جست‌وجوی استاد و پژوهشگر

Shuran Songمشاهده پروفایل

استادیار

• Computer Vision
• Robotics
• Robot Learning
• +۴ مورد دیگر

Shuran Song is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. Previously, she was faculty at Columbia University. She holds a Ph.D. in Computer Science from Princeton University and a BEng from HKUST. Her research focuses on the intersection of computer vision and robotics, particularly in embodied AI, robot manipulation, and sensorimotor learning. Song's work emphasizes learning from physical interactions to enable robots to perform complex tasks autonomously. She leads the Robotics and Embodied AI Lab (REAL@Stanford) and has received prestigious awards, including the NSF Career Award, Sloan Fellowship, and Microsoft Faculty Fellowship. Education: Ph.D., Computer Science, Princeton University; BEng, HKUST Affiliations: Stanford School of Engineering, Department of Electrical Engineering Research interests include deformable object manipulation, visuomotor policy learning, and generalizable robot skills. Her lab develops algorithms for robots to learn through interaction, with applications in household assistance (e.g., TidyBot) and industrial automation. Notable contributions include the TossingBot and Diffusion Policy frameworks. Publications span robotics, computer vision, and AI conferences (RSS, ICRA, CVPR), focusing on policy learning, deformable object handling, and embodied intelligence. Awards highlight her impact in advancing robot learning and perception. Advises doctoral and master's students in robotics and AI, and collaborates on grants from NSF, DoD, and industry partners. Teaches courses on robot perception and embodied AI at Stanford.

Jared Weinsteinمشاهده پروفایل

استاد

• Number Theory
• Algebraic Geometry
• Arithmetic Geometry
• +۳ مورد دیگر

Jared Weinstein is a Professor in the Department of Mathematics and Statistics at Boston University, serving as the Departmental Ombud. He specializes in Number Theory and Algebraic Geometry, with a focus on p-adic geometry, shtukas, and moduli spaces. His research explores connections between arithmetic geometry and homotopy theory, including contributions to the Langlands program and local Shimura varieties. Education: AB from Harvard University (undergraduate), PhD from University of California, Berkeley. Postdoctoral work at UCLA and the Institute for Advanced Study before joining BU in 2011. Research interests include arithmetic geometry, p-adic Hodge theory, and the geometry of moduli spaces. His work often intersects with topics like perfectoid spaces, diamonds, and chromatic homotopy theory. Recent articles highlight advancements in modularity of elliptic curves over function fields and the Kottwitz conjecture for local shtuka spaces. No scientific awards are explicitly listed, but his extensive publications reflect significant contributions to his field. Advising and grants details are not provided here. His work is closely tied to the v-topology and related geometric frameworks in algebraic geometry.

Siyu Tangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Digital Humans
• +۵ مورد دیگر

Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.

• Computer Graphics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Frederick Li is an Associate Professor in the Department of Computer Science at Durham University, UK. He holds editorial roles as Associate Editor of Frontiers in Education (Digital Education) and Editorial Board Member of Virtual Reality & Intelligent Hardware. His research focuses on Computer Graphics, Machine Learning, Geometric Modelling, Collaborative Virtual Environments, Visual Aesthetics, and Educational Technologies. He earned his B.A. (Hons) and M.Phil. from The Hong Kong Polytechnic University and his Ph.D. in Computer Graphics from City University of Hong Kong. Prior roles include Assistant Professor at HK PolyU and project manager of a Hong Kong Government ITF-funded project. **Education**: B.A. (Computing Studies) and M.Phil. from HK PolyU; Ph.D. in Computer Graphics (CityU Hong Kong). **Research Interests**: His work spans mesh saliency detection, human-object interaction recognition, cloud modeling, face beautification, and educational technology. Recent achievements include awards for papers (e.g., Best Paper at ITiCSE 2014) and recognition such as EPSRC Peer Review College membership. He leads Durham's Undergraduate Board of Examiners and has been an external examiner at Northumbria University. **Awards**: Best Paper (ACM ITiCSE 2014), Outstanding Paper (ICALT 2013), EPSRC Peer Review College (2024), Outstanding BMVC 2024 Reviewer. **Grants & Labs**: His research is supported by grants from EPSRC and others. He collaborates with the Centre for Vision and Visual Cognition, VIViD, and AIHS group at Durham.

Lawrence H. Staibمشاهده پروفایل

استاد

• Medical Image Analysis
• Machine Learning
• Nonrigid Registration
• +۶ مورد دیگر

Lawrence H. Staib is a Professor of Biomedical Engineering at Yale University, with additional academic appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. He holds a Ph.D. from Yale University and specializes in automated medical image analysis, including techniques like model-based segmentation, nonrigid registration, and diffusion tensor imaging (DTI). His research focuses on applications in neuroscience, cardiology, and cancer imaging, emphasizing machine learning and functional MRI analysis. His key contributions include advancements in white matter tractography via anisotropic wavefront evolution, real-time neural tract parcellation (Fasciculography), and noise reduction in diffusion tensor fields. Staib is a Fellow of the American Institute for Medical and Biological Engineering (2015), recognizing his impactful work in medical imaging technologies. Staib's research also encompasses statistical deformation models, perturbation-based shape analysis, and 3D deformable models for volumetric segmentation. He has developed patented 3D ultrasound computed tomography systems (USPTO #6878115, 7025725). His work bridges clinical needs with computational methods, addressing challenges in image registration, structural connectivity analysis, and medical robotics.

• Computer Graphics
• 3D Reconstruction
• Deep Learning
• +۵ مورد دیگر

Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France , and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs. Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching. Scientific Honors include: ERC Consolidator Grant (VEGA Project, 2023) ERC Starting Grant (2017) ACM SIGGRAPH 2023 Test-of-Time Award Best Paper Awards at 3DV 2021 and 3DV 2022 Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.

• Computer Graphics
• Computer Vision
• Human-Computer Interaction
• +۷ مورد دیگر

Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.

Steven Ceronمشاهده پروفایل

استادیار

• Swarm robotics
• Multi-agent systems
• Swarm intelligence
• +۵ مورد دیگر

Steven Ceron is an Assistant Professor in Robotics at the University of Michigan's College of Engineering. His research focuses on swarm robotics, multi-agent systems, and programmable self-organization of micro- and macro-scale robot swarms. He leads the Synergetic Adaptive Machinas (SAM) Lab, which develops reconfigurable robot swarms for biomedical applications and smart materials integration. Key research areas include microrobot fabrication, heterogeneous swarm coordination, and self-reconfigurable modular systems. His work envisions seamless integration of robot swarms into daily life through innovations in design, control, and scalability. Recent publications emphasize swarmalator dynamics, strain-based coordination in soft robots, and scalable fabrication methods. His lab explores both theoretical frameworks and practical implementations, bridging micro-scale and macro-scale robotics applications. Though no awards were explicitly listed, his contributions to novel fabrication techniques and modular robotics suggest ongoing recognition in the field. Advising and grant details are not provided here, but his lab's focus on biomedical and aerospace applications indicates active collaborative projects.

Martin Achmusمشاهده پروفایل

استاد

• Geotechnical Engineering
• Offshore Engineering
• Soil Mechanics
• +۳ مورد دیگر

Prof. Dr.-Ing. Martin Achmus is a Professor at Leibniz University Hannover, affiliated with the Faculty of Civil Engineering and Geodetic Science and the Institute for Geotechnical Engineering. He holds multiple leadership roles, including Designated Dean of Civil Engineering and Executive Director of the Institute of Geotechnical Engineering. His research focuses on geotechnical engineering challenges in offshore wind energy, cyclic soil-pile interactions, and foundation design for renewable energy infrastructure. Positions: Designated Dean, Executive Director (Institute of Geotechnical Engineering), Board Member (Testzentrum Tragstrukturen), Exchange Coordinator (Civil Engineering/Computer-Assisted Engineering). Key Projects: Collaborative Research Center (SFB) 'Offshore-Megastructures', DFWind, ProBucket, Ho-Pile, and SEALENCE, focusing on offshore foundations, cyclic loading, and soil-structure interactions. Research Interests: Load-bearing behavior of offshore foundations, soil-pipeline interaction, cyclic loading effects on piles, and geotechnical design methodologies. His work bridges experimental testing (e.g., model tests, particle image velocimetry) and numerical simulations to address challenges in renewable energy infrastructure and geotechnical systems. Publications: Over 150 peer-reviewed articles, emphasizing cyclic soil responses, monopile foundations, suction bucket behavior, and district heating pipeline interactions. Recent work explores novel p-y curve models, capacity degradation under cyclic loading, and scaling approaches for sand behavior. Grants & Collaborations: Funded by DFG, BMWi, and EU initiatives. Collaborates with industry partners on offshore wind turbine foundations and geotechnical innovations. Labs/Teams: Leads research teams within the Institute of Geotechnical Engineering, focusing on experimental and computational geotechnics, with active participation in national and international research networks.

Jeffrey Brockمشاهده پروفایل

استاد

• Low-dimensional geometry and topology
• Hyperbolic geometry
• Teichmüller theory
• +۳ مورد دیگر

Jeffrey F. Brock is the Zhao and Ji Professor of Mathematics and Dean of the School of Engineering & Applied Science at Yale University. He also serves as the inaugural Dean of Science in Yale's Faculty of Arts and Sciences. His research focuses on low-dimensional geometry and topology, particularly hyperbolic geometry and its applications to data science. He has held leadership roles including Chair of Brown University's Mathematics Department (2013–2017) and founding Director of Brown’s Data Science Initiative (2016). Roles: Dean of Engineering, Mathematics Professor Key Affiliations: Yale University, Brown University Education: B.S. from Yale University, Ph.D. in Mathematics from U.C. Berkeley (under Curtis McMullen). Postdoctoral positions at Stanford and University of Chicago. Extensive academic leadership experience, including administrative roles at both Brown and Yale. Research Interests: Hyperbolic 3-manifolds, Teichmüller theory, geometric structures in data science. Notable contributions include geometric classification of hyperbolic 3-manifolds (with R. Canary and Y. Minsky) and advancing topological methods for analyzing complex data sets. Publications: Over 40 peer-reviewed articles, including foundational work on the ending lamination conjecture and Weil-Petersson geometry. Recent work applies geometric methods to machine learning and medical imaging. Awards: John Simon Guggenheim Fellowship (2008), Fellow of the American Mathematical Society (2017). Labs/Initiatives: Brown’s Data Science Initiative, geometric and topological data analysis projects.

Davoud Cheraghi Homeمشاهده پروفایل

دانشیار

• Complex Analysis
• Dynamical Systems
• Geometric Analysis
• +۳ مورد دیگر

Dr Davoud Cheraghi Home is a Reader (Associate Professor) in Pure Mathematics at Imperial College London, affiliated with the Pure Analysis and PDEs and Geometry research groups. His research focuses on complex analysis and dynamical systems, particularly holomorphic maps, rigidity phenomena, and small divisors problems, bridging analysis, geometry, and combinatorics. He has organized numerous conferences and workshops, including events at Imperial College and the School of Mathematics in Tehran. Dr Cheraghi teaches advanced courses in geometric complex analysis, analysis, and dynamical systems at Imperial College, as well as at the University of Warwick and Stony Brook University. He has mentored PhD students and postdoctoral researchers, contributing significantly to the field through his publications and collaborations. His research interests encompass geometric analysis (quasi-conformal mappings, elliptic PDEs), renormalization operators, and low-dimensional analytic dynamics. Notable publications include studies on irrationally indifferent attractors, Siegel polynomials, and combinatorial rigidity. Dr Cheraghi’s work is supported by extensive teaching and mentorship activities, reflecting his commitment to advancing both research and education in pure mathematics.

Jia-Bin Huangمشاهده پروفایل

دانشیار

• Computer Vision
• Computer Graphics
• Machine Learning
• +۴ مورد دیگر

Jia-Bin Huang is an Associate Professor in the Department of Computer Science at University of Maryland, College Park , with a secondary appointment at the University of Maryland Institute for Advanced Computer Studies . His work bridges computer vision , computer graphics , and machine learning . His research focuses on 3D scene reconstruction , neural radiance fields , generative models , and multimodal foundation models . He has made significant contributions to video super-resolution , text-driven 3D modeling , and inverse rendering techniques. 15 recent publications (2024-2025) at top venues: CVPR , NeurIPS , SIGGRAPH Asia , 3DV , and ECCV Pioneering work in Urban Scene Inverse Rendering , Generative Video Editing , and 3D Human Digitization He has received multiple awards including the 3M Non-Tenured Faculty Award , ETRA Best Paper , and NSF Grants . His lab trains 12 PhD students and has graduated 18 Masters/PhD students now at institutions like Stanford , Meta , and Google .

Alexander Tumanovمشاهده پروفایل

استاد

• Several Complex Variables
• Differential Geometry
• Partial Differential Equations
• +۳ مورد دیگر

Professor Alexander Tumanov is a faculty member in the Department of Mathematics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. He holds the rank of Professor and has been active in research and teaching since at least 2000. His current teaching includes Math 220 Calculus in Fall 2025. Education & Affiliations: His academic career is deeply rooted at the University of Illinois, with no indications of former roles or retirement status. Research Interests: Tumanov specializes in Several Complex Variables, Differential Geometry, and Partial Differential Equations. His work explores topics such as CR mappings, stationary discs, pseudoholomorphic curves, symplectic geometry, and nonlinear PDEs. Notable contributions include studies on automorphism groups of bounded domains, boundary value problems, and applications of Gromov's methods in symplectic topology. Publications: His recent work (2025–2013) focuses on advanced topics like non-linear Lie groups, q-convex manifolds analysis, outer billiards dynamics, and symplectic non-squeezing in infinite dimensions. Earlier contributions include boundary regularity for elliptic equations and minimal energy configurations on spheres. Awards & Grants: No specific awards are listed in the provided materials, though his extensive publication record reflects sustained scholarly impact. No grants or advising data are explicitly mentioned. Labs & Teams: Tumanov collaborates extensively with researchers like A. Sukhov, L. Baracco, and B. Coupet, as evidenced by co-authored papers. His work intersects pure mathematics with geometric analysis and mathematical physics.

Olesya Zhupanskaمشاهده پروفایل

استاد

• Mechanics of Composites
• Structural Composites in Extreme Environments
• Multi-Field Effects (Mechanical, Thermal, Electromagnetic)
• +۱۲ مورد دیگر

Dr. Olesya Zhupanska is a Professor in the Department of Aerospace and Mechanical Engineering at the University of Arizona, where she holds a faculty position and is a member of the Graduate Faculty. Her research focuses on the mechanics of composite materials, especially under extreme multi-field conditions involving mechanical, thermal, and electromagnetic loads. Education: PhD in Mechanics of Solids and Applied Mathematics, Taras Shevchenko National University of Kyiv, Ukraine, 2000 BS/MS in Mechanics and Applied Mathematics (with Highest Honors), Taras Shevchenko National University of Kyiv, Ukraine, 1996 Her research interests span mechanics of composites, impact and damage, micromechanics, multi-field effects, and structural health monitoring, with applications in aerospace, wind energy, and smart materials. She has made significant contributions to understanding lightning strike damage, electrified composites, and thermostructural response of advanced materials. Her work integrates experimental, analytical, and computational methods to solve complex engineering problems. The 15 most recent publications highlight a strong trend in composite materials under electrical and thermal loads, with a focus on damage mechanisms, contact mechanics, and predictive modeling. Her research bridges mechanics, materials science, and electromagnetics, with increasing integration of machine learning for damage detection. Applications span aerospace structures, hypersonic vehicles, and wind turbine blades. Scientific Awards and Honors: DARPA Young Faculty Award (2011) Elsevier Young Composites Researcher Award (2008) ASME/Boeing Structures & Materials Award (2007) Multiple ASC Best Paper Awards National Research Council Senior Research Associateship Award (2022, 2015) Air Force Summer Faculty Fellowships (multiple years) Woman of Impact Award, University of Arizona (2022) Fellow, ASME Associate Fellow, AIAA ASME Dedicated Service Award (2023) Dr. Zhupanska has advised numerous graduate students, many of whom have won prestigious awards such as the DoD SMART Scholarship and NASA Fellowships. Her research has been funded by DARPA, NSF, NASA, AFOSR, AFRL, and industry partners. She has served on technical review boards including ARL and actively promotes engineering education and inclusion through NSF-funded initiatives. She holds leadership roles in professional societies, currently serving as President of the American Society for Composites (ASC) and as a member of the ASME IMECE Steering Committee Senate. She also serves as a Topic Editor for Composites and Advanced Materials and on the editorial board of Applied Composite Materials.

Jacopo De Simoiمشاهده پروفایل

استاد

• Hyperbolic Dynamics
• Billiards
• Rigidity
• +۴ مورد دیگر

Jacopo De Simoi is a Professor in the Department of Mathematics at the University of Toronto, holding appointments at both the St. George and Mississauga campuses. His research focuses on dynamical systems, particularly hyperbolic dynamics, billiards, and rigidity phenomena. He has held roles at institutions like Université Paris Diderot and the University of Maryland, College Park, and currently teaches courses such as Game Theory and Real Analysis. His work explores the interplay between deterministic systems and stochastic processes, with contributions to topics like Fermi acceleration and KAM theory. Education: Ph.D. in Mathematics from the University of Maryland (2009), Diploma di Licenza in Physics from Scuola Normale Superiore (2005), and M.Sc./B.Sc. in Physics from Università di Pisa. Research interests include stochastic properties of dynamical systems, conservative dynamics, and the ergodic theory of billiards. He has published extensively on spectral rigidity, entropy rigidity, and applications of renormalization group techniques. His recent work addresses inverse problems in billiard geometry and the statistical behavior of fast-slow systems. Teaching includes undergraduate and graduate courses in analysis, calculus, and dynamical systems. Collaborations span institutions globally, and he serves on editorial boards for journals like Communications in Mathematical Physics.