جست‌وجوی استاد و پژوهشگر

• Functional Data Analysis
• Spatial and Spatio-Temporal Statistics
• Time Series Analysis
• +۷ مورد دیگر

Pramita Bagchi is an Assistant Professor at the Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University. She holds a Ph.D. in Statistics from University of Michigan and completed postdoctoral research at Ruhr Universitat Bochum, Germany. Doctor of Philosophy (Ph.D.) in Statistics - University of Michigan, Ann Arbor (2015) Postdoctoral Researcher - Department of Mathematics, Ruhr Universitat Bochum, Germany (2015-2018) Master of Statistics - Indian Statistical Institute, Kolkata, India (2010) Bachelor of Statistics - Indian Statistical Institute, Kolkata, India (2008) Her research focuses on developing computationally efficient statistical methodologies for analyzing high-dimensional dependent data , including longitudinal, spatial, and time series observations. Applications span climate science, protein sequencing, medical imaging , and financial data . Methodologically, she explores functional data analysis, shape-constrained inference, asymptotic theory , and non-parametric methods . Current projects include frequency band analysis for functional time series and clinical data analytics for heart failure biomarkers . Dr. Bagchi has received research grants from the National Science Foundation (2022-2025) and INOVA Hospital (2020-2023). Her work emphasizes modeling data with minimal structural assumptions , addressing computational challenges in high-dimensional contexts.

Johannes Tauschمشاهده پروفایل

استاد

• Numerical approximation
• Boundary integral equations
• Computational electromagnetics
• +۴ مورد دیگر

Johannes Tausch is a Professor at Southern Methodist University specializing in numerical approximation and fast methods for boundary integral equations. His research spans computational electromagnetics, optics, fluid mechanics, shape optimization, and high-dimensional quadrature. Applications include heat transfer, anomalous diffusion, wave propagation, and electromagnetic analysis. Research focuses on developing efficient computational techniques for boundary integral reformulations of PDEs. Key methodologies include fast multipole methods, adaptive quadrature, Galerkin formulations, and mesh-free approaches for complex geometries. Current work emphasizes parabolic problems, moving boundaries, and high-dimensional integration. Publications demonstrate consistent focus on accelerating integral equation solvers through hybrid algorithms, matrix compression techniques, and specialized quadrature. Recent advancements target time-dependent domains and multiphysics coupling. No awards or student advising information is provided in the source materials.

Jeff Borggaardمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Science
• Control Theory
• +۷ مورد دیگر

Jeff Borggaard is a Professor of Mathematics at Virginia Tech, affiliated with the College of Science and the Interdisciplinary Center for Applied Mathematics (ICAM). His research focuses on numerical analysis, computational science, and control theory, with emphasis on optimization and control of systems governed by partial differential equations (PDEs). He specializes in sensitivity analysis, reduced-order modeling, and their applications in fluid dynamics and engineering systems. His work includes developing computational methods for PDE-constrained optimization, control of fluid flows, and uncertainty quantification. Key collaborations involve researchers at institutions like Florida State University and École Polytechnique de Montréal. Borggaard has been funded by agencies including the Air Force Office of Scientific Research (AFOSR) and the National Science Foundation (NSF), supporting projects on model reduction, flow control, and energy-efficient building systems. Research highlights include advancements in proper orthogonal decomposition (POD) for turbulent flows, nonlinear balanced truncation techniques, and applications of reduced-order models in control and optimization. His contributions also extend to thermal energy modeling in buildings and parameter estimation in groundwater flow systems. Borggaard holds positions at both the Department of Mathematics (McBryde Hall) and ICAM (Wright House), and maintains active involvement in professional societies such as the Society for Industrial and Applied Mathematics (SIAM) and the American Mathematical Society (AMS).

Olalekan Babaniyiمشاهده پروفایل

استادیار

• Inverse Problems
• Biomechanical Imaging
• Computational Mechanics
• +۳ مورد دیگر

Olalekan Babaniyi is an Assistant Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. His research focuses on inverse problems with applications in biomechanical imaging, computational mechanics, and PDE-constrained optimization. He develops mathematical techniques to improve medical imaging and environmental modeling, including real-time imaging of soft tissue mechanical properties and ice flow prediction. He teaches courses such as Probability and Statistics, Boundary Value Problems, and Partial Differential Equations. His work has been highlighted in media for innovative applications in noninvasive disease diagnosis and climate science. Babaniyi runs the Inverse Problems Seminar at RIT (InvPrS) and has published extensively in journals like The Cryosphere, Journal of Nonlinear and Variational Analysis, and Physics in Medicine & Biology. His research emphasizes uncertainty quantification, image denoising in biomedical contexts, and solving inverse problems in viscoelasticity and geophysics. While no personal awards are listed, his contributions have supported student achievements, such as Quinn Kolt’s Goldwater Scholarship in 2021.

Xiaobin Xiongمشاهده پروفایل

استادیار

• Legged Locomotion
• Humanoid Robots
• Dynamics and Control
• +۵ مورد دیگر

Xiaobin Xiong is an Assistant Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison. His research focuses on legged robot motion synthesis, combining dynamics modeling, control theory, optimization, and machine learning to achieve human-like physical intelligence in bipedal systems. He is affiliated with the College of Engineering and actively contributes to robotics research through open-source platforms like STRIDE. Education: PhD 2021, California Institute of Technology MS 2015, Northwestern University BE 2013, Tongji University Research interests include legged locomotion, humanoid robotics, dynamics and control, motion planning, and robot design. He explores hybrid dynamics and underactuated systems to develop efficient algorithms for real-world robotic applications. Recent work emphasizes data-driven predictive control, state estimation, and robust adaptation for bipedal systems. His articles span topics like flying-hopping locomotion, exoskeleton control, and energy-efficient gait synthesis. Awards include the 2024 UW-Madison Engineering EXPO Research Group Award, Amazon Fellowship (2019), and multiple scholarships from Tongji University. He teaches courses such as Dynamic Systems (ME 340) and supervises graduate research. His lab develops platforms like STRIDE, a low-cost bipedal robot for research and education, advancing both hardware and control methodologies.

Berwin Turlachمشاهده پروفایل

دانشیار

• Computational Statistics
• Statistical Computing
• Smoothing Methods
• +۵ مورد دیگر

Berwin Turlach is an Associate Professor in the School of Physics, Maths and Computing at The University of Western Australia, with a primary affiliation to the Mathematics and Statistics department. He holds secondary appointments in the UWA Medical School and the Institute for Paediatric Perioperative Excellence. His research focuses on computational statistics, smoothing methods, machine learning, and applications in healthcare analytics, dentistry, and sports science. Key research areas include nonparametric smoothing techniques using splines and wavelets, statistical computing for big data analysis, and methodological development in regression modeling with shape constraints. His work bridges theoretical statistics with applied domains like human milk composition studies, dental materials efficacy, and spatial epidemiology. Recent publications span topics from sensor-based gait assessment to geographic healthcare demand analysis. He has led projects on statistical software development (e.g., quadprog package) and contributed to interdisciplinary collaborations in biomedical research and public health policy analysis. Turlach has secured research grants including a project on shape-constrained smoothing techniques and an aging population study funded by the Channel 7 Telethon Trust. His work addresses UN Sustainable Development Goals related to health equity and innovation.

Elahe Araniمشاهده پروفایل

استادیار

• Continual Learning
• Self-Supervised Learning
• Scene Understanding
• +۶ مورد دیگر

Elahe Arani is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. She also holds external positions as Head of AI Research at Wayve (since October 2023) and previously served as Senior AI Manager and Senior Research Scientist at Wayve from September 2020 to September 2023. Her research interests are centered around Continual Learning, Self-Supervised Learning, and Learning under Noisy Labels. She focuses on developing algorithms for efficient, reliable, and adaptable AI models, particularly in the context of Scene Understanding and Multi-Task Learning. Her work also explores the application of AI in Autonomous Vehicles and integrates insights from Neuroscience to design more biologically plausible AI systems. Key areas include improving generalization in neural networks, mitigating catastrophic forgetting, and leveraging shape-awareness for robust model training. Elahe Arani's recent publications highlight advancements in Continual Learning and Self-Supervised Learning techniques, with a focus on improving model efficiency and adaptability. Her work also delves into applications for Autonomous Vehicles, such as vision-language alignment in SimLingo and generative world models in Gaia-2. She has contributed to frameworks addressing catastrophic forgetting and neural network optimization, often combining biological plausibility with computational methods. No scientific awards are explicitly mentioned in the provided text. No supervised students or specific grant information is listed. Her research emphasizes practical applications and theoretical contributions to AI, with a notable focus on reducing data dependency and environmental impact through efficient model designs. While no specific lab or team names are mentioned, her research involves collaborations in AI-driven systems, including work on road maintenance inspection and autonomous driving technologies. These collaborations aim to bridge academic and industry applications of AI.

Megan Greischarمشاهده پروفایل

استادیار

• Community Ecology
• Population Biology
• Evolutionary Patterns and Processes
• +۴ مورد دیگر

Megan Greischar is an Assistant Professor in the Department of Ecology and Evolutionary Biology at Cornell University, co-leading the Diversity and Inclusion initiative. She holds a Ph.D. from Pennsylvania State University (2014) and a B.S. from Indiana University (2007). Her research focuses on parasite evolution, particularly malaria infections, examining how ecological factors influence transmission strategies, host-parasite interactions, and disease dynamics. Key areas include developmental synchrony in malaria, plasticity in parasite responses to host environments, and the impact of vector ecology on disease spread. She leads the Greischar Lab, which employs mathematical modeling and statistical approaches to address these questions. Courses taught include Evolutionary Medicine and seminars on infectious disease ecology. Her work bridges theoretical and applied research, with implications for public health and drug resistance. Research interests span community ecology, population biology, and evolutionary processes, with a focus on malaria’s life history strategies. Her team develops models to predict parasite behavior under variable ecological conditions and analyzes time-series data to uncover adaptive cues parasites use. Recent work explores how resource limitations constrain virulence evolution and how vector ecology shapes transmission patterns. The lab also investigates pre-symptomatic disease transmission and synchronization mechanisms tied to host biological rhythms. Publications emphasize quantifying synchrony, transmission investment, and evolutionary trade-offs in malaria. While no scientific awards are listed, her contributions to malaria epidemiology and ecological modeling are notable. She advises students in the graduate programs of Ecology & Evolutionary Biology and mentors undergraduate research projects. The Greischar Lab collaborates with affiliated centers at Cornell, contributing to interdisciplinary efforts in infectious disease research and biodiversity conservation.

• Robotics
• Autonomous Vehicles
• Machine Learning
• +۴ مورد دیگر

Dr Charika De Alvis is a Research Fellow at the School of Computer Science , University of Sydney. Her work focuses on robotics and autonomous systems, integrating advanced machine learning and computer vision techniques. Research Interests : Robotics Autonomous Vehicles Machine Learning Computer Vision Sensor Fusion Data Science Her recent publications emphasize robust autonomy , scene segmentation using Lidar-constrained CRFs , sensor fusion for moving platforms, and uncertainty estimation in tracking systems. These studies highlight trends in autonomous vehicle perception , Lidar-camera integration , and probabilistic modeling . Grants : 2022: National Intelligence Postdoctoral Grant for "Extreme Class Imbalance Handling with Sample Characterized Margin Loss"

Zoltán Szabóمشاهده پروفایل

استاد

• Statistical Machine Learning
• Kernel Methods
• Information Theoretical Estimators
• +۳ مورد دیگر

Zoltán Szabó is a Professor of Data Science at the Department of Statistics, London School of Economics (LSE). His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation. He has held roles such as Programme Director of the MSc Data Science program and is actively involved in academic service, including Area Chair positions at top conferences like NeurIPS and ICML. His work bridges theory and applications, with contributions to fields like safety-critical learning, economics, climate data analysis, and natural language processing. Education and Affiliations: While specific educational details are not explicitly listed, his career trajectory indicates advanced training in statistics and machine learning. He is affiliated with the LSE's Department of Statistics and the Turing Institute, contributing to academic leadership and interdisciplinary projects. Research Interests: His work emphasizes kernel-based methods, including kernel Stein discrepancies, Hilbert-Schmidt independence criteria, and shape-constrained prediction. Applications span finance, economics, robotics, and environmental data analysis. Recent projects include developing outlier-robust estimators and scalable algorithms for high-dimensional data. Publications: His recent work includes advancements in kernel methods for dependency testing, shape-constrained regression, and robust estimation. Key themes include improving computational efficiency, theoretical guarantees for kernel approximations, and practical applications in interdisciplinary domains. Awards and Grants: While no explicit awards are listed, his contributions to NeurIPS (e.g., a Best Paper Award in 2017) and his role in securing grants (e.g., Europlace Institute of Finance) highlight his impactful research. He also serves on editorial boards, including JMLR and ACM Transactions on Probabilistic Machine Learning. Students and Labs: Supervises PhD students in areas like functional data analysis and scalable computation. Collaborates with researchers on projects such as distribution regression and safety-critical learning, contributing to both theoretical and applied outcomes.

André Schulzمشاهده پروفایل

استاد

• Graph Theory
• Computational Geometry
• Algorithm Design
• +۳ مورد دیگر

Prof. André Schulz is a Professor at the Faculty MI, Department of Theoretical Computer Science, Distance University. His research focuses on graph theory, computational geometry, and algorithm design, with a strong emphasis on geometric graphs, graph drawing algorithms, and topological representations. He has published extensively in top-tier journals and conferences such as Discrete & Computational Geometry , Journal of Graph Algorithms and Applications , and Graph Drawing and Network Visualization . His work spans topics like adjacency graphs of polyhedral surfaces, Lombardi drawings of knots, and experimental analysis of graph visualization techniques. He collaborates internationally, co-authoring over 30 peer-reviewed articles since 2015. His research addresses challenges in geometric algorithms, graph embeddings, and combinatorial optimization, with applications in social network analysis and geometric topology. Key contributions include minimizing geometric primitives in planar graph drawings and developing efficient algorithms for 1-planar and cubic graphs. His recent work (2023–2024) explores orthogonal circle arrangements and schematic hypergraph layouts. Despite no explicit mention of awards or grants, his prolific publication record underscores his academic influence.

Kamesh Munagalaمشاهده پروفایل

استاد

• Theoretical Computer Science
• Approximation Algorithms
• Online Algorithms
• +۵ مورد دیگر

Kamesh Munagala is a Professor in the Computer Science Department at Duke University's Pratt School of Engineering. His academic career spans theoretical computer science with a focus on approximation algorithms, online algorithms, and computational economics. He has made significant contributions to resource allocation, decision making, and provisioning problems across various applications including data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Professor Munagala's research interests span several key areas in theoretical computer science: Theoretical foundations of approximation algorithms and online algorithms Computational economics and market design Resource allocation with fairness constraints Algorithmic game theory and mechanism design Persuasion and information revelation in optimization contexts Group fairness based on proportionality and stability His recent publications demonstrate a strong focus on fairness in algorithmic decision-making, particularly in societal contexts like school assignment and participatory budgeting. He has also made significant contributions to the theory of Bayesian persuasion and information disclosure in competitive settings. His work bridges theoretical computer science with practical applications in social choice, economics, and policy-making. Notable scientific achievements include: Best paper award at WINE 2018 for 'A simple mechanism for a budget constrained buyer' Multiple publications in top theoretical computer science conferences including STOC, SODA, and FOCS Significant contributions to the understanding of fairness in resource allocation Innovative work on metric distortion in social choice Professor Munagala has advised numerous students and collaborators, with recent work involving researchers such as Govind S. Sankar, Yiheng Shen, and Kangning Wang. His research has been supported by various grants, though specific grant details aren't provided in the available information. He teaches advanced courses in algorithms, including Algorithm Design, Randomized Algorithms, and Algorithmic Game Theory, shaping the next generation of theoretical computer scientists. His work has implications for real-world systems requiring fair and efficient decision-making, from school assignment algorithms to data exchange markets and civic budgeting platforms. He is actively engaged in both theoretical advancements and practical implementations of his research.

• Nonparametric statistics
• Causal inference
• Survival analysis
• +۳ مورد دیگر

Ted Westling is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on nonparametric statistics, causal inference, survival analysis, and shape-constrained inference. He holds a PhD in Statistics from the University of Washington and a BS in Mathematics from Stanford University. His work emphasizes methodological advancements in causal inference, particularly in observational studies and survival analysis. Notable contributions include developments in nonparametric causal effect estimation, machine learning integration for treatment-specific survival curves, and isotonic regression techniques for monotone functions. Westling's recent publications explore topics such as debiased covariate-adjusted regression, clustered observational studies, and statistical robustness in causal hypothesis testing. His research bridges theoretical statistics with applied problems in healthcare, epidemiology, and social network analysis. He is actively involved in interdisciplinary collaborations, addressing challenges in global health, vaccine efficacy evaluation, and emergency medical services demand forecasting. His methodological innovations aim to enhance the reliability and applicability of statistical methods in real-world settings.

Chuan-Fa Tangمشاهده پروفایل

استادیار

• Statistics
• Survival Analysis
• Statistical Machine Learning
• +۹ مورد دیگر

Chuan-Fa Tang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, affiliated with the School of Natural Sciences and Mathematics. He joined UTD in 2019 and teaches graduate-level courses including Applied Linear Models, Linear Statistical Models, and Nonparametric Methods. Ph.D. in Statistics from University of South Carolina (2017) M.S. and B.S. in Mathematics from National Taiwan University (2010, 2007) His research focuses on order-restricted inference , shape-constrained inference , survival analysis , and statistical machine learning , with particular interest in: Nonparametric and semiparametric models Empirical processes and likelihood methods Post-selection inference Stochastic ordering and heavy-tailed distributions His work includes developing goodness-of-fit tests for Cox models and stochastic ordering frameworks, with software implementations in R . Key publications examine uniform stochastic ordering comparisons, positive quadrant dependence testing, and Taylor's laws for heavy-tailed data. Scientific awards include: NSF DMS 2311292 grant (2023-2026) for monotonic hazard trend research He has contributed computational tools like TestUSO and LLGOF_UniCoxPH packages for statistical analysis.

• Econometrics
• Industrial Organization
• Antitrust
• +۴ مورد دیگر

Dong Li is a Professor of Economics at the University of Texas at Dallas (UT Dallas), affiliated with the School of Economic, Political and Policy Sciences. His research focuses on econometrics, industrial organization (particularly antitrust issues), financial economics, and the Chinese economy. He holds a Ph.D. in Economics from Texas A&M University (2000), an M.A. in Quantitative Economics from Huazhong University of Science & Technology (1994), and a B.A. in Quantitative Economics from the same institution (1991). Li's work emphasizes methodological contributions to panel data models, spatial econometrics, and semiparametric estimation techniques. His recent studies include analyses of cartel behavior in agricultural markets, military aid's impact on terrorism, and the implications of securities transaction taxes in emerging markets. His research bridges theoretical econometrics with applied policy questions, particularly in antitrust and financial regulation contexts. His articles span topics ranging from Bayesian auction analysis to China's economic policies, showcasing interdisciplinary rigor. While no specific awards are noted, his extensive publication record reflects sustained academic influence. His research often addresses practical economic challenges, such as optimizing college admissions systems and evaluating currency valuation impacts on macroeconomic variables like inflation and output growth.