جست‌وجوی استاد و پژوهشگر

Luciano Spinelloمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Robotics
• SLAM (Simultaneous Localization and Mapping)
• +۳ مورد دیگر

Luciano Spinello is a Research Fellow affiliated with the University of Freiburg's Department of Computer Science, working within the AIS Lab led by Prof. W. Burgard. Previously, he held roles at Amazon Research (Seattle), ETH Zurich (PhD under Prof. Roland Siegwart), and EPFL Lausanne as a research assistant. His research focuses on the intersection of computer vision and robotics, specializing in robot perception, SLAM, and autonomous systems. He has contributed to projects involving RGB-D data processing, terrain classification, and socially-aware navigation algorithms. Education: PhD in Computer Science from ETH Zurich (2009), Electrical Engineering degree from Rome, Italy. Academic activities include organizing workshops (RSS 2014, IROS 2012), serving on program committees for robotics conferences, and editorial roles (IROS associate editor). His work emphasizes multimodal sensing, object detection in 3D environments, and robust localization across dynamic conditions. Key technical contributions include methods for RGB-D fusion, adaptive domain adaptation, and large-scale place recognition. His research bridges theoretical advancements with practical applications in autonomous robotics, including navigation systems and human-robot interaction protocols.

SueYeon Chungمشاهده پروفایل

استادیار

• Computational Neuroscience
• Deep Learning
• Statistical Physics
• +۵ مورد دیگر

SueYeon Chung is an Assistant Professor of Neural Science at New York University and a Project Leader at the Flatiron Institute's Center for Computational Neuroscience. She will join Harvard University in 2025 as an Assistant Professor in the Department of Physics, affiliated with the Kempner Institute for Natural and Artificial Intelligence and the Center for Brain Science. Her work bridges computational neuroscience and deep learning, focusing on neural computation principles in biological and artificial systems. Education: Ph.D. in Applied Physics from Harvard University (2017), B.A. in Physics and Mathematics from Cornell University (2011). Postdoctoral fellowships at Columbia University and MIT. Research Interests: Neural population geometry, representational learning, statistical physics of neural networks, and interdisciplinary approaches to understanding brain-inspired AI. She develops theoretical frameworks to analyze high-dimensional neural systems and designs biologically plausible neural network models. Recent Work Trends: Focus on representation geometry, manifold capacity, and task-driven neural dynamics. Recent articles explore feature learning beyond traditional dichotomies, alignment with primate visual cortex, and spectral theories of neural networks. Her work emphasizes geometric structures in both biological and artificial systems. Awards: NeurIPS Spotlight Presentations (2021, 2023), Physical Review Letters Editors' Suggestion (2023), ICML Best Paper Award (2020). Grants & Advising: Active in interdisciplinary grants spanning neuroscience and physics. Advises on computational neuroscience projects and co-organizes workshops (e.g., Analytical Connectionism Summer School, CCN Junior Workshop). Teaches courses in statistical mechanics and computational neuroscience. Labs & Affiliations: Leads research groups at NYU and Flatiron Institute. Collaborates with the CILVR Group and Harvard’s Kempner Institute. Active in conferences like COSYNE and NeurIPS, delivering keynote talks globally.

• Neural Mechanisms of Information Processing
• Color Vision
• Vision and Eye Movements
• +۳ مورد دیگر

Prof. Dr. Thomas Wachtler is a Professor and Group Leader in the Department of Computational Neuroscience at the Faculty of Biology, Ludwig-Maximilians-Universität München. He directs the INCF G-Node, a key neuroinformatics initiative focused on data management standards. His research bridges sensory neuroscience and computational modeling, emphasizing invariant representations in visual processing, color perception, and the impact of eye movements on neural coding. Collaborating with interdisciplinary teams, he develops tools for neurophysiological data storage and sharing (e.g., odML, NIX format) to ensure reproducibility in neuroscience. Education: Doctorate in Neuroscience (exact details not specified in text) Affiliations: German Neuroinformatics Node (G-Node), RTG 2175: Perception in Context and its Neural Basis Research interests include: - Contextual computations in color vision - Neural mechanisms compensating eye movement effects - Neuroethology of honeybee communication (vibration signals) - Open science practices and FAIR data standards Key contributions: - Developed metadata standards (odML) and data formats (NIX) - Investigated Bayesian priors in hue perception - Analyzed saccadic remapping of visual context Lab members include PhD candidates like Reema Gupta and Felix Schrader, focusing on perception, neuroethology, and data workflows. Active in national (NFDI-Neuro) and international (INCF) initiatives to advance neuroscience research infrastructure.

Vincenzo Cagliotiمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Image Processing
• +۵ مورد دیگر

Vincenzo Caglioti is a Professor at the Department of Electronics, Information and Bioengineering (DEIB) of Politecnico di Milano. His research focuses on computer vision, robotics, and image processing, with emphasis on motion analysis, sensor fusion, and multi-robot systems. He has contributed extensively to fields such as augmented reality, motion blur recovery, and uncalibrated camera systems. Key research areas include: Development of algorithms for 3D reconstruction from motion-blurred images Design of efficient exploration strategies for mobile robots Integration of sensor data for environmental monitoring and autonomous navigation Analysis of geometric properties in catadioptric systems His work spans theoretical advancements in computer vision alongside practical applications in robotics and surveillance. Over 30 years of publications demonstrate a sustained contribution to advancing perception systems for autonomous systems. No scientific awards explicitly listed in provided texts Labs/Teams: Likely affiliated with DEIB's Robotics and Computer Vision research groups, though specific lab names not explicitly stated in text.

Kailai Liمشاهده پروفایل

استادیار

• Robotic Perception
• Sensor Fusion
• State Estimation
• +۲ مورد دیگر

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

• Computer Vision
• Robotics
• Remote Sensing
• +۳ مورد دیگر

Boying Li is a Research Fellow in the Department of Data Science & AI at Monash University. Their research focuses on advancing computer vision, robotics, and remote sensing technologies. Key contributions include developing SLAM algorithms using semantic planar text features, self-supervised depth estimation systems, and SAR datasets for ship interpretation. Research interests span neuro-symbolic AI frameworks, autonomous navigation, and sensor data fusion. Their work contributes to the UN Sustainable Development Goals through applications in maritime surveillance and autonomous systems. Collaborations involve structural regularities in indoor environments and satellite imagery analysis. Notable outputs include the OpenSARShip dataset (2017–2018) and recent advancements in Hier-SLAM++ (2025). Awards and grants are not explicitly listed in available texts. No lab affiliations or future works are detailed.

Justin Dimmelمشاهده پروفایل

دانشیار

• Mathematics Education
• Instructional Technology
• Virtual Reality in Education
• +۳ مورد دیگر

Justin Dimmel serves as Associate Dean for Academics and Student Engagement in the University of Maine College of Education and Human Development and holds a dual appointment as Associate Professor of Mathematics Education and Instructional Technology in the School of Learning and Teaching. He leads the Immersive Mathematics in Rendered Environments (IMRE) Lab, which pioneers VR/AR tools for STEM education. His 2022 NSF CAREER Award supports research on 3D geometry learning through immersive technologies. Education: Ph.D. and M.S. in Mathematics (University of Michigan); B.S. in Mathematics and Philosophy (Hartwick College). Research Focus: Dr. Dimmel’s work reimagines spatial and geometric education through virtual/augmented realities. He explores how movement-based interactions with 3D diagrams enhance mathematical understanding, particularly in geometry. His SunRule project—a solar-powered multiplication/division tool—exemplifies translating lab innovations into public-facing educational tools. Awards: National Science Foundation CAREER Award (2022) Outreach & Grants: Leads a professional development program for secondary math teachers integrating VR/AR. Spearheads rural Maine STEM outreach via immersive environments, aiming to democratize access to advanced educational technologies. Collaborates with the Maine RiSE Center and UMaine’s MIRTA accelerator. Labs/Teams: Director of IMRE Lab, fostering collaborations with undergrad/grad researchers on projects like SunRule and VR teaching tools. Active in STEM education policy and teacher training initiatives.

• Sustainable Aviation
• Aerospace Computation
• Aircraft Design Optimization
• +۷ مورد دیگر

Dr. Rhea Liem is an Associate Professor in Sustainable Aviation at Imperial College London's Department of Aeronautics, affiliated with the Brahmal Vasudevan Institute for Sustainable Aviation. She holds a PhD from the University of Toronto and prior degrees from MIT and Nanyang Technological University. Her work emphasizes computational science integration into sustainable aviation, focusing on operation-aware aircraft design and air traffic optimization. Her research bridges computational methods with real-world applications, addressing systemic inefficiencies through data-driven frameworks. Education: PhD in Aerospace Engineering (University of Toronto, 2015) S.M. in Aeronautics and Astronautics (MIT, 2010) S.M. in Computation for Design and Optimization (MIT, 2007) B.Eng. in Mechatronics (Nanyang Technological University, 2003) Research Interests: Optimization of aircraft design across flight missions Data-infused physics-based models for fuel efficiency Integration of operational constraints into design frameworks Noise and fuel tradeoffs in trajectory optimization Her work on AirTrafficSim and AeroFuse platforms highlights contributions to education and industry. Awards include the Amelia Earhart Fellowship and Royal Aeronautical Society Fellowship. She has held roles at Hong Kong University of Science and Technology and industry positions in Singapore.

Matt Thomsonمشاهده پروفایل

استاد

• Biological Engineering
• Cellular and Developmental Biology
• Molecular Biology
• +۸ مورد دیگر

Matt Thomson is a Professor of Computational Biology and Investigator at the Heritage Medical Research Institute (HMRI) at the California Institute of Technology (Caltech). He holds a B.A. from Harvard University (2001) and a Ph.D. from Caltech (2011). His academic progression includes roles as Visiting Associate (2016), Assistant Professor (2017-24), and his current full Professor position since 2024. His research focuses on applying quantitative methods to engineer cellular differentiation and control biological systems. Key areas include synthetic biology, single-cell genomics, and active matter systems. Techniques employed include mathematical modeling, machine learning, and single-cell RNA sequencing. Notable achievements include developing optical differentiation systems, computational tools for cell trajectory analysis, and stochastic models of cellular self-organization. Thomson's work bridges biology and engineering, with applications in regenerative medicine, cancer immunotherapy, and foundational research in developmental biology. His lab, the Thomson Lab, collaborates with the SPEC initiative to advance programmable biological systems. He also explores AI-driven approaches for protein engineering and large language model optimization in biological contexts. No scientific awards are explicitly listed in the provided materials. His research is supported by grants and institutional resources at Caltech and HMRI. He advises students and researchers in his lab but no specific names are provided in the text.

Maria Terrellمشاهده پروفایل

مدرس ارشد

• Geometry
• Mathematics Education
• Tensegrities
• +۳ مورد دیگر

Maria Terrell is a retired Senior Lecturer in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. She holds a Ph.D. from the University of Virginia (1978). Her research focuses on geometry, particularly tensegrity structures and the historical development of geometrical optics and linear perspective. She is also deeply involved in mathematics education, co-leading the GoodQuestions project to enhance calculus instruction through structured classroom discussions. Her work emphasizes improving undergraduate pedagogy and active learning strategies. Key contributions include co-authoring Calculus With Applications (2014) and publishing on educational methodologies in journals like PRIMUS and the American Mathematical Monthly. Terrell has collaborated with researchers like Robert Connelly and her spouse, R. Terrell, on projects blending geometry with visual perception and structural mechanics. Email: mst1@cornell.edu

Ioannis Ivrissimtzisمشاهده پروفایل

دانشیار

• Computer Graphics
• 3D Modeling and Processing
• Surface Reconstruction
• +۷ مورد دیگر

Dr. Ioannis Ivrissimtzis is an Associate Professor in the Department of Computer Science at Durham University. He holds a BSc in Mathematics from the University of Thessaloniki and a PhD in Mathematics from the University of Southampton. Before joining Durham, he was a lecturer at Coventry University's Department of Creative Computing, and previously conducted research at the Max-Planck Institut für Informatik and Cambridge University's Computer Laboratory. His research focuses on computer graphics, including subdivision surfaces, polygonal mesh encoding, and statistical learning methods for surface reconstruction from scan data. Key areas of interest include 3D model security (steganography/steganalysis), statistical surface reconstruction techniques, discrete geometry, and spectral graph theory. Notable contributions include work on 3D steganalysis, mesh quantization, and geometric algorithms. He has supervised multiple postgraduate students including Elizabeth Callander and Jindi Wang. His publications span conferences like ACM SIGGRAPH and journals such as IEEE TVCG and ACM TOMCCAP.

Tai Sing Leeمشاهده پروفایل

استاد

• Computational Neuroscience
• Systems Neuroscience
• Neural Circuits
• +۵ مورد دیگر

Tai Sing Lee is a Professor in the School of Computer Science and a core faculty member of the Neuroscience Institute at Carnegie Mellon University. His research focuses on computational neuroscience, systems neuroscience, and the intersection of machine learning with biological neural systems. Key areas include neural circuit characterization, learning mechanisms, and perceptual inference. His work combines theoretical modeling with empirical data from neurophysiological experiments, particularly in visual cortex regions like V1, V2, and V4. Notable contributions include studies on neural encoding of natural scenes, recurrent cortical circuit dynamics, and the application of self-supervised learning principles in artificial vision systems. Lee's research also explores how neural systems integrate contextual information and adapt to environmental statistics, with implications for both biological understanding and AI development. He has pioneered methods for analyzing large-scale calcium imaging datasets to uncover organizational principles in visual cortex.

• Robotics
• Artificial Intelligence
• Multi-Agent Systems
• +۳ مورد دیگر

Woojun Kim is a Postdoctoral Fellow at the Robotics Institute of Carnegie Mellon University, working in the Advanced Agent - Robotics Technology Lab. His research focuses on multi-agent reinforcement learning, human-agent collaboration, and autonomous navigation systems. Key research areas include: Multi-agent systems for decentralized robotics Large language model integration in robotic tasks Collision avoidance and path planning algorithms Offline reinforcement learning frameworks His work spans robotics, computer science, and artificial intelligence, with recent publications addressing collaborative strategies, geometric decomposition for grasping, and domain adaptation in visual learning tasks.

Isa Verdinelliمشاهده پروفایل

استاد

• Statistics
• Bayesian Statistics
• Machine Learning
• +۵ مورد دیگر

Isa Verdinelli is a Professor in Residence in the Department of Statistics and Data Science at Carnegie Mellon University. She maintains an office in Baker Hall 232 H in Pittsburgh, PA, and can be reached at isabella@stat.cmu.edu. She has co-authored a book titled "All of Regression" with L. Wasserman. Dr. Verdinelli's research interests span several areas of statistics and data science: Bayesian Statistics and Inference Nonparametric Statistics and Estimation Machine Learning and Statistical Learning Experimental Design and Optimization Manifold Learning and Geometric Statistics Feature Selection and Variable Importance Her recent work demonstrates a strong focus on developing novel statistical methodologies with applications in machine learning, particularly in the areas of feature importance, manifold learning, and nonparametric estimation. She has made significant contributions to the understanding of Bayesian experimental design, with publications spanning several decades. Her research often bridges theoretical statistical developments with practical applications across various domains. Dr. Verdinelli has received recognition for her contributions to statistical methodology, though specific awards are not mentioned in the available information. She has co-authored numerous influential papers in top statistical journals and has contributed to the advancement of statistical science through her research on Bayesian methods, nonparametric techniques, and statistical learning theory. Her work on the book "All of Regression" suggests she is also committed to statistical education and the dissemination of statistical knowledge.

Dan Huttenlocherمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Social Networks
• +۴ مورد دیگر

Prof. Dan Huttenlocher is a Professor at the Massachusetts Institute of Technology (MIT), recognized for interdisciplinary contributions spanning computer vision, artificial intelligence, social computing, and robotics. His work bridges theoretical foundations with practical applications in areas like autonomous systems, online platforms, and societal impacts of AI. Research focuses include large-scale image understanding, epidemic modeling, social network dynamics, and human-AI collaboration. Notable projects involve developing robust perception systems for autonomous vehicles and analyzing Wikipedia's evolving role in the AI era. Publications span over two decades, addressing topics from early computer vision techniques (e.g., structure-from-motion) to modern challenges in AI ethics and governance. His work frequently appears in top venues across computer science, economics, and social sciences.