جست‌وجوی استاد و پژوهشگر

Marie-Therese Wolframمشاهده پروفایل

استاد

• Applied Partial Differential Equations
• Mathematical Modeling in Socio-Economic and Life Sciences
• Inverse Problems
• +۴ مورد دیگر

Professor Marie-Therese Wolfram holds a faculty position at the Mathematics Institute of the University of Warwick. Her research focuses on applied partial differential equations, mathematical modeling in socio-economic and life sciences, and inverse problems. She is actively involved in organizing international workshops and serves on editorial boards for journals like ESAIM M2AN and Kinetic and Related Models. Her academic journey includes grants such as the Royal Society International Exchange (2022-2024) and the EPSRC First Grant (2017-2019). Notable awards include the Excellence in Gender Equality Award (2023) and the Whitehead Prize (2023). She co-leads the EDI committee at Warwick and contributes to initiatives like the Young Academy of the Austrian Academy of Sciences. Her research spans opinion dynamics, crowd modeling, and optimal transport theory. Recent work explores consensus-breaking in social networks and applications of mean-field games to knowledge growth. She collaborates internationally on projects such as the 'Multiscale Modelling of Crowded Transport' and the 'Wasserstein Gradient Flows' initiative. Teaching responsibilities include MA265 Methods for Mathematical Modelling and MA4M2 Inverse Problems. Her work bridges theoretical analysis with practical applications, reflecting her dual expertise in pure mathematics and interdisciplinary modeling.

Mingtao Xiaمشاهده پروفایل

مدرس

• Mathematical Modeling
• Numerical Methods
• Kinetic Theory
• +۳ مورد دیگر

Mingtao Xia is a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University (2023–present). He holds a PhD in Applied Mathematics from UCLA (2019–2023) and a B.S. in Information and Computing Science from Peking University’s School of Mathematical Sciences (2015–2019). His research focuses on mathematical modeling, numerical methods, and machine learning applications in kinetic theories, stochastic systems, and unbounded-domain differential equations. He has contributed to interdisciplinary areas including biological systems, environmental science, and epidemic control through computational methods. Education: PhD in Applied Mathematics, UCLA, 2019–2023 B.S. in Information and Computing Science, Peking University, 2015–2019 His work integrates advanced numerical techniques like spectral methods and Wasserstein-distance frameworks with real-world problems such as gene regulation dynamics, solar wind analysis, and ozone pollution impacts. Recent publications emphasize efficient algorithms for high-dimensional systems and uncertainty quantification in stochastic models. While no awards are listed, his research demonstrates strong contributions to mathematical modeling across diverse domains. Advising and grant details are currently unavailable. His affiliation with the Courant Institute positions him at the forefront of computational mathematics and its applications in interdisciplinary research.

• Mathematical Statistics
• Biostatistics
• Medical Research
• +۳ مورد دیگر

Lu Ting is a Professor at the Department of Mathematics within the Courant Institute of Mathematical Sciences, New York University. Their research integrates mathematical statistics and biostatistics with applications in oncology, environmental health, and precision medicine. Key focuses include molecular pathway analysis in glioma, biomarker development for drug resistance, and statistical methods in translational research. Research interests span mathematical modeling of cancer therapies, environmental exposure impacts on human health, and optimization algorithms. Notable projects include longitudinal studies on World Trade Center (WTC) exposure effects and sphericity testing in high-dimensional covariance matrices. Publications from 2015–2024 reflect interdisciplinary work combining mathematical rigor with biomedical challenges, particularly in cancer treatment optimization and environmental health surveillance. No scientific awards or grants are explicitly documented in the provided text.

Avanti Athreyaمشاهده پروفایل

استاد پژوهشی

• Probability
• Stochastic Processes
• Network Inference
• +۳ مورد دیگر

Avanti Athreya is an Associate Research Professor at the Department of Applied Mathematics and Statistics, Johns Hopkins University, within the Whiting School of Engineering. Her research focuses on probability, stochastic processes, and network inference methodologies. She holds a BS from Iowa State University (1997), an MS from the University of Washington (2000), and a PhD from the University of Maryland College Park (2009). Her work emphasizes spectral analysis of random matrices, stochastic blockmodel graphs, and dynamic network inference. Recent publications explore Gaussian mixture models for language networks, mirror distance-based clustering, and change-point detection in organoid networks. She develops algorithms for community detection, anomaly identification, and geometric approaches to network dynamics. Key research trends include advancing methodologies for analyzing time-series networks, integrating vertex covariates into spectral algorithms, and applying Euclidean geometric principles to network structures. Her contributions bridge theoretical probability frameworks with practical applications in biology and machine learning.

Orimar Sauri Arreguiمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Stochastic Processes
• Nonparametric Estimation
• +۷ مورد دیگر

Orimar Sauri Arregui is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, Denmark. His research lies at the intersection of mathematical statistics, stochastic processes, and financial modeling. Research Interests: His work focuses on ambit fields , trawl processes , Lévy and infinite divisible random fields , and nonparametric estimation in continuous time. He investigates asymptotic behavior, limit theorems, and statistical inference for complex stochastic models, with applications in financial market microstructure and energy flux modeling. The analysis of his recent publications reveals a strong trend in theoretical statistics and probability, particularly in developing and analyzing models driven by non-Gaussian noise and long-range dependence. His work often involves high-frequency data and contributes to the foundations of spatiotemporal modeling. Scientific Contributions: Developed mathematical frameworks for financial market microstructure. Advanced theory for nonparametric estimation of trawl processes. Derived asymptotic error distributions for numerical schemes in stochastic delay equations. Proved local limit theorems for energy fluxes in random fields. Advising and Research Activity: He has been involved in PhD supervision and maintains an active research output, primarily through preprints on arXiv and SSRN. His collaborations span topics in financial econometrics and statistical physics. Though specific grants are not listed, his consistent publication record suggests ongoing research funding. Laboratory and Teams: While no formal lab is mentioned, his work is part of the broader research network in mathematical statistics and financial mathematics at Aalborg University, with notable collaborations in stochastic modeling and econometrics.

Laurent Clavierمشاهده پروفایل

استاد

• Digital Communications
• Wireless Networks
• Internet of Things (IoT)
• +۷ مورد دیگر

Laurent Clavier is a Professor at IMT Lille Douai (now IMT Nord Europe), affiliated with the Institut Mines-Télécom and conducting research within the CNRS laboratories IEMN (UMR 8520) and IRCICA (USR 3380). His work centers on digital communications and the physical layer of wireless networks for IoT applications, especially in energy-autonomous and ultra-dense environments. His research focuses on interference modeling in wireless networks, particularly under non-Gaussian and heavy-tailed conditions such as those found in IoT scenarios. He investigates the impact of interference in LoRa-like and SCMA-based networks, with a strong emphasis on statistical modeling, channel access mechanisms, and capacity analysis under α-stable noise. The recent publications highlight a consistent trend in analyzing and modeling real-world interference in low-power, large-scale wireless systems. Key themes include LoRa network optimization, unsupervised signal detection, multiuser access, and theoretical limits in non-Gaussian channels—indicating a deep integration of information theory, signal processing, and practical IoT deployment challenges. Scientific Awards: No scientific awards mentioned in the provided text. Laurent Clavier is actively involved in research projects such as ALPAGA and CASTREAU, contributing to advancements in resilient communication systems and drone-based technologies. While no formal list of advisees is provided, his HDR degree and professorial status suggest supervision of doctoral candidates. He has contributed to book chapters and high-impact journals, reflecting sustained research productivity. He is associated with research teams focusing on multi-robot systems and disaster-resilient communication frameworks, as evidenced by his participation in the Robot-IA events and comments on resilient systems. His work bridges theoretical communication models with practical IoT implementations.

Charles S. Peskinمشاهده پروفایل

استاد

• Immersed Boundary Method
• Mathematical Physiology (Heart, Neuro)
• Partial Differential Equations in Biology
• +۷ مورد دیگر

Charles S. Peskin is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He specializes in mathematical modeling and simulation of biological systems, particularly cardiovascular physiology and neurophysiology. His work focuses on the Immersed Boundary Method, a computational framework for fluid-structure interaction problems. Peskin has authored influential textbooks such as Modeling and Simulation in Medicine and the Life Sciences (2nd ed., 2002) and developed simulation tools used in biomechanics research. His research spans mathematical biology, computational fluid dynamics, and stochastic processes in biological systems. He advises PhD students on interdisciplinary projects at the interface of mathematics and life sciences. Key contributions include modeling heart valve dynamics, neural signaling mechanisms, and biomolecular motor systems. Affiliations: Courant Institute, NYU Research: Immersed Boundary Method, Cardiac Mechanics, Neurophysiology Modeling Publications: 100+ articles, 2 books Teaching: Courses on PDEs in Biology, Biomolecular Motors, and Simulation Techniques Software: MATLAB programs for cardiovascular system simulations His work bridges applied mathematics with biomedical engineering, emphasizing computational methods for complex biological systems. Recent work includes studies on entropy principles in biology and rotary molecular motor mechanisms.

Nuutti Hyvönenمشاهده پروفایل

استاد

• Inverse Problems
• Electrical Impedance Tomography
• Numerical Analysis
• +۳ مورد دیگر

Nuutti Hyvönen is a Professor and Head of Department at the Department of Mathematics and Systems Analysis, Aalto University, School of Science. His research focuses on inverse problems, electrical impedance tomography, and numerical analysis with applications in biomedical imaging and engineering. He holds a Doctor of Science (Technology) degree and has extensive experience in developing mathematical methods for imaging and reconstruction algorithms. His work emphasizes improving the accuracy and robustness of tomographic techniques, particularly in handling modeling errors and electrode configurations. Key research areas include: Inverse problems for partial differential equations Electrical impedance tomography (EIT) and its biomedical applications Bayesian experimental design and uncertainty quantification Numerical methods for nonlinear imaging problems Recent publications highlight advancements in: Optimal electrode positioning Series reversion methods in Calderón problems Feasibility of EIT for monitoring intracerebral hemorrhages Edge-enhancing reconstruction algorithms He has contributed to over 79 peer-reviewed articles, with notable work on monotonicity-based reconstruction methods and stochastic Galerkin finite element approaches. His research bridges mathematical theory and practical applications in medical imaging, engineering, and computational science.

• Machine Learning
• Computer Vision
• Robotics
• +۵ مورد دیگر

Roberto Javier López Sastre is an Associate Professor of Signal Theory and Communications at the University of Alcalá. He holds a Ph.D. from the same institution (2010), focusing on 'Visual vocabularies for category-level object recognition' under Dr. Saturnino Maldonado Bascón's supervision. His research spans machine learning, computer vision, robotics, and astrophysics. Key areas include Bayesian neural networks for stellar dating, semantic navigation in robotics, and AI-driven medical assistive technologies. Affiliation: Department of Signal Theory and Communications, University of Alcalá Research Group: GRAM (Multisensorial Recognition and Analysis Group) His work interfaces computational methods with real-world applications, such as assistive robotics for neurodevelopmental disorder patients and deep learning models for astrophysical parameter estimation. Recent contributions include semantic segmentation-driven navigation systems and Bayesian approaches to black hole solutions. Publications emphasize interdisciplinary applications of AI, including live video processing, continual learning frameworks, and physics-informed neural networks. Ongoing research explores the intersection of robotics, healthcare, and astrophysics through collaborative projects. López Sastre's GRAM group focuses on multisensory data analysis for recognition and navigation challenges. His technical expertise spans signal processing, probabilistic modeling, and embedded AI systems.

• Causal Inference
• Data Management
• Machine Learning
• +۳ مورد دیگر

Babak Salimi is an Assistant Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego. Previously, he was a postdoctoral research associate at the University of Washington's Department of Computer Science and Engineering, working with Prof. Dan Suciu. He holds a Ph.D. in Computer Science from Carleton University, advised by Prof. Leopoldo Bertossi. His research focuses on integrating causal inference, theoretical data management, and machine learning to build decision-support systems that enhance interpretability for diverse users. Key areas include causal relational learning, fairness in data-driven decisions, and robust offline evaluation frameworks. He has pioneered work on causal DAG summarization, adversarial data corruption testing, and model-agnostic data valuation systems like KAIROS. Salimi's contributions have been recognized with prestigious awards including the ACM SIGMOD Research Highlight Award and Best Paper Award, and the VLDB Best Demonstration Paper Award. His work bridges foundational data science principles with societal impact, emphasizing ethical AI practices and algorithmic accountability. Education: Ph.D., School of Computer Science, Carleton University Research Lab: HDSI's Causal Data Science Group Key Projects: Development of CausaLens, Zorro, and DEMA

Yin Tat Leeمشاهده پروفایل

دانشیار

• Convex Optimization
• Convex Geometry
• Spectral Graph Theory
• +۵ مورد دیگر

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has been faculty since 2017 (initially as Assistant Professor until 2022). He also holds a position as Senior Principal Researcher at Microsoft AI since 2024, having previously served as Principal Researcher (2022-2024) and Visiting Researcher (2018-2022) at Microsoft Research. His academic career spans prestigious institutions including MIT, where he completed his PhD in Mathematics. 2024-Now: Member of Technical staff / Senior Principal Researcher in Microsoft AI 2022-2024: Principal Researcher in Microsoft Research 2022-Now: Associate Professor in University of Washington 2017-2022: Assistant Professor in University of Washington 2018-2022: Visiting Researcher in Microsoft Research 2016-2017: Postdoc in Microsoft Research Dr. Lee received his PhD in Mathematics from MIT (2012-2016) and his undergraduate degree in Mathematics from the Chinese University of Hong Kong (2008-2012). His exceptional academic journey was recognized with the MIT Presidential Fellowship and the Charles W. and Jennifer C. Johnson Prize. Lee's research fundamentally advances algorithms across multiple domains, particularly in convex optimization, convex geometry, spectral graph theory, and online algorithms. His work bridges continuous and discrete mathematics to develop state-of-the-art algorithms for fundamental problems in computer science and optimization. Notably, he has developed breakthrough approaches for linear programming, maximum flow problems, and optimization in high-dimensional spaces. His research has evolved from foundational theoretical work to more applied areas including differential privacy and connections to machine learning. Analysis of his recent publications reveals a strong trajectory toward practical applications of theoretical optimization, with significant contributions to differentially private machine learning, efficient sampling methods, and connections between optimization theory and deep learning. His work consistently demonstrates how deep theoretical insights can yield practical algorithmic improvements across computer science. Lee's exceptional contributions have been recognized with numerous prestigious awards including the Packard Fellowship, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, A.W. Tucker Prize, and multiple Best Paper Awards at top theoretical computer science conferences (FOCS, SODA, NeurIPS). He has also received the NSF CAREER Award and MIT's Sprowls Award for his doctoral thesis. Packard Fellowship (2020) Sloan Research Fellowship (2020) Microsoft Research Faculty Fellowship (2019) Best Paper Awards at FOCS, SODA, and NeurIPS A.W. Tucker Prize NSF CAREER Award As an advisor, Lee has mentored PhD students including Haotian Jiang, whose work earned a Best Student Paper award at SODA. His research has been supported by significant grants from NSF and Microsoft Research. Lee actively contributes to the academic community through service on program committees for FOCS, SODA, and other major conferences, as well as organizing workshops on continuous approaches to discrete optimization. He has also taught graduate courses including Theory of Optimization and Continuous Algorithms and undergraduate courses on algorithms. Lee's work bridges theoretical computer science and practical applications, with his recent research expanding into differential privacy for machine learning and connections between optimization theory and deep learning. His collaborative work spans institutions including MIT, Microsoft Research, and the University of Washington, reflecting his position at the intersection of theoretical and applied computer science.

• Time Series Analysis
• Financial Econometrics
• Volatility Modeling
• +۵ مورد دیگر

Professor Shelton Peiris is an Associate Professor at the School of Mathematics and Statistics , University of Sydney , where he has been since 1990. He holds visiting appointments at institutions worldwide, including University of Waterloo , University of Manitoba , and University of Malaya . Currently, he serves as Sub Dean (Student Affairs) in the Faculty of Science and coordinates interdisciplinary teaching with the School of IT for MIT/MDS degrees. His research focuses on time series analysis, financial econometrics, and technology integration in statistics education. Education: PhD in Statistics, Monash University , 1987 Shelton's research interests include statistical analysis of stationary and non-stationary time series, theory and applications of estimating functions, financial time series modeling, saddlepoint and Edgeworth approximations, and exploring technology's role in statistics education. He is a member of the Statistics Research Group at the University of Sydney and leads projects in financial econometrics, generalized autoregressive models, and nonlinear time series analysis. Recent publication trends highlight his work in financial econometrics, particularly volatility and duration modeling, stochastic processes, and hybrid forecasting methods combining traditional statistical techniques with machine learning advancements like GANs and neural networks. His collaborations span Australia, Canada, Malaysia, and Indonesia, with notable grants including ARC Linkage and University of Malaya Research Grants . Scientific Awards: 2012: Faculty of Science Teaching Citation 2011: Bronze Medal, University Putra Malaysia 2007: Bronze Medal, University Putra Malaysia 1983: Monash University Graduate Scholarship Elected Member, International Statistical Institute (ISI) Fellow, Royal Statistical Society (FRSS) Honorary Fellow, Institute of Applied Statistics, Sri Lanka (FIASSL) Current Research Students: Leonard Mushunje - High-Dimensional Financial Functional Time Series Data Grants: ARC Linkage Grant (2005-2007): Modelling Stock Market Liquidity ARC Bridging Grant (2012-2014): Financial Duration Modeling University of Malaya Research Grants (2011-2015): Volatility Models, GARCH MOHE Malaysia Grant (2013-2015): Robust Control Charts Professor Peiris contributes to editorial boards such as the Journal of Statistical Computation & Simulation and Sri Lankan Journal of Applied Statistics . His teaching roles include MATH1015 (Statistics for Life Science) , MATH1905 (Statistics Advanced) , and advanced honors courses in time series analysis.

Robert L. Carrawayمشاهده پروفایل

استاد

• Data Analytics
• Decision Sciences
• Optimization
• +۴ مورد دیگر

Robert L. Carraway is a Professor at the University of Virginia's Darden School of Business, where he has been a faculty member since 1984. He currently serves as the Quantitative Analysis (QA) area head and previously held the position of Senior Associate Dean for MBA Programs from 2006-2011. Carraway teaches Data Analytics and Decision Sciences courses in Darden's MBA program, including First Year Decision Analysis, Managerial Quantitative Analysis, Optimization Models, and Darden Business Projects: Venturing. His global executive education work spans Europe, Asia, and Latin America. Education: B.S., East Carolina University M.B.A., East Carolina University Ph.D., Purdue University Carraway's research spans multiple areas of quantitative business analysis. His original work focused on Optimization, but he has expanded his focus to the broader role of analytics in decision making. He examines the relationship between intuition and rational analysis, exploring how these approaches complement one another in business contexts. His work investigates how analytical methods enhance innovation processes, making them more effective and efficient. A significant portion of his recent research addresses 'Big Data' applications in business. He has authored articles such as 'Big Data, Small Bets' and 'Meeting the Big Data Challenge: DON'T Be Objective,' which explore practical approaches to leveraging large datasets for competitive advantage. His work emphasizes combining analytical rigor with business judgment rather than relying solely on data-driven approaches. Carraway's publication record demonstrates a consistent focus on quantitative methods for business decision-making. His earlier work centered on optimization models and mathematical programming, while more recent publications address analytics, probability distributions, and risk analysis. A notable trend is his exploration of how decision-makers integrate quantitative analysis with intuitive judgment in complex, uncertain environments. Award Recognition: Multiple teaching honors at Darden School of Business Carraway has significantly influenced business education through his case method teaching, with numerous cases used in Darden classrooms worldwide. His global executive education work extends his impact to practicing managers across continents. While specific doctoral students aren't listed, his long tenure suggests substantial mentoring experience. As QA area head, he oversees faculty specializing in quantitative business analysis and collaborates with colleagues like Sam Bodily, Phillip Pfeifer, and Sherwood Frey on research teams focused on decision sciences.

Caitlin Wardمشاهده پروفایل

استادیار

• Bayesian Statistics
• Infectious Disease Modeling
• Spatio-Temporal Modeling
• +۴ مورد دیگر

Caitlin Ward, PhD is an Assistant Professor at the University of Minnesota's Division of Biostatistics & Health Data Science. Her work bridges methodological development and interdisciplinary collaboration. Education : PhD/MSc in Biostatistics (University of Iowa), BS in Statistics (Iowa State) Methodologically, she specializes in Bayesian modeling for complex systems, particularly infectious disease dynamics , spatio-temporal analysis , and multiplex imaging data . Collaboratively, she engages with: Nursing (dementia care communication) Neuroradiology (brain tumor classification) Veterinary medicine (Mycoplasma bovis transmission) Public health (vaccine hesitancy) Education (open educational resources) Her 15 most recent publications span epidemiological modeling , healthcare communication , and machine learning applications . Key contributions include: Bayesian SEIR models for behavioral change Network analysis of care coalitions Evaluation of elderspeak communication impacts Development of open-source BayesSEIR R package Statistical methods for imperfect diagnostics Recognitions include the CANSSI Distinguished Postdoctoral Fellowship and Outstanding Teaching Assistant Award . Current projects focus on making biostatistics education more accessible through open resources.

Lan Ma Nygrenمشاهده پروفایل

دانشیار

• International Business
• Management Sciences
• Business Analytics
• +۳ مورد دیگر

Lan Ma Nygren is an Associate Dean of Business Programs and faculty member at Rider University, affiliated with the Department of Information Systems, Analytics and Supply Chain Management. Her research spans international business, management sciences, and quantitative finance, with a focus on statistical modeling of Broadway show survival and stock market dynamics. Ph.D. in Statistics, Stern School of Business, New York University Master of Arts in Economics, The Ohio State University Master of Science in Statistics, Renmin University, Beijing Her work applies statistical and probabilistic models to business challenges, including portfolio optimization, financial risk assessment, and entertainment industry analytics. She has pioneered the use of Malliavin calculus in hedging strategies and employed hierarchical Bayesian frameworks for analyzing Broadway show success. Recent publications analyze stock market co-movements post-crisis and survival analysis in cultural industries. She has received multiple teaching recognitions and Who's Who distinctions, along with summer research fellowships and teaching grants at Rider University. Marquis Who's Who in America (2007, 2009) Faculty Advisor Appreciation Award (2008) Multiple Academic Excellence Scholarships (1989-1996) Beta Gamma Sigma Honor Society (2003) Dr. Nygren has secured multiple research grants at Rider University (2003-2011), including Davis Fellowships and Deming Scholarships, while maintaining active participation in international academic conferences across the US, Europe, and China.