جست‌وجوی استاد و پژوهشگر

Alex Savachkinمشاهده پروفایل

دانشیار

• Resilience Engineering
• Operations Research
• Public Health
• +۵ مورد دیگر

Alex Savachkin is an Associate Professor and Graduate Director in the Department of Industrial & Management Systems Engineering at the University of South Florida, Tampa. His academic career is complemented by industry experience as a technology feasibility analyst at MBS Consulting and industrial engineer at Integral Inc., both in Minsk, Belarus. He holds a Ph.D. in Industrial Engineering from Texas A&M University (2005), an M.S. in Management of Technology from the University of Colorado (1998), and a B.S. in Industrial Engineering/Applied Math from Belarusian State University (1996). Savachkin's research focuses on applying applied stochastic processes to design resilient cyber-physical-social systems. Key research areas include stochastic measures of system resilience, assessment of resilience in physical and social systems, and multi-objective optimization models for resilience enhancement. His work bridges theoretical frameworks and practical applications in infrastructure and healthcare systems. His academic contributions are highlighted by teaching awards: the IIE Annual Award for Excellence in the Teaching of Operations Research (2013) and USF Outstanding Undergraduate Teaching Award (2012) . In advising, he has guided PhD students such as Daniel Romero (2018), Sandro Paz (2014), and co-advised Dayna Martinez (2012) and Diana Prieto (2012). Current advisee Weimar Ardilla is pursuing a Ph.D. His research team collaborates on projects spanning pandemic preparedness and infrastructure resilience. His research trends emphasize balancing costs/benefits in resilience decisions (2025), critical reviews in system resilience (2025), and pandemic influenza outbreak modeling (2024–2010). He specializes in resilient distribution networks, heuristic algorithms for optimization, and cross-regional resource allocation strategies.

Georgi S. Medvedevمشاهده پروفایل

استاد

• Dynamical Systems
• Stochastic Analysis
• Mathematical Neuroscience
• +۳ مورد دیگر

Georgi S. Medvedev is a Professor in the Department of Mathematics at Drexel University. He earned his PhD from Boston University (1999) and was a Veblen Research Instructor at Princeton University and the Institute for Advanced Study before joining Drexel in 2002. His research investigates dynamical systems, stochastic analysis, and mathematical neuroscience. Research develops mathematical frameworks for understanding complex network dynamics. Primary areas include synchronization in coupled oscillator systems, metastability in stochastic networks, and continuum limits of large-scale dynamical systems. Applications span neuroscience, statistical physics, and engineering. Recent publications focus on the Kuramoto model, exploring bifurcations in networked oscillators, stability of twisted states, and continuum approximations for sparse graphs. The work bridges discrete network models with continuum descriptions using graph limit theory. He has developed interdisciplinary courses including Mathematical Neuroscience and Dynamical Networks. His research is supported by multiple NSF grants focusing on network metastability, mean-field analysis, and synchronization phenomena. Service includes editorial roles for Discrete and Continuous Dynamical Systems and Networks and Heterogeneous Media. He mentors undergraduate researchers in projects investigating metastability in oscillator networks.

Zafeiria Roumeliotiمشاهده پروفایل

استادیار

• Seismology
• Geophysics
• Earthquake Source Mechanics
• +۳ مورد دیگر

Zafeiria Roumelioti is an Assistant Professor at the Department of Geology, University of Patras. Her research focuses on seismology, geophysics, and earthquake engineering, with a strong emphasis on earthquake source mechanics, ground motion simulation, and seismic hazard assessment. She leads projects such as ARGONET and PROION, involving advanced geophysical instrumentation and multidisciplinary monitoring networks. Education: BSc in Geology MSc in Geophysics PhD in Seismology from Aristotle University of Thessaloniki Research Interests: Kinematic modeling of earthquake sources Site effect studies using vertical arrays Strong ground motion simulation (deterministic/stochastic) Real-time earthquake monitoring systems Nonlinear soil response during seismic events Regional seismicity and tectonic dynamics Her work integrates geophysical data from accelerometers, GNSS, and distributed sensors to improve earthquake early warning systems and infrastructure resilience. Key contributions include the ARGONET vertical array and PROION project’s multi-parametric monitoring framework.

Sévérien Nkurunzizaمشاهده پروفایل

استاد

• Statistical Inference
• Statistical Modeling
• Change-Point Analysis
• +۵ مورد دیگر

Sévérien Nkurunziza is a Professor in the Department of Mathematics and Statistics at the University of Windsor, within the Faculty of Science. His research expertise lies in statistical inference, stochastic modeling, change-point analysis, and applied probability. He holds a Ph.D. from UQAM, Canada. His research focuses on statistical methodologies including change-point detection, shrinkage estimation, tensor regression, and inference in diffusion processes. He has supervised over 10 undergraduate students and 30 graduate students, contributing to topics such as neuroimaging modeling, high-dimensional data analysis, and mean-reverting processes. Recent work includes advancements in generalized CIR models, improved matrix estimators in high-dimensional settings, and inference methods in time-varying diffusion processes. His contributions span theoretical developments and applied statistical challenges. Advising and supervision highlights include mentoring current PhD students (Sathish Pichika, Ran Sun) and M.Sc. students (Dilay Gumus, Lavanya Perera Dharmasena). Past students have contributed to tensor regression, change-point modeling, and diffusion processes. His research is published in journals like Annals of Applied Probability, Journal of Multivariate Analysis, and Statistical Inference for Stochastic Processes.

Hye-Won Kangمشاهده پروفایل

دانشیار

• Stochastic Modeling
• Mathematical Biology
• Biochemical Reaction Networks
• +۷ مورد دیگر

Hye-Won Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on stochastic modeling of biochemical systems, particularly in understanding complex biological processes such as enzyme clustering, microRNA signaling pathways in lung cancer and COPD, and multiscale reaction-diffusion systems. She holds a Visiting Research Fellowship from Merton College, University of Oxford. Her work integrates mathematical analysis, simulation, and experimental data to address challenges in systems biology. Key research areas include: Multiscale approximations for stochastic chemical networks Stochastic analysis of reaction-diffusion processes Modeling enzyme kinetics in metabolic diseases Gene regulatory networks and pattern formation Recent contributions include studies on glucosome condensate formation (2024), chemical systems with limit cycles (2023), and the role of microRNAs in lung cancer (2013). Her research has been published in high-impact journals such as Physical Biology , Bulletin of Mathematical Biology , and SIAM Multiscale Modeling and Simulation . She has advised PhD student Luan Chip Nguyen and has taught advanced courses in mathematical biology, statistics, and differential equations at UMBC since 2013.

Nageswari Shanmugalingamمشاهده پروفایل

استاد

• Geometric Analysis
• Metric Measure Spaces
• Potential Theory
• +۴ مورد دیگر

Nageswari Shanmugalingam is a Professor and Distinguished Research Professor (STEMM) in the Department of Mathematical Sciences at the University of Cincinnati's College of Arts and Sciences. She specializes in geometric analysis, focusing on metric measure spaces, potential theory, and quasiconformal maps. Her research explores connections between analytic inequalities and geometric properties, with contributions to functions of bounded variation (BV) and Besov spaces. Education: PhD from the University of Michigan (1999), BSc from the University of Rochester (1994). Research interests include geometric implications of Poincaré inequalities, BV functions, and boundary theories in potential analysis. She has secured significant grants from NSF and Simons Foundation, totaling over $1.2M, supporting projects on potential theory, metric geometry, and hyperbolicity. Her publications span over 100 peer-reviewed articles, including works on Mazurkiewicz distance, BV trace theorems, and Besov spaces. Awards include the 2008 McMicken Dean's Award and 2017 Graduate School Fellowship. Grants include NSF funding for 'Notions of curvature and potential theory' (2024–2027) and 'Role of Gromov hyperbolicity' (2021–2024). She actively contributes to editorial boards of journals like Revista Matemática Complutense and serves on international conference committees.

Erik Van Vleckمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Numerical Methods for PDEs
• +۳ مورد دیگر

Erik Van Vleck is a Professor of Mathematics at the University of Kansas, affiliated with the College of Liberal Arts & Sciences. His research focuses on applied mathematics, numerical analysis, and numerical methods for partial differential equations (PDEs), with particular emphasis on data assimilation and reaction-diffusion systems. His work bridges theoretical advancements and practical applications in computational dynamics, environmental modeling, and ecological systems. Van Vleck’s research interests include multi-scale data assimilation techniques, adaptive mesh methods, and the numerical analysis of traveling wave solutions in discrete and continuous systems. He has contributed to understanding pattern formation in ecological models, particularly in savanna ecosystems and fire-driven dynamics. His methodological innovations span numerical methods for PDEs, reduced-order modeling, and stochastic filtering approaches for complex systems. His recent work highlights trends in developing robust adaptive frameworks for data assimilation, integrating advanced numerical techniques with real-world applications in geophysics and environmental science. This includes projects on metric tensor-based mesh adaptation and projected data assimilation using sliding window methods. His contributions to theoretical foundations include spectral stability analyses for discrete dynamical systems and bifurcation studies in reaction-diffusion equations. Van Vleck has collaborated extensively across disciplines, applying mathematical tools to ecological, climatological, and biomedical systems. His research often addresses computational challenges in high-dimensional spaces, emphasizing efficiency and accuracy in modeling complex phenomena. Despite the breadth of his contributions, no specific grants or advising roles are detailed in the provided materials.

Peter Jan van Leeuwenمشاهده پروفایل

استاد

• Data Assimilation
• Physical Oceanography
• Geophysical Fluid Dynamics
• +۴ مورد دیگر

Peter Jan van Leeuwen is a Professor of Data Assimilation and Physical Oceanography at Colorado State University (since 2018) and a Professor of Data Assimilation at the University of Reading (since 2009). He holds leadership roles, including Director of the Data Assimilation Research Centre (DARC) and former Director of the National Centre for Earth Observation (NCEO). He earned his Ph.D. in Fluid Dynamics from Delft University of Technology (1992) and holds multiple advanced degrees in Theoretical Physics and Astrophysics from Dutch universities. His research focuses on advancing data assimilation techniques (including machine learning), causal discovery in non-intervenable systems, and their applications to geophysical fluids like oceans, atmosphere, and cryosphere. Key areas include ocean dynamics, air-sea interaction, sea-ice prediction, cloud microphysics, and hurricane modeling. He has been funded by NSF, NOAA, NASA, and European agencies like the ERC and NERC. Notable awards include the George T. Abell Outstanding Faculty Research Award (2025) and the ERC Advanced Investigator Award (2016). He has authored over 50 peer-reviewed articles and co-edited the open-access textbook Data Assimilation Fundamentals . Van Leeuwen leads the Center for Nonlinear Data Assimilation, Causal Discovery, and Prediction, emphasizing cutting-edge methods for high-dimensional systems and climate predictability.

Sergey Nadtochiyمشاهده پروفایل

استاد

• Financial Mathematics
• Probability Theory
• Partial Differential Equations
• +۲ مورد دیگر

Sergey Nadtochiy serves as Professor of Applied Mathematics within the College of Computing at Illinois Institute of Technology. His research bridges theoretical mathematics with financial applications through rigorous probabilistic and analytical frameworks. His academic foundation includes: Ph.D. in Operations Research & Financial Engineering from Princeton University (2009), advised by R. Carmona M.A. in Operations Research & Financial Engineering from Princeton University (2008), advised by R. Carmona Specialist (M.Sc.) in Mathematics, summa cum laude, from Moscow State University (2005), advised by A. Shiryaev Nadtochiy's research program centers on Financial Mathematics, Probability Theory, and Partial Differential Equations with applications to market microstructure, optimal contract design, and systemic risk. His work demonstrates deep integration of Stochastic Control and Game Theory to model complex financial phenomena like price impact and limit order book dynamics, while recent publications extend into mathematical physics through Stefan problem analysis. Analysis of his 15 most recent publications reveals a strategic evolution from pure financial mathematics toward interdisciplinary connections with mathematical physics. The 2022-2024 works show increasing focus on the Stefan problem and diffusion-limited aggregation, while maintaining core expertise in market microstructure through studies on price impact concavity, brokerage contracts, and liquidity effects. His scientific recognition includes: Award for excellence (honorific fellowship) from Moscow State University (2001-2005) SIAG/FME Junior Scientist Prize from SIAM (2012) Charlotte Elizabeth Procter Honorific Fellowship from Princeton University (2008-2009) Gordon Y.S. Wu Honorific Fellowship from Princeton University (2005-2009) Nadtochiy has secured significant research funding as sole Principal Investigator on two NSF awards: a CAREER grant (DMS-1855309, 2017-2022) and earlier grant DMS-1411824 (2014-2017). While no formal student advisees are listed in the source materials, his collaborative publications with researchers like M. Shkolnikov and S. Shreve demonstrate active mentorship within research teams focused on probabilistic methods and financial modeling.

Carlos Cardonhaمشاهده پروفایل

استادیار

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +۶ مورد دیگر

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

Laura Balzanoمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Signal Processing
• +۷ مورد دیگر

Laura Balzano is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, with a courtesy appointment in the Department of Statistics within the College of Literature, Science, and the Arts. Her research focuses on optimization, machine learning, and signal processing, particularly in high-dimensional data analysis, subspace learning, and low-rank structures. She holds affiliations with the Department of Statistics and is located at 323 West Hall, Ann Arbor, MI. Contact information includes girasole@umich.edu . Her work bridges theoretical foundations and practical applications, addressing challenges in heterogeneous data, online learning, and robust algorithms. Key research areas include subspace clustering, tensor decomposition, and model compression for deep learning systems. Recent contributions explore dynamic networks, control theory integration, and energy-efficient algorithms for large-scale models. Laura Balzano's publications span topics like PCA variants for noisy data, optimization on Riemannian manifolds, and neural collapse analysis. Her methods emphasize scalable solutions for real-time applications, such as energy disaggregation and medical imaging. While no explicit awards are listed, her extensive publication record reflects recognition in her field. Advising and grant details are not provided in the available text.

Jessica Linمشاهده پروفایل

دانشیار

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +۳ مورد دیگر

Jessica Lin is an Associate Professor in the Department of Mathematics and Statistics at McGill University, holding the Canada Research Chair in Partial Differential Equations and Probability (Tier 2). She specializes in stochastic homogenization, elliptic and parabolic equations, and reaction-diffusion systems. Prior to McGill, she was a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison, and earned her Ph.D. from the University of Chicago in 2014 with undergraduate studies at NYU. Her research focuses on partial differential equations and probability, particularly in nonlinear phenomena, stochastic processes, and their applications to mathematical physics and materials science. Key areas include front propagation in stochastic environments, nondivergence form equations, and phase transition models in periodic media. Dr. Lin has contributed to foundational work on quantitative homogenization estimates for elliptic and parabolic equations, asymmetric cooperative motion, and barycentric Brownian systems. Her recent articles explore generalized front propagation, anisotropic surface tensions, and KPZ universality in random growth models. She has been recognized with the Canada Research Chair (Tier 2), and her work spans over 15 peer-reviewed articles since 2013. Her research combines analytical techniques with probabilistic methods, addressing multiscale problems in applied mathematics.

• Complex Systems
• Non-linear Dynamics
• Statistical Physics
• +۴ مورد دیگر

Professor Eduardo Goldani Altmann is a faculty member in the Department of Mathematics at the University of Sydney. His research focuses on understanding complex systems through mathematical models and computational techniques, with a particular emphasis on nonlinear dynamics, statistical physics, and data science. He explores applications ranging from network theory to text analysis and biological systems like honeybee colony health. Research Interests: Altmann investigates the interplay between complexity and data-driven approaches, including chaotic systems, network dynamics, and the statistical properties of language. His work bridges disciplines such as physics, computer science, and biology. Grants & Collaborations: 2023: Learning the meso-scale organization of complex networks (Australian Research Council) 2021: Reducing the Morbidity of Head and Neck Cancer Treatment (Cancer Institute NSW) 2019: A complex systems approach to preventing colony failure in honey bees (Australian Research Council) Labs/Teams: Altmann leads a research group focused on complex systems analysis, with a webpage dedicated to their work: Group Website .

Jacob Carruthمشاهده پروفایل

پژوهشگر

• Adaptive Control Theory
• Whitney Extension Problems
• Fourier Analysis
• +۳ مورد دیگر

Jacob Carruth is a Postdoctoral Research Associate in the Department of Mathematics at Princeton University. His academic focus includes adaptive control theory, Whitney extension problems, and Fourier analysis. He earned his PhD in 2019 from the University of Texas at Austin under the advisement of Arie Israel. His research explores theoretical control systems and mathematical analysis, with notable contributions to adaptive control strategies for unknown linear dynamics and extensions of Whitney's theorem in functional spaces. Education: PhD in Mathematics, 2019, University of Texas at Austin (Advisor: Arie Israel) Research Interests: Adaptive control theory with applications to uncertain linear systems Whitney extension problems in Sobolev spaces and geometric analysis Fourier analysis methods in extremal problems and approximation theory His work bridges pure mathematics and applied control theory, addressing fundamental questions in system dynamics and function extensions. Recent Research Trends: His publications emphasize regret minimization strategies, optimal control under parameter uncertainty, and functional analysis techniques. Key themes include developing bounded-regret algorithms for linear systems and advancing understanding of extension operators for function spaces. Lab/Team Affiliations: Currently affiliated with Princeton University Mathematics Department's research groups in analysis and control theory.

• Geotechnical Engineering
• Computational Mechanics
• Soil Mechanics
• +۴ مورد دیگر

James Doherty is an Associate Professor in the Department of Civil, Environmental and Mining Engineering at The University of Western Australia (UWA), affiliated with the UWA Oceans Institute. His work focuses on geotechnical engineering and cloud computing technologies, emphasizing automation of data interpretation and optimization of engineering design workflows. He leads research in numerical methods, soil-structure interaction, and offshore wind infrastructure, with over 80 peer-reviewed publications. Doherty founded the open-access geocalcs.com platform, serving 1,000+ users globally with engineering design tools derived from his research. He has developed cloud-based platforms for companies like Fugro, achieving significant computational efficiency gains. His expertise aligns with UN Sustainable Development Goals related to infrastructure and sustainable energy.