جست‌وجوی استاد و پژوهشگر

• Computational Biology
• Structural Biology
• Bayesian Inference
• +۱۰ مورد دیگر

Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science , Yale University. He is affiliated with the Quantitative Biology Institute (QBio) , the Applied Math Program , the Institute for Foundations of Data Science (FDS) , and the Wu Tsai Institute (WTI) . He was awarded the Sloan Research Fellowship (2023) . He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018) . Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University. Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning. Research Areas: Dr. Lederman works at the intersection of computational biology , structural biology , Bayesian inference , numerical analysis , and machine learning . His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms . Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis , manifold learning , Hamiltonian Monte Carlo , and Zernike polynomials . Scientific Awards: Sloan Research Fellow (2023) Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series . His lab develops open-source software (e.g., prolate function implementation ) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.

• Stochastic Processes
• Queueing Theory
• Probability Theory
• +۷ مورد دیگر

Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.

Martin Wainwrightمشاهده پروفایل

استاد

• High-dimensional statistics
• Information theory
• Statistical machine learning
• +۳ مورد دیگر

Martin Wainwright is a Professor at the University of California at Berkeley with joint appointments in the Department of Statistics and the Department of Electrical Engineering and Computer Sciences (EECS). His research spans high-dimensional statistics , information theory , statistical machine learning , and optimization theory . He has made significant contributions to understanding computational and statistical trade-offs in high-dimensional settings, as well as developing advanced message-passing algorithms for graphical models. His educational background includes a Bachelor's degree in Mathematics from the University of Waterloo and a Ph.D. in EECS from MIT . His work has been recognized with prestigious awards such as the COPSS Presidents' Award (2014) , IEEE Joint Paper Award (2012) , and Sloan Research Fellowship (2005) . He has advised numerous prominent researchers, including Nihar Shah , John Duchi , and Yuchen Zhang . Publications by Wainwright reflect trends in machine learning , high-dimensional data analysis , and graphical model inference . Notable works include advancements in Markov Chain Monte Carlo algorithms , pairwise comparison models , and distributed computation methods . He has also contributed extensively to signal processing and LDPC codes . COPSS Presidents' Award (2014) IEEE Joint Paper Award (2012) Institute of Mathematical Statistics Fellow (2011) NSF CAREER Award (2006) Okawa Research Grant (2005) Sloan Research Fellow (2005)

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +۷ مورد دیگر

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

• Statistical Inference
• Stochastic Processes
• Biomathematics
• +۵ مورد دیگر

Susanne Ditlevsen is a Professor at the Department of Mathematical Sciences , University of Copenhagen. Her research focuses on statistical inference for stochastic processes , mathematical modeling of physiological systems , nonlinear dynamics , neuroscience , and biomathematics . Research : She develops statistical methods for diffusion processes, hidden Markov models, and stochastic differential equations, with applications in biomedical data and marine mammal behavior. Teaching : Covers basic statistics, probability, stochastic processes, regression, and generalized linear models. Publications highlight her work on climate tipping points (2023, Nature Communications ), nonlinear neuronal systems (2017), and statistical ecology (2020). Her collaborations span Denmark, France, and international institutions.

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Optimization
• +۴ مورد دیگر

Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.

James Sweeneyمشاهده پروفایل

استاد

• Machine Learning
• Time Series Analysis
• Geospatial Statistics
• +۳ مورد دیگر

James Sweeney serves as Professor in the Department of Mathematics and Statistics at the University of Limerick, concurrently holding memberships in the Centre for Battery and Energy Materials Research and the Mathematics Applications Consortium for Science and Industry (MACSI). Actively accepting PhD students, his research bridges theoretical mathematics with practical industry applications across diverse sectors including energy materials, real estate, and public health. His research portfolio demonstrates exceptional interdisciplinary range, with core expertise in machine learning algorithms (particularly time series classification and neural networks), geospatial statistics for property valuation, and epidemiological modeling for disease surveillance. Key methodological contributions include evolutionary algorithms for optimization, dissimilarity-preserving representation learning, and flexible geospatial smoothing techniques that address complex real-world data challenges. Analysis of his 23 publications (2015-2024) reveals accelerating scholarly output since 2020, with 2024 being particularly prolific. His work consistently targets high-impact applications: developing diagnostic thresholds for bovine tuberculosis, modeling COVID-19 transmission dynamics in Dublin, and creating neural network solutions for geodemographic clustering. This trajectory reflects deepening engagement with computational approaches to solve pressing societal problems through mathematical innovation. As a PhD supervisor, he cultivates next-generation researchers in advanced computational methods. His collaborative framework extends through MACSI's industry partnerships and the Centre for Battery and Energy Materials Research, where mathematical modeling directly informs energy technology development. These dual affiliations position him at the critical intersection of academic research and industrial application, particularly in Ireland's growing tech and energy sectors. His laboratory activities center around computational mathematics teams within MACSI, focusing on applying statistical learning to battery materials research and real-world data challenges. Current projects involve time series analysis for sensor data, geospatial modeling for economic forecasting, and optimization algorithms for veterinary epidemiology – demonstrating remarkable methodological versatility across traditionally disparate domains.

Keith LeGrandمشاهده پروفایل

استادیار

• Multi-sensor Multi-object Tracking
• Spacecraft Navigation
• Space Domain Awareness
• +۵ مورد دیگر

Keith LeGrand is an Assistant Professor in the School of Aeronautics and Astronautics at Purdue University, part of the Cislunar Space Initiative. He holds a Ph.D. in Aerospace Engineering from Cornell University (2022), an M.S. (2015), and a B.S. (2014) in Aerospace Engineering from Missouri University of Science and Technology. His research focuses on multi-object tracking, spacecraft navigation, space domain awareness, and intelligent sensor control. Key projects include developing probabilistic filters for cislunar space object tracking and information-driven autonomy systems. LeGrand leads the Sensing, Controls, and Probabilistic Estimation (SCOPE) Group, which advances space surveillance and autonomous systems. Notable awards include the 2025 AFOSR and ISIF Young Investigator Awards, 2023 AMOS Best Paper Award, and the E.F. Bruhn Teaching Award for excellence in undergraduate instruction. LeGrand advises a diverse group of graduate and undergraduate students in astrodynamics and space applications. His work is supported by grants from AFOSR, ISIF, and partnerships with institutions like Sandia National Laboratories and Draper Labs. The SCOPE lab collaborates on projects such as lunar landing navigation, satellite proximity operations, and multi-sensor fusion for space surveillance.

Ivan Canayمشاهده پروفایل

استاد

• Econometric Theory
• Partially Identified Models
• Moment Inequalities
• +۵ مورد دیگر

Ivan Canay is a Professor of Economics and Director of the Mathematical Methods in the Social Sciences Program at Northwestern University’s Weinberg College of Arts & Sciences. He holds a PhD from the University of Wisconsin, Madison (2008). His research focuses on econometric theory, particularly developing statistical methods for assessing partially identified models, including tests for moment inequalities and randomization-based inference techniques. Recent work addresses challenges in clustered data analysis, covariate-adaptive randomization, and regression discontinuity designs. Canay’s academic contributions include advancing methodologies for handling non-ignorable cluster sizes and improving the robustness of inference in settings with limited data. He serves as an associate editor for the Journal of Econometrics , Journal of Business and Economic Statistics , and Econometrics Journal . His work bridges theoretical econometrics with practical applications in policy evaluation and causal inference. Key research themes include: Partially identified models and moment inequality frameworks Bootstrap methods for clustered data Covariate-adaptive randomization in clinical trials Statistical software development (e.g., Stata modules) His publications emphasize methodological rigor while addressing real-world complexities in economic data. Current projects likely expand his work on inference under structural constraints and improving accessibility of econometric tools for applied researchers.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +۳ مورد دیگر

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

Guillaume Balمشاهده پروفایل

استاد

• Inverse Problems
• Medical Imaging
• Geophysical Imaging
• +۴ مورد دیگر

Guillaume Bal is a Professor at the University of Chicago, holding joint appointments in the Departments of Statistics and Mathematics, and affiliated with the Committee on Computational and Applied Mathematics (CCAM). His research focuses on inverse problems in medical and geophysical imaging, partial differential equations with random coefficients, and the mathematical analysis of topological insulators. He explores applications in wave propagation, uncertainty quantification, and hybrid imaging modalities. His recent work delves into theoretical and computational aspects of stochastic partial differential equations and topological edge states. Research Interests: Inverse Problems (Geophysical/Medical Imaging) Topological Insulators and Edge States Wave Propagation in Heterogeneous Media Uncertainty Quantification Publications Trends: Recent articles emphasize topological insulator dynamics, hybrid imaging techniques, and stochastic PDE models. Key themes include bulk-edge correspondence, dual-energy CT optimization, and semiclassical propagation in curved interfaces.

• Signal Processing
• Wireless Communications
• Underwater Acoustic Communications
• +۷ مورد دیگر

Professor Yue Rong is a Full Professor at Curtin University's Department of Electrical and Computer Engineering, within the School of Electrical Engineering, Computing and Mathematical Sciences. He holds editorial roles at IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters. His research focuses on signal processing for communications, underwater acoustic systems, wireless networks, and healthcare IoT. Rong has authored over 140 journal and conference papers and received multiple awards, including the 2010 Young Researcher of the Year Award. Education: B.E. (Electrical Engineering), Shanghai Jiao Tong University (1999) M.Sc. (Electrical Engineering), University of Duisburg-Essen (2002) Ph.D. (Electrical Engineering), Darmstadt University of Technology (2005) Research Interests: Rong's work spans cooperative MIMO communications, underwater acoustic systems, OFDM modulation, radar-based healthcare monitoring, and secure wireless protocols. His innovations include adaptive modulation schemes for underwater environments and radar-based vital signs detection. Recent trends in his publications emphasize AI-driven signal processing for healthcare IoT and underwater optical communication systems. Awards: Best Paper Awards (WCSP 2011, APCOMM 2010) Chinese Government Award (2004) DAAD/ABB Fellowship (2001-2002) Grants & Labs: His research is supported by grants focusing on UAV-enabled data collection and underwater network optimization. He leads projects in the Distributed Data Fusion and Emerging Technologies (DDFE) lab, advancing radar-cardiography and wearable health monitoring systems.

Hamed Rahimianمشاهده پروفایل

استادیار

• Stochastic Optimization
• Data-Driven Optimization
• Risk-Averse Optimization
• +۳ مورد دیگر

Dr. Hamed Rahimian is an Assistant Professor in the Department of Industrial Engineering at Clemson University. He holds a Ph.D. in Industrial and Systems Engineering from The Ohio State University (2018), an M.Sc. from The University of Arizona (2012), and B.Sc./M.Sc. degrees from Sharif University of Technology (2008/2011). Prior to Clemson, he was a Postdoctoral Research Fellow at Northwestern University under Prof. Sanjay Mehrotra. His research focuses on data-driven decision-making under uncertainty, including stochastic optimization, distributionally robust optimization, and risk-averse methodologies. He has published in top journals such as Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Education: Ph.D. Industrial and Systems Engineering, The Ohio State University (2018) M.Sc. Industrial Engineering, The University of Arizona (2012) B.Sc./M.Sc. Industrial Engineering, Sharif University of Technology (2008/2011) His research has been recognized with awards including the Harold W. Kuhn Award (2022), Runner-Up INFORMS Computing Society Student Paper Award (2017), and 2nd Place IISE Pristker Dissertation Award (2019). He serves as an Associate Editor for INFORMS Journal on Computing and Sharif Journal of Industrial Engineering & Management . Grants & Advising: He secured an Air Force grant (2024) on multistage stochastic programming. His research group focuses on advancing optimization under uncertainty with applications in healthcare, energy, and supply chains. Labs/Teams: Leads a research group in Clemson’s Industrial Engineering department, collaborating on projects in distributionally robust optimization and data-driven decision-making.

Ray Baiمشاهده پروفایل

استادیار

• Bayesian Statistics
• High-Dimensional Modeling
• Scalable Machine Learning
• +۷ مورد دیگر

Ray Bai is an Assistant Professor in the Department of Statistics at the University of South Carolina (USC), part of the McCausland College of Arts and Sciences. Effective August 2025, he will join the George Mason University (GMU) Department of Statistics as a faculty member. His research focuses on Bayesian statistics, deep learning, and causal inference, with applications to biomedical and public health challenges such as genomic studies, drug repositioning, and electronic health records analysis. Bai holds a PhD in Statistics from the University of Florida (2018), an MS in Applied Mathematics from the University of Massachusetts Amherst, and a BA from Cornell University. His work has been supported by the National Science Foundation (NSF). Education: PhD in Statistics, University of Florida (2018) MS in Applied Mathematics, University of Massachusetts Amherst BA, Cornell University Research interests include scalable algorithms for high-dimensional data, nonconvex optimization, and distributed inference methodologies. His work bridges statistical theory with practical applications in healthcare, emphasizing robustness and computational efficiency. Recent contributions address challenges in single-index models for skewed data, generative quantile regression, and Bayesian varying-coefficient models. Advising includes supervising PhD students Zile Zhao and Shijie Wang, who have contributed to survival analysis and deep learning frameworks. Future openings for students at GMU focus on Bayesian methodology and machine learning. Labs/Teams: Collaborates on projects involving interdisciplinary teams in biostatistics and computational biology.

Marta Casanellasمشاهده پروفایل

استاد

• Algebraic Geometry
• Computational Biology
• Phylogenetics
• +۴ مورد دیگر

Marta Casanellas is a full professor in the Department of Mathematics at the Polytechnic University of Catalonia (UPC) and a researcher at the Centre de Recerca Matemàtica. She teaches at the Faculty of Mathematics and Statistics, ETSEIB, and FIB. She earned her PhD in mathematics from the University of Barcelona under RM Miró-Roig, focusing on algebraic geometry and liaison theory. After completing a postdoc at UC Berkeley with a Fulbright Scholarship, she shifted her research to applications of algebraic geometry in computational biology, particularly phylogenetics. Her educational background includes a PhD from the University of Barcelona (2002), followed by a postdoctoral fellowship at UC Berkeley (2002-2003) supported by a Fulbright Scholarship. She obtained a prestigious Ramón y Cajal contract at UPC in 2003, which marked her transition to interdisciplinary research at the intersection of mathematics and biology. Casanellas' research focuses on applying algebraic and geometric techniques to phylogenetics, with particular emphasis on evolutionary models, phylogenetic invariants, and computational methods for genomic data analysis. Her work bridges pure mathematics (particularly algebraic geometry) with biological applications, developing mathematical frameworks to reconstruct evolutionary histories and understand genomic relationships. She has published extensively in both mathematics journals like Advances in Mathematics and biology journals like Molecular Biology and Evolution. Her recent publications demonstrate a consistent focus on developing algebraic methods for phylogenetic analysis, with increasing attention to heterogeneous evolutionary processes across lineages, time-reversible models, and computational implementations of theoretical results. The trend shows progression from theoretical foundations in algebraic geometry toward increasingly sophisticated and applicable computational methods for biological data. Fulbright Scholarship for postdoctoral research at UC Berkeley Ramón y Cajal contract (2003) Casanellas has supervised PhD students including A. Kedzierska (co-supervised with R. Guigó of the CRG). She has served as principal investigator for three competitive Spanish government projects involving fifteen researchers each. She has held significant academic leadership roles including Deputy Director of Research of the Department of Mathematics at UPC (2015-2018), head of studies for the Degree in Data Science and Engineering at UPC (2018-2022), and currently coordinates UPC's PhD in Bioinformatics program and Bachelor's Degree in Bioinformatics. She leads the BIO-GEOMAP research group focused on applying mathematical techniques to biological problems.