جست‌وجوی استاد و پژوهشگر

J. Ian Munroمشاهده پروفایل

استاد

• Data Structures
• Space Efficient Data Structures
• Algorithm Design and Analysis
• +۷ مورد دیگر

J. Ian Munro is a University Professor and Canada Research Chair in Algorithm Design at the Cheriton School of Computer Science, University of Waterloo. He has been a faculty member at Waterloo since completing his PhD at the University of Toronto in 1971 and was appointed University Professor in 2006, one of the highest honors the university bestows on its faculty. Dr. Munro's research focuses on the design, analysis, and implementation of efficient algorithms and data structures, with particular emphasis on space-efficient solutions. His work spans several key areas including succinct data structures, tree algorithms, text search methods, priority queues, and memory management for data structures. His research has had significant impact on how we understand the theoretical limits of data representation while maintaining efficient query performance. The trajectory of his recent publications (2020-2024) reveals a continued focus on pushing the boundaries of space efficiency in data structures, particularly for graphs and trees. His work increasingly addresses dynamic scenarios where data structures must be updated efficiently while maintaining query performance. The research shows strong connections between information theory, combinatorics, and practical algorithm design, with applications spanning database systems, computational geometry, and text processing. Fellow of the Association for Computing Machinery (FACM) Elected Fellow of the Royal Society of Canada (FRSC) in 2003 Canada Research Chair in Algorithm Design Dr. Munro has supervised over 24 PhD students throughout his career and has been actively involved in teaching advanced courses on data structures and algorithms. His research has been consistently funded through competitive grants, including his Canada Research Chair position. He maintains active collaborations with researchers worldwide, as evidenced by his extensive co-authorship network across numerous publications. As a member of both the Algorithms and Complexity Group and the Data Systems Group at Waterloo, Dr. Munro contributes to interdisciplinary research that bridges theoretical computer science with practical systems challenges. His work continues to influence both theoretical understanding and practical implementations of efficient data management solutions.

Andrea Scottمشاهده پروفایل

دانشیار

• Fluid Dynamics
• Machine Learning
• Remote Sensing
• +۸ مورد دیگر

Andrea Scott is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Faculty of Engineering. Her research focuses on fluid dynamics, remote sensing, and machine learning applications in environmental systems. She holds a Doctorate in Mechanical Engineering from the University of Waterloo (2008) and has taught courses such as ME 351 (Fluid Mechanics) and SYDE 621 (Numerical Methods). Notable awards include the 2022 Outstanding Performance Award and 2021 Distinguished Performance Award from the University of Waterloo. Research interests span turbulence modeling, data-driven approaches, and physically inspired neural networks. Her work includes developing algorithms for sea ice concentration estimation, SAR imagery analysis, and fluid flow simulations. She collaborates with groups like the Vision and Image Processing Lab and the Remote Sensing of Environmental Change group. Current projects involve small object detection in remote sensing and graph neural networks for unstructured grid problems. Education: PhD, Mechanical Engineering, University of Waterloo, Canada (2008) MASc, Mechanical Engineering, McMaster University, Canada (2001) BASc, Mechanical Engineering, University of Waterloo, Canada (1999) Teaching responsibilities include undergraduate and graduate courses in fluid mechanics and systems engineering. She actively mentors students through the IEEE GRSS Women-to-Women Mentorship program and serves as an Associate Editor for the AGU Journal of Machine Learning and Computation. Her lab oversees over 20 current and past graduate students, focusing on topics like space debris tracking, AI-driven environmental modeling, and sea ice dynamics. Research outputs include over 40 publications in journals such as Physical Review Fluids and IEEE Transactions on Geoscience and Remote Sensing .

• Machine Learning
• Theoretical Computer Science
• Data Science
• +۳ مورد دیگر

Lev Reyzin is a Professor of Mathematics, Statistics, and Computer Science at the University of Illinois Chicago (UIC) and Director of the IDEAL Institute. He specializes in the theory of machine learning, data science, and artificial intelligence, with affiliations to theoretical computer science and mathematical foundations. Prior roles include a Simons Postdoctoral Fellowship at Georgia Tech and an NSF Computing Innovation Fellowship at Yahoo! Research. He earned his Ph.D. from Yale University (NSF doctoral fellowship) and a bachelor’s degree from Princeton University. Research interests focus on algorithmic learning theory, computational complexity, and applications of machine learning to real-world problems. Notable work includes contributions to statistical learning algorithms, adversarial bandits, and graph theory. His research has been funded by NSF grants (e.g., ECCS-2217023, CCF-2307106), DOD programs, and others since 2015. He has received awards at leading conferences (ICML, COLT, AISTATS). Reyzin holds editorial roles, including Editor-in-Chief of Mathematics of Data, Learning, and Intelligence and Chair positions in major conferences like FOCS 2024 and ALT 2017. His leadership in academic organizations highlights his influence in shaping theoretical computer science and machine learning research.

Nils-Christian Deteringمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Energy Markets
• +۴ مورد دیگر

Nils-Christian Detering is an Associate Professor in the Department of Statistics & Applied Probability at the University of California, Santa Barbara (UCSB). He also serves as the Undergraduate Diversity, Equity, and Inclusion Officer. His research focuses on financial mathematics, probability theory, and their applications in systemic risk analysis, energy markets, and machine learning. Detering has contributed to understanding default contagion in financial systems, stochastic processes in energy derivatives, and neural network applications in functional data analysis. Research Interests : Financial systemic risk: Analyzing default contagion using random graphs and systemic stability metrics Infinite-dimensional stochastic analysis: Modeling energy markets via stochastic PDEs and forward curves Machine learning: Developing neural network frameworks for functional spaces and financial applications Teaching includes courses on stochastic processes, mathematical finance, and probability theory at both undergraduate and graduate levels. His work has been recognized with awards such as the Best Paper Award at the ACM International Conference on AI in Finance (2023). Key Publications address topics like reinforcement learning in banking networks, neural network calibration of energy curves, and integrated fire sales models. His research bridges theoretical probability with applied financial engineering challenges.

Ceyhun Eksinمشاهده پروفایل

دانشیار

• Networked Multi-Agent Systems
• Game Theory
• Distributed Optimization
• +۵ مورد دیگر

Ceyhun Eksin is an Associate Professor and the Corrie and Jim Furber '64 Faculty Fellow at the Texas A&M University Industrial & Systems Engineering Department. He is also affiliated with the Electrical & Computer Engineering Department. His research focuses on networked multi-agent systems, integrating game theory, distributed optimization, and control theory to address challenges in autonomous systems, energy systems, and epidemiological modeling. Education: Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2015), followed by a postdoctoral fellowship at Georgia Institute of Technology (hosted by Professors Jeff S. Shamma and Joshua S. Weitz). Research interests emphasize the design and analysis of complex systems, including distributed algorithms for autonomous teams, epidemic dynamics influenced by behavioral changes, and optimization in smart grids. His work bridges theoretical foundations with practical applications in cyber-physical systems and social networks. Notable awards include the NSF CAREER Award (2023) and TAMIDS Career Initiation Fellowship (2023). His research has been published in top journals like Proceedings of the National Academy of Sciences and IEEE Transactions . Lab activities center on the NetMaS (Networked Multiagent Systems) Lab, focusing on theoretical and algorithmic innovations for multi-agent systems. Collaborations span academia and industry, addressing real-world challenges in energy, healthcare, and robotics.

Zhang Yi-Chengمشاهده پروفایل

استاد

• Econophysics
• Information economics
• Statistical physics
• +۲ مورد دیگر

Zhang Yi-Cheng is a Full Professor of Theoretical Physics at the University of Fribourg, Switzerland, since 1992. His academic career includes visiting professorships at Nordita (Denmark) and INFN (Italy), and postdoctoral research at Brookhaven National Lab (USA). He specializes in interdisciplinary fields such as Econophysics , Statistical physics , and Complex network sciences , focusing on applications in financial markets, social systems, and global trade networks. His research explores topics like market dynamics, network structures, and algorithmic ranking systems. Notable awards include the 2011 Honorary Director of the Complexity Sciences Research Center and recognition as a 2011 Chinese '1000 Talents' awardee . His work bridges physics-based methodologies with socio-economic systems, addressing challenges in information-driven economies and networked societies. Zhang has contributed to influential studies on ranking algorithms, percolation theory in networks, and the interplay between economic complexity and trade. His interdisciplinary approach has led to advancements in understanding systemic risks, market inefficiencies, and the role of information in shaping global economic interactions.

Paul Rosenمشاهده پروفایل

دانشیار

• Scientific Visualization
• Topology-Based Methods
• Network Visualization
• +۳ مورد دیگر

Paul Rosen is an Associate Professor at the University of Utah, affiliated with the Scientific Computing and Imaging Institute and the Kahlert School of Computing. He holds a Ph.D. in Computer Science from Purdue University (2010). Prior to his current role, he was an Assistant/Associate Professor at the University of South Florida (2015–2022) and a Research Assistant Professor at the University of Utah's SCI Institute (2010–2015). Research Focus: Rosen specializes in topology-based visualization techniques, with emphasis on network visualization, uncertainty quantification, and perceptual studies. His work bridges computational methods with human perception, aiming to enhance data understanding through effective visual design. Awards & Recognition: National Science Foundation CAREER Award (2019) Best Paper Awards at PacificVis 2016, IVAPP 2016, and multiple other conferences Honorable Mentions for IEEE VIS and VAST Challenge submissions Leadership: As General Chair of IEEE VIS 2024, Rosen led the planning for this flagship visualization conference, emphasizing community-driven design and in-person collaboration. Education Contributions: His research includes pedagogical innovations, such as predictive modeling for student feedback and peer review analysis in visual literacy courses.

A. Stephen Morseمشاهده پروفایل

استاد

• Control Systems
• Network Science
• Multi-Agent Systems
• +۵ مورد دیگر

A. Stephen Morse is the Dudley Professor of Electrical & Computer Engineering at Yale University. He has been affiliated with Yale since 1970 and holds memberships in prestigious organizations such as the National Academy of Engineering and the Connecticut Academy of Science and Engineering. His research focuses on control systems, including hybrid systems, network science, multi-agent coordination, and sensor networks. He has received numerous awards, including the Bellman Control Heritage Award (2013) and the IEEE Technical Field Award (1999). Morse earned his BSEE from Cornell University, MS from the University of Arizona, and PhD from Purdue University. His work emphasizes logic-based switching, vision-based control, and distributed algorithms for autonomous systems. He has contributed to foundational papers on multi-agent consensus and formation control, as well as sensor network localization. Current projects include swarming dynamics and reactive control strategies for autonomous vehicles. His scientific contributions span over 200 publications, with recent work addressing distributed control algorithms, climate impact modeling, and game-theoretic network analysis. Morse advises graduate students like Ming Cao and Jia Fang, and his research group explores cutting-edge topics in systems theory and robotics.

• Human-Robot Interaction
• Social Robotics
• Group Dynamics
• +۳ مورد دیگر

Sarah Gillet is a Postdoctoral Researcher at the Division of Robotics, Perception, and Learning at KTH Royal Institute of Technology, where she focuses on developing social robot behaviors to foster collaboration and inclusion in human groups. Her research addresses challenges like in-group favoritism through computational approaches to shape group interactions. She holds a Doctoral Thesis (2024) titled Computational Approaches to Interaction-Shaping Robotics . Her work emphasizes group dynamics , robot-mediated inclusion , and pedagogical robotics , particularly in children and adolescent populations. Key areas include gaze behavior analysis, equitable participation promotion, and social robot roles such as mediators in educational settings. Dr. Gillet teaches the Social Robotics (DD2413) course and supervises master theses. Her recent publications explore robot gaze behaviors for participation balance, socially appropriate listening, and influence prediction models like RoSI. She actively participates in conferences like ACM/IEEE HRI and IEEE RO-MAN. Her research integrates computational methods with social science insights to design robots that actively improve human group interactions, with applications in education, collaboration, and bias mitigation.

Fariba Karimiمشاهده پروفایل

استاد

• Algorithmic Fairness
• Computational Social Science
• Network Science
• +۷ مورد دیگر

Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.

Renaud Lambiotteمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Temporal Networks
• +۷ مورد دیگر

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

Diego Patiñoمشاهده پروفایل

استادیار

• Machine Learning
• Geometric Computer Vision
• 3D Reconstruction
• +۷ مورد دیگر

Diego Patiño is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), a position he began in September 2024. He earned his Ph.D. in Computer Engineering from the National University of Colombia in 2020, following M.S. and B.S. degrees from the same institution. Prior to joining UTA, he served as a Postdoctoral Fellow at Drexel University and a Postdoctoral Researcher at the GRASP Laboratory, University of Pennsylvania. B.S. in Computer Engineering, National University of Colombia, 2010 M.S. in Computer Engineering, National University of Colombia, 2012 Ph.D. in Computer Engineering, National University of Colombia, 2020 Dr. Patiño's research centers on geometric computer vision and machine learning, with applications in robotics and 3D vision. His primary interests include 3D reconstruction, graph neural networks, symmetry detection, physics-informed machine learning, and reinforcement learning. He develops algorithms that integrate geometric priors and physical constraints into deep learning models to improve robustness and generalization in real-world robotic systems. His recent publications demonstrate a strong trend in leveraging implicit neural representations for 3D shape reconstruction, applying graph neural networks to swarm robotics, and enhancing computer vision tasks with self-supervised and physics-informed learning. Work spans high-impact venues such as IEEE RA-L, ICRA, ICPR, and MICCAI, showing a consistent focus on geometric reasoning, robotic perception, and medical imaging applications. His scientific contributions have been recognized with awards from the UTA Division of Student Affairs for exceptional dedication and positive impact (2024 and 2025). He is actively involved in securing research funding, with multiple grants under review from NSF, Air Force SBIR, and industry partners like Sony. Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (December 9, 2024) Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (April 30, 2025) Dr. Patiño advises and serves on committees for multiple graduate students in computer science and engineering, including doctoral and master’s candidates. He is also leading or co-leading several research grants under review, covering topics such as aerial swarm navigation, neuromorphic sensing, and industrial computer vision. He teaches graduate courses in computer vision and is involved in service roles including PhD admissions and faculty appointments committees. He is affiliated with research initiatives at UTA, including the UTARI Research Institute, where he has presented on geometric modeling and physics-informed learning. His lab focuses on developing next-generation computer vision algorithms for robotics, industrial inspection, and safety-critical systems.

Dong Gongمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.

• Optimal Control
• Aerospace Engineering
• Robotics
• +۳ مورد دیگر

Ran Dai is a Professor in the Department of Aeronautics and Astronautics at Purdue University's College of Engineering. His research focuses on optimal control theory, trajectory optimization, and robotics applications, with an emphasis on aerospace systems and energy-efficient solutions. He leads the Autonomous Optimization Lab (AOL) and has contributed extensively to advancements in learning-based control, mixed-integer programming, and deployable space systems. His work spans applications such as spacecraft guidance, unmanned vehicle path planning, and energy management for solar-powered systems. Notable contributions include algorithms for fuel-optimal powered descent, real-time trajectory optimization, and origami-inspired deployable mechanisms. He holds a Ph.D. in Aerospace Engineering and has published over 100 peer-reviewed articles. Research interests include: Optimal control and trajectory optimization Reinforcement learning for decision-making Autonomous systems and robotics Energy-efficient aerospace engineering Recent work emphasizes meta-reinforcement learning frameworks and adaptive optimization engines for complex systems.

Jure Leskovecمشاهده پروفایل

استاد

• Machine Learning
• Social Networks
• Data Mining
• +۴ مورد دیگر

Jure Leskovec is a Professor of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and the Center for Research on Foundation Models. He holds academic appointments in the Department of Computer Science and is a member of Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), and the Wu Tsai Neurosciences Institute. Leskovec earned his BSc from the University of Ljubljana (2004), PhD from Carnegie Mellon University (2008), and postdoctoral training at Cornell University. His research focuses on social networks, data mining, machine learning, and computational biomedicine, with contributions to graph neural networks, drug discovery, and AI applications in healthcare. His work has been applied to combat the COVID-19 pandemic and integrated into products at major tech companies. Leskovec’s publications reflect his expertise in network analysis, medical AI, and biological systems. His recent work includes foundational contributions to graph neural networks (e.g., PyG) and medical AI frameworks. His research has garnered numerous awards, including the Microsoft Research Faculty Fellowship and ICDM Research Contributions Award. Leskovec advises numerous doctoral and postdoctoral researchers, contributing to over 200 publications. His interdisciplinary collaborations span computational biology, healthcare analytics, and social systems, with a focus on leveraging AI to address real-world challenges.