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Eyal LubetzkyView profile

Professor

• Probability Theory
• Combinatorics
• Statistical Mechanics
• +3 more

Eyal Lubetzky is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on probability theory and combinatorics, with notable contributions to the study of Ising models, cutoff phenomena, and random graphs. He holds a Ph.D. in Mathematics from Tel-Aviv University (2007) and a B.Sc. in Mathematics and Computer Science from the same institution (2002). Education: Ph.D., Mathematics, Tel-Aviv University, Israel, 2007 B.Sc., Mathematics and Computer Science, Tel-Aviv University, Israel, 2002 His research interests span probability theory, combinatorics, and statistical mechanics, with a focus on phase transitions, stochastic processes, and the analysis of complex systems. Notable works include groundbreaking studies on the cutoff phenomenon in Ising models and the mixing times of Markov chains. He has authored numerous influential papers, including studies on the dynamics of SOS surfaces, critical wetting phenomena, and the mixing properties of spin systems. His work has been recognized in volumes like What's Happening in the Mathematical Sciences . Lubetzky serves as an Associate Editor for journals such as Probability and Mathematical Physics , Probability Theory and Related Fields , and Annals of Applied Probability . He has organized workshops on topics like Markov chain mixing times and probabilistic combinatorics.

Suryanarayana SankagiriView profile

Researcher

• Discrete Choice Models
• Recommendation Systems
• Blockchain Security
• +4 more

Suryanarayana Sankagiri is a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, affiliated with the Information and Network Dynamics (INDY1) group under Professor Matthias Grossglauser. Previously, he earned his Ph.D. in Electrical & Computer Engineering (2018-2022) from the University of Illinois at Urbana-Champaign , where he was supervised by Bruce Hajek and participated in the Coordinated Science Lab . He also holds an M.S. in Electrical & Computer Engineering from the University of Illinois (2016-2018) and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Bombay (2012-2016). Education : Ph.D., Electrical & Computer Engineering, University of Illinois (2018-2022) M.S., Electrical & Computer Engineering, University of Illinois (2016-2018) B.Tech., Electrical Engineering, IIT Bombay (2012-2016) Suryanarayana's research focuses on discrete choice models and their application to recommendation systems , with a particular emphasis on learning from choice data and developing novel models for human decision-making. His broader interests include blockchain security under adverse network conditions, network dynamics , probabilistic modeling , and algorithm design . Recent work explores nonconvex matrix factorization and contextual dueling bandits for recommendation systems. His publications span theoretical and applied domains, including high-impact venues like ICML , Stochastic Systems , and IEEE Transactions on Networking . Themes include blockchain efficiency , hidden community detection in preferential attachment graphs, and temporal analysis of Indian classical music. Current projects involve refining recommendation systems through sparse comparison data and designing protocols for resilient blockchain networks. Scientific Awards : zkCapital Paper of the Week (2021) Rambus Fellowship (2021) Mavis Future Faculty Fellowship (2019) List of Teachers Ranked as Excellent (2019) Nomination for IIT Bombay Undergraduate Colloquium (2016) Best Poster Award, IIT Bombay Undergraduate Research Symposium (2013) Suryanarayana has advised no students listed in the provided materials. His work has been supported by fellowships such as the Mavis Future Faculty Fellowship and Rambus Fellowship . He contributes to the INDY1 group at EPFL, which investigates information and network dynamics through interdisciplinary approaches combining probability , network theory , and algorithmic design .

• High-Frequency and Algorithmic Trading
• Stochastic Games between Takers and Makers of Liquidity
• Mathematical Finance
• +2 more

Prof. Leandro Sanchez Betancourt is a faculty member at the University of Oxford , affiliated with the Mathematical Institute . His work bridges Mathematical Finance and Stochastic Games , with a focus on high-frequency trading and market microstructure. He has been recognized with prestigious awards, including the Bruti Liberati Prize for Quantitative Finance research and the Gabino Barreda medal from UNAM. Education Doctor of Philosophy (DPhil) Master of Science (MSc) Bachelor of Science (BSc) Research Contributions Prof. Sanchez Betancourt’s research addresses critical challenges in algorithmic trading , latency impacts , and toxic order flow . His recent publications explore Nash Equilibrium in broker-trader dynamics, Mean Field Games for informed trading, and innovative applications of Lévy-Ito Processes and Quantum Measurement in financial modeling. These works highlight his expertise in integrating advanced mathematics with real-world trading strategies. Scientific Awards Bruti Liberati Prize for best PhD thesis in Quantitative Finance Best overall performance student award from King’s College London Gabino Barreda medal from Universidad Nacional Autónoma de México Labs and Teams He is a member of the Mathematical and Computational Finance research group at the University of Oxford, collaborating with leading experts like Álvaro Cartea, Sebastian Jaimungal, and others.

• Theory of Computation
• Discrete Algorithms
• Theoretical Computer Science
• +5 more

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. BhattacharyaView profile

Associate Professor

• nonparametric statistics
• distribution-free inference
• networks analysis
• +4 more

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 CunniffeView profile

Professor

• Epidemiological Modeling
• Plant Disease Ecology
• Computational Biology
• +3 more

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
• +7 more

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
• +4 more

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 BenakliView profile

Professor

• Geometric Group Theory
• Graph Theory
• Mathematics Education
• +3 more

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 HagerView profile

Assistant Professor

• Mathematical Finance
• Machine Learning
• Stochastic Control
• +4 more

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.

Franck IutzelerView profile

Professor

• Numerical optimization
• Statistical learning
• Stochastic programming
• +5 more

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 FortesView profile

Lecturer

• Signal Processing
• Localization Technology
• Tracking Systems
• +3 more

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 ShiuView profile

Professor

• 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

• Compositional Data Analysis
• Probability Theory
• Multivariate Analysis
• +5 more

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.

David StupplesView profile

Professor

• Space-based reconnaissance and surveillance systems
• Resilient position, navigation, and timing (PNT) systems
• Optical sensors for deep-space navigation
• +4 more

Professor David Stupples is a Professor of Electronic & Radio Engineering at City St George's, University of London , where he has served as Course Director for the MSc in Space Systems since 2021. He is also a Scientific Advisor to the UK Government (2012–present) and holds fellowships with the Royal Aeronautical Society, Institute of Measurement & Control, and other professional organizations. His research focuses on resilient position, navigation, and timing (PNT) systems for military and civilian applications, including the development of a solo micro-satellite to geolocate electromagnetic interference sources. He applies systems modeling and cyber warfare analysis to enhance decision-making and security in complex engineering environments. Key publications include: " Semantic Approach to Web-Based Discovery of Unknowns " (2013) – addressing intelligence gathering via natural language processing and grounded theory. " Probability Analysis of Cyber Attack Paths " (2013) – exploring risk in enterprise systems. " J-value: a universal scale for health and safety spending " (2006) – introducing a risk-assessment framework. Scientific awards include Fellow, Chartered Engineer, Institute of Electronic and Radio Engineers (1981–present) Fellow, Chartered Engineer, Royal Aeronautical Society (2016–present)