جستجوی استاد راهنمای دانشگاهی

David Jerisonمشاهده پروفایل

استاد

• Fourier analysis
• partial differential equations
• free boundary problems
• +۲ مورد دیگر

David Jerison is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), where he conducts research in Fourier analysis and partial differential equations. His work focuses primarily on free boundary problems and, more recently, on internal Diffusion Limited Aggregation (internal DLA), a stochastic growth model. He maintains an active research program with numerous publications in leading mathematical journals. Professor Jerison's research spans several interconnected areas of mathematical analysis. His primary interests include Fourier analysis and partial differential equations, with particular emphasis on free boundary problems. In recent years, he has expanded his research to include internal Diffusion Limited Aggregation, a stochastic growth model that has connections to probability theory and mathematical physics. His work often bridges geometric analysis, spectral theory, and probabilistic methods, demonstrating the deep connections between different branches of mathematics. Analysis of Professor Jerison's recent publications reveals a consistent focus on geometric aspects of partial differential equations, particularly free boundary problems. His research shows progression from classical PDE theory toward more stochastic and probabilistic approaches, as evidenced by his work on internal DLA. The publications demonstrate interdisciplinary connections between mathematical analysis, probability theory, and mathematical physics, with applications ranging from geometric measure theory to quantum mechanics. Professor Jerison is actively involved in teaching and mentoring at MIT. He has taught courses including Differential Equations (18.03), Fourier Analysis and Applications (18.103), and Differential Analysis (18.155). He also directs the Summer Program for Undergraduate Research (SPUR), which is exclusively for MIT undergraduates, and organizes the mathematics section of the Research Science Institute (RSI) for high school students. His teaching materials are available through MIT's Open Courseware platform, indicating his commitment to educational outreach and accessibility.

• Computational Mathematics
• Numerical Analysis
• Tsunami Modelling
• +۵ مورد دیگر

Stephen Roberts is a Professor at the Australian National University (ANU) in the College of Science, Department of Mathematics. He is the lead developer of the ANUGA open-source hydrodynamic modeling software, which simulates dam breaks, floods, and tsunamis for governments and engineers. Roberts has made significant contributions to computational mathematics, particularly in numerical methods for partial differential equations, sparse grid data fitting, and finite element approximations scaling to millions of data points. MSc, Flinders University (1980) PhD, University of California, Berkeley (1985) His research interests include: Computational methods for shallow water wave equations Development of Python-based scientific computing frameworks Global sensitivity analysis and uncertainty quantification Adaptive mesh algorithms for fluid dynamics Recent publications focus on energy-stable numerical schemes, multiscale flood simulation, and convergence analysis in sensitivity methods. Roberts actively collaborates with environmental agencies and has led major computational science education programs at ANU. Stephen serves as Treasurer of the Computational Mathematics Group (ANZIAM) and leads projects in: Parallelization of hydrodynamic models CO2 leak detection via atmospheric measurements Optimization of sparse grid combinations His work combines theoretical advancements with real-world applications in disaster risk reduction and climate policy.

Rick Hoyleمشاهده پروفایل

استاد

• Self-Regulation
• Adolescent Development
• Goal Pursuit
• +۵ مورد دیگر

Rick Hoyle is a Professor of Psychology and Neuroscience at Duke University, where he serves as Associate Chair of the Department of Psychology and Neuroscience. He is also a Faculty Network Member of the Duke Institute for Brain Sciences. His academic career spans decades, with significant contributions to understanding self-regulation and adolescent development. Dr. Hoyle earned his B.A. from Appalachian State University (1983), followed by an M.A. (1986) and Ph.D. (1988) from the University of North Carolina, Chapel Hill. He progressed from Assistant to Associate to Full Professor at the University of Kentucky from 1989 to 2003 before joining Duke University. His research focuses on how adolescents and emerging adults manage goal pursuit through self-regulation, taking a broad view that accounts for personality, environment, cognition, emotion, and social influences. He employs longitudinal methods with repeated assessments, sometimes spanning years with data collection multiple times per year, or intensive studies with assessments several times daily. His lab develops innovative measurement tools including self-report measures of self-control and grit, as well as unobtrusive approaches using mobile phones and wearable devices to track goal pursuit in natural settings. His recent publications reveal a strong focus on self-regulation in digital contexts, adolescent substance use, socioeconomic influences on development, and innovative measurement approaches. His work increasingly integrates technology (social media analysis, wearable devices) with traditional psychological research methods to understand real-world behavior. Fellow, Association for Psychological Science (2013) Dr. Hoyle has secured numerous research grants totaling millions of dollars, including the current Real-Time and Randomized Tests of Social Media and Mental Health Links in Early Adolescence (2024-2029), NCCU Duke - Substance Use Research & Education (2024-2029), and Mid-Life Health Inequalities in the Rural South: Risk and Resilience (2023-2028). His grant portfolio demonstrates sustained funding for research on adolescent development, substance use, self-regulation, and health disparities. He teaches advanced courses including Applied Structural Equation Modeling and Psychology and Neuroscience Grant Writing, mentoring the next generation of researchers in quantitative methods and research design. His work through the Center for the Study of Adolescent Risk and Resilience (2008-2025) has established a significant research infrastructure for longitudinal studies of adolescent development. Current projects examine social media effects on mental health, substance use patterns, and the impact of environmental factors on adolescent well-being using innovative digital tracking methods.

Benjamin Landonمشاهده پروفایل

استادیار

• Probability
• Mathematical physics
• Random matrix theory
• +۳ مورد دیگر

Benjamin Landon is an Assistant Professor in the Department of Mathematics at the University of Toronto, where he has been faculty since 2021. His office is located in the Bahen Centre for Information Technology, Room 6264. Prior to joining the University of Toronto, he was a CLE Moore Instructor at the Massachusetts Institute of Technology from 2018-2021. Education: PhD in Mathematics, Harvard University (2018). Advisor: Horng-Tzer Yau M.Sc. in Mathematics, McGill University (2013). Advisors: Vojkan Jaksic and Robert Seiringer B.Sc., McGill University (2012) Dr. Landon's research focuses on Probability and Mathematical Physics, with particular expertise in Random Matrix Theory. His work spans various aspects of spectral statistics, eigenvalue distributions, and universality phenomena in random matrix ensembles. He has made significant contributions to understanding the behavior of extremal eigenvalues, linear spectral statistics, and connections to other areas of mathematical physics such as spin glasses and the KPZ universality class. His research often involves developing novel analytical techniques to establish precise asymptotic behavior in complex random systems. Analysis of Dr. Landon's publication record reveals a strong focus on the intersection of probability theory and mathematical physics. His work consistently explores universality phenomena across different random matrix ensembles and related stochastic systems. A notable trend is his investigation of connections between random matrix theory and other areas of mathematical physics, particularly spin glass models and the KPZ equation. His research demonstrates both technical depth in establishing rigorous asymptotic results and breadth in connecting seemingly disparate areas of mathematical physics.

Gerold Alsmeyerمشاهده پروفایل

استاد

• Theory of stochastic processes
• Stochastic fixed-point equations
• Iterated function systems
• +۷ مورد دیگر

Prof. Dr. Gerold Alsmeyer is a faculty member at the Institute of Mathematical Stochastics, Department of Mathematics and Computer Science, University of Münster. He is an active researcher with a focus on stochastic processes, particularly stochastic fixed-point equations and iterated function systems. His work is supported by his role as an Investigator in Mathematics Münster in the project EXC 2044 - C1: Evolution and asymptotics. His primary research interests include the theory of stochastic processes, branching processes, Markov random walks, renewal theory, and the asymptotic analysis of random structures such as random trees and polytopes. He has made significant contributions to the understanding of fluctuation theory, perpetuities, and the smoothing transform. His recent publications (2017–2023) reveal a sustained focus on theoretical probability, with recurring themes in random difference equations, iterated function systems, and limit theorems for stochastic processes. The work spans pure mathematical theory and applications in mathematical biology and combinatorics, indicating a broad yet deep research profile. Prof. Alsmeyer has supervised numerous doctoral and master’s students, including Viet Hung Hoang, Christopher Eick, Philipp Godland, and Fabian Buckmann, whose dissertations cover topics in branching processes, random walks, and stochastic fixed-point equations. He has no listed scientific awards in the provided texts. He teaches courses in probability theory, mathematical statistics, branching processes, and stochastic recursion equations, demonstrating a strong commitment to academic mentoring and education.

Paul G Dupuisمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Control Theory
• +۴ مورد دیگر

Paul G Dupuis is the IBM Professor of Applied Mathematics at Brown University. His research focuses on applications of probability theory, stochastic processes, control theory, and numerical methods. He holds affiliations with the American Mathematical Society, Society for Industrial and Applied Mathematics (SIAM), and the Institute for Mathematical Statistics (IMS). His work emphasizes large deviation theory, Markov chain approximations, Monte Carlo simulation, and partial differential equations. Education: Ph.D. in Applied Mathematics from Brown University (1985), M.S. from Northwestern University (1982), and B.S. from Brown University (1981). Research Interests: Control of deterministic and stochastic processes, differential games, numerical methods, operations research, and stochastic processes. His contributions include foundational work on large deviation theory, risk-sensitive control, and queueing networks. Awards: Elected SIAM Fellow (2010), Fellow of the Institute for Mathematical Statistics (2011), IBM Professor of Applied Mathematics (2012), and AMS Fellow (2014). Previously held an NSF Postdoctoral Fellowship (1985-1988). Grants: Current funding from the Army Research Office and National Science Foundation. Key collaborations include work on stochastic approximation, constrained diffusions, and reflected Brownian motion. Teaching: Courses include Operations Research: Probabilistic Models, Information Theory, and Advanced topics in Probability and Stochastic Control.

Yvain Brunedمشاهده پروفایل

استاد

• Singular Stochastic Partial Differential Equations
• Regularity Structures
• Renormalisation Theory
• +۵ مورد دیگر

Yvain Bruned is a Professor of Mathematics at Université de Lorraine, Nancy, France, where he leads research in singular stochastic partial differential equations and related fields. He serves as Principal Investigator for the ERC Starting Grant LoRDeT (2023-2028), which focuses on advancing the theory of decorated trees and Hopf algebraic structures for solving singular SPDEs and dispersive PDEs at low regularity. Previously, he was a Lecturer at the University of Edinburgh (2019-2022) and completed postdoctoral work at Imperial College London and University of Warwick under Martin Hairer. His educational background includes: PhD in Mathematics (2012-2015), UPMC (Paris 6), on "Singular KPZ type equations" under Lorenzo Zambotti Master 2 in Probability and Statistics, ENS Cachan / Rennes 1, with honors Master 1 in Mathematics, ENS Cachan, with honors Bachelor in Mathematics and Computer Science, University of Rennes 1, with honors Student at ENS Cachan Brittany extension (2009-2013) Classes Préparatoires in Mathematics and Physics (2007-2009) Bruned's research centers on singular stochastic partial differential equations, with particular focus on Regularity Structures, renormalization theory, and their connections to Hopf algebras. His work bridges theoretical mathematics with applications in quantum field theory, wave turbulence, and numerical analysis. He has developed novel approaches using decorated trees to handle renormalization procedures for singular SPDEs and has extended these methods to dispersive PDEs with random initial data. His research program aims to establish existence and uniqueness results for quasilinear and dispersive SPDEs while developing algebraic tools through deformations of Hopf algebras. His extensive publication record demonstrates consistent contributions to the field of singular SPDEs, with a clear trajectory from foundational work on Regularity Structures to more recent applications in dispersive PDEs and numerical methods. The publications reveal a strong collaborative network with leading researchers in stochastic analysis, mathematical physics, and algebra. His work shows increasing sophistication in handling renormalization procedures through algebraic structures, with recent papers exploring connections between different mathematical frameworks. His major scientific recognition includes: ERC Starting Grant LoRDeT (2023-2028) Bruned actively supervises a large group of researchers, currently advising 4 PhD students and 2 postdoctoral researchers at Université de Lorraine, with several former PhD students having completed their degrees at the University of Edinburgh. His ERC grant has enabled him to organize multiple international workshops in Nancy, fostering collaboration between researchers in singular SPDEs, algebraic structures, and numerical analysis. The grant also supports the development of software platforms for decorated trees and their Hopf algebraic structures. As Principal Investigator of the ERC LoRDeT project, Bruned leads a vibrant research team based at the Elie Cartan Institute of Lorraine, which includes postdocs, PhD students, and visiting researchers. The team regularly organizes specialized workshops on topics including operads, symmetries for quantum field theory, and normal forms for singular dynamics, creating a dynamic research environment that bridges multiple mathematical disciplines.

Arno Bergerمشاهده پروفایل

استاد

• Dynamical Systems
• Ergodic Theory
• Benford's Law
• +۵ مورد دیگر

Dr. Arno Berger is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a Dipl.Ing. (ME) and Dipl.Ing. (MSc) in Mechanical Engineering and Applied Mathematics from TU Wien (Vienna University of Technology), followed by a Dr. techn (PhD) and Habilitation in Applied Mathematics from the same institution. His research focuses on dynamical systems, ergodic theory, Benford's Law, nonautonomous dynamics, bifurcation theory, applied probability, and dimensional analysis. He has held visiting positions at prestigious institutions including Georgia Tech, University of Warwick, Goethe University Frankfurt, and University of Canterbury. His recent work includes studies on Saint-Venant-Polya inequalities, planar curves with position-dependent curvature, and distributions of logarithmic functions. He co-authored the seminal book An Introduction to Benford's Law (2015), and maintains the Benford Online Bibliography. His teaching spans courses like Differential Equations and Real Variables. Dr. Berger’s research has explored Benford’s Law in diverse contexts, from stochastic processes to finite-time dynamics. His articles often bridge theoretical insights with practical applications, emphasizing the ubiquity of Benford’s Law in mathematical systems.

Barbara Drosselمشاهده پروفایل

استاد

• Complex Systems Theory
• Theoretical Ecology
• Random Boolean Networks
• +۷ مورد دیگر

Barbara Drossel is a Full Professor at the Institute of Solid State Physics within the Faculty of Physics at the Technical University of Darmstadt, where she has been conducting research since February 2002. Her work bridges theoretical physics, complex systems theory, and theoretical ecology, focusing on interdisciplinary approaches to understanding emergent phenomena in natural systems. She leads the AG Drossel research group that investigates the theoretical foundations of complex networks, ecological communities, and quantum systems. Professor Drossel's research spans multiple domains with emphasis on complex systems theory, where she has made significant contributions to understanding random Boolean networks, food web modeling, and the physics of ecological communities. Her work demonstrates how simple rules can lead to complex emergent behavior across different scales, from quantum systems to ecological networks. She investigates how top-down causation operates in complex systems and explores the relationship between microscopic dynamics and macroscopic patterns in diverse contexts. Analysis of her recent publications reveals a consistent focus on theoretical frameworks that connect physics with ecology. Her work shows increasing integration of quantum mechanics with ecological modeling, particularly in understanding emergence and time evolution in complex systems. She frequently employs network theory to analyze ecological communities and has developed innovative approaches to studying species interactions, mutualistic networks, and spatial dynamics in meta-communities. Minerva Fellowship Heisenberg Fellowship DFG Fellowship for research at MIT Professor Drossel has supervised numerous doctoral students whose work spans theoretical ecology, complex systems, and statistical physics. Her research group has secured funding for projects examining the stability of ecological networks, quantum decoherence, and the mathematical foundations of complex systems. She maintains active collaborations with researchers across Europe and has contributed to major theoretical advances in understanding how complexity emerges from simple interactions in diverse systems. The AG Drossel research group operates at the intersection of physics and theoretical biology, maintaining strong connections with both the physics and biology departments at TU Darmstadt. The group combines mathematical rigor with biological relevance, developing models that capture essential features of complex natural systems while remaining analytically tractable. Their work has influenced both theoretical physics and ecological theory, demonstrating the power of interdisciplinary approaches to complex systems.

Caterina Ida Zeppieriمشاهده پروفایل

استاد

• Calculus of Variations
• Elliptic PDEs
• Gamma-convergence
• +۵ مورد دیگر

Professor Caterina Ida Zeppieri is a distinguished mathematician at the Westfälische Wilhelms-University Münster (University of Münster) in Germany, where she leads the Research Group 'Analysis and Modelling' within the Institute for Analysis and Numerics. She has maintained a continuous academic presence since at least the Winter semester 2012/13 through to upcoming semesters in 2025/26, consistently teaching advanced mathematics courses and supervising research activities. Her research focuses on fundamental aspects of mathematical analysis with significant applications to materials science. She specializes in Calculus of Variations, Elliptic PDEs, Gamma-convergence, Homogenization theory, Free-discontinuity problems, Nonlinear elasticity, and Plasticity. Her work bridges theoretical mathematics with practical applications in understanding material behavior, particularly fracture mechanics and composite materials. Professor Zeppieri's publication record demonstrates a consistent and impactful research trajectory from 2007 through forthcoming publications in 2025. Her recent work shows a strong emphasis on stochastic homogenization techniques applied to free-discontinuity problems and singularly-perturbed functionals, revealing sophisticated mathematical approaches to modeling complex material behaviors across multiple scales. She regularly collaborates with leading researchers including Filippo Cagnetti, Gianni Dal Maso, and Lucia Scardia, contributing to significant advances in the mathematical understanding of material science phenomena. Her research has been published in top-tier mathematics journals including Calculus of Variations and Partial Differential Equations, Archive for Rational Mechanics and Analysis, and SIAM Journal on Mathematical Analysis. Within the department, Professor Zeppieri plays an active role in teaching advanced courses such as Partial Differential Equations, Calculus of Variations, and Advanced Topics in the Calculus of Variation, while participating in the department's Advanced Seminar in Applied Mathematics and Colloquium on Applied Mathematics.

Jonathan Weareمشاهده پروفایل

استاد

• Monte Carlo sampling
• Multiscale analysis
• Randomized numerical linear algebra
• +۲ مورد دیگر

Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.

Caterina Zeppieriمشاهده پروفایل

استاد

• Calculus of Variations
• Homogenization Theory
• Stochastic Analysis
• +۵ مورد دیگر

Professor Caterina Zeppieri is a faculty member at the Institute for Analysis and Numerics within the Faculty of Mathematics and Computer Science at the University of Münster, Germany. She serves as an investigator in Mathematics Münster and specializes in optimization and calculus of variations. Her research spans multiple collaborative projects within the Excellence Cluster EXC 2044. Her primary research interests focus on Calculus of Variations , Homogenization Theory , and Free-Discontinuity Problems , with significant contributions to the mathematical understanding of material science phenomena. Her work bridges theoretical mathematics with practical applications in material modeling, fracture mechanics, and multi-scale analysis. She has developed sophisticated mathematical frameworks for understanding stochastic homogenization, phase-field approximations, and gradient damage models in heterogeneous materials. Analysis of her recent publications reveals a consistent trajectory toward increasingly complex multi-scale problems involving randomness and discontinuities. Her work demonstrates deep connections between Γ-convergence theory, stochastic processes, and applications to material science, particularly in modeling fracture phenomena and composite materials. A notable trend is her development of global methods that unify deterministic and stochastic approaches to homogenization problems. Zeppieri actively contributes to teaching at the University of Münster, regularly offering courses on Partial Differential Equations, Calculus of Variations, and Advanced Topics in Mathematical Modeling. Her teaching spans both undergraduate and graduate levels, including specialized seminars on cutting-edge research topics in her field. She is deeply involved in the EXC 2044 collaborative research center, particularly in project units C1 (Evolution and asymptotics), C2 (Multi-scale phenomena and macroscopic structures), and C3 (Interacting particle systems and phase transitions), where she contributes her expertise in variational methods and homogenization to multi-disciplinary research efforts.

Stefan Hoderleinمشاهده پروفایل

استاد

• Econometrics
• Big Data
• Panel Data Analysis
• +۳ مورد دیگر

Stefan Hoderlein is a Professor in the Department of Economics at Emory University. His expertise lies in econometrics, with a focus on nonparametric methods, panel data analysis, and structural models. He holds a PhD from Bonn University and the London School of Economics (2002), and a Diplom Volkswirt from Bonn University (1997). His research interests include advanced econometric techniques such as instrumental variable estimation, demand analysis, and random coefficient models. He has contributed to methodologies addressing unobserved heterogeneity, endogeneity, and identification challenges in economic data. His work often explores applications in consumer behavior, market structure, and policy evaluation. Recent research trends in his publications emphasize nonparametric identification strategies, panel data methodologies, and the integration of big data into econometric frameworks. His technical contributions include Stata modules for statistical testing and frameworks for analyzing aggregate demand and welfare effects. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in econometric theory and applied economics. Advising details and grant information are not explicitly provided in the sources, though his work often involves collaborative research teams. His office is located in the R. Rollins Building (R428), and he maintains an active academic website.

• Longitudinal Modeling
• Psychometric Modeling
• Developmental Cognitive Neuroscience
• +۳ مورد دیگر

Ethan McCormick is an Assistant Professor in the School of Education at the University of Delaware, specializing in longitudinal and psychometric modeling. He holds a Ph.D. in Psychology from the University of North Carolina at Chapel Hill (2020) and a B.S. in Biochemistry from the University of Arkansas (2013). His research focuses on integrating short-term and long-term longitudinal models to study behavioral and cognitive changes across the lifespan, with recent emphasis on educational data analysis and nonlinear random effects modeling. He is a Resident Faculty member of the University of Delaware’s Data Science Institute and previously served as an Assistant Professor of Methodology & Statistics at Leiden University (2022–2024). Dr. McCormick’s grants include the NWO Veni SSH Grant (2024–2027) for tracking educational outcomes via statistical modeling and the Jacobs Foundation Fellowship (2024–2026) for studying complex growth in math ability. His work bridges methodological rigor with applied neuroscience, examining brain-behavior relationships in developmental contexts through large-scale collaborations. Professional Experience : Assistant Professor, University of Delaware (2024–present); Assistant Professor, Leiden University (2022–2024) Key Research Themes : Longitudinal modeling, time series analysis, psychometrics, developmental cognitive neuroscience Awards : NWO Veni SSH Grant, Jacobs Foundation Fellowship His recent articles emphasize improving time-series methodologies, addressing limitations of two-time-point studies, and advancing models for asymmetric temporal dynamics. He collaborates internationally on projects simulating developmental datasets and analyzing neural correlates of behavior.

Alexandros Kontogiannisمشاهده پروفایل

پژوهشگر ارشد

• Fluid Dynamics
• Applied Mathematics
• Bayesian Inference
• +۱۰ مورد دیگر

Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.