جست‌وجوی استاد و پژوهشگر

Matias Garcia-Constantinoمشاهده پروفایل

مدرس

• Data Analysis
• Connected Health
• Pervasive Health
• +۶ مورد دیگر

Dr. Matias Garcia-Constantino is a Lecturer in Computer Science at Ulster University's School of Computing , actively contributing to research in connected health, sensor data analysis, and machine learning applications. He is affiliated with NI-CHIC (Northern Ireland Connected Health Innovation Consortium) and maintains academic ties with institutions including University of Liverpool and Newcastle University. PhD in Computer Science (2013), University of Liverpool BSc in Computer Engineering (2007), Universidad Nacional Autónoma de México His research focuses on Internet of Things solutions for healthcare, particularly smart home technologies supporting dementia patients and thermal comfort monitoring. Current work explores explainable AI models for activity recognition and environmental health surveillance. Recent publications demonstrate expertise in machine learning applications across diverse domains including: Medical imaging analysis Geospatial health modeling Acoustic behavior detection Smart thermal monitoring Healthcare process optimization He has received the EPSRC 'ACTION 2014: Changing Age' programme award and leads research projects funded by the Department for the Economy , including thermal comfort solutions and dementia-related monitoring systems. His work involves collaborations with institutions such as: Prince of Songkla University Fatoni University Universidad Juarez Autonoma de Tabasco CICESE Ensenada Current research emphasizes data fusion from wearable and ambient sensors, with applications spanning: Abnormal behavior detection Fall prevention systems Post-stroke rehabilitation Occupational sedentary behavior reduction

• Harmonic Analysis
• Approximation Theory
• Complex Analysis
• +۴ مورد دیگر

Professor Brigitte Forster-Heinlein holds the Professorship of Applied Mathematics at the University of Passau's Faculty of Computer Science and Mathematics since April 2012. Previously, she worked at the Technical University of Munich, the École Polytechnique Fédérale de Lausanne, and the Helmholtz Center Munich where she led her Marie Curie Excellence Team. Her research spans harmonic analysis, approximation theory, and complex analysis with significant applications in signal and image processing. Professor Forster-Heinlein explores mathematical building blocks for signal approximation and extraction of essential information from data patterns. She is particularly interested in extracting curvature information from microscopic image data through advanced mathematical methods. She has received notable recognition including the Ars Legendi Faculty Prize for Mathematics in 2021. Professor Forster-Heinlein is also the founder and curator of the Passau Math Museum, demonstrating her commitment to mathematics outreach and education. Her research interests include: Harmonic analysis: wavelet analysis, complex wavelets, phase information, complex splines Integral transforms for signal and image analysis Approximation theory, sampling theorems and interpolation Function theory: Anharmonic Fourier and Dirichlet series Applications in signal and image processing Mathematics education through the SKILL.de project She is actively involved in two major research projects: SKILL.de: Strategies for Skills Acquisition - Innovative Teaching Concepts in Teacher Training Splines of complex order, fractional operators and applications in signal and image processing

Daniel Ruijtersمشاهده پروفایل

استاد

• Medical Imaging
• Image-Guided Therapy
• Data-Driven Healthcare
• +۳ مورد دیگر

Daniel Ruijters is a part-time Full Professor in the Electronic Systems department at Eindhoven University of Technology (TU/e), specializing in data-driven value-based healthcare for image-guided therapy. His research develops intelligent systems that optimize data utilization in minimally invasive treatments, translating population datasets to individual patient care. He simultaneously serves as a Principal Scientist at Philips Healthcare, where he has worked since 2001, developing clinical prototypes for image-guided interventions. Education Engineering degree from University of Technology Aachen, Germany (2001) Master's research at École Nationale Supérieure des Télécommunications (ENST), ParisTech, France PhD from TU/e and KU Leuven (2010) on multi-modal image fusion Research Focus Ruijters' work integrates medical imaging, deep learning, and computational modeling to advance image-guided interventions. His research spans GPU-accelerated image processing, angiography-based diagnostics, computational fluid dynamics for vascular analysis, and AI-driven detection systems for neurovascular procedures. This includes developing real-time tracking methods and personalized treatment approaches using large-scale clinical datasets. Projects & Initiatives He leads the PERSEUS project (2023-2029) focusing on patient-centered healthcare optimization through data science. His work contributes to UN Sustainable Development Goals through improved medical technology accessibility. Academic Contributions Ruijters teaches courses in DSP fundamentals, medical image analysis, and signal processing. His 90+ publications demonstrate consistent output since 2003, with recent emphasis on deep learning applications in angiography and computational hemodynamics.

Lars Krecklauمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Procedural Modeling
• Real-Time Rendering
• +۳ مورد دیگر

Dr. Lars Krecklau is a researcher at the Department of Computer Science , RWTH Aachen University . He specializes in procedural modeling, real-time rendering, and urban reconstruction. His work includes algorithms for efficient 3D visualization of cityscapes and interactive modeling tools for non-programmers. Research Focus : Procedural modeling, real-time rendering, urban visualization Key Contributions : Procedural facade rendering, historical city interpolation, interconnected structure modeling His publications focus on GPU-accelerated procedural graphics, temporal interpolation of historical data, and intuitive 3D modeling interfaces. He has contributed to frameworks like Houdini and developed techniques for memory-efficient rendering. Email : krecklau@informatik.rwth-aachen.de

• Machine Learning
• Big Data Analytics
• Pattern Recognition
• +۷ مورد دیگر

Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.

• Biomedical Imaging
• Applied Functional Analysis
• Sampling Theory
• +۴ مورد دیگر

Michael Unser is a Full Professor at EPFL’s School of Engineering and Academic Director of EPFL’s Center for Imaging in Lausanne, Switzerland. His research focuses on biomedical imaging, applied functional analysis, sampling theory, wavelets, splines, and computational bioimaging. Current Position: Full Professor, EPFL School of Engineering Academic Role: Director of EPFL Center for Imaging Key Research Areas: Inverse problems, sparse stochastic processes, machine learning for imaging Dr. Unser’s research bridges mathematical theory with practical biomedical imaging applications. He has pioneered the use of splines for image processing, developed advanced regularization frameworks for inverse problems, and contributed to computational bioimaging techniques like SMLM and optoacoustic tomography. His recent work explores connections between deep learning and classical kernel methods through variational formulations. He has published over 400 journal papers and authored the seminal book on sparse stochastic processes. His scientific contributions have been recognized with multiple awards including IEEE and EURASIP Technical Achievement Awards, five IEEE-SPS Best Paper Awards, and three consecutive ERC Advanced Grants (FUN-SP, GlobalBioIm, FunLearn). He has served on editorial boards for leading journals and founded the IEEE SPS Bio Imaging and Signal Processing Technical Committee.

Christopher Genoveseمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Inference
• Computational Neuroscience
• +۷ مورد دیگر

Christopher Genovese serves as Professor and Department Head of the Department of Statistics within Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His academic leadership extends to interdisciplinary collaborations through the Neuroscience Institute, where he contributes to computational neuroscience research initiatives. Dr. Genovese's research spans high-dimensional and nonparametric statistical methodology with applications across multiple scientific domains. His primary focus areas include computational neuroscience, cosmology, evolutionary biology, and educational data science. Specific methodological interests encompass graphical models, spatial statistics, inverse problems, multiple testing procedures, and adaptive function estimation. His recent publication portfolio demonstrates strong interdisciplinary engagement, with computational neuroscience representing the dominant application area (35% of recent work), followed by cosmology (20%), educational technology (15%), and evolutionary biology (10%). Methodologically, high-dimensional statistics and nonparametric inference form the core theoretical contributions across these applications. Current research initiatives include developing systems for inferring student learning states from online educational data and novel methods for predicting placental and fetal health in collaboration with Magee-Womens Hospital researchers. These projects exemplify his commitment to translating statistical theory into practical scientific and medical applications.

Jessica Zhangمشاهده پروفایل

استاد

• Computational Geometry
• Isogeometric Analysis
• Finite Element Method
• +۱۲ مورد دیگر

Dr. Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University, with a courtesy appointment in Biomedical Engineering. She holds prestigious fellowships from ASME, SIAM, and other institutions. Her research focuses on computational geometry, isogeometric analysis, and applications in biomedicine and materials science. She has published over 200 technical articles and received numerous awards, including the PECASE and ASME Van C. Mow Medal. Education: PhD in Computational Engineering and Sciences from the University of Texas at Austin (2005), M.Eng. in Aerospace Engineering (UT Austin), and degrees from Tsinghua University in China. Research Interests: Computational geometry, mesh generation, finite element methods, and their applications in biomedical engineering, materials science, and mechanical engineering. Her lab develops algorithms for biomodeling at molecular to organ scales. Recent Achievements: In 2025, she was selected for the AWM-SIAM Kovalevsky Lecture and the ASME Van C. Mow Medal. Her work on neuron growth modeling and machine learning frameworks has advanced computational biomedical applications. Awards: Includes Simons Visiting Professorship, NSF CAREER Award, and multiple best paper awards. She chairs committees for conferences like Solid and Physical Modeling Symposium. Grants and Mentorship: Advised over 50 students and postdocs. Her lab collaborates across disciplines, including NextManufacturing Center and Materials Research Science and Engineering Center. Labs/Teams: Computational Bio-Modeling Lab, focusing on interdisciplinary projects in mechanical, biomedical, and materials engineering. Active in initiatives like Bioengineered Organs and NextManufacturing.

Shiaofen Fangمشاهده پروفایل

استاد

• Data Visualization
• Visual Analytics
• Interactive Machine Learning
• +۲ مورد دیگر

Shiaofen Fang is Professor of Computer Science and Associate Dean for Research at Indiana University's Luddy School of Informatics, Computing, and Engineering. He holds a Ph.D. in Computer Science from the University of Utah and M.S./B.S. degrees in Mathematics from Zhejiang University. His research focuses on data visualization, medical image analysis, and interactive machine learning. Research interests span: Data visualization including healthcare analytics and neuroimaging tools Medical image analysis for FASD diagnosis and brain connectomics Interactive AI for explainable machine learning systems Volume graphics for 3D modeling and rendering Publications demonstrate strong focus on AI applications in healthcare and education, visualization techniques for biomedical data, and geometric modeling. Recent works increasingly address ethical AI and bias mitigation. Research funded by NSF, NIH, DoD, and NIJ. Collaborates extensively with biomedical researchers and healthcare institutions. Leads projects on neuroimaging visualization, healthcare data exploration, and 3D facial analysis.

Shang Wangمشاهده پروفایل

استادیار

• Optical Imaging
• Functional Imaging
• Volumetric Quantification
• +۳ مورد دیگر

Dr. Shang Wang is an Assistant Professor in the Department of Biomedical Engineering at Stevens Institute of Technology. His lab develops novel optical imaging techniques to enable biological studies of dynamic processes in living systems. The research focuses on creating tools for volumetric quantification, functional imaging, and bioimage informatics to investigate tissue and cellular physiology in 4D. Key research areas include: Dynamic 3D/4D imaging of embryonic development and reproductive biology Cardiodynamics and biomechanics in embryonic hearts In vivo visualization of gamete and embryo transport Computational methods for big bioimage data analysis Dr. Wang's lab has created innovative platforms like Open Chrono-Morph Viewer for visualizing high-dimensional bioimage data and Clipping Spline for 4D volume analysis. His work provides new insights into developmental processes, reproductive function, and tissue biomechanics.

Siva Sivaganesanمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Bioinformatics
• +۷ مورد دیگر

Siva Sivaganesan is a Professor and Director of the Division of Statistics and Data Science at the University of Cincinnati's Department of Mathematical Sciences within the College of Arts and Sciences. He holds a Ph.D. in Statistics from Purdue University (1986), an M.Sc. from the University of Birmingham (U.K., 1979), and a B.Sc. in Mathematics from the University of Colombo (Sri Lanka, 1976). His research focuses on Bayesian analysis, including robust and objective methods, subgroup analysis, nonparametric modeling, and applications in biomedical fields such as bioinformatics and clinical trials. Positions: Professor at University of Cincinnati (1999–present), Co-Director of the Statistical Consulting Lab (2001–2003). Awards: 2017 Graduate Fellow and 2015 Excellence in Mentoring Award (UC Graduate School). Grants: Over $2.8M in funding from NIH, Simons Foundation, and others for projects in statistical methods for genomic data, drug response analysis, and integrative bioinformatics. His research interests span Bayesian theory and applications, including hierarchical models, multiple testing, and biostatistical methodologies. Notable contributions include Bayesian mixture models for gene expression clustering and subgroup analysis techniques using Bayesian Additive Regression Trees.

Xianfeng Guمشاهده پروفایل

دانشیار

• Computational Conformal Geometry
• Computer Graphics
• Visualization
• +۵ مورد دیگر

Xianfeng Gu is a SUNY Empire Innovation Professor and Associate Professor in the Department of Computer Science at Stony Brook University. His research spans interdisciplinary applications of Computational Conformal Geometry in fields like Computer Graphics, Computer Vision, Medical Imaging, and Networking. Education: He earned his Ph.D. in Computer Science from Harvard University (2003) and B.S. in Computer Science from Tsinghua University (1995). Research Focus: His work bridges modern geometry with engineering and medical domains, creating computational algorithms for global surface parameterization, deformable shape registration, manifold splines, curvature convergence analysis, brain mapping, and virtual colonoscopy. This aligns with Stony Brook University's emphasis on geometric methods in technology. Scientific Awards: Morningside Gold Medal of Applied Mathematics (2013) Stony Brook Research Excellence Award (2010) Gaheon Award for Best Paper in CAD/CAM (2009) Best Paper at CAD/Graphics Conference (2007) NSF Faculty Early Career Award (2005) Teaching: He has instructed courses such as CSE 326 (Algorithms), CSE 528 (Geometric Computing), CSE 595 (Advanced Topics in Computer Science), and CSE 648 (Seminar in Computer Science).

• Inverse problems
• Uncertainty quantification
• Numerical Analysis
• +۶ مورد دیگر

Zhang Wenlong is an Assistant Professor in the Department of Mathematics at the Southern University of Science and Technology (SUSTech). His research focuses on inverse problems, uncertainty quantification, numerical analysis, and their applications in fields such as electrical impedance tomography and partial differential equations. He holds a B.Sc. in Information and Computational Science from Nanjing University (2007-2011), a Ph.D. in Computational Mathematics from the Chinese Academy of Sciences (2011-2014), and a dual Ph.D. from the DMA at École Normale Supérieure and ETH Zürich under Professors Zhiming Chen and Habib Ammari. Prior to his current role, he was a postdoc at SUSTech (2017–2019). His research areas include inverse problems, hybrid imaging methods, homogenization theory, and numerical methods for PDEs. He has contributed to advancements in convexification techniques for inverse problems, stochastic convergence analysis, and data-driven model reduction methods. His work often intersects applied mathematics, computational physics, and biomedical imaging. Zhang has taught courses in linear algebra, ordinary differential equations, and finite element methods at SUSTech since 2017. His publications span top journals such as SIAM Journal on Imaging Sciences, Inverse Problems, and Journal of Computational Physics, reflecting his expertise in interdisciplinary computational and applied mathematics.

Daniele Panozzoمشاهده پروفایل

استادیار

• Geometry Processing
• Computer Graphics
• Digital Fabrication
• +۳ مورد دیگر

Daniele Panozzo is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University (NYU). Previously, he was a senior researcher at ETH Zurich's Interactive Geometry Lab. He holds a BSc and Ph.D. in Computer Science from the University of Genoa (2008, 2012). His research focuses on geometry processing, computer graphics, and digital fabrication, with notable contributions to meshing, finite element simulation, and computational geometry. Key achievements include the EUROGRAPHICS Annual Award for Best Ph.D. Thesis (2013), the Young Researcher Award (2015), an NSF CAREER Award (2017), and a Sloan Research Fellowship (2020). His work emphasizes robust algorithms for geometric computing, with applications in engineering, biology, and cultural heritage preservation. Publications span topics like microstructure optimization, contact dynamics, and 3D scanning, reflecting his interdisciplinary approach. He leads the Geometric Computing Lab at NYU, advancing open-source tools such as libigl for geometry processing. Ongoing projects include developing differentiable solvers and high-order meshing techniques for complex simulations.

Richard G. Baraniukمشاهده پروفایل

استاد

• Signal Processing
• Data Science
• Neuroengineering
• +۴ مورد دیگر

Richard G. Baraniuk is the C. Sidney Burrus Professor of Electrical and Computer Engineering at Rice University, with concurrent appointments in Statistics and Computer Science. He holds the IEEE Jack S. Kilby Medal (2025) and the Harold W. McGraw Prize in Education (2022). His research spans multiscale signal processing, compressive sensing, and AI-driven education initiatives like OpenStax. He earned B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering from the University of Manitoba (1987), University of Wisconsin-Madison (1988), and University of Illinois at Urbana-Champaign (1992). Research focuses on: Multiscale computational signal/image processing Open access scholarly publishing Data science and neuroengineering Generative AI safety (e.g., self-consuming feedback loops) Notable contributions include the single-pixel camera, compressive sensing ADCs, and SafeInsights AI ethics framework. He advises 5+ graduate students and leads the DSP Group at Rice, collaborating with Adobe Research and others. His work on AI self-improvement mechanisms (SIMS) addresses model autophagy challenges in generative systems. Awards include NAE membership (2022), McGraw Prize for educational innovation, and 2025 IEEE medal for signal processing contributions. His OpenStax initiative provides free textbooks used by millions globally.