Find Academic Supervisors

Mylène BédardView profile

Professor

• Computational Statistics
• Bayesian Methods
• Markov Chain Monte Carlo (MCMC)
• +5 more

Mylène Bédard is a Full Professor in the Department of Mathematics and Statistics at Université de Montréal. She holds a Ph.D. from the University of Toronto (2006) and oversees the graduate programs in statistics. Her research focuses on computational statistics, Bayesian methods, and Markov Chain Monte Carlo (MCMC) algorithms, with emphasis on optimal scaling, algorithm efficiency, and robust statistical inference. She has collaborated on topics such as MALA algorithms, hierarchical models, and risk theory in actuarial science. Her teaching spans advanced courses in actuarial science (e.g., risk theory, financial mathematics) and statistical theory (e.g., Bayesian inference, survival analysis). She has supervised over 20 graduate students, many of whom have received prestigious awards including the Prix Serge-Tardif and Prix Constance-van Eeden. Her work integrates theoretical advancements with practical applications in finance, climatology, and healthcare. Key research themes include adaptive MCMC methodologies, Bayesian robustness, and computational techniques for high-dimensional data. Recent projects involve regional adaptive algorithms, non-stationary phase analysis of MALA, and geometric approaches to Bayesian marginalization. Her contributions bridge statistical theory and computational innovation, addressing challenges in sampling efficiency and algorithmic convergence.

Karim Anaya-IzquierdoView profile

Senior Lecturer

• Statistics
• Information Geometry
• Mechanical Engineering
• +2 more

Karim Anaya-Izquierdo is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath, where he specializes in statistical methodology with applications in engineering and computational mathematics. His research integrates information geometry, spatial statistics, and Bayesian inference to solve complex problems across domains including materials science, epidemiology, and environmental studies. His methodological work focuses on parametric modeling, tensor decompositions for high-dimensional data, and geometric approaches to statistical inference. Applied research includes collaborative projects on composite materials certification with aerospace industry partners and spatial analysis of disease intervention trials. Recent publications demonstrate a strong emphasis on computational statistics, with research bridging theoretical mathematics and practical applications in clinical trials, ecological modeling, and reliability engineering. Articles frequently develop novel methods for uncertainty quantification in complex systems. Dr. Anaya-Izquierdo is involved in the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and co-investigator on multiple research council projects. He teaches advanced statistics courses and supervises doctoral students in mathematical sciences.

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

Charles Smart is a Professor of Mathematics at Yale University, located at Kline Tower - 219 Prospect Street, New Haven, CT 06511. His research focuses on partial differential equations, probability theory, and stochastic homogenization, with notable contributions to elliptic equations, statistical physics, and discrete models such as the Abelian sandpile. He holds a prominent academic position within the Department of Mathematics at Yale's Faculty of Arts and Sciences. Smart's work bridges theoretical mathematics with applications in physics and engineering, addressing topics like unique continuation properties, localization phenomena, and scaling limits. His recent publications explore advanced concepts such as large-scale analyticity in elliptic systems, free boundary problems, and the interplay between singular integrals and mixing flows. He has contributed to foundational studies in stochastic homogenization, including quantitative error estimates and convex integral functionals. His research also extends to geometric analysis, such as convex hull peeling and facet formation in free boundary problems. No specific grants or awards are listed in the provided information. His advising record remains unspecified, though his academic standing suggests significant mentorship potential.

Eliza O’ReillyView profile

Assistant Professor

• Mathematics of Data Science
• Stochastic and Convex Geometry
• Point Processes
• +4 more

Eliza O’Reilly is an Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University (JHU) and a member of the Data Science and AI Institute. Her research focuses on the mathematical foundations of data science, combining stochastic and convex geometry, high-dimensional probability, and statistical learning theory. She develops geometric models and algorithms for analyzing complex data structures, with applications in machine learning, optimization, and high-dimensional statistics. Dr. O’Reilly holds a B.S. in Mathematics from the University of Pittsburgh (2013) and a Ph.D. in Mathematics from the University of Texas at Austin (2019), where she was advised by François Baccelli. She completed a postdoctoral fellowship at the California Institute of Technology (2019–2022), sponsored by Venkat Chandrasekaran and Joel Tropp. Her work has been supported by NSF fellowships, including a NSF Graduate Research Fellowship and a NSF Postdoctoral Research Fellowship. Her research interests include: Randomized partitioning algorithms (e.g., Mondrian forests) Regularization in statistical inference and inverse problems Convex and nonconvex optimization methods Determinantal point processes and repulsive models High-dimensional geometry of random convex sets Her publications span theoretical guarantees for machine learning algorithms, optimization frameworks for data-driven problems, and geometric analysis of stochastic processes. Notable contributions include minimax analyses for random tessellation forests and spectral methods for convex regression. She actively collaborates with researchers in applied mathematics, statistics, and computer science.

Edward ScheinermanView profile

Professor

• Discrete Mathematics
• Graph Theory
• Random Methods
• +3 more

Edward Scheinerman is a Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He currently serves as the Vice Dean for Special Projects, previously holding roles such as Vice Dean for Graduate Education and Chair of the Department of Applied Mathematics and Statistics. He is a Fellow of the American Mathematical Society and the Institute of Combinatorics and its Applications. His research focuses on discrete mathematics, including graph theory, random methods, and partially ordered sets. He is renowned for inventing random dot product graphs, which model social and biological networks. His work bridges theoretical mathematics with practical applications in networks and algorithms. Scheinerman authored multiple influential books, including Mathematics Lover’s Companion and textbooks like Invitation to Dynamical Systems and Mathematical Notation . He has received teaching awards such as the Distinguished Faculty Award and the Robert B. Pond, Sr. Excellence in Teaching Award. His academic journey includes a B.A. in Mathematics from Brown University (1980), and M.S. and Ph.D. from Princeton University (1981, 1984). He joined Johns Hopkins in 1984.

Fedor Levkovich-MaslyukView profile

Lecturer

• Mathematical Physics
• String Theory
• Integrable Models
• +3 more

Dr. Fedor Levkovich-Maslyuk is a Lecturer in Mathematics at City St George's, University of London, where he joined in 2024. His work lies at the intersection of theoretical physics and mathematics, with a focus on fundamental quantum systems. Research Interests: Dr. Levkovich-Maslyuk specializes in mathematical physics, with core expertise in string theory, integrable models, quantum field theory, and random matrix models. His research aims to develop innovative analytical methods to explore the deep structures of quantum systems. These areas bridge high-energy physics and advanced mathematics, contributing to the understanding of non-perturbative phenomena and exact solutions in quantum theories. Although no recent publications are listed in the provided text, his research trajectory suggests strong engagement with theoretical frameworks in high-energy and condensed matter physics, likely involving algebraic and geometric methods in quantum systems. Honors and Recognition: Prestigious Junior Research Chair at the École Normale Supérieure, Paris Advising and Grants: No information is available regarding graduate students or research grants at this time. Labs and Research Groups: No specific research group or laboratory affiliation is mentioned in the provided text. His office is located in C121, Tait Building, at the university’s Northampton Square campus in London.

Marni MishnaView profile

Professor

• Analytic Combinatorics
• Enumerative Combinatorics
• Algorithmic Solutions
• +5 more

Marni Mishna is a Full Professor in the Department of Mathematics at Simon Fraser University’s Faculty of Science. Her academic roles include Associate Dean, Equity, Diversity, and Inclusion (2023–2028) and PIMS Deputy Director (2019–2021). She has held visiting positions at institutions such as CNRS (France), LaBRI (University of Bordeaux), and the Fields Institute. Education: PhD in Mathematics (Université du Québec à Montréal, 2003), MSc in Pure Math (SFU, 2000), BMath (University of Waterloo, 1997). Her research spans analytic and enumerative combinatorics , focusing on generating functions , lattice paths , D-finite symmetric functions , and algorithmic solutions . She explores the intersection of combinatorics with art and design for mathematical communication. Recent articles analyze differential transcendence , Kronecker coefficients , and lattice path universality classes , emphasizing computational methods and asymptotic analysis. Her work often bridges graph theory , symmetric functions , and algebraic structures . She supervises students and postdocs in projects related to combinatorial theory , graph enumeration , and symmetric function applications . Her teaching includes advanced courses like MATH 443/743 (Combinatorics) and MATH 322 (Complex Analysis).

Steven M. HeilmanView profile

Assistant Professor

• Probability
• Analysis
• Geometry
• +5 more

Steven M. Heilman is an Assistant Professor at the University of Southern California , specializing in research at the intersection of Probability, Analysis, Geometry , and Theoretical Computer Science . His work bridges mathematical theory with applications in algorithmic hardness, social choice, and quantum computing. Affiliation: Department of Mathematics, University of Southern California Advisor: Professor Assaf Naor (Princeton University) Research Trends: From 2021–2025, Heilman's publications focus on noise stability of voting systems, geometric inequalities, Gaussian isoperimetric problems, and algorithmic challenges in quantum computing. Key themes include symmetric sets, tree structures, and applications of probability to computational complexity. Advising & Grants: He supervises high school and undergraduate research projects. In 2025, he received an NSF CCF Grant AF: Small ($200,000) for research on geometric inequalities, clustering hardness, and social choice theory. Conferences: He organized the HIM Conference on Information Theory, Boolean Functions, and Lattice Problems (2024) and the IPAM Conference on Calculus of Variations in Probability and Geometry (2022).

Omer BobrowskiView profile

Professor

• Topological Data Analysis
• Stochastic Topology
• Applied Topology
• +4 more

Omer Bobrowski is a Professor in Mathematical Data Science at Queen Mary University of London, affiliated with the School of Mathematical Sciences. His research focuses on stochastic topology, topological data analysis (TDA), and their applications in signal processing and natural language processing. He explores theoretical aspects like phase transitions in stochastic topology and noise distribution in TDA tools, while developing statistical methods for practical applications. His work has been supported by grants from the EPSRC (£396,018, 2024–2027) and the Leverhulme Trust (£330,778, 2024–2027). Collaborators include Primoz Skraba and others in the Centre for Probability, Statistics, and Data Science. Research Interests include: Random Topology, Applied Topology, Stochastic Geometry, and Probability Theory. His lab includes Research Staff Dr. Shu Kanazawa, Dr. Uzu Lim, and Dr. Duncan Parker. Bobrowski’s publications span foundational TDA theory and applied methodologies across diverse datasets.

• Coding Theory
• Combinatorics
• Error-Correcting Codes
• +2 more

Anina Gruica is a Research Fellow in the Algebra group at the Technical University of Denmark (DTU), specializing in coding theory with emphases on density questions, combinatorial structures of error-correcting codes, and DNA-based data storage systems. Her work bridges theoretical mathematics and practical applications in next-generation storage technologies. Her research program centers on Coding Theory and Combinatorics, investigating the geometric and probabilistic properties of codes in metric spaces. Key interests include rank-metric codes, MRD codes, and combinatorial optimization for DNA storage efficiency, where she develops novel approaches to random access and coverage depth challenges through algebraic and geometric frameworks. Dr. Gruica's publication portfolio reveals a strong interdisciplinary trajectory, with increasing focus on DNA storage applications since 2022. Her work combines deep theoretical insights in combinatorial geometry with practical storage system design, resulting in high-impact publications across SIAM journals, IEEE conferences, and arXiv preprints that address both classical coding problems and emerging biological storage constraints. She has received competitive recognition including: ALCOCRYPT conference travel award (February 2023) DIAMANT PhD travel grant (2000 EUR, January 2023) DIAMANT visitor grant (1900 EUR, October 2021) SIAM Travel Award (August 2021) As co-organizer of the Postgraduate International Coding theory Seminar (PICS), Dr. Gruica actively mentors junior researchers while maintaining extensive collaborations with A. Ravagnani, J. Sheekey, and E. Yaakobi. Her conference presentations at venues like ISIT and SIAM AG23 demonstrate her leadership in translating theoretical advances to storage applications. She contributes to DTU's Algebra group research ecosystem, focusing on algebraic structures for coding theory and cryptography. Her current projects, evident from 2024-2025 preprints, explore combinatorial geometry for DNA storage efficiency and advanced rank-metric code constructions, positioning her at the forefront of coding theory's application to biological data systems.

Eveliina PeltolaView profile

Associate Professor

• Mathematical Physics
• Probability Theory
• Conformal Field Theory
• +3 more

Eveliina Peltola is an Associate Professor at Aalto University's Department of Mathematics and Systems Analysis, within the School of Science. She is also affiliated with the University of Bonn's Hausdorff Center for Mathematics. Her research focuses on mathematical physics, combining algebraic, geometric, and probabilistic methods to study conformal field theory, random geometry, and scaling limits. Her academic positions include roles in Algebra and Discrete Mathematics, Mathematical Physics, and Analysis. She is a member of the Finnish Academy of Science and Letters and has been supported by grants such as the Academy of Finland, ERC Starting Grant, and German Research Foundation. Key research interests include Schramm-Loewner Evolution (SLE), conformal invariance, and logarithmic conformal field theory. Recent work involves connection probabilities of interfaces, large deviations, and degenerate conformal blocks. Notable publications span 2016–2025, addressing topics like SLE processes, Ising models, and geometric probability. Eveliina's contributions are supported by grants including the ISCoURaGe Finnish Centre of Excellence and the SFB Collaborative Research Centre 1060. Her research bridges mathematics and physics, with applications in statistical mechanics and random geometry. Awards and recognitions include her membership in the Finnish Academy of Science and Letters and funding from prestigious institutions like the ERC and Academy of Finland.

• Mathematics
• Geometric Function Theory
• Partial Differential Equations
• +4 more

Kari Astala is a Research Professor and Research Director at the Department of Mathematics and Statistics, University of Helsinki. He also serves as the Supervisor for the Doctoral Programme in Mathematics and Statistics. His research focuses on geometric function theory, partial differential equations, and quasiconformal mappings, with interdisciplinary applications in inverse problems and materials science. Affiliations: University of Helsinki, Department of Mathematics and Statistics, Doctoral Programme in Mathematics and Statistics Major research interests include the study of quasiconformal mappings, elliptic PDEs, and their connections to nonlinear elasticity and inverse problems. Astala has contributed to foundational results in the field, such as the solution to Calderón’s inverse conductivity problem in the plane and the development of theories around Beltrami equations and Schauder estimates. His publications (43) span decades, with recent work addressing global smoothness in Triebel-Lizorkin spaces and homogenization of singular integrals. Key articles include analyses of nonlinear Beltrami operators and principal maps, reflecting his deep engagement with geometric analysis and its applications. Awards: Salem Prize (1994), Finnish Science Foundation's Magnus Ehrnrooth Prize (2003), Finnish Cultural Foundation Prize of Recognition (2011) Astala has actively supervised doctoral students and led research projects like QUAMAP (2019–2024), exploring quasiconformal methods in analysis. He maintains international collaborations, including visiting positions at the Instituto de Ciencias Matematicas (ICMAT) and the Universidad Autonoma de Madrid.

• Fractals
• Geometric Measure Theory
• Metric Rectifiability
• +3 more

Tuomas Orponen is a Professor at the Department of Mathematics and Statistics, University of Helsinki, where he serves as a supervisor for the Doctoral Programme in Mathematics and Statistics. His research focuses on fractal geometry, geometric measure theory, and quantitative rectifiability, with particular emphasis on the interplay between geometric configurations and measure-theoretic properties in Euclidean and non-Euclidean spaces. He has led major research projects such as 'Incidences on Fractals' and 'Quantitative Rectifiability of Sets and Measures,' supported by the University of Helsinki. Orponen has published over 40 peer-reviewed articles and actively contributes to academic activities, including organizing conferences like 'Geometric Measure Theory and its Connections' and delivering invited talks at events like the Barcelona Analysis Conference 2019. His work bridges pure mathematics and applications in harmonic analysis and combinatorics, addressing questions about fractal structures, projection theorems, and metric dimensions. Orponen has collaborated internationally, visiting institutions such as the Institute of Mathematics of the Polish Academy of Sciences (IMPAN) and hosting researchers at the University of Helsinki. His research has advanced understanding in areas like Hölder-regular surfaces, self-similar measures, and continuous analogues of combinatorial problems.

Amirali Khodadadian GostarView profile

Senior Lecturer

• Machine Learning
• Data Analytics
• Multi-Object Tracking
• +5 more

Dr. Amirali Khodadadian Gostar is a Senior Lecturer at the School of Engineering, RMIT University. His research focuses on machine learning, data analytics, multi-object tracking, sensor management, and data-driven manufacturing. He has contributed to advancements in autonomous systems, anomaly detection, and multi-agent coordination through projects like distributed information fusion for connected vehicles and geometrically-informed tracking algorithms. Research Interests : Image processing, sensor fusion, robotics, control systems, and industrial automation. Key Projects : Development of electronic pre-tension systems for seatbelts, AI-driven logistics optimization, and stereo vision systems for defect detection. Publications : Over 60 peer-reviewed articles in top journals/conferences such as IEEE Transactions on Intelligent Transportation Systems and ISA Transactions, focusing on tracking algorithms, anomaly detection, and multi-agent systems. His work bridges theoretical frameworks (e.g., random finite set theory) with practical applications in robotics, transportation, and manufacturing. He actively supervises PhD/Master's students on topics ranging from quantum AI in logistics to eye gaze tracking for visual attention modeling.

• Photovoltaic Materials
• Quantum Dot Systems
• Complex Networks
• +5 more

Lucas Cuadra Rodríguez is a Full Professor in the Department of Signal Theory and Communications at Universidad Alfonso X El Sabio (UAX). He holds a PhD from Universidad Politécnica de Madrid (2004), specializing in intermediate band solar cell development. His research spans quantum dot systems, network science applications in materials and energy systems, and machine learning for environmental and engineering challenges. Key areas include photovoltaic materials, complex network modeling of disordered systems, and renewable energy optimization. Education: Doctorado en Física from Universidad Politécnica de Madrid (2004), supervised by Dr. Antonio Marti Vega and Dr. Antonio Luque López. His work bridges physics, computer science, and engineering, with a focus on interdisciplinary solutions for energy and environmental systems. Research Interests: His primary focus is on developing novel semiconductor solar cell structures, particularly quantum dot-based intermediate band solar cells, and applying network science to study carrier transport in nanomaterials. He also explores machine learning applications in Earth observation, wind energy prediction, and complex system analysis. His recent work emphasizes spatially embedded networks and their relevance to material science and renewable energy infrastructure. Notable Projects: Leading the GHEODE Research Group (Modern Heuristics and Network Design), he investigates optimization algorithms for smart grids and network robustness. Recent studies include modeling electron transport in van der Waals materials and analyzing fog event persistence through nonlinear dynamics. Awards: No specific awards mentioned in the provided text. Advising & Grants: While his PhD supervision details are noted, no current student advisees or grant projects are detailed here. His work often involves collaborative interdisciplinary projects with engineering and environmental science teams. Labs/Teams: Active in the GHEODE Group at UAX, focusing on heuristic algorithms and network-based solutions for engineering challenges.