Geometry Processing Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۹٬۰۵۳ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Paolo Prandoniمشاهده پروفایل

مدرس

Swiss Federal Institute of Technology in LausanneSwitzerland

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

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Taein Kwonمشاهده پروفایل

پژوهشگر ارشد

École Normale SupérieureFrance

Taein Kwon is a postdoctoral research fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working under Prof. Andrew Zisserman. Previously, he completed his PhD at ETH Zurich under Prof. Marc Pollefeys and earned master's and bachelor's degrees from UCLA and Yonsei University, respectively. His educational background includes: Bachelor's in Electrical Engineering from Yonsei University, Seoul, Korea Master's degree from UCLA PhD from ETH Zurich (defended July 2024) His research spans Egocentric Vision, Action Recognition, Hand-object Interaction, Video Understanding, AR/VR, and Multi-modal Learning, with emphasis on first-person perspective analysis for AI assistants and human-computer interaction. His work integrates 3D reconstruction, pose estimation, and multimodal signals to model complex human activities and physical interactions. Analysis of his 2021-2025 publications reveals a consistent focus on egocentric vision datasets (H2O, HoloAssist, EgoPressure) and novel frameworks for hand-object interaction, action recognition, and gesture understanding. His research demonstrates strong interdisciplinary connections between computer vision, robotics, and human-centered AI, with increasing emphasis on pressure sensing, co-speech gestures, and cross-modal alignment. His scientific recognition includes: CVPR Egovis 2022/2023 Distinguished Paper Award for HoloAssist (July 2024) SNSF Postdoc.Mobility fellowship (May 2024) He actively mentors students on egocentric vision projects, supervising master's theses, semester projects, and collaboration initiatives leading to publications at top conferences. His research is supported by the SNSF fellowship and industry collaborations with Meta Reality Labs and Microsoft Research. As part of Oxford's Visual Geometry Group, he contributes to cutting-edge computer vision research while maintaining strong ties with ETH Zurich's computer vision community through ongoing collaborations and dataset development efforts.

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David Mountمشاهده پروفایل

استاد

University of Maryland, College ParkUnited States

David Mount is a Professor in the Department of Computer Science at the University of Maryland, with an additional appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). His primary research focus is Computational Geometry, particularly in designing, analyzing, and implementing data structures and algorithms for geometric problems. Applications of his work span image processing , pattern recognition , information retrieval , and computer graphics . He is a Fellow of the ACM and has received the ACM Recognition of Service Award twice. A member of the Algorithms and Theory Group, Mount has authored over 200 publications, many of which are available on Google Scholar, DBLP, and ArXiV. Research Focus Computational Geometry Algorithm Design and Analysis Geometric Data Structures Nearest Neighbor and Range Searching Clustering Algorithms Recent Publications Mount's recent publications (2023-2025) emphasize non-Euclidean geometry (e.g., Hilbert metric), dynamic geometric structures , and approximation algorithms for polytopes, Voronoi diagrams, and Delaunay triangulations. Collaborative works with students and researchers address challenges in kinetic data compression , label tracking , and geometric software development (e.g., Ipelets for polygonal geometry). Professional Activities Editorial Board Member, TheoretiCS (2021-present) Senior Associate Editor, ACM Trans. on Spatial Algorithms and Systems (2013-2020) Program Committee Member, FOCS , ESA , SODA , and other major conferences Awards ACM Fellow ACM Recognition of Service Award (twice)

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Max Wardetzkyمشاهده پروفایل

استاد

University of GöttingenGermany

Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.

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Shayan Aziznejadمشاهده پروفایل

پژوهشگر

Swiss Federal Institute of Technology in LausanneSwitzerland

Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com

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Xuming Heمشاهده پروفایل

دانشیار

Swiss Federal Institute of Technology in LausanneSwitzerland

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

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Laurent Caraffaمشاهده پروفایل

پژوهشگر

National School of Geographic SciencesFrance

Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.

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Sean Andersonمشاهده پروفایل

مدرس ارشد

University of SheffieldUnited Kingdom

Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.

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Joseph Woodمشاهده پروفایل

استاد

City, University of LondonUnited Kingdom

Professor Joseph Wood is a Professor of Visual Analytics at City St George's, University of London, where he serves as a founding member of the giCentre. His academic career spans over three decades, with continuous contributions to Geographic Information Science and visualization since 1990. He previously served as Head of Department for Computer Science at City University between 2014 and 2017. Professor Wood's educational background includes a PhD in Geographical Information Science from the University of Leicester (1996), an MSc in the same field from the University of Leicester (1990), and a BSc in Physical Geography & Geology from the University of Sheffield (1989). His academic progression shows steady advancement from Research Scholar at the University of Leicester (1990-1992) through various lecturer and senior positions to his current professorship. His research interests center on visual analytics and data visualization, with particular expertise in geographic information science and terrain analysis. Professor Wood has developed innovative methods bridging GI Science, Data Visualization, and education domains. His specific interests include narrative of visual analytic design, computational thinking in pedagogy, and novel visualization design for geographic data. His work demonstrates a consistent focus on making complex spatial data understandable through innovative visualization techniques. Analysis of Professor Wood's recent publications reveals a strong emphasis on practical applications of visualization techniques across diverse domains including transportation, epidemiology, sports analytics, and historical migration patterns. His work shows an evolution from foundational geographic information science toward broader applications in visual analytics, with increasing focus on narrative structures, responsive design, and accessibility considerations in visualization. The interdisciplinary nature of his research is evident in collaborations spanning computer science, geography, urban planning, and public health domains. Professor Wood has been actively involved in the academic community, serving on organizing and program committees for major international conferences including IEEE Infovis and VAST, Eurovis, GIScience, Spatial Accuracy, and Geomorphometry. His contributions to the field have been recognized through invitations to deliver keynote talks at prestigious venues ranging from GeoComputation to TEDx, where he presented on topics such as visualizing movement behavior of cyclists. As an advisor, Professor Wood has supervised numerous PhD and Master's students, with current supervision of Julia Crossley (Student conceptualisation of abstraction in computer science) and Jude Nzemeke (Understanding student misconception in recursive algorithmic thinking). His extensive supervision history includes completed PhDs on topics ranging from cycling behavior to spatio-social relations in photographic archives. His academic leadership extends to software development, with contributions to tools like litvis, elm-vega/el-vegalite, giCentre Utils, handy, and LandSerf GIS. Professor Wood is an active member of professional organizations including IEEE (2007-present), Association of Computing Machinery (ACM) (2007-present), and Association of Geographic Information (AGI) (1997-present), demonstrating his commitment to interdisciplinary collaboration across computer science and geographic information domains.

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Oliver Johnsonمشاهده پروفایل

استاد

University of BristolUnited Kingdom

Professor Oliver Johnson is a faculty member at the School of Mathematics, University of Bristol, UK, where he serves as Head of School and holds the Professor of Information Theory position. His research bridges information theory, probability, and statistics, focusing on entropy convergence, group testing, and fundamental limits in data analysis. Current PhD students: Kieran Morris, Conor Crilly Ex-PhD students: Matt Aldridge, Leonardo Baldassini, Dan Cowley, Vaia Kalokidou, Tom Kealy, Jennifer Chakravarty, Zichen Gui, Chrys Paschou Ex-postdoc: Erwan Hillion His work includes ORCiD profile and collaborations across information theory, cybersecurity, and ecological modeling.

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Jingbo Liuمشاهده پروفایل

استادیار

University of Illinois Urbana-ChampaignUnited States

Jingbo Liu is an Assistant Professor in the Department of Statistics at the University of Illinois, Urbana-Champaign, with an affiliate appointment in Electrical and Computer Engineering. He received his B.E. (2012) from Tsinghua University, M.A. (2014) and Ph.D. (2018) from Princeton University, all in Electrical Engineering, followed by a postdoc at MIT IDSS. Education Ph.D. in Electrical Engineering, Princeton University (2018) M.A. in Electrical Engineering, Princeton University (2014) B.E. in Electronic Engineering, Tsinghua University (2012) His research focuses on statistical inference under systems constraints, information-theoretic inequalities, graphical models, and applications of high-dimensional probability to information sciences. Key areas include mutual covering bounds, hypercontractivity, Brascamp-Lieb inequalities, and their connections to machine learning and communication systems. Recent work applies information theory to generative AI, analyzing diffusion models' utility, privacy enhancements, and computational efficiency. He also investigates statistical physics techniques for high-dimensional problems like Lasso distributional limits and tensor model free energy, with applications in variable selection and PCA. Scientific awards include the Thomas M. Cover Dissertation Award (2018) and Princeton's Wallace Memorial Fellowship (2016). Courses taught include STAT 578 (High-Dimensional Statistics), STAT 430 (Nonparametric Statistics), and STAT 542 (Statistical Learning).

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Cem Evrendilekمشاهده پروفایل

استاد

Izmir University of EconomicsTurkey

Professor Cem Evrendilek is a faculty member in the Department of Computer Engineering at Izmir University of Economics, Turkey, holding the rank of Professor with current active status. His institutional email is cem.evrendilek@ieu.edu.tr. His research spans algorithmic complexity and geometric computation, with primary focus areas including: Computational Geometry (specializing in orthogonal polygon covering problems) Wireless Sensor Network Localization (energy-efficient methods and trilateration) NP-hard Problem Analysis (proving complexity for geometric and network problems) Approximation Algorithms for combinatorial optimization Analysis of his 2008-2018 publications reveals consistent work on geometric covering problems and sensor network localization. Key trends include proving NP-completeness for orthogonal polygon covering variants, developing energy-efficient mobile beacon localization techniques, and analyzing trilateration with noisy measurements. His work frequently intersects computational geometry with practical wireless network applications, demonstrating strong theoretical foundations applied to real-world constraints. Collaborative patterns show frequent co-authorship with Hüseyin Akcan (on localization algorithms), Burkay Genç, and Brahim Hnich (on computational geometry problems).

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Catherine Neishمشاهده پروفایل

دانشیار

Western UniversityCanada

Catherine Neish is an Associate Professor in the Department of Earth Sciences at The University of Western Ontario. She serves as the Associate Director of Research for Western Space and is a Co-Investigator on NASA's Dragonfly mission to Titan. Her research focuses on planetary radar observations, impact cratering processes, and the geological evolution of planetary surfaces, particularly on the Moon, Titan, and other Solar System bodies. Ph.D. in Planetary Sciences, University of Arizona (2008) B.Sc. in Combined Honours Physics and Astronomy, University of British Columbia (2004) Her recent publications highlight studies on lunar impact crater thermophysics, Titan's impact melt dynamics, and radar-based analyses of planetary surfaces. She has contributed to missions including Lunar Reconnaissance Orbiter (Mini-RF), Cassini RADAR, and Dragonfly. Scientific Awards: College of New Scholars, Royal Society of Canada (2021) Early Researcher Award, Ontario (2017) Minor Planet 16972 Neish (2017) AGU Ron Greeley Award (2014) NASA Postdoctoral Fellowship (2012) NASA Group Achievement Award (2010) NSERC Postgraduate Scholarship (2005-2008) Julie Payette-NSERC Research Scholarship (2004-2005) Dr. Neish supervises a dynamic lab with current and former students investigating planetary geology, impact cratering, and remote sensing. Her work bridges field studies (e.g., Earth analogs), laboratory experiments, and spacecraft data analysis.

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James Martinمشاهده پروفایل

مدرس

University of OxfordUnited Kingdom

James Martin is a Lecturer at the Department of Statistics, University of Oxford . He is affiliated with St Hugh's College and has been actively involved in organizing probability seminars since 2018. Research Interests Probability theory Random graphs and percolation Interacting particle systems Models of random growth and coagulation-fragmentation Queueing networks Combinatorial games Teaching Courses: Prelims Probability , Part A Probability , Part B Statistical Lifetime Models , Part C Probabilistic Combinatorics His publications focus on probability theory , statistical physics , and combinatorial structures . Recent work includes studies on last-passage percolation, multispecies exclusion processes, and integrable probability models. James Martin collaborates with researchers from institutions such as Uppsala University, University of Cambridge, Imperial College London, and Kyoto University. He has been a key organizer for the Oxford Probability Seminar since 2018.

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Matty Mookerjeeمشاهده پروفایل

استاد

Sonoma State UniversityUnited States

Matty Mookerjee is a Professor and Department Chair of the Department of Geological Sciences at Sonoma State University. He is a structural geologist specializing in three-dimensional kinematics of fault and shear zones. His contact email is matty.mookerjee@sonoma.edu , and his office hours are Monday 9-11 AM and Tuesday 1-2 PM in Darwin 123. Education Ph.D. in Geological Science, University of Rochester (2005) M.S. in Geological Science, University of Rochester (2001) B.A. in Geology, Oberlin College (1999) Indiana University Summer Field Camp (1999) His research integrates structural geology with cyberinfrastructure development, focusing on quantitative analysis of fault zones using grain shape and crystallographic texture studies, alongside creating digital tools for field data management. Recent projects span the San Andreas Fault, Denali Fault, Himalayas, and Rosy Finch Shear Zone. Mookerjee's publications highlight analog modeling of fault asperities, strain analysis in tectonic zones, and cyberinfrastructure tools for geological data. He has mentored students in collaborative research projects, particularly in kinematic studies and data repository development.

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