جست‌وجوی استاد و پژوهشگر

Debra McGivneyمشاهده پروفایل

استادیار

• Magnetic Resonance Imaging
• Magnetic Resonance Fingerprinting
• Computational Inverse Problems
• +۴ مورد دیگر

Debra McGivney, PhD, is an Assistant Professor in the Department of Biomedical Engineering at the Case School of Engineering, Case Western Reserve University. She also serves as Associate Chair of the Case School of Engineering. Her research focuses on mathematical modeling of biomedical applications, particularly in medical imaging. She specializes in magnetic resonance fingerprinting (MRF), inverse problems using Bayesian frameworks, and computational solutions for imaging challenges. Education: PhD in Applied Mathematics, Case Western Reserve University (2013) MS in Mathematics, John Carroll University (2006) BS in Mathematics, University of Notre Dame (2004) Her research interests include MRF techniques for quantifying tissue properties, tackling the partial volume effect, and improving imaging accuracy through pattern matching and statistical methods. Recent work emphasizes optimizing pulse sequences, reducing artifacts, and applying MRF to brain tumors and epilepsy diagnostics. Her publications highlight advancements in MRF simulation, error estimation, and integration with quantum-inspired algorithms. She has contributed to 3D imaging techniques, radiomic analysis, and automated sequence design. Labs and collaborations involve interdisciplinary teams focused on medical imaging innovation, though specific lab names are not mentioned.

• Video coding
• Video analysis
• Video adaptation
• +۳ مورد دیگر

Fernando Manuel Bernardo Pereira is a Full Professor at the Telecommunications Institute of Instituto Superior Técnico (IST), Lisbon. His research focuses on video coding, multimedia representation standards, and advanced multimedia systems. He actively contributes to academic and industry collaborations on standards like JPEG Pleno and security frameworks for media annotation. Key Affiliations : Instituto Superior Técnico, Telecommunications Institute Roles : Full Professor, Researcher in Multimedia Communication His scientific interests span video analysis, adaptation techniques, and multimedia services. Recent work emphasizes deep learning applications in point cloud coding, quality metrics for 3D data, and explainable AI in facial recognition systems. He collaborates internationally on projects involving light field technology and trustworthy media systems. Publications reflect a focus on cutting-edge areas like neural network-based image coding, compression artifact analysis, and capsule networks for expression recognition. While no awards are explicitly listed, his prolific publication record indicates significant contributions to the field. No advising students or grant details are provided in the text. His research teams likely engage in collaborative efforts given his work on standards and multi-institutional responses to industry calls for evidence.

Yaoyao Liuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Continual Learning
• +۵ مورد دیگر

Yaoyao Liu is an Assistant Professor at the University of Illinois Urbana-Champaign, holding joint appointments in the School of Information Sciences and Coordinated Science Laboratory. He is affiliated with the Siebel School of Computing and Data Science, National Center for Supercomputing Applications (NCSA), and Illinois Informatics. His research focuses on computer vision and machine learning, particularly in areas like continual learning, few-shot learning, and 3D geometry modeling. He completed his PhD at the Max Planck Institute for Informatics and a BS at Tianjin University. Research Interests: Continual learning and class-incremental learning Generative models and diffusion models 3D geometry and medical imaging applications Awards: ECVA PhD Award (2024). Professional Activities: Area Chair for CVPR, NeurIPS, ICLR, and other top conferences. Served as Senior Program Committee member for AAAI and IJCAI. Labs/Teams: Active in interdisciplinary projects at NCSA and Illinois Informatics, collaborating on medical imaging and 3D vision challenges.

Aydin Buluçمشاهده پروفایل

دانشیار مدعو

• Parallel Computing
• High Performance Graph Analysis
• Machine Learning
• +۴ مورد دیگر

Aydin Buluç is a Senior Scientist at Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Associate Professor of EECS at UC Berkeley. His work focuses on parallel computing, high-performance graph analysis, machine learning, sparse computations, and computational biology. He leads the Sparsitute DOE center and directs the PASSION Lab, emphasizing scalable algorithms and their applications in genomics and data science. Education: PhD in Computer Science from UC Santa Barbara (2010); BS in Computer Science and Engineering from Sabanci University, Turkey (2005). Research: Develops parallel algorithms for graph analysis, sparse linear algebra, and communication-avoiding techniques. Key projects include HipMCL for metagenome analysis and GraphBLAS for combinatorial computing. Awards: DOE Early Career Award (2013), IEEE TCSC Early Career Award (2015), ACM Gordon Bell Prize Finalist (2022). Teaching: Co-teaches CS267 (Applications of Parallel Computers) at UC Berkeley, emphasizing practical high-performance computing. Professional Roles: Editor of ACM Transactions on Parallel Computing, PC member for major conferences (SPAA, IPDPS), and leader in ExaGraph and DOE initiatives.

• Edge Computing
• Wireless Networks
• Mixed Reality
• +۱۲ مورد دیگر

Tao Han is an Associate Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT), where he conducts cutting-edge research in edge computing, wireless networks, mixed reality, and machine vision. He leads multiple federally funded research projects supported by the National Science Foundation (NSF), including CAREER and I-Corps initiatives. His research focuses on enabling next-generation intelligent systems through innovations in wireless edge computing , deep reinforcement learning for network automation , holographic telepresence , and energy-efficient mobile networks . His work integrates AI, IoT, and immersive technologies to create scalable, secure, and adaptive systems for real-world applications. The recent article trends highlight a strong emphasis on metaverse infrastructure , XR spatial communication , UAV-based edge networks , and real-time health monitoring . These publications reflect a cohesive vision of intelligent, human-centric, and sustainable computing environments. NSF CAREER Award NSF I-Corps Program Funding Tao Han has secured over 10 federal grants as Principal Investigator (PI), including major NSF projects such as AutoEdge , AirEdge , UbiVision , and Low-Cost Holographic Telepresence . These grants support his lab’s work in developing autonomous, efficient, and immersive communication systems. He actively mentors students and collaborates with researchers like Nirwan Ansari and Chen-Chi Chu. While specific advisees are not listed, his leadership in large research projects suggests significant advisory and team-building roles. His research is conducted through a dynamic lab environment focused on edge intelligence , wireless automation , and immersive computing , where interdisciplinary teams develop prototypes and deployable systems for future networks.

Luca Castelli Aleardiمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Discrete Mathematics
• Computational Geometry
• +۸ مورد دیگر

Luca Castelli Aleardi is an Assistant Professor at the Laboratoire d'Informatique (LIX) of École Polytechnique, France, since September 2008. His research focuses on theoretical computer science, computational geometry, and discrete mathematics, particularly on Schnyder woods , graph drawing , and compact data structures for planar and surface-embedded graphs. PhD in Computer Science from École Polytechnique (2006) DEA in Algorithmique from Université Paris 6 (2003) Laurea in Applied Mathematics from University of Milan (2002) Erasmus exchange at Université de Versailles-St. Quentin (1999) His work spans graph encoding , mesh compression , and higher genus triangulations , with applications to data visualization, geometric algorithms, and network analysis. Recent publications in SoCG and GD emphasize practical implementations of theoretical constructs, such as toroidal Schnyder woods and SCARST data structures . He has supervised 15+ research internships (2008–2024) on topics including planar graph drawing and dynamic Laplacian systems . Software tools developed by his team include Jcg (Java library for computational geometry) and SCARST implementations.

• Heat Transfer
• Thermodynamics
• Reduced Order Modeling
• +۶ مورد دیگر

Matt Jones is an Associate Professor in the Department of Mechanical Engineering at Brigham Young University, College of Engineering. His work spans heat transfer, thermodynamics, and optical property characterization, with a focus on reduced order modeling, biomass cookstoves, and radiative surface control. PhD in Mechanical and Industrial Engineering, University of Illinois at Urbana-Champaign (1993) MS in Mechanical and Industrial Engineering, University of Illinois at Urbana-Champaign (1990) BS in Mechanical Engineering, Brigham Young University (1988) Professor Jones conducts research in heat transfer , radiative property measurement , and reduced order modeling . His projects include dynamic control of radiative surfaces with origami-inspired designs , low-cost optical particle counters , and clean-burning biomass cookstoves . Articles show trends in radiative transfer modeling , thermal diagnostics , and energy system optimization . Certificate of Appreciation, ASME Heat Transfer Division (2005, 2007, 2016-2018) Certificate of Merit for Flow Visualization Animation Technical Paper (2015) He advises PhD students like Cameron Quist (Biomass Cookstoves) and Nicholas Wallace (Additive Manufacturing Thermography), and has advised 16 graduates since 2002. Jones serves as Committee Chair for ASME Heat Transfer Division and contributes to organizations like SeaPerch Utah and International Centre for Heat and Mass Transfer.

Sos S. Agaianمشاهده پروفایل

استاد

• Big and Small Data Analytics
• Computational Vision and Sensing
• Machine Learning
• +۱۴ مورد دیگر

Sos S. Agaian is a Distinguished Professor of Computer Science at the College of Staten Island and the Graduate Center, City University of New York (CUNY). Previously, he was the Peter T. Flawn Professor of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), where he also served in the Graduate School of Biomedical Sciences and led the Multimedia and Mobile Signal Processing Laboratory. He has held visiting positions at Tufts University and Tampere Institute of Technology and was a Leading Scientist at AWARE, Inc., Massachusetts. Dr. Agaian holds a PhD in Mathematics and Physics from the Steklov Institute of Mathematics, Russian Academy of Sciences, a Doctor of Engineering Sciences from the Institute of Control Systems, RAS, and an MS in Mathematics and Mechanics (summa cum laude) from Yerevan State University, Armenia. His research spans Big and Small Data Analytics, Computational Vision, Machine Learning, Digital Forensics, Information Fusion, and Fast Algorithms . His work emphasizes extracting meaning from visual content and developing intelligent systems for applications in healthcare, biomedical data mining, multimedia security, and urban computing. He has contributed foundational theories such as Agaian's Theorem , Agaian's Family , Agaian's Method , and Agaian’s Matrix , which are widely recognized in signal processing and combinatorics. His publications include over 650 peer-reviewed papers, ten books, and nineteen edited proceedings. His research has been cited extensively, reflecting broad impact across engineering and computer science. Trends in his recent work focus on real-time data analytics, neurocomputing, and secure multimedia systems, particularly in defense and medical imaging. SPIE Fellow (2005) AAAS Fellow (2010) IS&T Fellow (2013) IEEE Fellow (2017) Entrepreneurship Award (UTSA, 2016, 2013) Innovator of the Year (UTSA, 2014) Tech Flash Titans - Top Researcher (2014) Distinguished Research Award (UTSA, 2006) Dr. Agaian has mentored over 30 PhD and 60 Master’s students, many of whom now work at top institutions and companies including Intel, Raytheon, MIT-LL, Siemens, and Cisco. He has secured over $7 million in research funding from NSF, DARPA, U.S. Army, DOE, and AFOSR. He co-founded BA Logix, Inc. and the Center for Simulation, Visualization, and Real Time Prediction at UTSA, which received a $5M NSF grant. He holds over 44 U.S. and international patents, several of which are licensed commercially, including to Latakoo for use in media transmission (e.g., NBC’s Sochi Olympics coverage). He leads the Computational Vision, Machine Learning, and Data Analytics (CSMAD) Laboratory , which focuses on real-time intelligent systems, multimodal biometrics, cancer imaging, and urban computing. The lab trains students in cutting-edge research and collaborates with industry and government sponsors.

Shuai Huangمشاهده پروفایل

استادیار

• Machine Learning
• AI for Healthcare
• MRI
• +۳ مورد دیگر

Shuai Huang is an Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University, affiliated with the Neuroimaging Center. His research focuses on machine learning and AI applications in healthcare, particularly leveraging MRI and computational imaging to address neurodegenerative disorders like Alzheimer's and autism spectrum disorder. The Computational Neuroscience and Artificial Intelligence (CNAI) Lab, led by Huang, develops advanced algorithms for neuroimaging data analysis. Research Interests: Machine learning, AI for healthcare, MRI, computational imaging, numerical optimization, and medical image reconstruction. The CNAI Lab actively seeks motivated students (PhD/MS) to join projects in these areas. Publications reflect expertise in Bayesian methods, parameter estimation, and medical imaging techniques. Current work emphasizes improving quantitative susceptibility mapping (QSM) reliability and algorithmic solutions for neuroimaging challenges. No scientific awards listed, though ongoing contributions to medical AI are notable. Advising: Open to recruiting students interested in computational neuroscience and AI applications. Grants and lab activities focus on interdisciplinary collaborations between engineering and neuroscience. No specific grant details provided in text.

• Multimedia Signal Processing
• Image and Video Coding
• Event-Based Imaging
• +۸ مورد دیگر

Catarina Brites is an Assistant Professor in the Department of Information Science and Technology at ISCTE - Instituto Universitário de Lisboa and an Integrated Researcher at the Institute of Telecommunications - IUL, where she contributes to the Multimedia Signal Processing Group (MSP-Lx). She holds a PhD in Electrical and Computer Engineering from Instituto Superior Técnico (IST), University of Lisbon. PhD, Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2011 M.Sc., Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2005 B.Sc., Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2003 Her research focuses on multimedia signal processing , particularly 2D/3D visual data coding , event-based imaging , plenoptic imaging (light fields and point clouds) , machine learning for image coding , and visual quality assessment . She has authored over 70 scientific publications with more than 3,890 citations (Google Scholar) and an h-index of 30. Her recent publications highlight a consistent focus on point cloud quality assessment , event-based vision coding , and deep learning applications in image and video compression . Trends include the development of novel metrics for 3D visual data and the application of neural networks to lossless and efficient coding paradigms. Best Paper Award Finalist at 2021 IEEE International Workshop on Multimedia Signal Processing Best Paper Honorable Mention Award at 2019 IEEE International Conference on Multimedia and Expo Top 8% Reviewer at 2018 IEEE International Conference on Multimedia and Expo Top 10% Paper Award at 2017 IEEE International Workshop on Multimedia Signal Processing Best Reviewer Award at 2012 IEEE Visual Communications and Image Processing Catarina Brites has co-supervised several M.Sc. theses at IST in areas including image/video coding and deep learning-based coding. She has been actively involved in multiple national and European R&D projects, serving as Co-Principal Investigator in the DARING project and Technical Coordinator in the EVASION project. She has also contributed as an Associate Editor for IEEE journals and served on technical and organizing committees for major conferences including IEEE ICIP, ICME, VCIP, and ACIVS. She is a key member of the Multimedia Signal Processing Group (MSP-Lx) at the Institute of Telecommunications, where she leads research in next-generation visual data representation and compression. She is also involved in the Technical Commission CT-120 - Image, Audio and Multimedia Information Coding, contributing to national standardization efforts.

• Multimedia Signal Processing
• Light Field Coding
• 3D Holoscopic Video
• +۷ مورد دیگر

Paulo Jorge Lourenço Nunes is an Associate Professor in the Department of Information Science and Technology at ISCTE – Instituto Universitário de Lisboa, where he also serves as Coordinator of the Multimedia Signal Processing Group at the Institute of Telecommunications - IUL. He is an Integrated Researcher at the same institute, contributing to advanced research in multimedia and signal processing. PhD in Electrical and Computer Engineering, Higher Technical Institute - UTL (2007) Master's in Electrical and Computer Engineering, Higher Technical Institute - UTL (1995) Bachelor's in Electrical and Computer Engineering, Higher Technical Institute - UTL (1992) His research focuses on multimedia signal processing , particularly in light field coding , 3D holoscopic video , and scalable video representation . He applies deep learning and advanced signal processing techniques to problems in immersive systems and image compression. His work bridges theoretical innovation with practical implementation in next-generation visual media. The recent articles highlight a strong trend in light field processing , including segmentation, coding, disparity estimation, and view synthesis using neural networks. Keywords across publications include computer vision, image compression, deep learning, and immersive media, reflecting a cohesive and impactful research trajectory in next-generation visual technologies. Notable scientific contributions include highly cited works in IEEE Transactions and Signal Processing journals, with over 1300 citations on Google Scholar. While no formal awards are listed, his extensive publication record and leadership in funded research projects underscore significant recognition in the field. He actively supervises postdoctoral researchers, PhD, and Master’s students. His advising portfolio includes ongoing and completed theses on light field coding, deep learning for immersive systems, and multimedia communication. He has also secured and led multiple research projects in multimedia and telecommunications. His teaching includes courses such as Multimedia Communication Systems and Fundamentals of Computer Networks. He leads the Multimedia Signal Processing Group at the Institute of Telecommunications - IUL, a research team focused on advanced multimedia analysis, compression, and delivery. The group works on cutting-edge topics including light field technologies, error-resilient video coding, and machine learning for multimedia.

Zhu Liمشاهده پروفایل

استاد

• Point Cloud Compression
• Light Field Compression
• Deep Learning in Visual Compression
• +۴ مورد دیگر

Zhu Li is a full professor at the Department of Computer Science & Electrical Engineering (CSEE) at the University of Missouri, Kansas City (UMKC), and director of the NSF Center for Big Learning (CBL). He holds a PhD in Electrical & Computer Engineering from Northwestern University (2004). Previously, he served as an AFRL summer visiting professor at the US Air Force Academy (2016-18, 2020-24), Senior Staff Researcher at Samsung Research America and FutureWei Technology, and Assistant Professor at The Hong Kong Polytechnic University. His research focuses on point cloud and light field compression, deep learning for visual compression, remote sensing, and multimedia systems. Key achievements include 50+ patents and 200+ publications. He is an IEEE Senior Member and serves as Associate Editor-in-Chief for IEEE Trans on Circuits & System for Video Technology, and Associate Editor for IEEE Trans on Image Processing and others. Notable awards include the Best Paper Runner-up at PBVS 2023, Best Poster at ICME 2006, and Best Paper at ICIP 2007. His lab has secured grants from NSF, AFRL, and industry partners, advancing research in neural compaction, SAR image processing, and immersive media communication. Education: PhD in Electrical & Computer Engineering, Northwestern University (2004) Affiliations: NSF I/UCRC Center for Big Learning (Director), IEEE (Senior Member) Grants: NSF Trustee's N.T. Veatch Award (2025), AFRL Phase 2 STTR ($1.2M), NSF 5G/6G Networks Grant (2022) Labs/Teams: Multimedia Computing & Communication Lab, MCC Lab collaborating with global institutions

• Database Systems
• NoSQL Databases
• Cloud Data Management
• +۷ مورد دیگر

Norbert Ritter is a professor at the University of Hamburg, Germany, with a long-standing and active research career in database systems and data management. His work spans from foundational database theory to modern cloud and real-time systems, with significant contributions to NoSQL, polyglot persistence, and web performance optimization. He collaborates extensively with researchers such as Wolfram Wingerath, Felix Gessert, and Fabian Panse, and has published consistently in top-tier venues including VLDB, ICDE, and BTW. Research Interests: Database Systems NoSQL and Cloud Data Management Real-Time and Stream Processing Probabilistic and Incomplete Data Web Compression and Performance Spatiotemporal and Simulation Data His recent publications (2020–2024) show a strong focus on polyglot data management, adaptive systems, and efficient web encoding techniques. These works reflect trends toward heterogeneous storage, intelligent compression, and scalable simulation data handling, particularly in smart city and scientific contexts. The integration of machine learning with spatiotemporal data and graph-based modeling is also emerging as a key theme. Scientific Contributions: Co-authored influential tutorials and books on real-time data management and NoSQL systems. Contributed to reference works such as the Encyclopedia of Big Data Technologies . Editorial roles in journals like Datenbank-Spektrum and organizing roles in BTW workshops. Ritter has advised or collaborated with several researchers, though formal student listings are not available. His work often involves system design, benchmarking, and real-world deployment, suggesting involvement in grants and applied research projects. He has also contributed to the development of tools and frameworks for data integration, caching, and simulation environments.

• Visualization
• Geometric Modeling
• Topological Analysis
• +۵ مورد دیگر

Joshua A. Levine is an Assistant Professor in the Department of Computer Science at the University of Arizona, where he has been since 2016. Previously, he held positions at Clemson University’s School of Computing (2012–2016) and the University of Utah’s Scientific Computing and Imaging Institute (2009–2012). He earned his Ph.D. in Computer Science from The Ohio State University, along with BS and MS degrees in Computer Engineering and Mathematics from Case Western Reserve University. Education: Ph.D., Computer Science, The Ohio State University (2009) M.S., Computer Science, Case Western Reserve University (2005) B.S., Computer Engineering and Mathematics, Case Western Reserve University (2003) Research Interests: His work focuses on visualization , geometric modeling , topological analysis , and computational topology . He develops tools like the Topology ToolKit for analyzing complex datasets and explores applications in mesh generation, vector fields, and performance analysis. His methods bridge theory with practical systems for scientific and engineering domains. Key Contributions: His publications span top-tier venues like IEEE VIS, SIGGRAPH, and Computer Graphics Forum , emphasizing scalable visualization techniques and algorithms for high-performance computing. Notable work includes Neural Volume Compression and Particle Merging-and-Splitting . Awards: DOE Early Career Research Program (2018) Best Paper Honorable Mention (IEEE VIS SciVis 2017) Multiple Best Paper Awards at visualization and meshing conferences (2010–2017) Labs & Collaborations: Leads visualization research at the University of Arizona, contributing to the Topology ToolKit (TTK), an open-source platform for topological data analysis. Engages in interdisciplinary projects with civil engineering and natural language processing communities.

Pamela Abshireمشاهده پروفایل

استاد

• VLSI Circuit Design
• Bioengineering
• Information Theory
• +۷ مورد دیگر

Pamela Abshire is a Professor in the Department of Electrical and Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park. She holds the rank of Fischell Institute Fellow and is affiliated with the Maryland Robotics Center, Brain and Behavior Institute, and Robert E. Fischell Institute for Biomedical Devices. Her work bridges VLSI circuit design and bioengineering, focusing on performance-resource tradeoffs in natural/engineered systems. Education: B.S. Physics (Caltech, 1992), M.S. and Ph.D. in Electrical Engineering (Johns Hopkins University, 1997 and 2001). Pre-UMD career included R&D roles at Medtronic (1992-1995). Research focuses on CMOS biosensors, low-power microsystems, and bio-inspired designs for applications like cell-based sensing, robotics, and medical devices. Notable projects include nose-on-a-chip odor detection systems, ant-like microrobots, and lab-on-CMOS platforms for real-time cell monitoring. Awards include IEEE Fellow (2018), NSF CAREER Award (2003), and 2021 University Distinguished Scholar-Teacher honor. Active in academic leadership roles including ADVANCE Professor (2020-2021) and editorial work for IEEE Transactions on Circuits and Systems. Grants include NSF funding for olfactory sensing, AFOSR bio-inspired flight tech, and DARPA CogniSense initiatives. Her Integrated Biomorphic Information Systems Lab collaborates on semiconductor innovation through partnerships like the Mid-Atlantic Semiconductor Collaborative. Labs/Teams: Leads the Integrated Biomorphic Information Systems Lab and contributes to Microelectronics at Maryland group. Co-develops biohybrid systems integrating CMOS, MEMS, and biological components.