 PhD in Electrical and Computer Engineering, Higher Technical Institute - UTL, 2006 Bachelor's degree in Aerospace Engineering, Higher Technical Institute - UTL, 2000 His research focuses on signal processing for communications , MIMO systems , channel estimation , reconfigurable intelligent surfaces (RIS) , and optimization in wireless communications . He explores advanced techniques in hybrid precoding , index modulation , and energy-efficient 5G/6G network design , contributing significantly to next-generation wireless technologies. His recent publications (2020–2024) reveal a strong trend toward reconfigurable intelligent surfaces (RIS) , millimeter-wave and THz communications , molecular communication with deep learning , and anti-UAV systems using SDR-based jamming and spoofing . These works appear in high-impact journals such as IEEE Transactions, Sensors, and Applied Sciences, reflecting his leadership in cutting-edge wireless communication research. Dr. Souto has no scientific awards explicitly listed in the provided text. He has been actively involved in teaching, research supervision, and academic leadership. His collaborations span multiple institutions and include prominent researchers such as Pavia, Velez, Correia, Sebastião, and Dinis. He contributes to both theoretical advancements and practical implementations in wireless systems, including software-defined radio (SDR) applications for UAV security and scalable video broadcasting in cellular networks. Dr. Souto is affiliated with the Radio Systems Group at the Institute of Telecommunications - IUL, where his team conducts research on next-generation wireless networks, signal processing algorithms, and communication system optimization.

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

John W. Byersمشاهده پروفایل

استاد

• Computer Networking
• Internet Platforms
• Data Analytics
• +۴ مورد دیگر

John W. Byers is a Professor in the Department of Computer Science at Boston University since 1999. He holds dual roles as Founding Chief Scientist and Board Member at Cogo Labs (founded in 2005 as Adverplex, Inc.), where he oversees technology platforms for online advertising and data science analytics. His research bridges theoretical computer networking with empirical studies of Internet platforms like Airbnb and Groupon. Education: Ph.D. in Computer Science from U.C. Berkeley. Research Focus: Byers explores algorithmic challenges in transport protocols, network architecture design, and the sharing economy's economic impacts. His work on platforms like Airbnb and Groupon examines reputation systems, user behavior, and market dynamics. Recent projects include liquid wireless networking for rural applications and optimizing ride-hailing fleet coordination. Publications: Over 50 peer-reviewed articles spanning network architecture (e.g., XIA), streaming systems (liquid transport protocols), and sharing economy analyses. Key themes include data-driven experimentation and cross-disciplinary solutions for evolving internet challenges. Industry Engagement: Cogo Labs incubates tech startups leveraging his research in algorithmic marketing and data analytics. Byers' work frequently bridges academia and industry, addressing real-world scalability and economic factors in digital ecosystems.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +۵ مورد دیگر

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Anne-Marie Kermarrecمشاهده پروفایل

پژوهشگر

• Gossip-based networking
• P2P systems
• Social Networks
• +۶ مورد دیگر

Anne-Marie Kermarrec is a Senior Researcher at INRIA (France), with prior roles at Microsoft Research (UK) and the University of Rennes 1 (France). Her work focuses on decentralized systems, including gossip protocols, peer-to-peer networks, social networks, and collaborative filtering. PhD in Computer Science (Rennes, 1996) Her research spans epidemic algorithms, content-based search in large-scale networks, and scalable group communication. She pioneered gossip-based peer sampling and multicast infrastructures like Scribe and SplitStream. Her publications highlight applications in decentralized news recommendation (AllYours), network coding, and privacy-preserving social platforms (Gossple). Key article trends include gossip protocols , P2P systems , social network analysis , decentralized storage , and collaborative filtering . She received the Michel Monpetit Award (2011), ERC Starting Grant (GOSSPLE, 2008-2013), and an ICDCS Best Paper Award (2010). Michel Monpetit Award (2011) ERC PoC AllYours (2013) ERC Starting Grant GOSSPLE (2008-2013) ICDCS Best Paper Award (2010) Kermarrec led program committees for major conferences (e.g., EuroSys, Middleware) and chaired the ACM Software System Award. Her software contributions include AllYours (news recommender), GossipLib (gossip library), and GossipPeer (P2P platform maintenance).

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +۶ مورد دیگر

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

Kwon Oh-jinمشاهده پروفایل

استاد

• Image/video compression
• Image quality enhancement
• Image fusion
• +۶ مورد دیگر

Professor Kwon Oh-jin is affiliated with Sejong University , where he serves in the Department of Electronic Engineering . His academic career spans over three decades, including a PhD in Electrical and Computer Engineering from University of Maryland (1994) and MS in Signal/Image Processing from University of Southern California (1991). His research focuses on image/video analysis , compression algorithms , watermarking systems , and steganography . Recent work explores machine learning-driven compression , 360-degree imaging , and JPEG standards implementation . His 15 most recent articles address topics like cybersecurity using CNNs , dynamic gesture recognition , and novel multimedia platforms for emerging standards. Professor Kwon leads the Visual Communication Research Lab (Chung923), supervising numerous PhD and MS students working on point cloud coding , JPEG privacy , and AI-based image coding . He has received multiple government and industry grants from organizations including the Agency for Defense Development and Ministry of National Defense .