Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
جستجوی استاد راهنمای دانشگاهی
برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۱-۱۵ از ۵۰۴ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
- Fibrosis Research
- Liver Fibrosis
- Kidney Fibrosis
- +۹ مورد دیگر
Karen Piper Hanley is Professor of Molecular Medicine and Head of Division in the Division of Diabetes, Endocrinology & Gastroenterology at the University of Manchester. She serves as the fibrosis lead in the Wellcome Trust Centre for Cell-Matrix Research and previously served as Director of Core Research Technologies (2019-2022). She is also an editorial board member for Scientific Reports. Her research focuses on fibrosis (scarring) mechanisms across multiple organs, particularly liver, kidney, and lung fibrosis. Her lab investigates the cellular and molecular basis of fibrotic disease, with emphasis on how pathological scar components are produced and how they signal to surrounding cells. Key areas include the role of SOX9 transcription factor, integrin beta-1 signaling, Group 1 PAKs, and YAP1 in fibrosis progression. Her recent publications reveal trends toward increased clinical translation of fibrosis research, with studies on health inequalities in liver cancer care, post-COVID diabetes incidence, and innovative approaches to liver disease detection. Her work increasingly integrates spatial transcriptomics and advanced omics technologies to understand cellular transitions in liver disease. Career-track postdoctoral fellowship from the University of Southampton (2006) As senior postgraduate tutor, she has mentored numerous PhD students who have presented at key conferences and authored high-quality manuscripts. All her PhD students have submitted and been awarded their degrees within 4 years. She has successfully guided previous Academic Clinical Fellows to win MRC clinical training fellowships. Her teaching extends to undergraduate Personal & Professional Development and supervision of MRes and BSc students, many of whom have achieved first-class degrees and prestigious awards. Her lab maintains active collaborations with Professor Neil Hanley's group on human development, stem cells, and regenerative medicine, bridging developmental biology with fibrosis research to understand why regeneration is impaired in chronic diseases.
John Paisleyمشاهده پروفایل
دانشیار
- Machine Learning
- Bayesian Statistics
- Data Science
- +۳ مورد دیگر
John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.
Foong Shaohuiمشاهده پروفایل
دانشیار
Dr. Foong Shaohui is an Associate Professor and Associate Head at the Engineering Product Development (EPD) pillar of the Singapore University of Technology and Design (SUTD), with prior experience as a Visiting Assistant Professor at MIT's Mechanical Engineering department (2011). He leads the Aerial Innovation Research (AIR) Laboratory @ SUTD and actively collaborates with Singapore's Ministry of Defence (MINDEF) and medical institutions like National University Hospital (NUH) and Changi General Hospital (CGH). PhD, MS, and BS in Mechanical Engineering from Georgia Institute of Technology (2005-2010) Research Interests span multiple domains: Robotics & UAVs : Nature-inspired aerial craft design (Project MONOCO), hybrid flight dynamics, and transformable rotorcraft Medical Device Innovation : Magnetic localization systems for nasogastric tubes and ventriculostomy procedures Engineering Education : Design-Centric pedagogy and pre-university Aerial Craft Workshops Autonomous Systems : Deep tunnel sewer inspection drones (NRF/PUB funded) and soft robotics Scientific Contributions include patented magnetic localization technologies (licensed to Medergo Pte. Ltd.), over 20 peer-reviewed publications, and 5 granted patents. His work bridges aerospace engineering with biomedical applications through innovative mechatronic solutions. Best Application Paper Award at SCIS & ISIS (2014) Research Grants include projects funded by Singapore's National Research Foundation (NRF), Public Utilities Board (PUB), and National University Hospital partnerships. He mentors PhD/Master's students through interdisciplinary research in aerial robotics and medical device development.
Karoline Faustمشاهده پروفایل
دانشیار
Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .
- Robotics
- Computer Vision
- Human-Machine Interaction
- +۳ مورد دیگر
Gregory D. Hager is the Mandell Bellmore Professor of Computer Science at Johns Hopkins University, with joint appointments in Electrical and Computer Engineering, Mechanical Engineering, and the Department of Surgery at the School of Medicine. He serves as the head of the NSF's Computer and Information Science and Engineering Directorate (as of 2024) and is the founding director of the Johns Hopkins Malone Center for Engineering in Healthcare. Previously, he chaired the Department of Computer Science from 2010-2015 and served as deputy director of the NSF Engineering Research Center for Computer-Integrated Surgical Systems and Technology. Hager's research focuses on collaborative and vision-based robotics, time-series analysis of image data, and medical applications of image analysis and robotics. His work spans surgical robotics, human-machine collaboration, and computer vision with applications in healthcare. As director of the Computational Interaction and Robotics Lab (CIRL), he investigates dynamic spatial interaction at the intersection of imaging, robotics, and human-computer interaction. His research has led to real-world applications in surgical training, medical imaging, diagnostics, and computer-enhanced interventional medicine. Hager's publications demonstrate consistent advancement in surgical data science, with recent work focusing on 3D reconstruction from endoscopic video, surgical skill assessment using AI, and robotic assistance in neurosurgery. His research trajectory shows increasing integration of deep learning with surgical robotics, particularly in real-time guidance systems and objective skill assessment metrics. IEEE Fellow MICCAI Fellow ACM Fellow AIMBE Fellow AAAS Fellow MICCAI Best Paper Award (2006) Fulbright Junior Faculty Award (1988) Morris Ruben Outstanding Dissertation Award (1988) Hager has advised numerous PhD students who have become leaders in computer vision and medical robotics. His lab has secured significant research funding, including NSF Engineering Research Center support. He co-founded two successful startups: Clear Guide Medical (ultrasound-guided procedures) and Ready Robotics (industrial robot usability). As chair of the Computing Community Consortium and member of the International Federation of Robotics Research board, he has shaped national research agendas in computing and robotics. Hager leads the Computational Interaction and Robotics Lab (CIRL), which is associated with the NSF Engineering Research Center for Computer-Integrated Surgical Systems and Technology (ERC-CISST) and the Laboratory for Computational Sensing and Robotics (LCSR). His team collaborates extensively with clinicians at Johns Hopkins Hospital to translate robotics research into clinical practice.
Xiuwei Zhangمشاهده پروفایل
استادیار
Xiuwei Zhang is the J.Z. Liang Early-Career Assistant Professor in the School of Computational Science and Engineering (SCoSE) at Georgia Institute of Technology, part of the College of Computing. Her research focuses on computational biology and bioinformatics, particularly in developing machine learning methods for analyzing single-cell omics data, including multi-modal, temporal, and spatial data integration. She leads a lab that designs tools like scDART , scMoMaT , and scMultiSim , which address challenges in multi-omics integration, lineage reconstruction, and simulation. Before joining Georgia Tech, she held postdoctoral positions at UC Berkeley (Nir Yosef’s group), the European Bioinformatics Institute (EBI), and École Polytechnique Fédérale de Lausanne (EPFL). She earned her PhD in computer science from EPFL under Bernard Moret. Her Erdős number is 3, reflecting her collaborative work across computational fields. Her research spans four key areas: multi-batch/single-cell data integration, temporal analysis of cell differentiation, spatial-temporal omics dynamics, and simulation tools for benchmarking methods. She has received prestigious awards, including the NSF CAREER Award (2022) and NIH MIRA (2021). She actively participates in conferences (RECOMB, ISMB) and serves on editorial boards (Journal of Computational Biology). Her group’s recent work includes the scMultiSim simulator (2025), which generates multi-omics spatial data, and LinRace (2023), reconstructing cell lineage histories. She mentors over 15 students and collaborates internationally on projects like the InQuBATE Workshop on Single-Cell Transcriptomics.
- Cybersecurity
- Big Data
- Applied Machine Learning
- +۴ مورد دیگر
Dr. Baijian "Justin" Yang serves as the Associate Dean for Research at Purdue Polytechnic Institute and is a Professor in the Department of Computer and Information Technology at Purdue University. He earned his Ph.D. in Computer Science from Michigan State University, with Master's and Bachelor's degrees in Automation (EECS) from Tsinghua University. Dr. Yang has established himself as a leader in multiple interdisciplinary research domains. Dr. Yang's educational background includes: PhD in Computer Science, Michigan State University (2002) MS in Automation (EECS), Tsinghua University (1998) BS in Automation (EECS), Tsinghua University (1995) His research interests span multiple cutting-edge domains with practical applications: Cybersecurity : Developing novel approaches for threat intelligence, security education, and network defense Big Data : Creating innovative algorithms for dimension reduction, regression with categorical variables, and tensor decomposition Applied Machine Learning : Implementing AI solutions in healthcare, manufacturing, and forestry applications Digital Forestry : Using UAV imagery and remote sensing for forest management and tree species classification Dr. Yang's publication record demonstrates significant impact across multiple disciplines, with recent work focusing on spatial transcriptomics analysis (SiGra), delirium detection using limited-lead EEG, and visual localization technologies. His research bridges theoretical advances with practical applications in healthcare, manufacturing quality control, and environmental monitoring. The interdisciplinary nature of his work is evident in collaborations spanning computer science, healthcare, forestry, and manufacturing domains. His scientific achievements have been recognized with numerous awards: 2023 HRSA Building Bridges to Better Health Competition Winner (Phase 1) and 2nd place ($100,000 prize) in Phase 3 2023 Outstanding Faculty Award in Engagement, Department of Computer and Information Technology, Purdue University 2021 Leadership in Manufacturing Award, Manufacturing Times Digital (MxD) 2021 Good to Great Award, Purdue Polytechnic 2020 Outstanding Faculty Award in Discovery, Department of Computer and Information Technology 2019 University Faculty Scholars, Purdue University As an educator and mentor, Dr. Yang has advised numerous graduate students through their PhD and Master's research. His leadership extends to significant service roles including serving as Faculty Champion for the Holistic Safety and Security research impact area at Purdue Polytechnic from 2018 to 2021, board membership with ATMAE (2014-2016), and participation in the IEEE Cybersecurity Initiative Steering Committee (2015-2017). He holds valuable industry certifications including CISSP, MCSE, and Six Sigma Black Belt, demonstrating his commitment to bridging academic research with industry practice. Dr. Yang leads multiple research projects including "Digital Forestry" for developing tools to quantify forest function, "CHEESE" (Cyber Human Ecosystem of Engaged Security Education), and "CICI" (Supporting Controlled Unclassified Information with a Campus Awareness and Risk Management Framework). His work on "Applied Machine Learning" focuses on solving real-world problems, while his "Dimension Reduction and Memory Amnestic Big Data Regression" project innovates computational algorithms for large-scale data analysis.
Prof. Dr. Oliver Krüger is a behavioral ecologist and evolutionary biologist at Bielefeld University 's Faculty of Biology , where he leads the Department of Animal Behaviour since 2013. His research spans avian and marine mammal systems, focusing on life history strategies, parasite-host interactions, and environmental adaptation. Education: Biology studies at Bielefeld University (1994-1996) MSc in Oxford (1996-1997) PhD at Bielefeld University with Fritz Trillmich and Jan Lindström (1998-2000) Research Themes: Behavioral ecology, evolutionary biology, and population dynamics across tropical and temperate ecosystems. Key projects include NC³ (Niche Choice/Construction) and studies on Galápagos sea lions, common buzzards, and pinniped species. Scientific Leadership: Spokesperson, SFB TRR 212 "NC³" (2018-2025) Advisory Board member: German Ornithologists Union, IUCN SSC pinniped group, German Primate Centre Peer review roles: Humboldt Foundation, DFG, HFSP, NSF Awards: Leopoldina Prize (2001) Niko Tinbergen Award (2008) DFG Heisenberg Professorship (2010-2015)
- Digital Law
- Data Protection
- E-Commerce Regulation
- +۳ مورد دیگر
Florian Jacques is a Researcher at the University of Namur, affiliated with the Faculty of Law and the Namur Digital Institute. He holds a Doctorate from the University of Namur (2019). His research focuses on digital law, data protection, AI ethics, and EU regulatory frameworks. Jacques has led and collaborated on projects like the eIDAS legal study and a training initiative on government-to-government information exchange. His work bridges legal challenges posed by emerging technologies, with notable contributions to AI policy, pandemic governance, and digital identity regulations. He has published extensively across journals and edited volumes, including analyses of the DSA/DMA frameworks and APD jurisprudence. Education: Doctorate in Law, University of Namur (2019) His research interests center on digital transformation’s legal implications, particularly in data governance, e-commerce, and public policy. Jacques has analyzed how pandemic responses exposed gaps in legislative frameworks and explored ethical dimensions of AI applications like ChatGPT. His publications reflect a cross-disciplinary approach to modern legal challenges in digital spaces. Notable projects include a 2025 study on eIDAS modernization and a training program on international data exchange protocols. While no formal awards are listed, his active research output (24+ publications since 2019) underscores his academic engagement. He collaborates with institutions like the Centre de Recherche Information, Droit et Société (CRIDS) and contributes to policy-oriented research teams.
See Kiong Ng serves as Professor of Practice in the Department of Computer Science at the School of Computing, National University of Singapore (NUS), while concurrently holding leadership roles as Director of AI Technology at AI Singapore and Deputy Director of NUS's Institute of Data Science (IDS). His work focuses on translational data science research and developing integrated capabilities for Singapore's Smart Nation initiative through industry and public agency collaborations. His academic credentials include a B.S. in Applied Mathematics (Computer Science Track) from Carnegie Mellon University (1989), an M.S.E. in Computer & Information Science (Artificial Intelligence) from the University of Pennsylvania (1990), and a Ph.D. in Computer Science from Carnegie Mellon University (1998), supported by Singapore's National Computer Board overseas scholarship. Professor Ng's research bridges artificial intelligence with real-world applications across diverse domains. His primary interests span Data Mining, Machine Learning, Natural Language Processing, Smart Cities, and Computational Biology, with emphasis on extracting value from big data through interdisciplinary approaches. He actively pioneers applications in urban systems and bioinformatics, demonstrating data science's transformative potential beyond traditional boundaries. His publication record reveals consistent innovation in algorithm development for complex data challenges, with recent work focusing on taxonomy construction, single-cell genomics analysis, urban transportation systems, and imbalanced time series classification. These contributions demonstrate his commitment to solving practical problems through cutting-edge data science techniques. His major recognitions include: MTI Borderless Award (2014) as Green Growth Working Group project member Minister for National Development's R&D Award 2017 (Distinguished Award) for city-level analytics platform innovation A*STAR Borderless Award (2014) as Urban Systems Initiative team leader MTI Innovation Award (2013) for Strategic Technology Translation in Business Analytics Professor Ng has established significant research infrastructure including founding A*STAR's Data Analytics Department and leading the Urban Systems Initiative. His translational research model emphasizes industry partnerships and practical implementation, particularly in smart city development where he connects data science with urban planning challenges across Singapore's government agencies.
- Applied Mathematics
- Geothermal Energy
- Porous Media
- +۳ مورد دیگر
Inga Berre is a Professor at the Department of Mathematics, University of Bergen, and serves as Director of the Center for Modeling of Coupled Subsurface Dynamics (CSD). She leads the Porous Media Research Group and was appointed Argyris Visiting Professor at the University of Stuttgart's SimTech Cluster of Excellence in 2023. Research Interests: Mathematical modeling, partial differential equations, numerical methods for coupled thermo-hydro-mechanical-chemical processes in subsurface systems, and fault reactivation induced by injection/production. Scientific Leadership: Member of SIAM Council (2022-2027), Chair of SIAM GS activity group (2021-2022), Co-Chair of SET-Plan Deep Geothermal Implementation Working Group (2019-2021), and Chair of the Joint Program Geothermal, European Energy Research Alliance (2018-2021). Awards: 2011 Meltzer Award for Young Researchers. Advisory Roles: Member of Scientific Advisory Boards for GFZ (2024-2027) and SFB1313 (2018-), among others. Teaching: Developed courses on calculus, functional analysis, mathematical modeling, and numerical methods at the Bergen Summer Research School.
- Statistical Genomics and Bioinformatics
- High-Dimensional Data Analysis
- Bayesian Statistics
- +۴ مورد دیگر
Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.
- Computer Graphics
- Computer Vision
- Virtual Reality
- +۳ مورد دیگر
Xiaohu Guo is a Professor of Computer Science at the University of Texas at Dallas specializing in computer graphics, computer vision, and geometric modeling. His research develops algorithms for 3D/4D reconstruction, virtual reality, medical imaging, and physics-based simulations. Professor Guo has received significant recognition including a Best Paper Award at SIGGRAPH (2023) and an NSF CAREER Award (2012). His current research focuses on dynamic human capture, deformable models, and medical image computation. Education: PhD, Stony Brook University MS, Stony Brook University BS, University of Science and Technology of China Research Funding: Recently secured a $500,000 NSF grant for developing open-source 4D reconstruction frameworks for real-time dynamic human capture (2021). Editorial Roles: Serves on editorial boards of Graphical Models , Computer Animation and Virtual Worlds , and IEEE Transactions on Visualization and Computer Graphics .
- AI and Robotics
- Computer Vision
- Computer Graphics
- +۵ مورد دیگر
Ming Lin is a Distinguished University Professor at the University of Maryland, College Park, holding joint appointments in Computer Science (Department of Computer Science), the Institute for Advanced Computer Studies (UMIACS), Electrical and Computer Engineering (ECE), and the Maryland Robotics Center. She holds the Dr. Barry Mersky and Capital One E-Nnovate Endowed Professorships. Her research focuses on physically-based modeling, virtual environments, haptics, robotics, and AI applications in healthcare and urban computing. Education: Ph.D., M.S., and B.S. in Electrical Engineering & Computer Sciences from UC Berkeley. She previously spent 20 years at UNC Chapel Hill before joining UMD in 2018. Research interests include collision detection algorithms (e.g., Lin-Canny algorithm), real-time physics simulation, virtual/augmented reality systems, and medical imaging applications. Her work has led to over 2 million downloads of her group's software tools and licenses with 60+ companies. Notable contributions include the Oculus Rift-related VR technologies and Amazon's virtual try-on system. Awards: IEEE Fellow (2012), ACM Fellow (2011), NAI Fellow (2022), and Washington Academy of Sciences Distinguished Career Award (2020). Active in professional service, she serves on the CRA Board and chairs the Committee on Widening Participation in Computing Research. Advising: Supervises 12+ PhD/Master's students. Her lab (GAMMA Group) focuses on AI-driven robotics, autonomous systems, and physically-based simulations. Key projects include traffic simulation frameworks, medical VR applications, and 3D garment modeling.