Kevin Ferland serves as a Professor in the Department of Mathematics, Computer Science, and Digital Forensics at Bloomsburg University of Pennsylvania. His academic credentials include: Ph.D. from Syracuse University (1999) Dr. Ferland's research spans algebraic topology, graph theory, and combinatorics, with foundational work in algebraic topology through his doctoral dissertation. His scholarly focus integrates theoretical mathematics with discrete structures, emphasizing combinatorial applications in topological contexts and graph-based modeling. No scientific awards or honors were documented in the source material. Professional activities include advising and grant management, though specific students or funded projects were not referenced. Beyond academia, he actively participates in various sports, reflecting a strong personal commitment to athletic engagement.
Linda McGuire serves as the Truman Koehler Professor of Mathematics within the Department of Mathematics, Computer Science & Statistics at Muhlenberg College, where she integrates rigorous mathematical scholarship with transformative educational practices. Her academic credentials include a B.S. from Seton Hall University and M.S./Ph.D. degrees from Stevens Institute of Technology. As a first-generation college student from a blue-collar family, she brings unique perspective to navigating academic systems. McGuire's research spans combinatorics and graph theory applications for optimizing transit and communication networks, supplemented by growing work in applied statistics and mathematical modeling. A defining thread examines equity and inclusion in STEM education, investigating mathematical identity development, microaggressions, and culturally responsive pedagogy. Her scholarship consistently bridges theoretical mathematics with social justice imperatives. Recent publications reveal strong interdisciplinary trends connecting mathematics to literary arts and theater through concepts like mathematical dramaturgy, while advancing frameworks for inclusive STEM communities. This work demonstrates how mathematical thinking intersects with humanistic inquiry to address systemic inequities. Her scientific recognition includes: James P. Crawford Award for Distinguished Teaching (2023) Christian and Mary Lindback Award for Distinguished Teaching (2008) National Project NExT Fellow (1998) Bridge Builder Awards from the Academic Resource Center McGuire actively mentors students as faculty advisor for the Association for Women in Mathematics (AWM) and Women in STEM (WiSTEM) chapters, collaborating on grassroots initiatives for systemic change. Her National Project NExT fellowship underscores commitment to equitable classroom design and professional development for early-career faculty. While not leading a formal research laboratory, she cultivates collaborative environments through student groups and interdisciplinary projects. Her innovative course development—including first-year seminars blending analytical thinking with writing—fosters undergraduate research and problem-solving in accessible mathematical contexts.
Chuck Lundon is Professor and Department Chair in the Department of Mathematics at Linfield University, renowned for pioneering unconventional study abroad programs that integrate mathematics with cultural history across Russia, Germany, Switzerland, China, Japan, and Australia. His international teaching methodology follows historical mathematical legacies like Euler's work and explores connections between Chinese mathematics and Japanese wasan/sangaku. Dr. Lundon's academic credentials include: B.A. in Mathematics and Music from Lewis & Clark College M.S. in Mathematics from University of Illinois at Urbana-Champaign Ph.D. in Mathematics from Arizona State University His research centers on graph theory and combinatorics, specializing in competitive graph coloring algorithms developed through extensive undergraduate collaboration. With over 30 student co-authors across NSF-funded REU-RET programs (2008-2017), his work bridges theoretical mathematics with accessible educational frameworks for emerging researchers. Publications spanning 2004-2020 reveal consistent innovation in graph coloring games, with recent contributions including foundational texts for undergraduate research and analyses of k-degenerate graphs, tree structures, and higher-dimensional combinatorial paths. His work demonstrates evolving sophistication in modeling competitive coloring scenarios while maintaining educational applicability. Though no specific scientific awards are documented, Dr. Lundon's sustained NSF funding for undergraduate research initiatives highlights the significance of his scholarly contributions. He has mentored more than 30 undergraduate researchers through co-authored publications and NSF REU-RET programs, while collaborating internationally with scholars like Dr. Michael Crosser (Australia astronomy projects, Crisscrossing Science podcast) and Dr. Christopher Keaveney (China-Japan mathematics history studies). These partnerships create interdisciplinary learning environments connecting abstract mathematics to real-world scientific discovery. Dr. Lundon's educational philosophy, shaped by his own three undergraduate study abroad experiences (USSR, UK, China), manifests in immersive programs that contextualize mathematical concepts within global cultural frameworks, culminating in planned 2023 Australia courses examining Nobel-winning astronomical research.
Van Cyr is a Professor of Mathematics at Bucknell University, specializing in dynamical systems and ergodic theory within the Department of Mathematics. His research bridges theoretical mathematics with applications in combinatorics and symbolic dynamics. His educational background includes: Ph.D. from The Pennsylvania State University B.S. from University at Buffalo, The State University of New York Professor Cyr's research focuses on thermodynamic formalism for countable state Markov shifts exhibiting transience, multidimensional symbolic dynamics, and connections to combinatorics. His work explores spectral properties of Ruelle operators, geometric applications in billiard dynamics, and harmonic structures in graph theory. This interdisciplinary approach integrates ergodic theory with number-theoretic and combinatorial methods to analyze complex dynamical behaviors. Analysis of his publications reveals a consistent trajectory in symbolic dynamics, with increasing emphasis on multidimensional systems and combinatorial structures after 2011. His research demonstrates strong thematic continuity in Markov shift theory while expanding into graph labelings and geometric dynamics, reflecting a synthesis of pure and applied mathematical techniques across six publications from 2009-2014. No scientific awards are documented in the available information. No details regarding graduate student advising or research grants are provided in the source material. No specific laboratories or research teams are mentioned in the institutional profile.
David P. Williamson is a Professor at Cornell University in the School of Operations Research and Information Engineering (ORIE), with a significant leadership role as former Chair of the Department of Information Science in the Cornell Ann S. Bowers College of Computing and Information Science from July 2021 through December 2023. His academic journey began at MIT where he earned his B.S. in Mathematics (1989), followed by an M.S. (1990) and Ph.D. (1993) in Computer & Information Science under Professor Michel X. Goemans. After completing a postdoc at Cornell under Professor Éva Tardos, he worked at IBM Research at both the T.J. Watson Research Center and Almaden Research Center before joining Cornell University in 2004. B.S. (Mathematics), Massachusetts Institute of Technology (1989) M.S. (Computer & Information Science), Massachusetts Institute of Technology (1990) Ph.D. (Computer & Information Science), Massachusetts Institute of Technology (1993) Professor Williamson's research centers on discrete optimization, specializing in approximation algorithms for NP-hard optimization problems. His work spans network design, scheduling, facility location, clustering, ranking, and particularly the traveling salesman problem. He has made seminal contributions to the field, evidenced by his co-authored paper 'Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming' which earned the 2022 AMS Steele Prize. His research approach emphasizes simple yet powerful approximation algorithms with provable performance guarantees, bridging theoretical computer science and operations research. Analysis of his recent publications reveals a strong focus on the traveling salesman problem, with particular attention to integrality gaps of semidefinite programming relaxations, combinatorial algorithms for solving Laplacian systems, and novel approaches to cycle cut instances. His work consistently demonstrates how theoretical insights can yield practical algorithmic improvements, with applications spanning network design, revenue management, and graph theory. Williamson has also contributed significantly to educational resources through his textbook 'Network Flow Algorithms' (2019) and 'The Design of Approximation Algorithms' (2011, with David Shmoys). American Mathematical Society Steele Prize for Seminal Contribution to Research (2022) SIAM Fellow (2016) ACM Fellow (2013) Lanchester Prize for best contribution to operations research (2013) Professor of the Year (ORIE Undergraduate Voted) (2018) ACM STOC 30-year Test of Time Award (2024) Professor Williamson has demonstrated significant academic leadership through his service as Chair of the Department of Information Science and as former Editor-in-Chief for the SIAM Journal on Discrete Mathematics. His teaching portfolio includes undergraduate courses like ENGRI 1101 (introduction to operations research) and ORIE 1380 (introduction to data science), as well as graduate courses including ORIE 6330 (network flows) and ORIE 6334 (spectral graph theory and algorithms). His research has attracted substantial funding from the National Science Foundation, including awards for projects like 'AF: Small: Looking Under Rocks: A Search for a Provably Stronger TSP Relaxation' (2019) and 'AF: EAGER: Approximation algorithms for the traveling salesman problem' (2015). While specific laboratory affiliations aren't detailed in the available information, Professor Williamson's work is deeply embedded in Cornell's theoretical computer science and operations research communities. His research collaborations span multiple institutions, with frequent co-authorship with colleagues at Cornell and beyond. His recent publications indicate active engagement with current challenges in approximation algorithms, particularly those related to the traveling salesman problem and semidefinite programming relaxations, suggesting ongoing leadership in these critical areas of theoretical computer science and operations research.
Stephen Melczer is an Assistant Professor in Combinatorics and Optimization at the Cheriton School of Computer Science, University of Waterloo. His research advances analytic combinatorics, lattice path enumeration, and symbolic computation techniques. Research develops multivariate methods for asymptotic enumeration, including work on generating functions, lattice paths, and tree structures. Recent publications focus on SageMath implementations, AVL tree encodings, and singularity analysis. Publications demonstrate consistent innovation in combinatorial algorithms with applications to information theory and data structures. Work integrates symbolic computation with asymptotic analysis for rigorous combinatorial results.
Houcine Ben Dali is a Benjamin Peirce Fellow at Harvard University and a postdoctoral researcher at the Center of Mathematical Sciences and Applications . He earned his PhD from Université de Lorraine in June 2024 under the supervision of Valentin Féray and Guillaume Chapuy. His research focuses on algebraic and enumerative combinatorics, particularly connections between Jack and Macdonald polynomials and combinatorial objects such as non-orientable maps and Łukasiewicz paths. Education: PhD in Mathematics, Université de Lorraine (2024), supervised by Valentin Féray and Guillaume Chapuy. His work bridges algebraic combinatorics with mathematical physics, topology, and representation theory. He investigates integrality properties in the Matching-Jack conjecture, differential equations for hypermaps, and combinatorial interpretations of symmetric functions. Notable contributions include a new formula for Macdonald polynomials, differential equations for hypermap series, and proofs of Lassalle's conjecture. His publications appear in top venues such as the Electronic Journal of Combinatorics , Combinatorial Theory , and Transactions of the American Mathematical Society . Scientific Awards: Best Student Paper Award, FPSAC 2022 Contact: Email: bendali@math.harvard.edu Office: Science Center Office 238, Cambridge MA 02138
Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Data Science and AI Institute, Department of Mathematics, and Algorithms and Complexity Group. Prior to JHU, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from New York University’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the interplay between probability theory, mathematical statistics, and computational complexity, particularly in high-dimensional data and algorithmic limitations. He has held roles in software engineering (Google) and academic advising (ETH Zurich). Education: Bachelor’s in Mathematics, Princeton University PhD in Mathematics, Courant Institute, NYU Research Interests: His work examines computational thresholds in statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores the mathematical foundations of algorithmic performance, including information-computation gaps and pseudorandomness. Recent topics include nonlinear Laplacians, random circulant graphs, and low-coordinate-degree algorithms. Teaching & Outreach: In Fall 2025, he teaches Random Matrix Theory in Data Science . Past courses include Sum-of-Squares Optimization (Yale) and Mathematical Statistics (NYU). He advises graduate students like David Opalic and Nikolaus Doppelbauer on topics like Sherrington-Kirkpatrick Hamiltonians and mutually unbiased bases. Upcoming Engagements: BIRS Workshop on Combinatorics (June 2025) TTIC Workshop on Information-Computation Tradeoffs (June 2025) INFORMS Applied Probability Conference (July 2025) COLT Conference (July 2025) Labs/Teams: Active in JHU’s Data Science and AI Institute, collaborating on projects involving high-dimensional statistics, convex optimization, and algorithm design.
Dr. Sanjaye Ramgoolam is a Reader in Theoretical Physics at the School of Physical and Chemical Sciences, Queen Mary University of London. His research focuses on string theory, quantum field theory, representation theory, and combinatorics, with particular emphasis on gauge-string duality (AdS/CFT correspondence) and permutation invariant matrix models. He has pioneered mathematical frameworks using representation theory and combinatorics to explore the holographic map between quantum field theories and string theory. His work also extends to applications in financial matrix models and computational linguistics via matrix statistics. Teaching roles include advanced courses such as Mathematical Techniques 4 and Advanced Quantum Field Theory. He has supervised numerous PhD students, including Costis Papageorgakis and Tom Brown, and collaborates with researchers like Andreas Brandhuber and Rodolfo Russo on grants like 'Amplitudes, Strings and Duality' funded by STFC. Recent research highlights include Gaussian permutation invariant matrix models, quantum thermodynamics in large N systems, and combinatorial topological string theories. His work bridges fundamental physics with mathematical structures, offering insights into quantum gravity and dualities. Key grants include the Royal Society-funded 'Combinatorics and algorithms for quantum states in holography' (2025-2026) and STFC's 'Amplitudes, Strings and Duality' (2023-2026). His 15 most recent articles span topics like eigenvalue systems for multi-matrix invariants, permutation symmetry in quantum thermodynamics, and Kronecker coefficients from ribbon graphs. Though no explicit awards are listed, his extensive publications and grants reflect significant contributions to theoretical physics.
Dmitry Chelkak is the Keeler Professor of Mathematics at the University of Michigan's College of Literature, Science, and the Arts (LSA). His primary affiliation is within the Department of Mathematics, focusing on Probability Theory, Mathematical Physics, and Analysis. Chelkak holds a Ph.D. from the PDMI RAS (2003). His research interests center on critical phenomena in statistical physics, including the Ising model, dimer models, and conformal invariance. He has made significant contributions to understanding spin correlations, universality in lattice models, and the interplay between discrete and continuous systems. His work often employs advanced techniques from complex analysis and discrete geometry. Recent articles highlight investigations into universality in the Ising model on isoradial graphs, dimer models on planar graphs, and the application of tau-functions to cylindrical event probabilities. His studies frequently bridge combinatorial structures with probabilistic and geometric frameworks. No scientific awards are explicitly listed in the provided texts. Chelkak's advising record and grants are not detailed here, but his research has explored foundational topics in statistical mechanics and mathematical physics.
Matilde Lalín is a Full Professor at the University of Montreal's Department of Mathematics and Statistics, affiliated with the CICMA (Centre Interuniversitaire en Calcul Mathématique Algébrique) and the Montreal Number Theory Group. She holds a Licenciatura in Mathematics from the University of Buenos Aires, pursued graduate studies at Princeton University, and completed her PhD in Number Theory at the University of Texas at Austin under Fernando Rodríguez Villegas. Her research focuses on Mahler measure, L-functions, elliptic curves, and their connections to algebraic geometry and analysis. Her notable contributions include studies on Mahler measure’s relationship with hyperbolic volumes and L-functions, functional equations in genus-one curves, and statistical properties of zeta functions in function fields. She has been a Clay Mathematics Institute Liftoff Fellow and a PIMS postdoctoral fellow. Her work bridges analytic and algebraic number theory, with applications to arithmetic statistics and geometric structures. Recent articles highlight advancements in Mahler measure under variable transformations, Northcott properties of zeta functions, and symplectic conjectures in divisor function analysis. Collaborations span international institutions, and she actively organizes conferences like Women in Numbers and analytic number theory symposia. Teaching includes advanced courses on modular forms, algebraic number theory, and elliptic curves. Education: PhD (UT Austin), Licenciatura (Buenos Aires) Awards: Clay Liftoff Fellowship Key Research Themes: Mahler measure, L-functions, elliptic curves, arithmetic statistics Professional Roles: Conference organizer, instructor at summer schools, mentor in analytic number theory
Amanda Redlich is an Assistant Professor in the Department of Mathematics & Statistics at the University of Massachusetts Lowell (UML), part of the Kennedy College of Sciences. She holds a PhD in Mathematics from the Massachusetts Institute of Technology (2010) and a BA in Mathematics from the University of Chicago (2005), with additional studies at the Budapest Semesters in Mathematics (2003). Her research focuses on probabilistic combinatorics, randomized algorithms, random graphs, and applications to biological and social networks. Her work explores allocation processes, graph decomposition, and stochastic systems, with notable contributions to balanced and unbalanced allocation models. Publications highlight advancements in load balancing, graph theory, and combinatorial analysis. Redlich has been recognized with prestigious fellowships, including the NSF Mathematical Sciences Postdoctoral Research Fellowship (2010) and the Akamai Presidential Fellowship (2005). Her academic journey includes postdoctoral work at Rutgers University and the Institute for Computational and Experimental Research in Mathematics (ICERM) at Brown University, as well as teaching roles at Bowdoin College. She actively engages in research seminars and workshops, presenting on topics like network science and combinatorial games.
Prof. Dr. Patrick Felke is a Professor for IT Security at the University of Applied Sciences Emden/Leer, affiliated with the Department of Technology, Electrical Engineering and Informatics. His research focuses on advanced cryptographic techniques, including cryptanalysis of encryption algorithms, IoT security vulnerabilities (e.g., Z-Wave protocols), and mathematical foundations of cryptographic primitives. He leads the IT-Sec Lab (https://itsec-lab.hs-emden-leer.de/), which explores topics like multivariate cryptography, symmetric cipher design, and side-channel attack mitigation. His research spans both theoretical and applied aspects of information security, with notable contributions to cryptanalysis of TETRA encryption algorithms, analysis of multivariate encryption schemes (e.g., EFLASH), and identification of critical flaws in wireless communication standards. Felke also contributes to the Digital Hub Ostfriesland initiative, focusing on IT security advancements in regional technology ecosystems. Key areas of expertise include: Cryptographic protocol vulnerability analysis Z-Wave and IoT device security Design and cryptanalysis of symmetric/asymmetric encryption systems Mathematical foundations of nonlinear functions in cryptography Publications emphasize practical cryptanalysis methods, algorithmic decomposition techniques, and cryptographic standard evaluation. His work bridges academic research with real-world cybersecurity challenges in telecommunications and embedded systems.
Andrew Harder is an Associate Professor in the Department of Mathematics at Lehigh University. He specializes in algebraic geometry with strong connections to Hodge theory, mathematical physics, and symplectic geometry. Before joining Lehigh in 2019, he held a postdoctoral position at the University of Miami as part of the Simons Collaboration in Homological Mirror Symmetry. Education: Ph.D. in Mathematics, University of Alberta (2016) M.Sc. in Mathematics, Queen’s University (2011) B.Sc. in Mathematics, Queen’s University (2009) Research focuses on advanced topics including: Applications of mirror symmetry to Calabi-Yau varieties Hodge-theoretic analysis of Landau-Ginzburg models Interactions between holomorphic symplectic geometry and tropical geometry Connections between Feynman integrals and algebraic periods Recent work explores modular properties of Landau-Ginzburg models, motivic geometry of Feynman integrals, and P=W phenomena in hyper-Kähler manifolds. His research bridges pure mathematics with theoretical physics through geometric frameworks. Teaching includes advanced courses like Topics in Algebraic Geometry and graduate-level algebraic structures. Active in mentoring through graduate seminars and advising on thesis projects.
Adrien Kassel is a Researcher at CNRS based at École Normale Supérieure de Lyon, working within the Unit for Pure and Applied Mathematics (UMPA) in the Probabilities team. His research spans the intersection of probability theory, combinatorics, and mathematical physics, with particular focus on random structures on graphs and surfaces. Dr. Kassel's research centers on probabilistic combinatorial structures, especially determinantal point processes , random spanning trees and forests , and loop models . His work connects deep mathematical concepts from statistical mechanics with geometric and topological structures. He investigates scaling limits of discrete models, connections to conformal field theory, and applications to mathematical physics. His research often reveals profound connections between seemingly disparate areas of mathematics through the lens of probability. His publications demonstrate a consistent focus on the interplay between combinatorial structures and probabilistic phenomena. The research trajectory shows increasing sophistication in handling geometric aspects of random processes, with recent work exploring connections to quantum gravity and conformal field theory through Schramm-Loewner evolution. Dr. Kassel received the prestigious Paul R. Halmos - Lester R. Ford Award for his article "The Looping Rate and Sandpile Density of Planar Graphs" co-authored with David B. Wilson. This award recognizes expository excellence in mathematical writing. He has advised Héloïse Constantin, who successfully defended her PhD thesis on "Spanning forests and phase transition" in June 2023. Dr. Kassel teaches advanced courses including "Determinantal processes" at the Master's level and "Integration and Probability" at the undergraduate level. He has co-organized numerous academic events including the ICJ-UMPA probability seminar, workshops on random maps and matrices, and meetings between ENS Lyon and SISSA. As coordinator of MathαLyon from 2017-2022, he actively engaged in mathematical outreach, bringing exhibits to middle and high schools across the Lyon region. His commitment to popularization extends to writing for Images des Mathématiques and participating in various math circles and outreach programs.
