جست‌وجوی استاد و پژوهشگر

Yashar Hezavehمشاهده پروفایل

دانشیار

• Dark Matter
• Machine Learning and Deep Learning
• Galaxy Clusters
• +۳ مورد دیگر

Yashar Hezaveh is an Associate Professor at the University of Montreal's Faculty of Arts and Sciences, Department of Physics. He holds the Canada Research Chair in Astrophysical Data Analysis and Machine Learning. His work focuses on using gravitational lensing and machine learning to map dark matter distributions in galaxy halos, advancing our understanding of dark matter's nature. He completed his PhD at McGill University in 2013, earning recognition for groundbreaking research on high-redshift dusty star-forming galaxies. Education: PhD in Physics (McGill University, 2013) Affiliations: Kavli Institute for Theoretical Physics, Flatiron Institute's Center for Computational Astrophysics Research interests include applying deep learning to analyze gravitational lensing data, Bayesian neural networks for dark matter mapping, and cosmological simulations. Notable projects include the CASTOR mission and advances in radio interferometry image reconstruction. His work bridges astrophysics and machine learning, addressing challenges in cosmic structure analysis. Awards: Hubble Fellowship (2015), Top 10 Quebec Science Discoveries (2013). Grants: Leads multiple projects on dark matter, AI-driven stellar mass measurement, and astrophysical data analysis funded by NSERC, FQRNT, and the Simons Foundation. Students: Supervised four Master's theses on topics like Bayesian lensing inversion and machine learning for galactic archaeology. He contributes to collaborative initiatives like the Centre de recherche en astrophysique du Québec (CRAQ), fostering interdisciplinary astrophysics research.

Frederic Angمشاهده پروفایل

دانشیار

• Agricultural Economics
• Environmental Economics
• Business Economics
• +۷ مورد دیگر

Dr. Frederic Ang is an Associate Professor in the Business Economics Group at Wageningen University & Research since 2023, specializing in agri-food sector productivity and sustainability. He holds a joint Ph.D. from KU Leuven and Hasselt University (2015), followed by postdoctoral roles at the University of Reading and Swedish University of Agricultural Sciences. His research focuses on sustainable intensification, dynamic optimization, and agricultural productivity analysis. He currently serves as Associate Editor of the Journal of Productivity Analysis and Co-Editor of the Journal of Agricultural Economics . Key honors include the 2023 AES Outstanding Contribution Award and 2020 Best Referee Award. His work addresses agricultural efficiency, environmental policy, and farm-level decision-making. Education: Joint Ph.D. in Bioscience Engineering and Business Economics (2015) Master of Advanced Studies in Economics (2015) M.Sc. in Tropical Natural Resources Management (2009) B.Sc. in Biosystems Engineering (2007) Research Interests: Dr. Ang examines agricultural productivity through lenses of production economics, exploring sustainable practices and environmental-economic trade-offs. His work bridges theoretical models (e.g., Hicks-Moorsteen productivity indicators) with applied analyses of farm-level data, emphasizing nitrogen management, energy resilience, and microbial innovations. Awards & Grants: Recognized for contributions to agricultural economics journals and policy research. Active in OECD initiatives on productivity and environment. Projects include leading the SIMBA initiative on microbiome applications and the MINDSTEP project modeling farmer decisions under EU policies. Labs & Collaborations: Engaged with interdisciplinary teams in the Business Economics Group and international partners via projects like MIXED (agroforestry systems) and MINDSTEP. Coordinates research on circular economy principles and climate-smart agriculture.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +۵ مورد دیگر

Nisar Ahmed is an Associate Professor at the University of Colorado within the Aerospace Engineering Sciences department. His research focuses on the intersection of Artificial Intelligence , Robotics , and Autonomous Systems , emphasizing decision-making under uncertainty, sensor fusion, and human-machine collaboration. Key research interests include: Active Inference for autonomous planning Decentralized Data Fusion in multi-robot systems Machine Self-Confidence and competency assessment Reinforcement Learning for spacecraft and robotic guidance Uncertainty Quantification in dynamic environments Recent publications highlight trends in Pareto-optimal decision-making , Bayesian optimization , contextual bandits , and trust calibration for UAS and planetary rovers. His work integrates probabilistic modeling with real-time autonomy , ensuring robustness in applications like search-and-rescue missions and lunar exploration. Contact: Nisar.Ahmed@Colorado.EDU

Norman R. Swansonمشاهده پروفایل

استاد

• Financial Econometrics
• Forecasting
• Machine Learning
• +۶ مورد دیگر

Norman R. Swanson is a Distinguished Professor and James Cullen Chair in Economics at Rutgers University. He holds a PhD from the University of California, San Diego, and a degree from the University of Waterloo. Primary Affiliations: Department of Economics, Rutgers University Previous Positions: Pennsylvania State University, Texas A&M University, Purdue University, IBM Canada His research focuses on financial econometrics , forecasting , machine learning and big data , and time series analysis . He has published over 100 peer-reviewed articles and served as editor for journals like the Journal of Econometrics and Journal of Business and Economic Statistics . His work often bridges theoretical econometrics with practical applications in finance and macroeconomics, emphasizing robustness and predictive accuracy. The articles listed reflect his expertise in volatility modeling , jump detection , data reduction , and forecasting methodology . Key trends include the use of shrinkage methods, factor models, and simulation-based testing in high-frequency financial and macroeconomic contexts. Scientific Awards: Fellow of the Journal of Econometrics Fellow of the International Association of Applied Econometrics He has acted as a visiting scholar at institutions like the University of Maryland and the Federal Reserve Bank of Philadelphia. His consulting work spans firms such as Union Bank of Switzerland and DFA Capital Management, with expertise as a legal expert witness in financial services cases.

• Statistics
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.

Raaz Dwivediمشاهده پروفایل

استادیار

• Causal Inference
• Reinforcement Learning
• Personalized Decision-Making
• +۲ مورد دیگر

Raaz Dwivedi is Assistant Professor in Operations Research and Information Engineering at Cornell University and Cornell Tech. His research develops statistical and computational methods for personalized decision-making, focusing on causal inference, reinforcement learning, and distribution compression. Recent publications advance kernel thinning techniques, counterfactual inference methods, and adaptive nearest-neighbor algorithms with applications in healthcare and recommendation systems. Research appears in top venues with 15+ publications since 2022. Awards and honors: Blackwell-Rosenbluth Award (2024) ASA Best Student Paper Award (2022) MIT LIDS Best Presentation Award Harvard Teaching Excellence Award FODSI Postdoctoral Fellowship Holds PhD in EECS from UC Berkeley and BTech from IIT Bombay.

Marc Henryمشاهده پروفایل

استاد

• Microeconometrics
• Optimal Transport
• Inequality Measurement
• +۳ مورد دیگر

Marc Henry is a Liberal Arts Professor of Economics at Pennsylvania State University, serving as the Graduate Program Director in the Department of Economics. He holds a Ph.D. from the London School of Economics (1999). His research focuses on microeconometrics, optimal transport, inequality measurement, and environmental engineering applications in pollution control. Key research interests include the application of the Roy model to analyze occupational segregation and STEM participation, optimal transport theory for inequality measurement, and experimental design for nanoparticle removal in waste incineration systems. His work bridges econometric theory with practical policy analysis, particularly in gender economics and environmental technology. Recent publications highlight themes such as multidimensional inequality indices, hedonic equilibrium identification, and the design of scrubbers for particulate matter control. His contributions span both theoretical advancements (e.g., vector copulas, Sklar theorem extensions) and applied environmental engineering solutions. Dr. Henry’s research has been disseminated through venues like Google Scholar, with a focus on combining rigorous statistical methods with real-world policy implications. His academic activities include advising on graduate programs and maintaining a research website: https://sites.google.com/site/mabhenry/ .

Kasper Green Larsenمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +۳ مورد دیگر

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

Xiyu Jiaoمشاهده پروفایل

مدرس ارشد

• Econometrics
• Outlier Robustness
• Nonlinear Cointegration
• +۶ مورد دیگر

Xiyu Jiao is a Senior Lecturer in the Department of Economics at the University of Gothenburg, where he joined in 2023. Previously, he served as a Postdoctoral Research Fellow at Nuffield College, Oxford (2019–2023). He holds a DPhil (and MPhil) in Economics from the University of Oxford (2019), an MSc in Statistics (Research) with Distinction from the London School of Economics (2012), and a BSc in Computational Mathematics from Northwest University, China (2011). His research focuses on econometrics, statistics, and machine learning, with particular emphasis on outlier robustness, nonlinear cointegration, and model selection. His work bridges theoretical advancements and practical applications in addressing econometric challenges. He is affiliated with the Institute for New Economic Thinking and Climate Econometrics at the University of Oxford. Xiyu’s research trends reflect a strong emphasis on robust statistical methodologies, particularly in outlier detection and regression analysis. His articles explore asymptotic theory, instrumental variables, and algorithmic approaches to econometric problems. He has no listed academic awards but maintains active collaborations through his associations with interdisciplinary institutes. No grants or advising records are explicitly mentioned in the provided texts. Xiyu contributes to research teams at Climate Econometrics and the Institute for New Economic Thinking, focusing on econometric applications in climate and economic systems.

Bin Liمشاهده پروفایل

دانشیار

• Quantum Optics
• Quantum Information Science
• Optical Engineering
• +۳ مورد دیگر

Bin Li is an Associate Professor and Associate Chair of Actuarial Science in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Applied Mathematical and Computational Sciences (2013, University of Iowa), and master's and bachelor's degrees in Computational Mathematics from Xi’an Jiaotong University (2008 and 2005, respectively). His research focuses on quantum optics and optical engineering, particularly in developing robust laser-driven systems for quantum light sources, solid-state emitters, and trapped ion quantum computing. He has pioneered techniques like Notch-filtered Adiabatic Rapid Passage (NARP) and advanced femtosecond pulse engineering for high-performance quantum systems. His work bridges theoretical quantum physics with experimental optical innovations. Research interests include: Optical driving schemes for quantum emitters Adiabatic inversion and rapid passage methodologies Individual ion addressing in quantum simulators Spin dynamics in 2D perovskite materials Publications span 2018–2025, emphasizing advancements in quantum engineering, ultrafast optics, and atomic-scale systems. While no scientific awards are explicitly listed, his impactful contributions to quantum technologies suggest active recognition in the field. Advising and grants: No specific advisees or grants are documented in the provided texts, though his research activities imply involvement in graduate supervision and funding programs.

Rémi Flamaryمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Domain Adaptation
• +۷ مورد دیگر

Rémi Flamary is a Professor in the Applied Mathematics department at École Polytechnique, France, and a member of the CMAP Laboratory. Previously, he held an Associate Professor position at Université Côte d'Azur in the Department of Electronics and Lagrange Laboratory. He completed his PhD at Rouen University under Alain Rakotomamonjy, focusing on statistical signal processing and optimization. His research interests span machine learning, optimal transport, domain adaptation, and their applications in biomedical engineering, energy, and remote sensing. He leads the development of the POT (Python Optimal Transport) library and contributes to projects like SKADA for domain adaptation. Education: PhD in Applied Mathematics, Rouen University (LITIS Laboratory) Research Focus: Flamary's work emphasizes leveraging optimal transport theory for machine learning tasks such as graph prediction, signal normalization, and cross-domain adaptation. His contributions include novel algorithms for unbalanced transport, semi-relaxed Gromov-Wasserstein distances, and end-to-end graph generation frameworks. He actively collaborates on applications in neuroscience, astronomy, and energy systems. Professional Activities: He has presented at NeurIPS and other top conferences, supervised PhD students like Cédric Vincent-Cuaz, and contributed to open-source software. His teaching includes courses on signal processing and machine learning at École Polytechnique.

• Differential Privacy
• Algorithms
• Theoretical Computer Science
• +۳ مورد دیگر

Teresa Anna Steiner serves as an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, specializing in algorithmic research with emphasis on privacy-preserving computational methods and theoretical computer science. Her research centers on differential privacy mechanisms, where she investigates trade-offs between data utility and privacy guarantees through rigorous analysis of noise injection techniques like Laplace and Gaussian distributions. She extends this work to dynamic graph databases requiring real-time privacy protections and develops novel text indexing approaches for regular expression pattern matching, contributing to foundational advancements in algorithm design for sensitive data environments. Recent 2025 publications reveal a cohesive research trajectory focused on practical implementations of differential privacy across diverse data structures, with particular attention to variance optimization in noise mechanisms, edge-level privacy in evolving graphs, and efficient indexing for textual pattern recognition. These works collectively address critical challenges in balancing computational efficiency with robust privacy guarantees in modern data systems. No scientific awards were documented in the available information. Details regarding student advising or research grant funding were not specified in the provided materials.

James V. Burkeمشاهده پروفایل

استاد

• Convex Optimization
• Non-smooth Optimization
• Variational Analysis
• +۷ مورد دیگر

James V. Burke is a Professor of Mathematics at the University of Washington with extensive contributions to optimization theory and its applications. His academic career spans several decades, during which he has developed fundamental theories in nonsmooth and convex optimization, variational analysis, and computational methods for complex optimization problems. Research Focus Burke's primary research centers on convex-composite optimization , where he has established critical theoretical foundations and practical algorithms. His work on weak sharp minima has become foundational in optimization theory, providing essential insights into solution stability and error bounds. He has made significant advances in gradient sampling algorithms for nonsmooth, nonconvex optimization problems, which have broad applications in engineering and data science. More recently, Burke has applied optimization techniques to state estimation problems , particularly developing robust Kalman smoothing methods using Student's t-distributions and other non-Gaussian models. His research bridges pure mathematical theory with practical computational methods, demonstrating consistent innovation across multiple subfields of optimization. Academic Contributions Burke has taught numerous graduate-level courses including Math 509 (Optimal Control), Math 554 (Linear Analysis), and specialized courses on convex analysis and optimization. His research collaborations span multiple institutions, with frequent co-authorship with leading optimization researchers such as Tim Hoheisel, Adrian Lewis, and Michael Overton. He regularly presents his work at major conferences including SIAM Optimization and ICCOPT, with his most recent presentation at the SIAM Conference on Optimization in Seattle (June 2023).

• Statistics
• Data Science
• Applied Probability
• +۳ مورد دیگر

Brian Wainwright is a Lecturer in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB). His specialization lies in Statistics and Data Science, contributing to both academic and practical applications within these fields. Brian's educational background includes a PhD obtained in 2019. The specific institution for this degree is not detailed in the provided information. Brian's research interests are centered around advanced statistical methodologies and their application in data science. He focuses on developing robust models for predictive analytics and exploring computational techniques to address complex data challenges. His work often bridges theoretical foundations with practical problem-solving in real-world scenarios. In terms of advising and grants, Brian currently oversees a number of students in the Department of Statistics and Applied Probability, though specific advisees are not listed here. Information regarding his grants or current funding is also not provided in the available text.

Laurence Perreault-Levasseurمشاهده پروفایل

دانشیار

• Cosmology
• Machine Learning
• Gravitational Lensing
• +۵ مورد دیگر

Laurence Perreault-Levasseur is an Associate Professor at Université de Montréal and an Associate Member of Mila. She specializes in applying machine learning methods to cosmology, with affiliations at the Flatiron Institute and Perimeter Institute. Her research focuses on gravitational lensing, dark matter, and precision cosmology. She holds a Canada Research Chair in Computational Cosmology and Artificial Intelligence. Education: PhD (University of Cambridge, 2015), M.Sc. and B.Sc. (McGill University). Research Interests: Machine learning for cosmological inference, strong gravitational lensing, galaxy cluster characterization, and dark matter studies. Affiliations: CRAQ (Québec Astrophysics Research Centre), Mila (Quebec AI Institute). Her work includes developing Bayesian methods for inverse problems and neural networks for astrophysical data analysis. Key projects involve precision cosmology via machine learning and reconstructing early-universe conditions using generative models.