جست‌وجوی استاد و پژوهشگر

Raffaella Mulasمشاهده پروفایل

استادیار

• Spectral Theory of Graphs and Hypergraphs
• Discrete Laplacians
• Non-backtracking Operators
• +۷ مورد دیگر

Raffaella Mulas is an Assistant Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. She previously served as a Group Leader and Minerva Fast Track Fellow at the Max Planck Institute for Mathematics in the Sciences, where she maintains an ongoing affiliation. Her research lies at the intersection of spectral graph theory, discrete mathematics, and network science. Research Interests: Her work focuses on the spectral theory of graphs and hypergraphs, particularly the properties of discrete Laplacians and non-backtracking operators. She investigates extremal combinatorics problems such as graph coloring and the Turán problem, often applying spectral methods to derive sharp bounds. Her research has strong applications in modeling and analyzing complex networks. Recent Research Trends: Analysis of the 15 most recent publications reveals a consistent focus on spectral characterizations of graphs and hypergraphs, including signed and complex unit hypergraphs. She frequently studies the normalized Laplacian and its extremal eigenvalues, develops non-backtracking operators, and explores measure-theoretic and geometric representations of networks. A strong thread connects spectral bounds to combinatorial invariants like chromatic number. VU Startpremie Grant Elected Member, European Mathematical Society Young Academy (EMYA) Elected Member, Elisabeth-Schiemann-Kolleg, Max Planck Society Minerva Fast Track Fellow, Max Planck Institute Advising and Grants: While no formal students are listed, she is an active researcher with significant grant funding, notably the VU Startpremie Grant. She collaborates internationally and supervises research projects in spectral graph theory and network analysis. Her affiliation with both VU Amsterdam and MPI-MiS enables broad academic mentorship and collaborative supervision. Labs and Research Groups: Raffaella Mulas leads research within the Mathematics Department at VU Amsterdam and is affiliated with the research group at the Max Planck Institute for Mathematics in the Sciences. Her work contributes to advancing theoretical foundations in discrete mathematics with applications in data science and network modeling.

• Probability Theory
• Complex Analysis
• Mathematical Physics
• +۴ مورد دیگر

Professor Amanda Turner is a faculty member in the School of Mathematics at the University of Leeds, part of the Faculty of Engineering and Physical Sciences. Her research lies at the intersection of probability, analysis, and mathematical physics, with a focus on understanding the macroscopic behavior of complex random systems. Her research interests include: Probability theory Complex analysis Mathematical physics Random growth models Scaling limits of stochastic models She is affiliated with key research groups such as Statistics, Probability and Financial Mathematics, and Statistical Methodology and Probability. Her current work investigates the scaling limits of random growth models, contributing to foundational understanding in statistical mechanics and stochastic processes. Professor Turner welcomes PhD students and research collaborators interested in these areas, with active postgraduate research opportunities available at the university. She has no listed scientific awards in the provided text. She advises potential graduate students to contact her regarding ongoing projects, particularly in the domain of stochastic modeling and probabilistic analysis. While specific grants are not listed, her research group is actively engaged in theoretical and applied probability research. She is involved in the Probability and Financial Mathematics research group, contributing to collaborative work in theoretical probability and its applications.

• Theory of Computation
• Discrete Algorithms
• Theoretical Computer Science
• +۵ مورد دیگر

Michael Saks is a Distinguished Professor of Mathematics at Rutgers, The State University of New Jersey . He is affiliated with both the Department of Mathematics and the Computer Science Department as a graduate faculty member. His research focuses on theory of computation and discrete algorithms , with applications in computational complexity, combinatorics, and algorithmic analysis. His recent publications include work on edit distance approximations , randomized algorithms , discrepancy of random matrices , and Boolean function analysis , reflecting a strong emphasis on theoretical foundations. He has contributed to journals such as Combinatorica , Journal of Graph Theory , and Discrete Applied Mathematics , and served on editorial boards and conference program committees, including the 2014 IEEE Conference on Computational Complexity . Michael Saks maintains active research and teaching pages , including guidance for graduate applicants and links to seminars like the Discrete Mathematics/Theory of Computing Seminar . He also participates in initiatives such as the Center for Computational Intractability and DIMACS .

Bhaswar B. Bhattacharyaمشاهده پروفایل

دانشیار

• nonparametric statistics
• distribution-free inference
• networks analysis
• +۴ مورد دیگر

Bhaswar B. Bhattacharya is an Associate Professor of Statistics and Data Science at The Wharton School of the University of Pennsylvania, with a secondary appointment in the Department of Mathematics. His research spans several interconnected areas at the intersection of statistics, probability, and computational geometry. Dr. Bhattacharya received his Ph.D. in Statistics from Stanford University in 2016 under the supervision of Persi Diaconis. Prior to that, he earned both his Bachelor of Statistics (2009) and Master of Statistics (2011) from the Indian Statistical Institute in Kolkata. His research interests focus on three main pillars: nonparametric statistics (including distribution-free inference, nearest-neighbor methods, and inference on networks), combinatorial probability (covering counting problems in random graphs, random colorings, and graph limit theories), and discrete and computational geometry (including facility location problems, Voronoi games, and geometric Ramsey problems). His work often bridges theoretical developments with practical applications in network analysis, statistical learning, and geometric optimization. Recent publications demonstrate a strong trajectory in developing distribution-free methods for network analysis, with significant contributions to understanding fluctuations in graphon-based random graphs and developing optimal tests for inhomogeneous random graph models. His work also shows increasing focus on higher-order network structures through hypergraph models and applications to real-world problems like vaccination site optimization. NSF Career Award (2021-2026) Alfred P. Sloan Research Fellowship (2021) Probability Dissertation Award, Stanford University (2016) Sabyasachi Roy Memorial Gold Medal for best master's thesis, Indian Statistical Institute (2009-2011) Dr. Bhattacharya teaches advanced courses in mathematical statistics at both undergraduate and graduate levels at Wharton. His research program involves collaborations across multiple institutions and disciplines, with recent work applying statistical methods to public health challenges such as optimizing vaccination site locations. He maintains active research collaborations with colleagues in statistics, computer science, and applied mathematics departments.

Nik Cunniffeمشاهده پروفایل

استاد

• Epidemiological Modeling
• Plant Disease Ecology
• Computational Biology
• +۳ مورد دیگر

Nik Cunniffe is a Professor in the Department of Plant Sciences at the University of Cambridge, specializing in epidemiological modeling of plant pests and pathogens. His research focuses on developing theoretical and applied models to inform policy decisions regarding disease detection, control, and evolutionary dynamics. Key Research Areas: Theoretical epidemiology (deterministic, stochastic, and spatial models) Applied plant disease management (Phytophthora ramorum, ash dieback) Computational simulation techniques at large spatial scales Projects: Approximate models for complex disease management Stakeholder behavior impact on control efficacy Optimal fungicide resistance strategies Trap/repellent companion plants for virus control Collaborations: Cambridge NERC Doctoral Training Partnership (C-CLEAR DTP) NERC-funded CREATES program Email: njc1001@cam.ac.uk

• Geophysics
• Machine Learning
• Joint Inversion
• +۷ مورد دیگر

Jiajia Sun is an Associate Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston. Her research focuses on advancing subsurface imaging, uncertainty quantification, and mineral exploration through interdisciplinary approaches combining geophysics, machine learning, and computer vision. Education : PhD in Geophysics (2015, Colorado School of Mines); BS in Geophysics (2008, China University of Geosciences, Wuhan). Research Interests : Jiajia specializes in deep learning for geophysical inversion, multi-physics data integration, and probabilistic geological modeling. Her work leverages computational resources like GPUs and clusters to solve inverse problems and tackle magnetic remanence challenges. Recent Publications : Her research includes applying Bayesian frameworks, deep generative models, and joint inversion algorithms to airborne geophysics for critical mineral mapping and hydrogen reservoir detection. She emphasizes open-source tools like SimPEG for reproducibility. Awards : J. Clarence Karcher Award (SEG) Advising & Collaborations : She mentors PhD students in geophysics and collaborates with institutions like Amazon’s Generative AI Innovation Center, Stanford University, and University College Dublin. Her team also tests drones and magnetometers at the UH Coastal Center.

• Numerical Analysis
• Applied Mathematics
• Scientific Computing
• +۴ مورد دیگر

Virginie Ehrlacher is a Professor at CERMICS, École des Ponts ParisTech (ENPC), France. She specializes in applied mathematics with a focus on high-dimensional problems, numerical analysis, and computational modeling. Her work bridges quantum chemistry, materials science, and machine learning through innovative mathematical frameworks. Education includes: PhD in Mathematics (2012) from ENPC: Mathematical models in quantum chemistry and uncertainty quantification Habilitation (2020) from Université Paris-Dauphine: Mathematical and numerical analysis of high-dimensional and multiscale problems in materials science Research spans multiscale modeling, tensor decompositions for high-dimensional systems, cross-diffusion equations, and scientific machine learning. Her work frequently addresses challenges in quantum mechanics, materials science, and computational physics using advanced numerical techniques. Publications emphasize: Algorithms for high-dimensional PDEs and eigenvalue problems Model reduction techniques (tensor networks, reduced basis methods) Cross-diffusion systems with biological/physical applications Neural networks for scientific computing Awards and distinctions: Irène Joliot-Curie Prize (2023) Chevalier de l’Ordre National du Mérite (2025) Leadership includes: ERC Starting Grant HighLEAP (2023–2028) ERC Synergy project EMC2 (2020–2026) ANR JCJC project COMODO (2019–2023) She co-leads the EMS Topical Activity Group on Scientific Machine Learning. Affiliated with the CERMICS laboratory, she collaborates on interdisciplinary teams tackling multiscale and data-driven modeling challenges.

Nadia Benakliمشاهده پروفایل

استاد

• Geometric Group Theory
• Graph Theory
• Mathematics Education
• +۳ مورد دیگر

Dr. Nadia Benakli is a Professor of Mathematics at New York City College of Technology (City Tech), part of the City University of New York system, within the School of Arts & Sciences. She coordinates the Computer Science and Applied Mathematics internship programs, bridging academic theory with industry practice. Her career spans decades of contributions to both pure mathematics and educational innovation. Her educational foundation includes a Ph.D. in Mathematics from Paris-Sud University, France. Research interests center on Geometric Group Theory (hyperbolic groups, Coxeter groups, polygonal complexes), Graph Theory , and Mathematics Education innovations. She integrates computational tools like R and Maple into calculus and data analysis instruction, emphasizing visualization and hands-on projects to enhance student comprehension in foundational mathematics courses. Publication trends reveal an evolution from theoretical work (1991-2002) on geometric structures to applied educational research (2011-2017). Early papers established frameworks for hyperbolic geometry and group boundaries, while recent work focuses on computational thinking, faculty mentoring models, and internship program design—demonstrating her commitment to translating abstract mathematics into practical educational strategies. As internship coordinator, she mentors students through industry placements and co-developed SIAM News-published frameworks for applied mathematics internships. Her teaching portfolio spans foundational courses (MAT065, MAT1175) to advanced topics (MAT440, MAT4901), reflecting deep engagement with curriculum development across all undergraduate levels.

Paul Peter Hagerمشاهده پروفایل

استادیار

• Mathematical Finance
• Machine Learning
• Stochastic Control
• +۴ مورد دیگر

Paul Peter Hager serves as an Assistant Professor in the Department of Statistics and Operations Research at the University of Vienna, where he teaches courses including Linear Algebra and Applied Optimization. Previously, he held a junior research group leader position at Technische Universität Berlin. His research centers on: Mathematical Finance Machine Learning Stochastic Control Mean-Field Games Fractional Processes Gaussian Multiplicative Chaos Volatility Modeling Hager pioneers applications of rough path signatures in financial mathematics, developing novel frameworks for stochastic control and calibration problems. His work bridges theoretical probability with practical machine learning implementations, particularly in volatility modeling using fractional processes and log-correlated fields. Recent publications reveal a dominant trend in signature-based methods for optimal stopping and mean-field games, with significant contributions to fractional Brownian motion theory. His collaborative work with leading researchers like Peter Friz and Christian Bayer consistently targets high-impact journals in applied probability and financial mathematics. Dr. Hager maintains active research collaborations and has delivered invited talks at institutions including KAUST, focusing on computational implementations of signature methods in finance.

• Applied Analysis
• Numerical Analysis
• Data Science
• +۶ مورد دیگر

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

Franck Iutzelerمشاهده پروفایل

استاد

• Numerical optimization
• Statistical learning
• Stochastic programming
• +۵ مورد دیگر

Franck Iutzeler is a Professor of Applied Mathematics at Université de Toulouse, working within the Statistics & Optimization team of the Institut Mathématique de Toulouse and teaching in the Department of Mathematics. He previously served as an Assistant Professor at Université Grenoble Alpes from 2015 to 2023 and completed his Habilitation à Diriger des Recherches in 2021. His research focuses on the intersection of optimization, statistics, and optimal transport theory to develop robust data-driven models. Key areas include numerical optimization, statistical learning, stochastic programming, and optimal transport. He is particularly interested in distributionally robust optimization using Wasserstein metrics and has developed the skwdro Python library for implementing these methods. Iutzeler's recent publications demonstrate a strong focus on Wasserstein Distributionally Robust Optimization (WDRO), with multiple papers in top venues like NeurIPS and SIAM Journal on Optimization. His work bridges theoretical guarantees with practical implementation, particularly through the skwdro library which provides efficient code for WDRO in machine learning applications. ANR JCJC grant for project STROLL: Harnessing Structure in Optimization for Large-scale Learning Co-PI of ANITI chair on Trust and Responsibility in Artificial Intelligence led by JM. Loubes and J. Bolte Iutzeler actively supervises PhD students including Yu-Guan Hsieh (awarded Université Grenoble Alpes's PhD award), Gilles Bareilles, Waïss Azizian, and Victor Mercklé. He has secured research funding through the ANR (MAD project on Automatic Differentiation) and ANITI. His current research includes statistical fairness using optimal transport theory and automatic differentiation for stochastic optimization. He leads the development of the skwdro library for Wasserstein Distributionally Robust Optimization and is involved with ANITI (Toulouse's AI Cluster), where he also took responsibility for the 2nd year of the Master SID in Data Science & Engineering in September 2024.

Jana Fortesمشاهده پروفایل

مدرس

• Signal Processing
• Localization Technology
• Tracking Systems
• +۳ مورد دیگر

Jana Fortes is a Lecturer at the Faculty of Electrical Engineering and Informatics, Technical University of Košice, Slovakia. She teaches Applied Statistics, Cryptography, and Mathematics courses, including Numerical Mathematics and Probability and Statistics. Her research focuses on signal processing for localization and tracking systems using Ultra-Wideband (UWB) radar and sensor networks. Teaching: Applied Statistics, Cryptography, Mathematics I/II, Numerical Mathematics, Probability and Statistics, Mathematics Tutoring Research: UWB radar signal processing for person localization and tracking, software development (MATLAB, LabVIEW), cluster analysis for movement detection. Publications: Recent work includes studies on 2D/3D localization algorithms, UWB sensor system implementations, and mathematical analyses of graph crossing numbers. Contact: Email: jana.fortes@tuke.sk , Room N32 615, Phone: +421 55 602 3252.

Elias Shiuمشاهده پروفایل

استاد

• Statistics
• Actuarial Science

Elias Shiu is a Professor in the Department of Statistics and Actuarial Science at the University of Iowa. His research focuses on foundational and applied aspects of statistics and actuarial science, contributing to theoretical advancements and practical implementations in risk assessment, probability modeling, and financial mathematics. Research Areas: Statistics, Actuarial Science Contact: elias-shiu@uiowa.edu

• Probability Theory
• Interacting Particle Systems
• Hydrodynamic Limits
• +۷ مورد دیگر

Makiko Sasada is a Professor at the Graduate School of Mathematical Sciences, The University of Tokyo. Her research focuses on probability theory, interacting particle systems, and classical integrable systems, with an emphasis on hydrodynamic limits, spectral gaps, and non-gradient models. She has made significant contributions to understanding energy diffusion in stochastic systems and integrable cellular automata like the box-ball system. 2010: MSJ Takebe Katahiro Prize for Encouragement of Young Researchers 2011: Second JSPS Ikushi Prize 2011: University of Tokyo President's Prize Her recent work explores scaling limits in the box-ball system, thermal conductivity in stochastic models, and topological structures in large-scale systems. She actively participates in international conferences, serves as an associate editor for leading journals, and organizes educational initiatives like the FoPM program, which trains students to apply basic science to societal challenges through mathematical and physical innovation. Selected publications include: The incompressible Navier–Stokes limit from the discrete-velocity BGK Boltzmann equation (2025) Characterization of Gradient Condition for Asymmetric Partial Exclusion Processes (2025) Topological structures of large-scale interacting systems (2024)

• Compositional Data Analysis
• Probability Theory
• Multivariate Analysis
• +۵ مورد دیگر

Jakob Bergman serves as Senior Lecturer, Associate Professor, and Director of Studies at Lund University's Department of Statistics. His academic profile demonstrates significant expertise in compositional data analysis, where he studies vectors of proportions (compositions) that arise across diverse fields including geochemistry, household economics, and political science. His research focuses on developing innovative statistical methodologies, particularly his compositional loess method for smoothing compositional time series data. Bergman maintains active interdisciplinary collaborations with archaeologists like Mikael Larsson (on cereal grain analysis and early farming practices) and philosophers like Martin Jönsson (on post-hoc interventions and gender bias in research funding systems). Bergman's scholarly output reveals strong interdisciplinary trends, with recent publications bridging statistics with archaeology, political science, and philosophy. His work consistently addresses practical methodological challenges while maintaining theoretical rigor, particularly in analyzing party shares over time and archaeological specimen compositions. As an educator, Bergman teaches theoretical and applied statistics across undergraduate, advanced, and PhD programs, with special emphasis on sampling/survey research and regression analysis. His outreach extends to providing statistical expertise to numerous NGOs, authorities, and companies. His research contributes to UN Sustainable Development Goals related to quality education and gender equality. Bergman has led significant projects including 'Post-hoc Interventions' at Pufendorf IAS and 'Manure matters' investigating early farming practices through nitrogen analysis of archaeological crop assemblages.