جستجوی استاد راهنمای دانشگاهی

Alex Wongمشاهده پروفایل

استادیار

• Sensor fusion
• Unsupervised learning
• 3D vision
• +۲ مورد دیگر

Alex Wong is an Assistant Professor of Computer Science at Yale University, specializing in computer vision, robotics, and medical imaging. His research focuses on sensor fusion, unsupervised learning, 3D vision, robust perception under adverse conditions, and medical image analysis. He holds degrees from the University of California, Los Angeles (UCLA), including a B.S., M.S., and Ph.D. in Computer Science. Wong’s work bridges theoretical advances with practical applications, particularly in depth estimation, autonomous systems, and medical diagnostics. He has received prestigious awards such as the NeurIPS Outstanding Student Paper Award (2011) and the ICRA Best Paper Award in Robot Vision (2019). His research often addresses challenges in unstructured environments, emphasizing robustness and adaptability. Recent projects include developing novel frameworks for unsupervised depth completion, adversarial robustness in vision systems, and multimodal fusion techniques. His contributions span conferences like CVPR, ICCV, and ICRA, with a strong focus on advancing AI for real-world applications in healthcare and robotics. Education: B.S., Computer Science, UCLA M.S., Computer Science, UCLA Ph.D., Computer Science, UCLA Awards: NeurIPS Outstanding Student Paper Award (2011) ICRA Best Paper Award in Robot Vision (2019) His lab at Yale Engineering focuses on AI-driven solutions for perception challenges, collaborating across disciplines to advance medical imaging and autonomous systems. Current efforts explore generative models, continual learning, and vision-language integration for robust scene understanding.

Joan Brunaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Signal Processing
• +۵ مورد دیگر

Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.

Amir Ali Ahmadiمشاهده پروفایل

استاد

• Optimization Theory
• Control Theory
• Dynamical Systems
• +۷ مورد دیگر

Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.

Daniel M. Kaneمشاهده پروفایل

استاد

• Number Theory
• Combinatorics
• Complexity Theory
• +۳ مورد دیگر

Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.

Michal Kolesárمشاهده پروفایل

استاد

• Econometrics
• Causal Inference
• Instrumental Variables
• +۴ مورد دیگر

Michal Kolesár is a Professor in the Department of Economics at Princeton University , holding this position since July 2020. Previously, he served as Assistant Professor (2014-2020) with dual appointments in Economics and the Woodrow Wilson School (2018-2020), and as Visiting Assistant Professor at MIT (2016-2017). His research focuses on econometrics , particularly causal inference , instrumental variables , nonparametric regression , and robust statistical methods . His work addresses fundamental challenges in high-dimensional data analysis, treatment effect heterogeneity, and finite-sample inference validity. Current projects include developing bias-aware methods for regularized regression and analyzing dynamic causal effects in nonlinear systems. Kolesár's recent publications reveal a strong emphasis on methodological rigor with practical applications. His work spans instrumental variable techniques (addressing contamination bias, weak identification), regression discontinuity designs (discrete running variables, measurement error), and high-dimensional inference (sparsity fragility, honest confidence intervals). Key recurring themes include finite-sample optimality, coverage probability guarantees, and robustness to model misspecification. Fellow of the International Association for Applied Econometrics (2023) Journal of Econometrics best associate editor award (2023) Sloan Research Fellowship (2019) NSF grants for high-dimensional data inference (2021-2025) and nonparametric regression (2016-2019) Graduate Economics Club teaching awards (2017, 2018) As an educator, Kolesár teaches advanced econometrics courses at both undergraduate (ECO 312, ECO 313) and graduate levels (ECO 517, ECO 519, ECO 539b). He serves as Co-editor of the Journal of Business & Economic Statistics (2024-2027) and sits on editorial boards of Econometrica , American Economic Journal: Applied Economics , and others. His professional activities include extensive peer review for top journals and organization of major econometrics conferences including the 2026 Econometric Society Winter Meeting.

Carlos Cinelliمشاهده پروفایل

استادیار

• Causal Inference
• Statistical Methodology
• Machine Learning
• +۵ مورد دیگر

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.

Andrea Montanariمشاهده پروفایل

استاد

• High-dimensional statistics
• Machine learning theory
• Statistical physics
• +۴ مورد دیگر

Andrea Montanari is a Professor of Mathematics and Statistics at Stanford University, affiliated with the Department of Mathematics and Statistics. His research focuses on high-dimensional statistics, machine learning theory, optimization algorithms, and statistical physics, with applications to neural networks and complex systems. He has contributed extensively to understanding generalization in overparametrized models, spin glass theory, and algorithmic methods like approximate message passing. His work bridges theoretical computer science and mathematical physics, addressing challenges in data analysis and learning from high-dimensional datasets. Notable themes include analyzing neural network dynamics, optimizing high-dimensional landscapes, and developing efficient algorithms for sparse and low-rank matrix estimation. Montanari’s publications explore topics such as the interplay between statistical and computational limits, the behavior of gradient-based methods, and the theoretical foundations of modern machine learning. His recent research demonstrates a focus on fundamental questions in learning theory, including the study of phase transitions in statistical estimation, the role of overparametrization in generalization, and the mathematical underpinnings of contemporary algorithms. While no specific awards are listed here, his contributions reflect significant impact in interdisciplinary fields.

• Quantum Computation
• Quantum Algorithms
• Computational Complexity
• +۳ مورد دیگر

Ashley M R Montanaro is a Professor of Quantum Computation at the School of Mathematics, University of Bristol . Active in quantum computing research since at least 2014, they lead projects at the intersection of quantum algorithms , computational complexity , and quantum information theory , affiliated with the Bristol Quantum Information Institute. Research interests focus on quantum algorithm design , computational complexity analysis , and quantum simulation . Key work includes developing variational quantum algorithms for phase transition detection, Hamiltonian simulation techniques, and quantum-classical hybrid methods for solving complex problems in physics and optimization. Recent publications demonstrate expertise in: Quantum phase diagram simulation with low-depth circuits Quantum speedups for constraint satisfaction problems Quantum communication complexity of machine learning tasks Quantum-enhanced optimization heuristics Hamiltonian simulation with time-dependent product formulas Quantum algorithm complexity analysis Scientific awards include: EPSRC Fellowship (2014-2019) - "New insights in quantum algorithms and complexity" Active in quantum software development through projects like: "Quantum Algorithms from Foundations to Applications" (ERC-2018-COG) "Quantum Computing and Simulation Hub" (2019-2024) "Prosperity Partnership in Quantum Software" (2019-2023)

• Machine Learning
• Deep Learning
• Signal Processing
• +۹ مورد دیگر

Prof. Dr.-Ing. Ahmad Osman is a Professor at the Saarland University of Applied Sciences (htw saar), specializing in Test Technologies and Test Methods within the Faculty of Engineering. He also holds an Adjunct Professor position at Laval University in Quebec, Canada, in the Department of Electrical Engineering and Computer Science. His research focuses on Artificial Intelligence applications in Signal and Image Processing for Non-destructive Testing (NDT) , with extensive work on Deep Learning , 3D Ultrasound Tomography , and Sensor Data Fusion in industrial contexts. Engineering Artificial Intelligence Signal Processing Image Processing Non-destructive Testing Quality Control Augmented Reality Osman leads the AutomaTiQ research group and serves as Head of the Algorithms/Signal and Data Processing Department at Fraunhofer IZFP . His recent publications (2017–2022) emphasize Deep Learning for defect detection in CFRP , Terahertz Imaging for artwork diagnostics, and Acoustic Sensors for agricultural quality control. He has organized international conferences on Structural Health Monitoring and contributed to Springer books on NDT technologies. His projects include ComforTex-AI (2024) and development of 3D positioners for ultrasound measurements. Collaborations span institutions in Germany, Canada, Italy, and Brazil, with advisory roles in the German Society for NDT and technical committees for conferences in Montreal and Egypt.

Markus Bachmayrمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +۴ مورد دیگر

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.

Richard Nicklمشاهده پروفایل

استاد

• Mathematical Statistics
• High-dimensional Inference
• Bayesian Nonparametrics
• +۳ مورد دیگر

Richard Nickl is a Professor of Mathematical Statistics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Statistical Laboratory. His research focuses on high-dimensional inference, Bayesian nonparametrics, statistics for partial differential equations, and inverse problems. He has held significant grants, including an ERC Advanced Grant (2024–2029) and an EPSRC Programme Grant (2022–2027). His work bridges statistics, probability, and analysis, with contributions to theoretical foundations and computational methods in non-linear inverse problems. Key research interests include Bayesian posterior consistency, statistical inference for diffusions, and polynomial-time algorithms for high-dimensional posteriors. Notable publications include foundational monographs such as Mathematical foundations of infinite-dimensional statistical models (2016), which earned a PROSE Award, and recent advancements in Bayesian nonparametric inference for McKean-Vlasov models (2025). His group organizes workshops, such as the 2024 Statistical Aspects of Non-Linear Inverse Problems conference. Awards: 2017 PROSE Award in Mathematics. Grants: ERC Advanced Grant, EPSRC Programme Grant. Lab/Team: Research Group in Mathematical Statistics at DPMMS, focusing on inverse problems and Bayesian methodology.

Arnab Senمشاهده پروفایل

دانشیار

• Probability Theory
• Discrete Harmonic Analysis
• Statistical Physics
• +۳ مورد دیگر

Arnab Sen is an Associate Professor at the School of Mathematics, University of Minnesota. His research focuses on probability theory and discrete harmonic analysis, with emphasis on models from statistical physics such as spin glasses, random graphs, random matrices, and random polynomials. PhD in Statistics, UC Berkeley (2010), advised by Steven N. Evans and Elchanan Mossel Postdoctoral Fellow, Statistical Laboratory, University of Cambridge His research spans discrete probability , statistical physics , and random matrix theory , addressing topics like disorder chaos in spin glasses, eigenvalue distributions, and quantum percolation. He has taught graduate and undergraduate courses including Random Matrix Theory , Introduction to Stochastic Processes , and Multivariable Calculus . His recent publications analyze spin glass models, random matrices, and combinatorial systems.

Soosan Beheshtiمشاهده پروفایل

استاد

• Signal Processing
• Statistical Signal Processing
• Information Theory
• +۶ مورد دیگر

Soosan Beheshti is a Professor and Program Director in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. She holds a B.S. from Isfahan University of Technology and M.S./Ph.D. from MIT. Her research focuses on signal processing, statistical learning, and information theory, with applications in biomedical systems, data denoising, and system modeling. She has received awards such as the Dean's Teaching Award (2010) and the EECS Carlton E. Tucker Award (1998). Education: B.S., Electrical Engineering, Isfahan University of Technology (1996) M.S. & Ph.D., Electrical Engineering, MIT (2002) Research Interests: Statistical Signal Processing Information Theory Data Denoising & Compression System Modeling & Control Machine Learning Applications Awards: Dean's Teaching Award (2010) Gold Paper Award (PacRim 2009) Best Paper Award (Remote Sensing 2008) MIT Teaching Excellence Award (1998) Teaching: Courses include Signals and Systems, Control Systems, and Statistical Inference. She has supervised numerous graduate students and postdocs in her Signal and Information Processing (SIP) Lab. Labs/Teams: Director of the SIP Lab, conducting research in signal processing, information theory, and biomedical applications. Collaborates with industry partners like Myant Inc. and Huawei Technologies.

Keming Yuمشاهده پروفایل

استاد

• Quantile Regression
• Bayesian Statistics
• Survival Analysis
• +۱۲ مورد دیگر

Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.