Find Academic Supervisors

Zafeiria RoumeliotiView profile

Assistant Professor

• Seismology
• Geophysics
• Earthquake Source Mechanics
• +3 more

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 NkurunzizaView profile

Professor

• Statistical Inference
• Statistical Modeling
• Change-Point Analysis
• +5 more

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 KangView profile

Associate Professor

• Stochastic Modeling
• Mathematical Biology
• Biochemical Reaction Networks
• +7 more

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 ShanmugalingamView profile

Professor

• Geometric Analysis
• Metric Measure Spaces
• Potential Theory
• +4 more

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 VleckView profile

Professor

• Applied Mathematics
• Numerical Analysis
• Numerical Methods for PDEs
• +3 more

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 LeeuwenView profile

Professor

• Data Assimilation
• Physical Oceanography
• Geophysical Fluid Dynamics
• +4 more

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 NadtochiyView profile

Professor

• Financial Mathematics
• Probability Theory
• Partial Differential Equations
• +2 more

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 CardonhaView profile

Assistant Professor

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +6 more

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 BalzanoView profile

Associate Professor

• Machine Learning
• Optimization
• Signal Processing
• +7 more

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 LinView profile

Associate Professor

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +3 more

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
• +4 more

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 CarruthView profile

Researcher

• Adaptive Control Theory
• Whitney Extension Problems
• Fourier Analysis
• +3 more

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
• +4 more

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.

Audrey RepettiView profile

Associate Professor

• Optimization
• Fixed Point Algorithms
• Deep Learning
• +4 more

Audrey Repetti is an Associate Professor at Heriot-Watt University, affiliated with both the School of Mathematical and Computer Sciences and the School of Engineering and Physical Sciences. She holds a PhD in optimization from Université Paris-Est Marne-la-Vallée (2015) and an MSc in applied mathematics from Université Pierre et Marie Curie (2011). Her research focuses on optimization (convex/nonconvex/stochastic), deep learning, Bayesian inference, and inverse problems in imaging and graph processing. She received the Royal Society of Edinburgh Fellowship (2022) for her work in optimization for data science. Her teaching includes courses like Bayesian Inference and Computational Methods. She collaborates internationally on projects in radio astronomy imaging, medical imaging, and computational optics. Repetti’s work bridges optimization theory with practical applications, emphasizing scalable algorithms for large-scale data problems.

Alessandro ChiusoView profile

Professor

• Control Systems
• Machine Learning
• System Identification
• +3 more

Professor Alessandro Chiuso is a Full Professor in the Department of Information Engineering at the University of Padova, Italy. His research focuses on control systems, machine learning, system identification, and their applications to neuroscience and medical imaging. He has contributed to advancing data-driven control methodologies, regularization techniques, and brain network modeling. His work bridges theoretical control systems with practical applications, including predictive control under uncertainty, nonlinear system identification, and understanding brain dynamics through fMRI and PET data. Notable areas include developing algorithms for adaptive control, optimizing brain region interactions, and analyzing metabolic-functional couplings in neurological conditions. Recent publications highlight advancements in policy gradient methods for LQR control, sparse dynamic causal models for brain aging studies, and predictive control frameworks for stochastic systems. His research emphasizes the integration of machine learning with classical control theory to solve complex real-world problems.