جست‌وجوی استاد و پژوهشگر

Felipe Thomazمشاهده پروفایل

دانشیار

• Marketing Strategy
• Digital Marketing
• AI Ethics in Business
• +۶ مورد دیگر

Felipe Thomaz is an Associate Professor of Marketing at Saïd Business School, University of Oxford, and Deputy Director of the Oxford Future of Marketing Initiative. He holds a PhD in Marketing from the University of Pittsburgh and previously taught at the University of South Carolina. His research focuses on marketing strategy, AI ethics, illicit markets, and ESG integration, with notable contributions to frameworks like Ad Net Zero for net-zero advertising emissions. He collaborates with UN agencies, NGOs, and tech companies to address global sustainability goals and wildlife trafficking networks. Education: PhD in Marketing (University of Pittsburgh), MSc in Marketing & Finance (University of Pittsburgh), BSc in Animal Sciences (University of Florida). Research interests include digital marketing channels, brand performance via social networks, AI-driven marketing strategies, and conservation science linked to wildlife trade. His work bridges academia and industry, resulting in spinouts and IP transfers from Saïd Business School. Key projects include: Ad Net Zero: Global standard for reducing advertising emissions UN collaboration on wildlife trafficking through dark web analysis UNESCO partnerships on eliminating stereotypes in advertising His interdisciplinary approach spans marketing, mathematics, and conservation science, with publications in top journals like Journal of Marketing and Conservation Science and Practice .

• Algorithms
• Computational Complexity
• Convex Optimization
• +۴ مورد دیگر

James R. Lee is a Professor in the Department of Computer Science at the University of Washington. His research spans theoretical computer science, probability, and geometry. He has held visiting scientist roles at Microsoft Research (2023, 2018, 2017) and participated in programs at the Simons Institute (2023, 2020, 2018, 2017, 2014). Research Interests: Algorithms, complexity theory, convex optimization, metric embeddings, spectral graph theory, probability, stochastic processes, and the interplay between discrete and continuous analysis. Teaching: Courses on modern algorithms, quantum computing, optimization theory, and spectral methods in theoretical computer science. Scientific Contributions: Developed sparsification algorithms for generalized linear models and norms with near-linear size guarantees (STOC'24, FOCS'23). Extended Cheeger-type inequalities to higher eigenvalues (STOC'12, STOC'18). Proved super-polynomial lower bounds for LP/SDP relaxations in constraint satisfaction (STOC'15, FOCS'13). Disproved Benjamini-Papasoglou conjectures on annular separators (Discrete Comp. Geom. 2024). Advanced understanding of random walks in geometric and unimodular graphs (Israel J. Math. 2023, GAFA 2023). Scientific Awards: Best Paper Award, STOC 2015

• Algebraic Graph Theory
• Combinatorial Optimization
• Quantum Information Theory
• +۳ مورد دیگر

Aida Abiad Monge is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), where she chairs the Algebraic Combinatorics group. She holds a 0.1fte position as an Associate Professor at Vrije Universiteit Brussel and is a guest researcher at Ghent University. Her research focuses on algebraic graph theory, including spectral graph properties, combinatorial optimization, and applications in quantum information theory and coding theory. Her academic career includes postdoctoral fellowships at Ghent University (2020-2023) and Vrije Universiteit Brussel (2020-2023). She has been awarded grants such as the NWO VIDI and BOF Postdoctoral Fellowship. Her editorial roles include serving on the boards of the Electronic Journal of Linear Algebra and The Electronic Journal of Combinatorics. Research Interests: Spectral graph theory, coding theory, quantum information, finite geometry Key Achievements: Over 60 publications, including works on sum-rank metric codes and cospectral hypergraphs Teaching: Courses like Algebraic Combinatorics and Combinatorics and Applications Her work bridges theoretical advancements with practical applications, particularly in optimizing coding schemes and analyzing graph structures through algebraic methods.

Benny Sudakovمشاهده پروفایل

استاد

• Extremal Graph and Hypergraph Theory
• Ramsey Theory
• Random Structures
• +۵ مورد دیگر

Benny Sudakov is a Professor of Mathematics at ETH Zurich, where he conducts research in combinatorics. He has previously held positions at UCLA, Princeton University, and the Institute for Advanced Study. His work is supported by the SNSF grant 200021_196965. Research Interests: His primary research areas include Extremal Graph and Hypergraph Theory, Ramsey Theory, Random Structures, and the application of Algebraic and Probabilistic Methods in Combinatorics, with strong connections to Theoretical Computer Science. He investigates fundamental structural properties of discrete systems, such as the existence of regular subgraphs, extremal configurations, and the behavior of random combinatorial objects. The recent popular science articles on his work highlight a consistent trend of solving long-standing open problems in extremal combinatorics using sophisticated probabilistic and algebraic techniques. His research spans topics like equiangular lines, graph decompositions, and the emergence of cycles in sparse graphs, demonstrating a deep focus on the interplay between structure and randomness. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: He has advised numerous Ph.D. students, many of whom have gone on to become professors at top universities (e.g., Oxford, Stanford, CMU, ETH, Princeton). His research is currently funded by the Swiss National Science Foundation (SNSF). He has organized workshops and seminars, such as the Theory of Combinatorial Algorithms Mittagsseminar at ETH and a workshop at UCLA on Extremal and Probabilistic Combinatorics. Labs and Teams: He is a key member of the combinatorics group at ETH Zurich and co-organizes the Theory of Combinatorial Algorithms Mittagsseminar, a central forum for research discussions in discrete mathematics at the institution.

Stefan Glockمشاهده پروفایل

استادیار

• Discrete Mathematics
• Extremal Combinatorics
• Probabilistic Combinatorics
• +۴ مورد دیگر

Professor Stefan Glock is an Assistant Professor of Discrete Mathematics at the University of Passau's Faculty of Computer Science and Mathematics, a position he has held since September 2022. Prior to this appointment, he spent three years as a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich, following the completion of his doctorate at the University of Birmingham. Stefan Glock received his mathematics education at Technische Universität Ilmenau from 2009 to 2014, then pursued his PhD at the University of Birmingham, which he completed in 2018. His doctoral dissertation, "Decompositions of Graphs and Hypergraphs," was the runner-up for the Richard-Rado-Preis 2018. Professor Glock's research focuses on discrete mathematical structures, with particular emphasis on their asymptotic properties. His work spans several interconnected fields of combinatorics: Extremal Combinatorics : Investigating the maximum or minimum possible size of mathematical structures satisfying certain properties Probabilistic Combinatorics : Applying probability theory to solve combinatorial problems Graph Theory : Studying properties of graphs and networks Ramsey Theory : Examining conditions under which order must appear in large structures Design Theory : Creating arrangements of elements satisfying specific balance properties Discrete Geometry : Analyzing geometric problems with discrete structures Analysis of Professor Glock's recent publications reveals a consistent focus on solving long-standing open problems in combinatorics using innovative methods that combine probabilistic techniques with structural insights. His work often bridges theoretical mathematics with applications in theoretical computer science, particularly in the analysis of algorithms and network structures. A significant portion of his research addresses fundamental questions about graph and hypergraph decompositions, which have implications for coding theory, cryptography, and network design. Professor Glock has received notable recognition for his contributions to mathematics: Runner-up for the Richard-Rado-Preis 2018 for his dissertation "Decompositions of Graphs and Hypergraphs" Awarded funding through the prestigious DFG Emmy Noether Programme in 2024 for his research group on "the interplay of structure and randomness in mathematics" As a faculty member at the University of Passau, Professor Glock leads the Discrete Mathematics research group and actively collaborates with mathematicians worldwide. He has established a strong research program that has attracted funding for academic visitors and supports multiple research projects. His approach to mathematical problems emphasizes developing new methods that have far-reaching implications beyond the specific problems being solved. Professor Glock's research group at the University of Passau focuses on the interplay between structure and randomness in discrete mathematics. The group maintains active collaborations with leading institutions including ETH Zurich, University of Birmingham, and various research centers across Europe. Through the DFG Emmy Noether Programme funding, his group is expanding its research on combinatorial structures and their applications.

• Probabilistic Combinatorics
• Extremal Combinatorics
• Random Combinatorial Structures
• +۳ مورد دیگر

Guillem Perarnau is an Associate Professor in the Department of Applied Mathematics at Universitat Politècnica de Catalunya (UPC), affiliated with CRM, IMTech, and BGSMath. He holds a PhD from UPC (advised by Oriol Serra) and was a CARP Postdoc Fellow at McGill University (2013–2015). From 2016 to 2019, he was a Lecturer at the University of Birmingham. His research focuses on Probabilistic and Extremal Combinatorics, Random Combinatorial Structures, and Discrete Stochastic Processes. Education: Bachelor, Master, and PhD in Mathematics at UPC. His academic journey includes postdoctoral research at McGill University and a Lecturer role at the University of Birmingham. Research interests include Percolation Theory, Random Graphs, Stochastic Processes, and Algorithmic Combinatorics. His recent work explores topics like percolation on dense graphs, random walk mixing times, and synchronization in automata. He is co-PI of the COCOA grant and coordinates the Spanish Discrete and Algorithmic Mathematics Network. He participates in the RandNET MSCA Exchange Programme. Key contributions include studies on giant components in directed graphs, phase transitions in Glauber dynamics, and extremal problems in graph colorings. His publications span top journals like Annals of Applied Probability , SIAM Journal on Discrete Mathematics , and Random Structures & Algorithms .

Renaud Lambiotteمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Temporal Networks
• +۷ مورد دیگر

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete Optimization
• +۷ مورد دیگر

Nikhil Bansal holds the prestigious Patrick C. Fischer Professorship of Theoretical Computer Science in the Department of Computer Science & Engineering at the University of Michigan's College of Engineering. His research program has established him as a leading figure in theoretical computer science, with significant contributions to algorithm design and analysis, particularly in discrete optimization problems. Bansal's research focuses on theoretical computer science with emphasis on design and analysis of algorithms for discrete optimization problems. His work spans multiple areas including discrepancy theory, approximation algorithms, randomized algorithms, combinatorial optimization, complexity theory, machine learning theory, and probability. He has made significant contributions to understanding the limits of approximation algorithms and developing novel techniques for combinatorial optimization problems. Analysis of Bansal's recent publications reveals a strong focus on discrepancy theory, online algorithms, and combinatorial optimization. His work often bridges theoretical computer science with discrete mathematics and probability theory. A recurring theme across his publications is the development of novel algorithmic techniques for solving NP-hard problems with provable guarantees. His research has evolved from foundational work in approximation algorithms to more recent contributions in quantum computing complexity and stochastic optimization. Patrick C. Fischer Professor of Theoretical Computer Science Bansal has advised numerous PhD students including Marek Elias, Shashwat Garg, and Greg Koumoutsous, as well as mentoring several postdoctoral researchers. He has served on editorial boards for top journals including Journal of the ACM, Theory of Computing, and Stochastic Models, and has been active on program committees for major conferences such as STOC, FOCS, SODA, and ICALP, including serving as chair for ICALP 2021. Bansal has organized multiple academic workshops including the STOC 2020 Workshop on Recent Advances in Discrepancy and Applications, several SDP Days at CWI Amsterdam, and the Semester on Bridging Continuous and Discrete Optimization at UC Berkeley in Fall 2017.

Nikhil Bansalمشاهده پروفایل

استاد

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete Mathematics
• +۳ مورد دیگر

Nikhil Bansal is a Professor in Theoretical Computer Science at the University of Michigan, Ann Arbor. He earned his PhD from Carnegie Mellon University and previously worked at IBM Research, TU Eindhoven, and CWI Amsterdam. His research focuses on algorithm design, discrepancy theory, and combinatorial optimization. Education: PhD, Carnegie Mellon University Bansal's work bridges classical and quantum computing, with recent publications exploring k -Forrelation, vector balancing, and stochastic scheduling. His algorithmic approaches often combine geometric insights and probabilistic methods. Scientific Awards: Patrick C. Fischer Professor of Theoretical Computer Science NSF Career Award (2023) He has advised numerous PhD and postdoctoral researchers, including Marek Elias, Shashwat Garg, and Makrand Sinha. Bansal actively contributes to program committees (ICALP 2021, STOC 2020, FOCS 2018) and organizes workshops on discrepancy theory and optimization.

• Algorithm Design
• Discrete Optimization
• Approximation Algorithms
• +۲ مورد دیگر

Nikhil Bansal is Patrick C. Fischer Professor of Theoretical Computer Science at the University of Michigan. His research focuses on algorithms for discrete optimization problems, discrepancy theory, and scheduling. Research Contributions: Developed foundational techniques in randomized rounding and algorithmic discrepancy minimization. Current investigations include online algorithms, approximation methods for scheduling, and quantum-classical complexity separations. Teaching: Offers courses on randomized algorithms, linear algebra, and approximation algorithms. Created instructional materials on discrepancy theory and semidefinite programming. Professional Service: Serves on editorial boards for Journal of the ACM and Theory of Computing. Organized international workshops on discrepancy theory and optimization.

• Zeta Functions
• Group Theory
• Representation Theory
• +۴ مورد دیگر

Prof. Dr. Christopher Voll is a full Professor at the Faculty of Mathematics, Bielefeld University , Germany. He leads multiple subprojects in the Transregional Collaborative Research Center TRR 358 'Integral Structures in Geometry and Representation Theory' , including A1 (lattices), A4 (Euler products), and A6 (quiver zeta functions). His research bridges algebra, number theory, and combinatorics through zeta functions, subgroup growth, and representation theory.

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.

• Discrete Mathematics
• Probability
• Theoretical Computer Science
• +۶ مورد دیگر

Prasad Tetali is a Regents' Professor at the Georgia Institute of Technology , with appointments in both the School of Mathematics and the School of Computer Science . He also holds an adjunct professor position at Emory University's Mathematics and Computer Science departments. Education: PhD in Mathematics (1991) from the Courant Institute of Mathematical Sciences at NYU; MS in Mathematics (1987) from the Indian Institute of Science; Postdoc at AT&T Bell Labs His research spans Discrete Mathematics, Probability Theory, and Theoretical Computer Science , focusing on Markov chains, Isoperimetry, Combinatorics, Computational number theory, and Algorithms. Recent work includes applications to statistical physics models and hypergraph structures. He has served as Director of the ACO Ph.D. Program since 2019 and held leadership roles like Interim Chair of the School of Mathematics. His publications reflect a blend of discrete geometry, stochastic processes, and algorithmic analysis . Key Honors: AMS Fellow (2012) SIAM Fellow (2009)

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.

Jonas L. Juulمشاهده پروفایل

استادیار

• Network Science
• Social Networks
• Epidemiology
• +۵ مورد دیگر

Jonas L. Juul is an Assistant Professor in the Computer Science Department at the IT University of Copenhagen . With a background in network science and complex systems, he employs statistical methods, mathematical modeling, and computer simulations to study social networks, spreading processes, and human behavior. Focus areas include: information diffusion in social networks Disease spread mitigation in human populations Interdisciplinary collaboration with medical doctors, economists, and computer scientists Recent research highlights include improving statistical models for pandemic forecasting through the InForM project funded by the Novo Nordisk Foundation , and groundbreaking work on contact tracing optimization and information cascade dynamics. Notable recognitions: 2025 H.C. Ørsted Research Talent Prize 2024 Novo Nordisk Foundation Data Science Emerging Investigator Grant 2025 Young Academy membership He has contributed to mathematical modeling efforts during Denmark's COVID-19 reopening in 2020 and maintains active collaborations with institutions including Cornell University , Technical University of Denmark , and Niels Bohr Institute .