Find Academic Supervisors

• Geometric Group Theory
• Algebraic Topology
• Cohomology Theory
• +3 more

Dr. Benjamin Brück is an Investigator in Mathematics at the University of Münster, affiliated with the Faculty of Mathematics and Computer Science and the Institute for Mathematical Logic and Foundational Research. He is a member of the Collaborative Research Center (CRC) 1442: Geometry: Deformations and Rigidity. His research focuses on geometric group theory, algebraic topology, and model theory, with emphasis on cohomology of arithmetic groups, symplectic groups, and topological data analysis. Brück holds a PhD and has contributed to projects such as T1 (K-Groups and Cohomology) and T3 (Models and Universes) within the Mathematics Münster framework. His work bridges geometric and combinatorial structures, including studies on Coxeter complexes, handlebody groups, and persistent homology applications in cosmology. Recent publications include breakthroughs in the cohomology of SLn(Z), symplectic groups, and the interplay between graph complexes and cyclic operads. His research often intersects with algebraic geometry and geometric topology, addressing questions in arithmetic groups and topological invariants. No formal academic awards are listed, though his active participation in collaborative projects highlights his role in advancing interdisciplinary mathematical research. He is involved with the CRC Geometry initiative and contributes to the Graduate School’s academic programs.

Son K. LamView profile

Professor

• Sales Management
• Organizational Ambidexterity
• Organizational Identification
• +6 more

Son K. Lam is a Professor in the Department of Marketing at the Terry College of Business, University of Georgia. His research focuses on sales management, organizational ambidexterity, customer-centricity, and relationship marketing from a social identity theory perspective. PhD in Marketing, University of Houston (2009) Research Interests span salesperson judgment and decision-making, dual agency in B2B negotiations, and leveraging data integration in frontline management. His work often applies social identity theory to address organizational challenges. Article Trends highlight his contributions to resolving empirical challenges in marketing dualities, buyer-seller interactions, and sales force performance metrics across journals like Journal of Marketing and Journal of the Academy of Marketing Science . Scientific Awards: Emily H. and Charles M. Tanner, Jr., Chair in Sales Management (2023) Marketing Science Institute (MSI) Scholar (2023) Terry Dean’s Advisory Council Distinguished Professorship (2019-2022) Outstanding Research Award (2015) Editorial & Service Roles include Developmental Editor for Journal of Marketing (2023–present), Area Editor for Journal of the Academy of Marketing Science (2017–present), and advisory roles in UGA Study Abroad and Master in Marketing Research Program.

Jeb F. WillenbringView profile

Professor

• Representation Theory
• Discrete Mathematics
• Mathematical Physics
• +3 more

Jeb F. Willenbring is a Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee. His academic career spans multiple institutions, including Yale University, where he held a research position under NSF VIGRE funding (2000-2003). He serves as the Graduate Program Chair and is actively engaged in research, teaching, and academic collaboration. Educational Background: PhD in Mathematics from the University of California at San Diego (2000) BS in Mathematics from North Dakota State University (1995) Research Interests: His work focuses on Representation Theory, Discrete Mathematics, and Mathematical Physics. Key themes include quantum entanglement, symmetry analysis, and the interplay between algebraic structures and geometric invariants. Scientific Contributions: NSF VIGRE funding recipient for research at Yale University Published extensively on Hilbert series, Howe duality, and branching rules Collaborated with prominent mathematicians like Thomas J. Enright and Roger Howe Academic Engagement: Willenbring mentors graduate students and collaborates on research projects. His publications highlight interdisciplinary applications in quantum mechanics and combinatorics. He maintains an open-door policy for students and colleagues at his office (E461 EMS Building) and welcomes inquiries via email at jw@uwm.edu .

Oded YacobiView profile

Associate Professor

• Representation Theory
• Algebraic Groups
• Categorification
• +3 more

Associate Professor Oded Yacobi is affiliated with the University of Sydney's Faculty of Science. His research focuses on advanced algebraic structures, including representation theory, categorification, and geometric methods in quantum groups. He holds a Ph.D. from the University of California, San Diego (2009), followed by postdoctoral roles at the University of Toronto and Tel Aviv University. Yacobi is actively involved in grants exploring dualities in representation theory and braid group dynamics. His work bridges algebraic geometry and combinatorics, with recent articles addressing Weyl groups, braid group actions, and affine Grassmannians. He leads a vibrant research program with collaborations spanning North America and Australia. Education: Ph.D. in Mathematics, University of California, San Diego (2009) Postdoctoral Fellowships: University of Toronto, Tel Aviv University Research Interests: Representation theory of algebraic groups and quantum groups Categorification techniques and their applications Geometric methods in Lie theory and combinatorics Braid groups and their algebraic structures Key Grants: 2023: ARC DP Grant (Braid groups via representation theory and machine learning) 2018: ARC DP Grant (New Dualities in Modular Representation Theory) Professional Activities: Maintains an active research group and collaborates internationally. His work has been published in top journals like Mathematische Zeitschrift and Advances in Mathematics. He is also engaged in teaching advanced algebra courses at the University of Sydney.

Pavlos MotakisView profile

Assistant Professor

• Functional Analysis
• Operator Theory
• Banach Space Theory
• +4 more

Pavlos Motakis is an Assistant Professor in the Department of Mathematics and Statistics at York University, Toronto, Canada. He holds a PhD in Mathematics from the National Technical University of Athens. Previously, he was a J.L. Doob Research Assistant Professor at the University of Illinois at Urbana-Champaign and a Visiting Assistant Professor at Texas A&M University. His research focuses on functional analysis, particularly the study of operators on Banach spaces, algebras of operators, and the local and asymptotic structure of Banach spaces. Key interests include hereditarily indecomposable Banach spaces, L∞-spaces, and metric characterizations of non-local properties. Recent work explores topics such as orthogonal factors of operators on Rosenthal spaces, variants of the James Tree space, and reflexive Calkin algebras. His publications reflect a deep engagement with advanced structural properties of Banach spaces and operator algebras. Dr. Motakis currently holds no listed scientific awards but maintains an active research agenda in pure mathematics. His advising and grant activities are not detailed in available records, though his academic career demonstrates consistent contributions to functional analysis through teaching and research. He is affiliated with the Faculty of Science at York University, contributing to both undergraduate and graduate programs in mathematics and statistics. His office is located in the Ross Building, S618.

Suhan ZhongView profile

Visiting Assistant Professor

• Optimization
• Operations Research
• Mathematical Programming
• +3 more

Suhan Zhong is a Visiting Assistant Professor in the Department of Mathematics at Texas A&M University, mentored by Prof. Jianxin Zhou. He holds a Ph.D. in Mathematics from UCSD (2022), supervised by Prof. Jiawang Nie. His research focuses on optimization theory, with expertise in operations research, mathematical programming, and their applications to data science and machine learning. He employs techniques from real algebraic geometry, moment problems, and stochastic optimization to tackle complex problems like multilevel optimization, equilibrium models, and tensor analysis. His work includes advancements in generalized Nash equilibrium problems, distributionally robust optimization, and global solution methods for nonconvex stochastic programs. He teaches MATH 311 and contributes to the TAMU Math Circle as an instructor. Current research trends include polynomial optimization under uncertainty, robust constraint handling, and algorithmic development for bilevel systems. His articles reflect contributions to stochastic programming, equilibrium analysis, and tensor decomposition. Presently seeking tenure-track or postdoctoral positions, Zhong emphasizes interdisciplinary approaches to optimization challenges. His work bridges theoretical foundations with practical applications in finance, data science, and engineering systems.

Tony JebaraView profile

Professor

• Machine Learning
• Computer Vision
• Graph Theory
• +3 more

Professor Tony Jebara is a faculty member in the Department of Computer Science at Columbia University, where he chairs the Center on Foundations of Data Science and directs the Columbia Machine Learning Laboratory. His research focuses on machine learning with applications in vision, graphs, and spatio-temporal data. He holds a PhD from MIT (2002) and has advised startups including Sense Networks, Evidation Health, and Agolo. Notable awards include the NSF Career Award (2004), Best Paper at ICML 2009, and recognition as one of Esquire's Best and Brightest (2008). His work has been featured in major media outlets. Jebara's academic contributions span over 100 peer-reviewed papers and a textbook on machine learning. He served as General Chair for ICML 2017 and Program Chair for ICML 2014. His research explores generative and discriminative models, Bayesian inference, and optimization techniques. Current projects include neural ensemble analysis in neuroscience and robust learning algorithms for environmental modeling. Recent publications emphasize scalable methods for collaborative filtering, survival analysis in online experiments, and graphical model applications in neuroscience. His work bridges theoretical advancements with practical systems, including contributions to privacy-preserving algorithms and recommendation systems.

Ralf WernerView profile

Professor

• Computational Statistics
• Data Analysis
• Business Mathematics
• +6 more

Professor Ralf Werner serves as Professor of Business Mathematics at the University of Augsburg, where he leads the Computational Statistics and Data Analysis working group within the Institute of Mathematics at the Faculty of Mathematics, Natural Sciences and Technology. His academic career spans both theoretical research and practical industry applications in quantitative finance. Werner's research interests encompass: Computational Statistics and Data Analysis Optimization under Uncertainty Financial Engineering and Risk Management Actuarial Science and Insurance Mathematics Portfolio Optimization and Asset Allocation His scholarly output demonstrates a consistent focus on robust mathematical methods applied to financial problems, particularly in replicating portfolios for insurance applications, credit risk modeling, and statistical approaches to financial risk management. Werner's publications appear in leading journals across operations research, mathematical finance, and actuarial science. Professional qualifications include his habilitation at the Karlsruhe Institute of Technology (2011) and doctorate from Friedrich-Alexander University Erlangen (2001). He maintains active industry connections through his role as Scientific Advisor for DEVnet since 2010. Werner serves as Internship Coordinator and DAV (German Actuarial Society) correspondent, supporting students pursuing actuarial careers. He is an active member of multiple professional organizations including the Society for Operations Research (GOR), German Mathematical Society (DMV), and German Society for Insurance and Financial Mathematics (DGVFM).

Hammou ElbarmiView profile

Professor

• Order Restricted Inference
• Survival Analysis
• Categorical Data Analysis
• +3 more

Hammou Elbarmi is a Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, CUNY. His academic journey includes a PhD in Statistics from the University of Iowa (1993), an MS in Statistics (1989), a DEA in Applied Mathematics from University Mohamed V, Morocco, and a BS in Applied Mathematics from the same institution. PhD, Statistics, University of Iowa (1993) MS, Statistics, University of Iowa (1989) DEA, Applied Mathematics, University Mohamed V, Morocco BS, Applied Mathematics, University Mohamed V, Morocco Elbarmi specializes in Order Restricted Inference , Survival Analysis , and Categorical Data Analysis . His research focuses on statistical methods for comparing survival and cumulative incidence functions under stochastic ordering constraints, with applications to competing risks and biased sampling. He has developed novel nonparametric estimation techniques for distributions with type I/II bias and pioneered empirical likelihood approaches for hypothesis testing under inequality constraints. His work spans over two decades of funded research projects from PSC-CUNY , including grants for "Nonparametric estimation under stochastic precedence" (2020-2022) and "Consistent estimation of survival functions under uniform stochastic ordering" (2023-2024). He has delivered over 30 presentations at institutions like Rice University, University of Iowa, and Joint Statistical Meetings. Elbarmi has received the Faculty Scholarship and Creative Achievement Award at Baruch College annually from 2003-2011 and served as a Co-Chair for the Seminar Series in Statistics & Operations Research. His editorial and peer-review roles include associate editorships for journals like Statistica Sinica and Journal of Nonparametric Statistics .

Alberto RavagnaniView profile

Associate Professor

• Coding Theory
• Network Communication
• Combinatorics
• +4 more

Alberto Ravagnani is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), the Netherlands. He specializes in the mathematical foundations of communication theory, with a focus on coding theory, network communication, and combinatorics. His research addresses challenges in error-correcting codes, network coding, quantum error correction, and applications in data storage and cryptography. He holds a PhD from the University of Neuchâtel, Switzerland, and has held academic positions at institutions including University College Dublin, Ireland, and the University of Zurich. His work has been recognized with awards such as the KNAW Early Career Award (2021), Ambizione Research Grant (2019), and the Nexans Prize (2017). Ravagnani’s research spans theoretical and applied domains, including rank-metric codes, network decoding, and combinatorial structures. His recent publications explore eigenvalue bounds for codes, densities in metric spaces, and evasive subspaces. He is actively involved in editorial roles for journals like IEEE Transactions on Information Theory and Designs, Codes and Cryptography. His teaching includes courses on coding theory and combinatorics, and he has mentored multiple PhD students in areas such as DNA storage and quantum error correction. Key grants include a NWO Gravitation Grant (EUR 10M) and an EuroTech PhD Program Grant (EUR 235K).

• Convex Optimization
• Nonconvex Optimization
• Monotone Operators
• +5 more

Radu Ioan Boț is a Professor at the Faculty of Mathematics at the University of Vienna . Since 2020, he has served as Dean of the Faculty of Mathematics and Head of the Institute of Mathematics . He is also the Speaker of the FWF DK Vienna Graduate School on Computational Optimization and a Founding Member of the Research Platform Data Science@Uni Vienna . Education Ph.D. in Mathematics (2003, Chemnitz University of Technology, Summa cum laude ) M.Sc. in Mathematics (1999, Babeş-Bolyai University, Grade: 10 ) Diploma in Mathematics (1998, Babeş-Bolyai University, Grade: 10 ) Research Interests focus on convex and nonconvex optimization , monotone operators , duality theory , and proximal algorithms . His work bridges continuous and discrete time models , emphasizing fast convergence rates and Tikhonov regularization for problems in image processing , machine learning , and inverse problems . Recent Publications highlight advancements in primal-dual splitting , inertial dynamics , and stochastic optimization , with applications to GANs , phase retrieval , and structured convex minimization . Scientific Awards include the 2003 University Prize of Chemnitz University of Technology , 1998 award for graduating with the best grade from Babeş-Bolyai University, and fellowships during his Ph.D. and pre-Ph.D. research stays. Editorial Roles include Editor-in-Chief of SIAM Journal on Optimization (2026–2029) and editorial board membership for journals like Mathematical Programming , Computational Optimization and Applications , and Journal of Optimization Theory and Applications .

• Operational Research
• Mathematical Optimization
• Simplex Algorithms
• +2 more

Richard J. Caron is a Professor at the University of Windsor's College of Engineering, specializing in operational research and mathematical optimization. He has supervised students including Nooshin Nekoiemehr, Qiqi Zhang, and Anqi Chen. His team won the first practice prize at the national conference of the Canadian Operational Research Society. His research focuses on mathematical programming, constraint analysis, and optimization algorithms. Recent publications examine simplex algorithms, matrix inequalities, and probabilistic methods for extreme point identification. Dr. Caron's awards include the First Practice Prize from the Canadian Operational Research Society for developing cargo optimization tools for Air Canada. His work demonstrates consistent contributions to optimization theory and computational mathematics throughout his career.

Roghayeh HajizadehView profile

Lecturer

• Discrete Optimization
• Operations Research
• Mathematical Modeling
• +4 more

Roghayeh Hajizadeh is a University Lecturer at Linköping University's Department of Mathematics (MAI) and Applied Mathematics (TIMA) research group. Her work focuses on discrete optimization techniques for real-world problems like urban snow removal and air traffic control, using mathematical modeling and algorithm development. PhD in Optimization of Snow Removal in Cities (2023) Member of Mathematics and Algorithms for Intelligent Decision Making research group Key projects: Optimization of Snow Removal in Cities (2017-present) and OPT@ATM (2023-present) Research keywords include: Discrete Optimization, Operations Research, Sustainable Urban Planning, Air Traffic Control Algorithms . Teaching includes Combinatorial Optimization for Computer Science/Software Engineering programs and Supply Chain Optimization for Industrial Economics/Mechanical Engineering students. Publications (2020-2024) demonstrate expertise in: Lagrangian relaxation techniques , vehicle coordination algorithms , and tree elimination methods for snow removal; Dantzig-Wolfe decomposition for aircraft scheduling.

Radu BalanView profile

Professor

• Applied Mathematics
• Harmonic Analysis
• Signal Processing
• +6 more

Radu Balan is a Professor of Applied Mathematics in the Department of Mathematics at the University of Maryland, College Park. He is affiliated with the Center for Scientific Computation and Mathematical Modeling (CSCAMM) and the Norbert Wiener Center for Harmonic Analysis and Applications. His research bridges harmonic analysis, signal processing, and machine learning, with a strong focus on phase retrieval, frame theory, and deep neural network analysis. He earned his Ph.D. in Applied Mathematics from Princeton University in 1998, advised by Ingrid Daubechies, and holds B.Sc. degrees in Physics and B.E. in Electrical Engineering from Romanian institutions. His academic journey includes a postdoctoral stint at IMA and IBM, followed by research roles at Siemens before joining the University of Maryland faculty in 2006 as an Associate Professor, rising to full Professor in 2011. His research interests include harmonic analysis, statistical signal processing, machine learning, phase retrieval, and mathematical modeling. He has made significant contributions to the theory of frames, phaseless reconstruction, and the stability of deep neural networks. His recent work explores permutation-invariant representations, Lipschitz bounds in CNNs, and graph deep learning. His recent publications (2020–2024) reveal a strong trend toward theoretical machine learning and its mathematical foundations, particularly in the stability and invertibility of neural networks and representations. He frequently publishes in top-tier journals such as IEEE Transactions, SIAM Journal, and Foundations of Computational Mathematics, often on arXiv, indicating an active and current research program in mathematical signal processing and deep learning theory. Radu Balan has authored or co-authored over 60 peer-reviewed papers, edited five volumes of Excursions in Harmonic Analysis , and holds 25 U.S. and international patents in signal processing and source separation. His work has been supported by the National Science Foundation (NSF), Army Research Office (ARO), Simons Foundation, Siemens, and Verisk. He has advised numerous students and collaborators, with research projects spanning statistical signal processing, blind source separation, and deep learning. His lab and research group are associated with CSCAMM and the Norbert Wiener Center, fostering interdisciplinary collaboration in mathematical modeling and computation.

• Graph Algorithms
• Convex Optimization
• Fine-Grained Complexity Theory
• +3 more

Rasmus Kyng is an Assistant Professor at the Department of Computer Science, ETH Zurich, since Fall 2019. He focuses on developing fast algorithms for graph problems, convex optimization, and structured linear equations, with a specialization in fine-grained complexity theory and dynamic graph algorithms. Assistant Professor, Department of Computer Science, ETH Zurich (2019–present) Postdoc, Theory of Computation Group, Harvard (2018–2019) Research Fellow, Simons Institute, UC Berkeley (Fall 2017) His research explores the intersection of graph algorithms, optimization, and complexity, particularly through dynamic graph data structures, sparsification, and applications in machine learning. Recent work includes breakthroughs in almost-linear time algorithms for network flow problems. Key trends in his publications include: Advancements in dynamic graph algorithms and data structures Applications of convex optimization to graph theory Connections between matrix theory and algorithmic efficiency Derandomization techniques for algorithmic speed Expander graph-based oblivious routing Sparsification and low-congestion vertex sparsifiers Scientific recognition includes the FOCS Best Paper Award (2022), ICBS Frontiers of Science Award (2022), and the Machtey Award for Best Student Paper (2017). His group mentors PhD candidates and postdocs, including Ming Ding , Federico Soldà , Simon Meierhans , Aurelio Sulser , and Wuwei Yuan . Research is supported by Swiss National Science Foundation grants (project no. 200021 204787, starting grant no. TMSGI2 218022).