جست‌وجوی استاد و پژوهشگر

• Applied Topology
• Dynamical Systems
• Algebraic Topology
• +۵ مورد دیگر

Robert Ghrist is the Andrea Mitchell University Professor at the University of Pennsylvania with dual appointments in the Department of Mathematics and the Department of Electrical and Systems Engineering. He serves as Associate Dean for Undergraduate Education for Penn Engineering. His educational background includes a B.S. in Mechanical Engineering from the University of Toledo (1991), and M.S. and Ph.D. degrees in Applied Mathematics from Cornell University (1994, 1995). Ghrist's research bridges pure and applied mathematics, focusing on applied algebraic topology , dynamical systems , and geometric methods in data science. His work extends to network theory, topological data analysis, and computational geometry, with applications spanning robotics, neuroscience, and social dynamics. Key innovations include developing sheaf-theoretic approaches for networked systems and persistence homology techniques for high-dimensional data. Analysis of his recent publications reveals a strong emphasis on lattice-theoretic frameworks , topological robotics , and network dynamics , with emerging applications in neural data interpretation and geometric computing. His research consistently integrates category theory with real-world engineering challenges. Significant scientific recognition includes: Presidential Early Career Award (PECASE, 2004) Scientific American 'Top 50' Research Leader (2007) Mathematical Association of America's Chauvenet Prize (2013) University of Pennsylvania Lindback Award for Distinguished Teaching (2015) DoD National Security Science and Engineering Faculty Fellowship (NSSEFF, 2015) Ghrist leads multiple federally funded research initiatives supported by AFOSR, DARPA, NSF, and ONR. He directs the development of educational tools including the Calculus BLUE/GREEN Project video series and custom GPTs for mathematical pedagogy. His open online courses have reached over 100,000 learners globally.

Stefan Leuteneggerمشاهده پروفایل

استادیار

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +۷ مورد دیگر

Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۱۱ مورد دیگر

Andrea Tagliasacchi is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), where he holds the Visual Computing Research Chair. He is also a part-time (20%) Staff Research Scientist at Google DeepMind in Toronto and holds an associate professor (status only) appointment in the Department of Computer Science at the University of Toronto. Education: PhD in Computing Science – Simon Fraser University (NSERC Alexander Graham Bell Fellow) Postdoctoral Research – École Polytechnique Fédérale de Lausanne (EPFL) MSc in Computer Science – Politecnico di Milano (Gold Medalist) His research lies at the intersection of computer vision, computer graphics, and machine learning, with a focus on 3D visual perception. Key areas include neural radiance fields (NeRF), 3D Gaussian splatting, inverse rendering, and geometric deep learning, with applications in robotics, augmented reality, and autonomous systems. His work emphasizes robust and efficient scene understanding and reconstruction from visual data. His recent publications, appearing in top venues like CVPR, SIGGRAPH, NeurIPS, and ECCV, demonstrate a strong emphasis on neural fields, 3D reconstruction, and generative modeling. Trends include improving rendering efficiency, enhancing robustness to noise and distractors, and enabling controllable and 3D-aware generation. His group has made significant contributions to Gaussian splatting, NeRF optimization, and diffusion-based 3D/4D synthesis. Scientific Awards: 2024 CVPR Best Paper Award (Honorable Mention) 2020 CVPR Best Student Paper Award 2015 SGP Best Paper Award NSERC Alexander Graham Bell Canada Graduate Scholarship MITACS Best Paper Award (SIGGRAPH Asia 2009) NSF Best Poster Award (SGP 2012) He has advised numerous PhD and MSc students, many of whom are now researchers at leading institutions and companies. His research has been supported through collaborations with Google, Intel, and academic partners. He serves the community as a Senior Area Chair for CVPR 2025, Associate Editor for IEEE TPAMI (2024–2026), Guest Editor for IEEE TPAMI on 3D GenAI, and Program Chair for 3DV 2024. He leads a vibrant research lab at SFU focused on pushing the boundaries of 3D scene understanding with machine learning.

Jiaxuan Youمشاهده پروفایل

استادیار

• Graph Neural Networks
• Relational Data
• Machine Learning Systems
• +۹ مورد دیگر

Jiaxuan You is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, leading the U Lab focused on achieving Artificial General Intelligence (AGI) in digital environments. His research spans graph neural networks (GNNs), relational data, foundation models, and machine learning systems. PhD and MS in Computer Science from Stanford University (2021) Developed GraphGym and PyTorch Geometric (PyG) for graph learning Core member at Kumo AI (2021-2023) His research explores: Graph-enhanced LLMs: Integrating relational structures into foundation models AGI Development: Self-optimizing AI agents and tool utilization ML Systems: Scalable architectures and redundancy-free computation Interdisciplinary Applications: Financial networks, crop yield prediction, and metro systems Recent publications focus on temporal reasoning, multi-agent dynamics, and hybrid architectures for LLMs. He actively develops open-source tools like DBGYM and GraphRouter. Scientific recognition includes: JPMC PhD Fellowship Baidu Scholarship Best Student Paper at AAAI 2017 World Bank Big Data Innovation Challenge winner He mentors PhD and intern students, emphasizing machine learning systems expertise. His lab collaborates on AGI workshops (e.g., ICLR 2024) and industry projects.

• Uncertainty Quantification
• Scientific Machine Learning
• Stochastic Optimization
• +۳ مورد دیگر

Hadi Meidani is a Clinical Associate Professor at the Carle Illinois College of Medicine , specifically within the Department of Biomedical and Translational Sciences at the University of Illinois at Urbana-Champaign . He teaches courses in Civil and Environmental Engineering, including topics like Systems Engineering & Economics , Machine Learning in CEE , and Uncertainty Quantification . Ph.D., Civil Engineering, University of Southern California (2012) M.S., Electrical Engineering, University of Southern California (2012) M.S., Structural Engineering, Sharif University of Technology (2005) B.S., Civil Engineering, K.N. Toosi University of Technology (2002) Dr. Meidani's research focuses on uncertainty quantification , scientific machine learning , and optimization under uncertainty for engineering systems. His work spans stochastic multiscale analysis , physics-informed machine learning , and model reduction techniques. His recent publications emphasize machine learning for infrastructure systems , graph neural networks , physics-informed models , and traffic assignment . Key trends include deep learning , multi-fidelity modeling , and neural operator transformers applied to metamaterial design , seismic reliability , and autonomous freight delivery .

Ram Vasudevanمشاهده پروفایل

دانشیار

• Optimization and control of nonlinear systems
• Legged robot locomotion
• Autonomous systems safety
• +۴ مورد دیگر

Ram Vasudevan is an Associate Professor and Associate Chair of Graduate Studies in the Department of Robotics at the University of Michigan. His research focuses on developing tools for safe and robust deployment of robotic systems, emphasizing optimization, nonlinear control, and real-world applications. Key areas include legged robot locomotion, shared control systems, and safety-critical autonomous systems. Research Interests: Optimization and control of nonlinear systems, locomotion of legged robots, shared control active safety systems, and automation of diagnostic/rehabilitative tasks. His ROAHM Lab prioritizes mathematical guarantees for robotic performance, with applications in medical robotics, autonomous vehicles, and soft robotics. Recent work emphasizes trajectory optimization, sensor fusion, and safety-aware control strategies. He has contributed to benchmarks for autonomous vehicle perception and novel methods in thermal image restoration using neural radiance fields. Awards: None explicitly listed in provided text. Labs/Teams: Directs the ROAHM Lab, collaborating on projects like robotic tail mechanics, real-time motion planning, and sensor data analysis. Active in academic conferences including RSS and ICRA.

Alessio Lomuscioمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning Verification
• Neural Network Verification
• +۵ مورد دیگر

Alessio Lomuscio is a Professor of Safe Artificial Intelligence at Imperial College London, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies and recognized as an ACM Distinguished Member. He leads the Safe AI Lab, which focuses on developing methods and tools for the verification of AI systems to ensure their safe and secure deployment in applications of societal importance. His research spans verification and robust learning for neural networks and decision trees, robust machine learning in aviation and finance, monitoring of machine learning systems, assurance for autonomous systems and AI, and verification and validation of neuro-symbolic systems. Lomuscio has made significant contributions to formal verification methods for AI systems, particularly in the context of safety-critical applications. His recent publications demonstrate a strong focus on neural network verification techniques, with applications across multiple domains including finance, aviation, and autonomous systems. His work bridges theoretical advances in formal methods with practical applications in real-world AI systems, addressing critical challenges in AI safety and trustworthiness. Scientific Awards: Royal Academy of Engineering Chair in Emerging Technologies ACM Distinguished Member Lomuscio has served in numerous leadership roles, including as Co-Director (2023-present) and Deputy Director (2019-2023) of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence. He has also held positions as Director of Strategy and Planning (2017-2020), Member of Management Committee (2013-2020), and Deputy Head of Department (2016-2017). His editorial service includes roles as Associate Editor for Artificial Intelligence Journal and Editorial Board Member for Journal of Artificial Intelligence Research and Journal of Autonomous Agents and Multi-agent Systems. His research group actively mentors students and researchers, with current openings for PhD and postdoctoral positions focused on AI verification and safety. Lomuscio's work has established him as a leading figure in the field of safe and verifiable AI systems, with significant contributions to both theoretical foundations and practical applications.

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۴ مورد دیگر

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

Angjoo Kanazawaمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +۶ مورد دیگر

Angjoo Kanazawa is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She leads the Kanazawa AI Research (KAIR) lab under the Berkeley Artificial Intelligence Research (BAIR) umbrella and serves on the advisory board of Wonder Dynamics. Her research focuses on the intersection of computer vision, computer graphics, and machine learning, with a particular emphasis on 4D reconstruction of dynamic scenes, neural radiance fields (NeRF), and systems that model human-environment interactions from 2D visual data. Education : Ph.D., Computer Science (2017), University of Maryland, College Park BA, Mathematics and Computer Science (2012), New York University (NYU) Her work aims to build systems that can capture, perceive, and understand complex 3D/4D worlds from photographs and videos, enabling applications in scene reconstruction, motion analysis, and generative modeling. She has pioneered techniques for scaling NeRFs across GPUs (NeRF-XL), developing open-source tools like nerfstudio and gsplat. Her recent publications focus on topics like self-occluded avatar recovery (SOAR), decentralized diffusion models, and 4D reconstruction of articulated objects for robotics. Kanazawa's research has been recognized with prestigious awards including the IEEE CS TCPAMI Young Researcher Award (2024) , Sloan Research Fellowship (2023) , and Google Faculty Research Award (2021) . Her lab has trained numerous students who now hold positions at leading institutions and companies like Anthropic, Meta Reality Labs, and Luma AI. Key Scientific Awards : IEEE CS TCPAMI Young Researcher Award (2024) Sloan Research Fellow (2023) Hellman Fellow (2022) Bakar Fellows Spark Award (2022) Google Faculty Research Award (2021) Her KAIR lab collaborates extensively with industry partners and academic institutions, including the Max Planck Institute and Google Research. She has served as an advisor for PhD students and postdocs who now lead teams at UC Berkeley, MIT, Stanford, and Luma AI, while her teaching includes graduate courses like CS 280A (Computer Vision) and CS 294-173 (Learning for 3D Vision).

Thomas Brinkhoffمشاهده پروفایل

استاد

• Geoinformatics
• Geodatabase Systems
• Spatio-temporal Data Processing
• +۵ مورد دیگر

Prof. Dr. Thomas Brinkhoff is Chair of the Institute Board and Chair of Geoinformatics at Oldenburg University of Applied Sciences. He leads the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and contributes to institutions like the Association for the Promotion of Geoinformatics in Northern Germany (GiN e.V.) and the Oldenburg Research and Development Institute for Computer Science (OFFIS e.V.). Education: Diploma in Computer Science (Informatik), Universität Bremen (1990) Doctorate in Computer Science (Dr. rer. nat.), Ludwig Maximilian University of Munich (1994) Brinkhoff's research spans geodatabase systems, spatiotemporal data processing, geosensor analytics, and location-based services. His work addresses Volunteered Geographic Information (VGI), web-based geospatial visualization, and mobile data integration, with applications in traffic management and forensic science. Recent projects include ProSaDi (Digital Provenance and Collection Research) and contributions to the Laboratory for optical 3D metrology . He has served on program committees for ACM SIGSPATIAL (2002-2019), AGILE conferences (2010-2025), and editorial boards of journals like GeoInformatica and TGIS. Notable Lectures: 2024: Forensic applications of tachograph data 2023: Geoinformatics in homicide investigations 2022: Spatiotemporal analysis for sustainability projects 2015: Open geodata standards at FOSSGIS 2014: Mobile sensor data processing

Paul Goldbergمشاهده پروفایل

استاد

• Algorithmic Game Theory
• Computational Complexity Theory
• Machine Learning
• +۳ مورد دیگر

Paul Goldberg is a Professor of Computer Science and Director of the MSc in Mathematics and Foundations of Computer Science (MFoCS) at the University of Oxford. He holds a BA in Mathematics from Oxford University and a PhD in Computer Science from the University of Edinburgh. His research focuses on algorithmic game theory, computational complexity, and machine learning, with notable contributions to equilibrium computation, complexity classes of total search problems, and decentralized systems. Affiliations: Department of Computer Science, Oxford; Editorial Board of ACM Transactions on Economics and Computation. Education: PhD in Computer Science (1993), University of Edinburgh MSc in Computer Systems Engineering (1989), University of Edinburgh and Université Paris-Sud BA in Mathematics (1988), Oxford University Research Interests: Algorithmic game theory, computational complexity (especially total search problems like CLS and PPAD), decentralized computation of equilibria, and applications in machine learning and AI. His work bridges theoretical computer science and economics, with a focus on algorithm design and complexity analysis. Publications and Awards: Over 120 papers, including influential work on Nash equilibrium complexity (2009), gradient descent (2023), and fair division algorithms. Notable awards include the ACM SIGecom Test of Time Award (2022) and a SIAM Outstanding Paper Prize (2011). Grants and Students: Leads EPSRC-funded projects on game theory and machine learning. Supervised 11 PhD graduates and currently advises Giannis Tyrovolas and others. Active in mentoring MSc and undergraduate projects. Labs/Teams: Part of the Algorithms and Complexity Theory group at Oxford, contributing to research on optimization, equilibrium dynamics, and fair division.

Lijie Chenمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Computational Complexity Theory
• Quantum Complexity Theory
• +۴ مورد دیگر

Lijie Chen is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, where he is part of the Berkeley Theory Group. Previously, he was a Miller Research Fellow at UC Berkeley, hosted by Avishay Tal and Umesh Vazirani, and earned his Ph.D. from MIT under Ryan Williams. His research focuses on theoretical computer science, particularly computational complexity theory, with applications to quantum physics and AI safety. Education: Ph.D. in Computer Science from MIT (2022), B.Sc. from Yao Class at Tsinghua University. Research Interests: Complexity theory, quantum complexity, derandomization, circuit lower bounds, and foundational aspects of AI safety. Chen has made significant contributions to understanding fundamental questions in complexity theory, including circuit lower bounds and the connections between randomness and computation efficiency. His work often bridges theoretical insights with practical implications in quantum computing and algorithm design. Awards and Honors: Machtey Award for Best Student Paper (2019). Danny Lewin Best Student Paper Award (2019). Invited to SICOMP Special Issues for FOCS and STOC papers. He has organized workshops on complexity theory and derandomization, and his research has been recognized in venues like STOC, FOCS, and the Journal of the ACM.

Olga Sorkine Hornungمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Geometry Processing
• +۳ مورد دیگر

Prof. Olga Sorkine Hornung is a Full Professor of Computer Science at ETH Zürich, leading the Interactive Geometry Lab. She holds a BSc and PhD from Tel Aviv University (2000 and 2006) and conducted postdoctoral research at Technical University Berlin. Her research focuses on computer graphics, geometric modeling, and geometry processing, with applications in shape editing, digital fabrication, and animation. She has received numerous accolades, including the ACM Fellowship (2020), ERC Consolidator Grant (2020), and the Golden Owl Teaching Award (2021). Her work bridges theoretical foundations and practical algorithms, addressing challenges in parameterization, surface compression, and interactive design tools. Her research interests span: Computer Graphics & Visualization Geometric Modeling & Processing 3D Content Creation & Digital Fabrication Garment Design & Simulation Human Motion Analysis & Animation Awards and grants include: 2024: Best Paper Honorable Mention (EUROGRAPHICS) 2023: Member of Swiss Academy of Engineering Sciences (SATW) 2020: ERC Consolidator Grant 2017: Rössler Prize (ETH Zurich) Her lab focuses on developing novel methods for interactive geometry processing, with recent advancements in garment modeling (e.g., AIpparel, Rags2Riches) and motion retargeting systems like WalkTheDog. She actively collaborates on interdisciplinary projects, including biomedical applications and sustainable fashion technology.

Sebastian Schererمشاهده پروفایل

استاد پژوهشی

• Robotics
• Unmanned Aerial Vehicles
• Autonomous Navigation
• +۱۳ مورد دیگر

Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments. Education: Ph.D. in Robotics, Carnegie Mellon University (2010) MS in Robotics, Carnegie Mellon University (2007) BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004) His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy . He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010). His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround , BETTY , and SubT-MRS , and simulation tools like Pegasus Simulator , indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments. Scientific Awards: Popular Science Best of What's New 2010 Award AIAA@Infotech Best Paper Runner-up Award (2010) Siebel Scholar Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles. He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.