جست‌وجوی استاد و پژوهشگر

Diana Chua Halikiasمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Scientific Machine Learning
• Randomized Linear Algebra
• +۴ مورد دیگر

Diana Chua Halikias is a Ph.D. Candidate in Mathematics at Cornell University, with a focus on numerical analysis and scientific machine learning. Starting in January 2026, she will join New York University as a Courant Instructor in the Department of Mathematics, collaborating with Chris Musco. Her current role as a Simons-Berkeley Research Fellow underscores her expertise in computational mathematics and machine learning. Education: B.S. in Mathematics from Yale University (2020) Ph.D. in Mathematics from Cornell University (2025) Her research bridges randomized linear algebra, matrix theory, and operator learning, with applications in scientific computing and machine learning. Recent work includes hierarchical matrix approximation and data-efficient PDE learning. She has held internships at the Flatiron Institute and Lawrence Berkeley National Laboratory. Scientific Awards: Simons-Berkeley Research Fellow Graduate Student Funding and Awards at Cornell Halikias actively mentors undergraduate researchers, including directing projects in machine learning and spectral graph theory. She also contributes to outreach through the Cornell REU program and local math circles in Ithaca.

• Dynamical Systems
• Riemann Surfaces
• Moduli Spaces
• +۵ مورد دیگر

Benjamin Dozier is an Assistant Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His research focuses on dynamical systems and Riemann surfaces, particularly their moduli spaces. He explores the interplay between dynamics on individual surfaces and abstract moduli space properties, with recent work involving moduli space compactifications and connections to algebraic geometry and probability theory. Education: Ph.D. in Mathematics from Stanford University (2018). Research Interests: Dynamical systems, Riemann surfaces, algebraic geometry, hyperbolic geometry, ergodic theory, and geometry of numbers. His work bridges pure mathematical theory with applications in topology and geometric analysis. Publications highlight studies on strata of differentials, geodesic subvarieties, and probabilistic graph theory, demonstrating cross-disciplinary engagement with geometry and topology. Teaching: Instructs courses in Linear Algebra, Multivariable Calculus, Topology, and supervised research/reading programs. His academic contributions include advancing geometric analysis and dynamical systems theory through rigorous mathematical frameworks.

Christos Masourosمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• Green Communications
• +۵ مورد دیگر

Christos Masouros is a Professor of Signal Processing and Wireless Communications at University College London (UCL), affiliated with the Institute for Communications and Connected Systems. He holds a Ph.D. from the University of Manchester (2009) and has held research positions at Philips Research Labs, Queen’s University Belfast, and UCL. His expertise spans wireless communications, signal processing, and integrated sensing and communications (ISAC). Key roles include coordinating the EU-funded PAINLESS (2018–2022) and ISLANDS (2024–2028) projects, which focus on energy-autonomous networks and next-generation vehicular networks, respectively. Education: Diploma in Electrical and Computer Engineering, University of Patras (2004) MSc by Research, University of Manchester (2006) PhD in Electrical and Electronic Engineering, University of Manchester (2009) Research Interests: Green Communications Large-Scale Antenna Systems ISAC (Integrated Sensing and Communications) Interference Mitigation in MIMO and Multicarrier Systems Awards: 2024 IEEE SPS Best Paper Award 2023 IEEE ComSoc Stephen O. Rice Prize Fellow of IEEE, AIIA, and AAIA Leadership Roles: Vice-Chair, IEEE Emerging Technologies Initiative on ISAC Chair, IEEE SPS ISAC Technical Working Group Editorial roles in IEEE Transactions on Wireless Communications, IEEE Open Journal of the Communications Society, and others Labs/Teams: Active in UCL’s Information and Communication Engineering Research Group, contributing to standards development via IEEE and ETSI working groups.

Michel Goemansمشاهده پروفایل

استاد

• Combinatorial Optimization
• Algorithms
• Approximation Algorithms
• +۳ مورد دیگر

Mitchell Goemans is the RSA Professor of Mathematics at the Massachusetts Institute of Technology (MIT), where he also serves as the Head of the Department of Mathematics. He is a member of the Theory of Computation group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on combinatorial optimization, approximation algorithms, and algorithmic mathematics. He is an ACM Fellow, recognizing his contributions to theoretical computer science and discrete mathematics. Goemans has taught numerous advanced courses at MIT, including Advanced Combinatorial Optimization , Algorithms , and Topics in Theoretical Computer Science . His work bridges foundational mathematics with computational challenges, emphasizing techniques like semidefinite programming and matroid theory. Key research areas include non-bipartite matchings, matroid intersection, submodular function optimization, and approximation algorithms for NP-hard problems. His contributions to the Traveling Salesman Problem and semidefinite programming relaxations are particularly influential. Awards and recognitions include the ACM Fellowship (2009) and leadership roles in academic administration, reflecting his dual impact on research and education.

Anna De Mier Vinueمشاهده پروفایل

دانشیار

• Combinatorics
• Graph Theory
• Matroid Theory
• +۳ مورد دیگر

Anna De Mier Vinue is an Associate Professor at the Department of Mathematics, Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona (FIB). She leads research in combinatorics, graph theory, and matroid theory as part of the UPC's GAPCOMB research group (Geometric, Algebraic and Probabilistic Combinatorics). Her work focuses on structural combinatorics, polynomial invariants, and discrete geometry. Education: Holds a PhD in Applied Mathematics from UPC. Research Interests: Combinatorial enumeration, graph polynomials (Tutte, U-polynomials), matroid representations, non-crossing structures, and algorithmic combinatorics. Her contributions bridge algebraic methods with geometric and probabilistic approaches. Publications: Over 77 works including articles in Discrete Mathematics, European Journal of Combinatorics, and SIAM Journal on Discrete Mathematics. Recent research explores marked graphs, Tutte polynomial evaluations, and lattice approximations in clutter theory. Active in international projects like COCOA and R+D+I initiatives. Grants & Collaborations: Co-leads projects on extremal combinatorics, geometric algorithms, and mathematical education. Collaborates with researchers from BarcelonaTech and international institutions. Involved in initiatives addressing gender equality in STEM education through statistical analysis. Labs/Teams: Core member of the UPC's GAPCOMB group, contributing to interdisciplinary projects at the Institut de Matemàtiques de la UPC-BarcelonaTech. Supervises research in combinatorial geometry and algebraic structures.

Arshak Minasyanمشاهده پروفایل

دانشیار

• Robust Statistics
• High-Dimensional Data Analysis
• Learning Theory
• +۳ مورد دیگر

Arshak Minasyan is an Associate Professor at CentraleSupélec – Université Paris-Saclay and a member of the Laboratoire Signaux et Systèmes (L2S), specifically the Modélisation et Estimation (GME) team. His research focuses on robust statistics and learning theory, particularly addressing computational and statistical complexities in high-dimensional data analysis. Education PhD in Mathematics from Yerevan State University (YSU), supervised by Prof. Victor Ohanian. Master’s in Mathematics from Skoltech. Bachelor’s in Mathematics from Higher School of Economics (HSE). Research Interests His work explores robust statistical methods for high-dimensional data, including outlier-robust algorithms, optimal estimation techniques, and the intersection of learning theory with statistical guarantees. Key areas include feature matching under noise, mean and covariance estimation, and generative modeling with distributional robustness. Key Contributions Notable contributions include the All-in-one Robust Estimator of the Gaussian Mean (Annals of Statistics, 2022) and outlier-tolerant methods for feature matching. His research emphasizes both theoretical foundations and computational tractability, with applications in signal processing and machine learning. Teaching Lecturer of “High-dimensional statistics” at Université Paris-Saclay. Teaching Assistant roles at CREST-ENSAE, HSE, and Skoltech in courses like Probability Theory, Stochastic Processes, and Biostatistics. Labs & Collaborations Affiliated with the L2S lab’s Modélisation et Estimation team, contributing to projects like COMEDY (Control of Dynamical Systems) and SYCOMORE (Signal Processing).

Elena Di Bernardinoمشاهده پروفایل

استاد

• Spatial Statistics
• Extreme Value Theory
• Environmental Risks
• +۵ مورد دیگر

Elena Di Bernardino is a full Professor at Université Côte d'Azur, leading research in spatial statistics, extreme value theory, and environmental risk modeling. She holds the prestigious Junior membership at the Institut Universitaire de France (2025-2030) and serves as Head of the Probability & Statistics team at the J.A. Dieudonné Laboratory. Her work integrates stochastic geometry, copula theory, and machine learning to address multidisciplinary challenges such as climate risk assessment and wildfire prediction. **Education**: Dual Italian-French national, Elena earned a PhD in Applied Mathematics from Université Lyon 1 (2011) and habilitation (HDR) from Université Pierre et Marie Curie (2017). Her academic journey includes postdoctoral research in Venezuela and Canada, alongside engineering studies at the Polytechnic of Milan. **Research**: Key focus areas include spatio-temporal dependence modeling, multivariate risk measures, and geometric inference on random fields. She leads major national projects like the ANR COCHAIRS initiative under France's PEPR Risques program, and collaborates internationally via Simons-CRM and MITACS grants. **Awards**: Recognitions include the Chercheurs Simons-CRM Prize (2019), PEDR grants (2015, 2019, 2023), and continuous funding through EU and French National Research Agency (ANR) programs. **Leadership**: Currently chairs the Scientific Council of the J.A. Dieudonné Laboratory and oversees strategic initiatives at the 3IA Côte d'Azur Institute. She actively mentors 8 current PhD/postdoc researchers and has supervised over 14 completed doctoral projects. **Teaching**: Teaches advanced courses in extreme value theory, risk modeling, and environmental statistics at both undergraduate and graduate levels.

Laszlo Kozmaمشاهده پروفایل

استادیار

• Data Structures
• Algorithms
• Combinatorics
• +۴ مورد دیگر

László Kozma is an Assistant Professor at Freie Universität Berlin in the Theoretical Computer Science department. He obtained his PhD at Saarland University under Raimund Seidel, followed by postdocs at Tel Aviv University and TU Eindhoven. His work focuses on data structures , combinatorics , and algorithmic adaptivity , with significant contributions to self-adjusting heaps, binary search trees, and geometric optimization. Recent research includes pattern-avoiding sequences and saddlepoint algorithms . His publications span exponential algorithms , TSP variants , and heap structures , with a recurring theme of connecting combinatorial geometry to algorithm design. Co-authors include leading researchers like Robert Tarjan, Uri Zwick, and Haim Kaplan. Key software implementations (e.g., smooth heap ) are publicly available. He has developed tools like Cuckoo Hashing Visualization and the historical WikipediaVision project, demonstrating practical engagement with algorithmic concepts. His mathematical genealogy traces back to classical researchers.

• Statistics
• Probability Theory
• Random Fields
• +۴ مورد دیگر

Chunsheng Ma is a Professor of Mathematics at Wichita State University, affiliated with the Department of Mathematics, Statistics, and Physics within the Fairmount College of Liberal Arts and Sciences. His research focuses on advanced topics in probability theory, stochastic processes, and spatial statistics, with particular emphasis on random fields, covariance models, and their applications. He holds an office in Jabara Hall and can be reached via email at cma@math.wichita.edu. His teaching responsibilities include courses such as STAT370 and STAT774. Research Interests: Dr. Ma's work spans statistical methodology development, including studies on vector random fields, Lévy processes, and isotropic Gaussian fields. He explores theoretical properties like stochastic orderings, strong local nondeterminism, and covariance structures on complex spaces such as spheres and manifolds. His contributions address both foundational aspects (e.g., metric-dependent covariance functions) and applied challenges in spatial-temporal modeling. Key Trends in Articles: Recent publications emphasize non-Gaussian random field constructions, power-law processes, and stochastic comparisons. Themes include extending classical Brownian motion frameworks to non-Euclidean settings, analyzing covariance matrices on homogeneous spaces, and developing simulation techniques for isotropic fields. His work often bridges pure probability theory with computational methods for practical spatial and temporal data analysis. Funding & Professional Activities: Links to statistical societies (e.g., ASA, IMS) and grant opportunities suggest active engagement in academic networks. His webpage provides access to teaching materials, research resources, and collaborative initiatives in the mathematical sciences. No specific grants or awards are explicitly listed, though his extensive publication record reflects sustained scholarly activity.

Bárány Imreمشاهده پروفایل

استاد پژوهشی

• Discrete Geometry
• Combinatorics
• Convex Geometry
• +۷ مورد دیگر

Imre Bárány is a Research Professor at the Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, and holds the Astor Professor of Pure Mathematics position at University College London (half-time). His career spans over four decades with visiting roles at Yale University, MSRI, Microsoft Research, and others. Bárány graduated from Eötvös University (1971), earned his PhD (1982) and Doctor of Sciences (1993) from the Hungarian Academy of Sciences, and became a member of the Hungarian Academy of Sciences in 2010. His research interests include discrete geometry, combinatorics, convexity, algebraic topology, and their applications in computer science and operations research. Key areas of focus are random polytopes, lattice points, Helly-type theorems, and geometric optimization. Bárány’s recent work, such as his 2020 paper on Carathéodory-Helly-Tverberg theorems and 2016 analysis of curve-hyperplane interactions, showcases advancements in geometric combinatorics. Over 170 publications span journals like Discrete & Computational Geometry and Annals of Probability. Awards: Rényi Prize (1988), Hungarian Academy Prize (1998), ERC Advanced Grant (2011), AMS Fellow (2012), Szechenyi Prize (2016). Grants & Leadership: Led the ERC-funded 'Discrete and Convex Geometry' project. Editorial roles include Discrete & Computational Geometry (since 1991) and Combinatorica (Editor-in-Chief since 2020).

Suzana Petrović Savićمشاهده پروفایل

دانشیار

• Production Engineering
• Manufacturing Processes
• Tribology
• +۳ مورد دیگر

Suzana Petrović Savić is an Associate Professor at the Department of Production Engineering, Faculty of Engineering, University of Kragujevac, Serbia. Her work integrates advanced manufacturing processes, materials science, and biomedical engineering with computational techniques like neural networks and machine learning. Her research focuses on optimizing machining processes, tribological behavior analysis, and biomechanical applications such as gait analysis and prosthetic design. Notable areas include abrasive waterjet machining, temperature prediction in milling, and AI-driven diagnostics for respiratory and musculoskeletal disorders. She also contributes to the development of smart healthcare technologies and IoT-enabled quality management systems. Recent studies emphasize interdisciplinary approaches, combining experimental mechanics with data-driven modeling. Her work spans from material characterization (e.g., dental ceramics, polymers) to clinical applications in orthopedics and neonatal care. Despite no awards listed, her publications reflect sustained contributions to production engineering and biomedical innovation. No formal advisee list or grant details are provided, but her involvement in laboratory activities related to the Faculty’s research centers is implied through her departmental affiliation.

Saroglou Christosمشاهده پروفایل

دانشیار

• Convex Geometry
• Functional Analysis
• Geometric Tomography
• +۴ مورد دیگر

Christos Saroglou is an Associate Professor in the Department of Mathematics at the University of Ioannina, Greece. His academic career includes prior roles as Assistant Professor (2018–2023), Visiting Assistant Professor at Texas A&M University, and postdoctoral positions at institutions including Kent State University and Tel Aviv University. He holds a Ph.D. in Mathematics from the University of Crete (2011), with a thesis on problems related to convex bodies. His research focuses on Convex and Affine Geometry, Isoperimetric Inequalities, Geometric Tomography, and Functional Analysis. Education: Ph.D. (2011) and M.Sc. (2005) from University of Crete; B.Sc. (2003) from Aristotle University of Thessaloniki. Key Research Areas: Convex Geometry, Functional Analysis, Geometric Probability, and Asymptotic Geometric Analysis. Recent Work: Contributions to the Lp-Minkowski problem, log-Brunn-Minkowski conjecture, and geometric inequalities involving Wulff shapes and projection bodies. His publications address topics such as symmetrization operators, convex body properties, and geometric inequalities. He actively participates in international conferences and collaborates with global researchers. Current advisees include Konstantinos Patsalos (PhD) and Lampros Athanassopoulos (MSc). His work bridges theoretical insights with applications in geometric functional analysis and convex geometry.

• Positional games
• Discrete geometry
• Computational geometry
• +۳ مورد دیگر

Miloš Stojaković is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Serbia. He has been with the university since 1999, progressing from student to Assistant Professor (2006-2011), Associate Professor (2011-2016), and Full Professor (2016-present). He leads the Foundations of Computer Science group and serves as an editor for Discrete Mathematics & Theoretical Computer Science and the Novi Sad Journal of Mathematics. Ph.D. in Computer Science, ETH Zurich (2005), advisors: Emo Welzl and Tibor Szabó M.Sc. in Computer Science, University of Novi Sad (2001), advisor: Joviša Žunić B.Sc. in Mathematics and Computer Science, University of Novi Sad (1999) Stojaković's research centers on positional games, discrete and computational geometry, discrete random structures, combinatorial algorithms, and graph theory. His work explores theoretical aspects of combinatorial games, particularly Maker-Breaker type games on graphs and other structures. He investigates geometric properties of discrete structures and develops algorithms for geometric problems. His research has applications in theoretical computer science and combinatorics, with significant contributions to understanding game strategies, structural properties, and computational complexity in discrete settings. Stojaković's recent publications (2016-2025) primarily focus on positional games, extending classical results to new settings like posets, generalized saturation, and constructor-blocker variants. He examines computational complexity aspects and structural properties of game outcomes. His work often involves collaborations with leading researchers including Dan Hefetz, Michael Krivelevich, and Tibor Szabó, demonstrating the ongoing vitality of combinatorial game theory and its connections to other areas of discrete mathematics. "Dr Z. Đinđić Award" for the best young scientist in Vojvodina (2008) "Best Student of University of Novi Sad Award" (1998/99) Stojaković has supervised three PhD students to completion: Mirjana Mikalački (2014), Marko Savić (2018), and Jelena Stratijev (2023). His research has been supported by various grants, including the 1 million RSD grant accompanying the Dr Z. Đinđić Award. He has organized multiple workshops including the Novi Sad Workshop on Foundations of Computer Science series (2015, 2017, 2024) and co-organized the Mini-Workshop on Positional Games at Oberwolfach Research Institute (2018) and the Positional Games workshop within A Sparse (Graphs) Coalition (2021). Since 2018, Stojaković has led the Foundations of Computer Science group at the University of Novi Sad. He maintains active international collaborations, having taught courses at Eötvös Loránd University Budapest, University of Buenos Aires, University of Szeged, University of Banja Luka, University of Groningen, and Graz University of Technology. His research group focuses on theoretical aspects of combinatorial games, discrete geometry, and related computational problems, contributing significantly to these fields through publications, workshops, and academic leadership.

• Positional games
• Discrete geometry
• Computational geometry
• +۳ مورد دیگر

Milos Stojakovic is a Full Professor in the Department of Mathematics and Informatics at the Faculty of Sciences, University of Novi Sad, Serbia. He has held this position since 2016, following appointments as Associate Professor (2011-2016) and Assistant Professor (2006-2011) at the same institution. Since 2018, he has led the Foundations of Computer Science research group. His academic foundation includes dual Bachelor's degrees in Mathematics and Computer Science (1999), a Master's degree in Computer Science (2001), and a Ph.D. in Computer Science from ETH Zurich (2005) under Emo Welzl and Tibor Szabó. Dr. Stojakovic's research focuses on: Positional games and their variants Discrete and computational geometry Discrete random structures Combinatorial algorithms Graph theory and applications His extensive publication record reveals a consistent focus on positional games, with recent work exploring Maker-Breaker games, Avoider-Enforcer games, and Constructor-Blocker games. His research bridges combinatorics, game theory, and computational geometry, with notable contributions to hypergraph coloring, graph searching algorithms, and geometric matchings. He co-authored the seminal book 'Positional Games' (2014) in the Oberwolfach Seminars series. Dr. Stojakovic has received significant recognition including the 'Dr Z. Đinđić Award' for best young scientist in Vojvodina (2008) and the 'Best Student of University of Novi Sad Award' (1998/99). He has successfully mentored three PhD students to completion: Mirjana Mikalački (2014, 'Positional games on graphs'), Marko Savić (2018, 'Efficient algorithms for discrete geometry problems'), and Jelena Stratijev (2023, 'Strong positional games'). His research has been supported by various grants, including the 1 million RSD grant accompanying the Dr Z. Đinđić Award. As head of the Foundations of Computer Science group, Dr. Stojakovic leads research in theoretical computer science and discrete mathematics. He has organized international workshops including multiple editions of the Novi Sad Workshop on Foundations of Computer Science (NSFOCS) and specialized workshops at the Oberwolfach Research Institute for Mathematics.

Ravi Montenegroمشاهده پروفایل

استاد

• Markov Chains
• Cryptography
• Combinatorics
• +۳ مورد دیگر

Ravi Montenegro is a Professor and Department Chair in the Department of Mathematics & Statistics at the University of Massachusetts Lowell (UML), within the Kennedy College of Sciences. He also serves as the GPS Math Coordinator. His research focuses on Markov chain convergence rates, combinatorics, isoperimetric inequalities, and cryptographic algorithms like Pollard's Rho and Kangaroo methods for discrete logarithm problems. Montenegro holds a Ph.D. in Mathematics from Yale University (2002) and a BS in Mathematics from the California Institute of Technology (1995). Education Background: Ph.D. in Mathematics, Yale University (2002) B.S. in Mathematics, California Institute of Technology (1995) Research Interests: Markov chain mixing times and convergence rates Cryptanalysis and birthday attack algorithms Combinatorial optimization and graph theory Isoperimetric inequalities and geometric bounds Notable Works: Developed rigorous analyses of Pollard’s Rho and Kangaroo methods Contributed to spectral profile and conductance-based mixing time bounds Explored evolving set processes and canonical path methods for Markov chains Awards and Honors: Japan Society for the Promotion of Science (JSPS) Invitation Fellowship (2013) NSF-VIGRE Postdoctoral Fellowship (2002) NSF-VIGRE Graduate Fellowship (1999) Professional Contributions: Guided multiple research papers on discrete logarithm algorithms and Markov chain theory Active in academic leadership roles, including graduate program coordination