Find Academic Supervisors

• Numerical Analysis
• Differential Equations
• Matrix-oriented Methods
• +10 more

Ivonne Sgura is a Full Professor of Numerical Analysis at the Department of Mathematics and Physics of the University of Salento in Lecce, Italy. She received her PhD in Mathematics from the University of Pisa in 1996 and has been affiliated with the University of Salento since 2000, first as a Senior Lecturer, then as Associate Professor from 2010, before achieving Full Professor status in 2017 following her Italian National Habilitation for Full Professor in Numerical Analysis. Her primary research interests focus on developing innovative numerical methods for differential equations with applications in multiple fields including nonlinear elasticity, electrochemistry, and DNA dynamics. She has made significant contributions to the understanding of pattern formation in reaction-diffusion systems, particularly Turing patterns relevant to battery technology and electrodeposition processes. Her work often bridges theoretical mathematics with practical engineering applications, especially in the development of computational tools for simulating complex physical phenomena. Analysis of her publication record shows a strong trend toward interdisciplinary research combining mathematical modeling with electrochemical systems, particularly for battery technology. Her recent work increasingly incorporates machine learning techniques, especially deep learning approaches for parameter identification in complex electrochemical systems. She has published extensively with over 90 papers and an H-index of 21 according to SCOPUS metrics. Italian National Award for Mathematics (INdAM-UMI-SIMAI) 2019 for PhD thesis supervision Member of the Istituto Nazionale di Alta Matematica (INdAM), Section of Scientific Calculus (GNCS) ORCID: 0000-0001-9207-5832 SCOPUS Author ID: 6603115525 Professor Sgura actively supervises graduate students and has developed a strong research group focusing on computational methods for physical applications. Her teaching responsibilities include Numerical Analysis for Master's degree students and Numerical Calculus for Bachelor's degree students in Mathematics. She maintains active collaborations with experimental researchers, particularly in the field of electrochemistry, where her mathematical models help interpret complex experimental observations.

Alexander KheifetsView profile

Professor

• Complex Analysis
• Harmonic Analysis
• Operator Theory
• +6 more

Alexander Kheifets is a Professor in the Department of Mathematics and Statistics at the University of Massachusetts Lowell, part of the Kennedy College of Sciences. He holds a Ph.D. in Mathematics from Kharkov National University (1990) and an MS in Mathematics from the same institution (1978). His research focuses on Complex Analysis, Harmonic Analysis, Operator Theory, Hilbert spaces of analytic functions, and scattering theory, with particular interest in the interplay between complex analysis and operator geometry on Hilbert spaces. He has presented at numerous international conferences, including the International Conference on Modern Analysis and the Ukrainian Mathematical Congress. Notable awards include the Excellence in Teaching Award (2004) and the Kharkov Mathematical Society's Prize for Young Mathematicians (1987). His teaching responsibilities include courses such as Complex Variables I and Real/Mathematical Analysis. Kheifets has authored over 30 research articles, covering topics like inverse scattering, functional models, and interpolation theory. His work often bridges pure mathematics with applications in mathematical physics and bioinformatics. He has collaborated with leading institutions worldwide, contributing to operator theory advancements and boundary asymptotics studies. Education: Ph.D. in Mathematics, Kharkov National University, 1990 MS in Mathematics, Kharkov National University, 1978 Key Research Areas: Complex Analysis, Operator Theory, Scattering Theory, Functional Models Recent Activities: Active in international workshops (IWOTA conferences) and mathematical physics seminars

• Computational Mathematics
• Numerical Analysis
• Computational Fluid Dynamics
• +4 more

Dr. Arash Ghasemi serves as an Adjunct Faculty member in the Department of Engineering Management and Technology within the College of Engineering and Computer Science at the University of Tennessee at Chattanooga (UTC). His academic role bridges computational mathematics and engineering applications through teaching and research activities focused on advanced numerical methods. His research spans computational mathematics, numerical analysis, and scientific computing with emphasis on high-order spectral/finite element methods, mesh generation, and preconditioning techniques. Key application areas include computational fluid dynamics (particularly compressible flow), structural engineering (crane mechanisms, non-destructive testing), climate modeling for infrastructure, and electromagnetic simulations. His methodological innovations consistently target improved accuracy, efficiency, and stability in engineering computations across civil, mechanical, and aerospace domains. Dr. Ghasemi's publication trajectory from 2006 to 2024 reveals sustained contributions to computational methods. Early work established foundations in wireless sensor networks for infrastructure and high-order ODE solvers, while his 2016 'spectral hull' breakthroughs revolutionized degree-of-freedom reduction in conservation laws. Recent publications (2021-2024) demonstrate expanding applications in polynomial approximation, data interpolation, and climate modeling, maintaining his signature focus on mathematical rigor coupled with practical engineering impact across fluid dynamics, structural mechanics, and environmental systems.

• Optimal Control
• Operations Research
• Game Theory
• +4 more

Dr. Loïc Cellier is an Associate Professor specializing in Mathematics and Computer Science at the School of Aerospace Engineering (ELISA Aerospace, ISAE group) in Bordeaux, France. He holds a Ph.D. in Applied Mathematics from the University of Toulouse and has extensive qualifications from the French National Council of Universities. His research focuses on Optimal Control, Operations Research, and Game Theory, with applications in Air Traffic Management, Combinatorial Games, and Mathematics Education. Education includes a Ph.D. from the University of Toulouse (2015), M.Sc. in Applied Mathematics from Sorbonne University (2010), and B.Sc. in Mathematics from the University of Montpellier (2008). He has received multiple awards, including top rankings in national competitions and international recognition for his work in Hex game theory and optimization. Research interests span Optimal Control (aircraft conflict avoidance), Operations Research (combinatorial optimization), Game Theory (Hex and Chomp), and Mathematics Education. He has published extensively in journals like Optimal Control Applications and Methods, and contributed to educational platforms like POEM for personalized learning systems. Teaching responsibilities include courses in Mathematics, Computer Science, and Numerical Analysis at universities in France, Azerbaijan, and Switzerland. His pedagogical materials cover advanced topics like Probability, Optimization, and Programming in Python/C++. Notable projects include leading the ATOMIC project on air traffic optimization, co-founding the HEX&CO organization to promote combinatorial games, and developing educational resources for UNESCO’s POEM initiative.

• Combinatorics
• Discrete Mathematics
• Algebra
• +3 more

Ayomikun Adeniran is an Assistant Professor of Mathematics at Colby College, specializing in combinatorics and discrete mathematics. He holds a Ph.D. from Texas A&M University (2020). His research focuses on algebraic combinatorics, parking functions, Gončarov polynomials, and partition lattices. He currently teaches courses in multi-variable calculus, linear algebra, mathematical reasoning, and graph theory. His work bridges combinatorial structures with algebraic methods, exploring topics such as parking sequences, lattice theory, and enumerative combinatorics. Recent publications (2019–2022) emphasize interdisciplinary approaches to discrete mathematics problems. No scientific awards or grants are explicitly mentioned in the provided information. He advises no listed students but teaches across multiple undergraduate mathematics courses.

Anna SoósView profile

Associate Professor

• Fractal Mathematics
• Stochastic Processes
• Brownian Motion
• +6 more

Anna Soós is an Associate Professor at the Hungarian Mathematics and Informatics Institute, Faculty of Mathematics and Informatics, Babes-Bolyai University (Cluj-Napoca, Romania). She holds a PhD in Mathematics (2002) and has been active in stochastic analysis, fractal theory, and approximation methods. Her academic career includes leadership roles such as Vice-Rector for Curriculum and Hungarian Studies (2012–present) and Vice-Dean of the Faculty of Mathematics and Informatics (2008–2012). Education: MSc in Mathematics (Babes-Bolyai, 1982), MSc in Computer Science (Eötvös Loránd University, 1996), PhD in Mathematics (Babes-Bolyai, 2002) Her research focuses on deterministic and random fractals, stochastic differential equations, and homogenization on fractal structures. She has contributed to stochastic analysis through: Developing contraction methods in probabilistic metric spaces Wavelet approximations for SDE solutions Fractal interpolation techniques Scientific awards include: Bolyai János Research Fellowship Erasmus and CEEPUS Fellowships Domus Hungarica Scientiarum et Artium Grants World Bank Project Participation Anna Soós has coordinated international research projects like ERASMUS IP DSL2013 and FP7/MASCIL, while organizing conferences including the International Symposium on Numerical Analysis and Approximation Theory and Central European Functional Programming Summer Schools.

Jeffrey HoganView profile

Associate Professor

• Harmonic Analysis
• Signal Processing
• Wavelet Analysis
• +5 more

Jeffrey Hogan is an Associate Professor in the School of Information and Physical Sciences (Mathematics), University of Newcastle. He holds academic appointments including Head of Mathematics Discipline and former Deputy Head of the School of Mathematical and Physical Sciences. His research focuses on harmonic analysis, wavelet theory, and applications in signal processing, including bandlimited signals, prolate spheroidal wave functions, and quaternion-valued wavelets. Hogan earned his BSc (Hons) and PhD from the University of New South Wales, followed by postdoctoral roles at institutions like the University of Texas and University of Arkansas before joining Newcastle in 2009. Education: Bachelor of Science, University of New South Wales PhD (Mathematics), University of New South Wales Research Interests: Jeffrey Hogan explores pure, applied, and computational harmonic analysis, including singular integrals, wavelet frames, Clifford-Fourier theory, and applications in mineral fractionation. His work emphasizes spatio-spectral limiting, multidimensional wavelet construction, and quaternionic signal processing. Key projects involve developing algorithms for EEG analysis, optimizing wavelet design via projection methods, and analyzing signals on graphs and hypercubes. Grant Funding: A$546,913: Enhanced Fractionation of Mineral Particles According to Density (ARC Discovery Project, 2020–2022) A$593,621: Relaxed Reflection Methods for Feasibility and Matrix Completion Problems (ARC Discovery Project, 2016–2018) Collaborations & Teams: Hogan collaborates with experts like Prof. Kevin Galvin (University of Newcastle), Prof. Joseph Lakey (New Mexico State University), and Dr. Matthew Tam (University of Göttingen). His work integrates interdisciplinary approaches, combining pure mathematics with applications in engineering and mineral processing. His lab focuses on computational harmonic analysis and optimization-driven wavelet design.

Ravi PethiyagodaView profile

Lecturer

• Fluid Mechanics
• Computational Mathematics
• Free-Surface Flows
• +3 more

Dr. Ravi Pethiyagoda is a Lecturer in Mathematics at the School of Information and Physical Sciences, University of Newcastle. His research focuses on fluid mechanics, computational mathematics, and free-surface flows, with particular emphasis on ship wave dynamics and tsunami propagation. He holds a PhD in Applied Mathematics from Queensland University of Technology (2016) and has conducted postdoctoral research on droplet impaction on plant leaves and nonlinear ship wave patterns. His work includes advancing numerical methods like Jacobian-free Newton-Krylov (JFNK) techniques to solve complex fluid dynamics problems. He has contributed to understanding the apparent wake angle, time-frequency analysis of ship wakes, and compressible ocean wave propagation. Current PhD supervision spans wave scattering, fluid dynamics with surface tension, and ocean wave problems. Key research areas include: numerical methods for nonlinear ship wave problems, analysis of wake patterns, spectrogram-based wave signal interpretation, and tsunami wave dynamics under variable bathymetry. His interdisciplinary studies bridge mathematics, physics, and engineering, with applications in oceanography and agricultural fluid dynamics.

Alexander GoncharovView profile

Lecturer

• Functional Analysis
• Basis Problem
• Locally Convex Spaces
• +7 more

Dr. Alexander Goncharov is a Lecturer in the Department of Mathematics at Bilkent University, Turkey. His research focuses on functional analysis, approximation theory, and potential theory, with significant contributions to basis problems and extension operators. Research areas include: locally convex spaces, Whitney functions, extremal polynomials, generalized Julia sets, harmonic measures, and Widom factors. Active in scientific guidance at Master’s and PhD levels. Coordinator of Bilkent’s Analysis Seminar and editorial board member for mathematics journals. Contact: Office SA-122, Tel: +90-312-290-1923, goncha@fen.bilkent.edu.tr His recent work explores Cantor-type sets, polynomial asymptotics, and metric properties of harmonic measures, reflecting interdisciplinary connections between approximation theory and complex dynamics.

Damek Shea DavisView profile

Assistant Professor

• Optimization
• Stochastic Algorithms
• Nonconvex Optimization
• +4 more

Damek Shea Davis is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, College of Engineering. He received his Ph.D. in Mathematics from UCLA in 2015 and joined Cornell in 2016. Davis focuses on mathematical foundations of data science, with particular expertise in optimization, statistical learning, and numerical algorithms. His research develops theoretical frameworks and efficient algorithms for nonconvex, nonsmooth, and stochastic optimization problems. Key contributions include convergence guarantees for stochastic subgradient methods on nonconvex functions, analysis of optimization landscapes for phase retrieval, and development of methods for high-dimensional statistical estimation. His work bridges mathematical optimization with machine learning and signal processing applications. Davis has received numerous honors including the Sloan Research Fellowship (2020) and INFORMS Optimization Society Young Researchers Prize (2019). His publications advance understanding of optimization algorithm behavior in statistically challenging regimes, with recent work exploring geometric convergence properties and optimality conditions.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Maryna ViazovskaView profile

Professor

• Sphere Packing
• Lattice Theory
• Modular Forms
• +3 more

Prof. Maryna Viazovska is a Full Professor and holds the Chair of Arithmetics at the Institute of Mathematics (EPFL SB MATH TN) at the École Polytechnique Fédérale de Lausanne (EPFL). She also serves as a Full Professor in the SMA-ENS division, part of the School of Basic Sciences (SB). Additionally, she leads the Theoretical Number Theory (TN) group , directs the MATH-GE unit, and is a member of the Bernoulli Center's Executive Committee (CIB) and the SB Faculty Board. Her roles reflect her leadership in both research and academic governance. Her research focuses on sphere packing , lattice theory , modular forms , and Fourier analysis . She is celebrated for solving the sphere packing problem in dimensions 8 and 24, a breakthrough integrating modular forms and harmonic analysis. Her work bridges number theory, discrete geometry, and mathematical physics, with implications for coding and cryptography. Prof. Viazovska has advised doctoral students including Matthias Gröbner Nihar Prakash Gargava Gauthier Leterrier Martin Peter Stoller Her teaching includes courses such as Discrete Mathematics , Riemann Surfaces , and Number Theory II.b - Modular Forms . She actively contributes to EPFL's academic infrastructure, including the Institute of Mathematics and the Bernoulli Center .

Boris LevitView profile

Professor

• Non-parametric estimation
• Semi-parametric models
• Optimal design in non-parametric regression
• +5 more

Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.

Alexander BelyaevView profile

Associate Professor

• Image Processing
• Signal Processing
• Computer Vision
• +6 more

Dr. Alexander Belyaev is an Associate Professor at the Institute of Sensors, Signals & Systems within the School of Engineering & Physical Sciences at Heriot-Watt University. His research focuses on advanced mathematical and computational methods in image processing, signal processing, and geometry processing. He has contributed to areas such as distance function approximation, image deblurring, dehazing, and texture analysis. His work often integrates variational methods and partial differential equations (PDEs), addressing challenges in computer vision and geometric modeling. Research interests include image enhancement techniques like multiplicative deblurring and reverse filtering, as well as geometric algorithms for shape interrogation and surface reconstruction. His publications span over 15 years, with recent emphasis on deep learning applications in image dehazing and variable exponent diffusion for texture separation. Collaborations involve international teams, particularly in signal processing and computer graphics. No scientific awards are explicitly listed. His advising and grants are not detailed in the provided texts, but his lab work is centered at the Institute of Sensors, Signals & Systems, focusing on interdisciplinary research in sensing technologies and computational methods.

Leili Rafiee SevyeriView profile

Assistant Professor

• Hybrid Symbolic-Numeric Computation
• Scientific Computing
• Computer Algebra
• +3 more

Leili Rafiee Sevyeri is an Assistant Professor in the Teaching Stream at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. Her research focuses on hybrid symbolic-numeric computation, scientific computing, and computer algebra. She holds a PhD and MSc in Applied Mathematics from Western University (2020 and 2016, respectively). Her teaching spans theoretical computer science, numerical methods in finance/business, and introductory computer science courses. She is based in location MC 6431 on campus and can be reached at leili.rafiee.sevyeri@uwaterloo.ca . Research interests include developing computational methods at the intersection of symbolic and numeric techniques, with applications in linear algebra, polynomial systems, and matrix theory. Recent work emphasizes interpolation methods, Bohemian matrix analysis, and approximate GCD algorithms. Her publications (e.g., Google Scholar ) reflect a focus on advancing computational tools for scientific and mathematical problems. No scientific awards are explicitly listed in the provided information. Advising and grant details are not mentioned, but her academic webpage provides further details on ongoing and past projects. She is actively involved in computational mathematics education and research within the university's Faculty of Mathematics.