Find Academic Supervisors

• Differential Geometry
• Riemannian Geometry
• Lorentzian Geometry
• +5 more

Kwok-Kun Kwong is a UOW CERL Fellow at the University of Wollongong, specializing in differential geometry, Riemannian and Lorentzian geometries, and mathematical relativity. His research focuses on integral formulas, curvature flows, isoperimetric inequalities, and quasi-local mass problems. He holds a PhD (2011) and M.Phil. (2008) from The Chinese University of Hong Kong, supervised by Prof. Luen-Fai Tam, along with a B.Sc. (2006). Research interests include geometric inequalities involving scalar curvature, eigenvalue estimates on manifolds, and rigidity theorems in warped product manifolds. Recent work explores Alexandrov-Fenchel inequalities, optimal transport applications, and geometric flows in spacetime contexts. He secured grants such as the Innovative Applications of Optimal Transport (2024) and Early Mid-Career Researcher Enabling Grant (2024). Current supervisions involve PhD topics like derivative pricing for geological risks and nonlinear PDE applications. Active in publishing high-impact geometric analysis papers, he contributes to foundational theories in geometric analysis and mathematical physics.

Bahman GharesifardView profile

Professor

• Systems and Control
• Distributed Control and Optimization
• Geometric Control Theory
• +9 more

Bahman Gharesifard is a Professor in the Department of Mathematics and Statistics at Queen's University, Canada. He holds a Ph.D. from Queen's University (2009) and advanced degrees from Shiraz University (B.Sc., 2002; M.Sc., 2005). His research focuses on systems and control theory, with emphasis on distributed control, optimization, geometric control, and their intersections with network sciences, machine learning, and game theory. He has been recognized with the First Year Instructor Teaching Award in Engineering & Applied Science (2014 & 2016). His academic journey includes postdoctoral research at the University of California, San Diego (2009–2012) and the University of Illinois, Urbana-Champaign (2012–2013). His work bridges theoretical foundations of control systems with practical applications in distributed optimization, neural networks, and contagion models on networks. Recent research trends include advancing Lyapunov-based methods for reinforcement learning, analyzing structural controllability in sparse systems, and developing models for network dynamics using Pólya urn frameworks. His articles explore topics like averaged controllability, flexible-step MPC, and stability in distributed algorithms. Education: Ph.D., Queen's University (2009) M.Sc., Shiraz University (2005) B.Sc., Shiraz University (2002) Awards: Engineering & Applied Science First Year Instructor Teaching Award (2014) Engineering & Applied Science First Year Instructor Teaching Award (2016) He collaborates on projects involving secure distributed optimization, epidemic modeling via Pólya contagion networks, and neural network approximation guarantees. His lab contributes to theoretical control advancements with practical implications in robotics, energy systems, and AI.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +3 more

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.

Son-Young YiView profile

Professor

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +4 more

Son-Young Yi is a Professor in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP), specializing in Computational Science. Her research focuses on numerical methods for partial differential equations, particularly finite element methods applied to solid mechanics, flow in deformable porous media, fluid-solid interactions, and multiscale analysis. She develops physics-preserving numerical schemes and contributes to the study of locally-conservative discretizations and reduced-order modeling. Her research interests span numerical analysis, scientific computing, and computational mechanics. Notable areas include enriched Galerkin methods for coupled thermo-poroelasticity systems, locking-free formulations for poroelasticity and linear elasticity, and numerical studies of financial models like European option pricing with non-Lévy jumps. Recent advancements in her work include projection-based reduced order modeling for iterative coupling schemes and immersed interface methods for interface problems. These contributions address challenges in accurately simulating complex physical phenomena across multiple scales and coupled processes. Dr. Yi's work has been supported by significant grants, including a $5 million award from UTEP for climate change mitigation research. Her publications reflect interdisciplinary collaborations in computational fluid dynamics, solid mechanics, and geophysical modeling.

Hichem Ben-El-MechaiekhView profile

Professor

• Nonlinear Functional Analysis
• Mathematical Economics
• Game Theory
• +5 more

Hichem Ben-El-Mechaiekh is a Professor of Mathematics at Brock University, where he has served since 1988. He previously held a visiting assistant professorship at the Technical University of Nova Scotia (now part of Dalhousie University) and visiting scholar positions at institutions including the Université Paris I, Panthéon-La Sorbonne, the American University of Sharjah, and the Canadian University Dubai. He earned his B.Sc., M.Sc., and Ph.D. in Mathematics from the Université de Montréal. His research focuses on Nonlinear Functional Analysis and its applications to mathematical economics, game theory, optimization, fixed point theory, and differential equations. He has held leadership roles, including Head of the Department for 8 years and Associate Dean for 6 years. He is an associate editor for journals in nonlinear functional analysis and co-founded the UAE Mathematics Day in 2003. He actively drives interdisciplinary initiatives, such as the proposed Big Data Science program at Brock University. Ben-El-Mechaiekh’s research interests span analytical and topological methods in game theory, nonsmooth optimization, set-valued analysis, and variational methods in differential equations. His work bridges pure mathematics with applied fields, emphasizing computational technologies in education. He has published extensively, with contributions to leading journals and edited volumes. His editorial roles include the Journal of Fixed Point Theory and Applications and Arabian Journal of Mathematics . He has taught a wide range of courses, from calculus to graduate-level functional analysis and topology. His research highlights include foundational contributions to fixed point theory, equilibrium problems in non-convex domains, and historical analyses of mathematical finance pioneers like Louis Bachelier. He collaborates internationally, integrating mathematical rigor with real-world applications. His academic service includes organizing conferences and mentoring students in mathematical economics and optimization.

• Process Control
• System Dynamics
• Process Design Optimization
• +5 more

Nikolaos A. Diangelakis is an Assistant Professor at the Technical University of Crete (TUC), affiliated with the School of Chemical Engineering (ChEnvEng) and the Division II: Process Development, Analysis and Design. His research focuses on system dynamics, process control, and integrated process design optimization. He holds a PhD from Imperial College London (2017), an M.Sc. from the same institution (2012), and a Diploma in Chemical Engineering from the National Technical University of Athens (2011). He has conducted a Visiting Ph.D. research at Texas A&M University (2017). Diangelakis specializes in multi-parametric optimization and control strategies, with applications in chemical, environmental, and energy systems. He has contributed to software tools like PAROC and POP, advancing explicit model predictive control (MPC) and robust optimization frameworks. His work integrates process design, scheduling, and control to enhance operational efficiency in industries such as pharmaceuticals and energy systems. He has secured research funding through projects like 'PAROC' (National Science Foundation) and 'U Psi Psi' (EPSRC), and has been recognized with awards including the 2017 Excellence Award for Outstanding PhD Thesis in Computer-Aided Process Engineering (CAPE). His academic contributions span over 30 peer-reviewed publications, emphasizing topics like microgrid optimization, rotary tablet press control, and energy-efficient industrial processes. Diangelakis teaches Linear Algebra at TUC and actively engages in academic conferences, delivering invited lectures and presenting innovative research on MPC strategies and process operability. His research group collaborates internationally, advancing the frontiers of model-based systems engineering.

Junsoo LeeView profile

Assistant Professor

• Control Theory
• Stochastic Systems
• Multiagent Networks
• +3 more

Junsoo Lee is an Assistant Professor in the Departments of Mechanical and Aerospace Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. His research centers on stability theory, stochastic systems, and distributed control of multiagent networks, with applications in aerospace systems and large-scale dynamical networks. Education credentials: Ph.D. in Aerospace Engineering, Georgia Institute of Technology (2022) M.S. in Mathematics, Georgia Institute of Technology (2021) M.S. in Aerospace Engineering, Seoul National University (2018) B.S. in Mechanical and Aerospace Engineering, Seoul National University (2016) Dr. Lee's research develops novel frameworks for finite-time and fixed-time stability in stochastic systems, creating control architectures based on thermodynamic principles. His publications demonstrate consistent focus on semistability, network consensus protocols, and optimal control of nonlinear discrete-time systems across deterministic and stochastic domains. Awards include multiple IEEE travel grants and the 2021 Faces of Inclusive Excellence honor. He serves as editorial reviewer for leading journals including IEEE Transactions on Automatic Control and Automatica, and participates in professional societies including IEEE, AIAA, and ASME.

Aritra DuttaView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Optimization
• +7 more

Aritra Dutta is an Assistant Professor at the AI Initiative of the University of Central Florida (UCF), primarily affiliated with the Department of Mathematics and secondarily with the Department of Computer Science. He also holds an affiliation with the Pioneer Centre for AI (P1), Denmark. His research focuses on optimization (stochastic/nonconvex), distributed computing (including federated learning), numerical linear algebra, machine learning, low-rank approximation, and computer vision applications such as image/video analysis and object detection/tracking. Dr. Dutta’s work emphasizes interdisciplinary approaches, blending mathematical rigor with computational efficiency. He actively seeks Ph.D. students and postdocs with strong foundations in mathematics (optimization, linear algebra) or computer science (ML, distributed systems), prioritizing candidates with publication records in top-tier venues. His teaching includes courses in applied mathematics and computational methods. His research outputs span communication-efficient distributed learning frameworks, convergence analysis of optimization algorithms, and vision transformer architectures. Notable projects include GAEA (geolocation-aware conversational models) and MAVREC (multi-view aerial visual recognition). Postdoc opportunities: Open for exceptional candidates. Labs/Initiatives: UCF AI Initiative (UCF Aii), UCF Center for Research in Computer Vision (CRCV). Grants: Competitive research assistantships with tuition support.

Alex SavachkinView profile

Associate Professor

• Resilience Engineering
• Operations Research
• Public Health
• +5 more

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. MedvedevView profile

Professor

• Dynamical Systems
• Stochastic Analysis
• Mathematical Neuroscience
• +3 more

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 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.