جست‌وجوی استاد و پژوهشگر

Stephen Vavasisمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science
• Scientific Computing
• +۳ مورد دیگر

Stephen Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo's Faculty of Mathematics, where he serves on the Steering Committee for the Math Business and Accounting Unit. His research is supported by NSERC grants. Education: B.A. in Mathematics, Princeton University (1984) M.A. (Part III Tripos) in Mathematics, University of Cambridge (1985) Ph.D. in Computer Science, Stanford University (1989) Research Focus: Vavasis specializes in continuous optimization with applications to data science, numerical linear algebra, and computational geometry. His work bridges theoretical foundations and practical implementations in scientific computing and mechanics, emphasizing algorithmic efficiency and real-world problem-solving. Awards & Honors: Guggenheim Fellowship Presidential Young Investigator Award Churchill Scholarship Hertz Graduate Fellowship Administrative & Teaching Roles: Served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science Graduate Programs (2021). Currently teaches optimization courses including CO372 (Portfolio Optimization), CO466/666 (Convex Optimization), and CO673/CS794 (Optimization for Data Science).

Sharat Ibrahimpurمشاهده پروفایل

پژوهشگر

• Combinatorial Optimization
• Approximation Algorithms
• Stochastic Optimization
• +۴ مورد دیگر

Sharat Ibrahimpur is a Postdoctoral Researcher at the Research Institute for Discrete Mathematics (University of Bonn, Germany). His research focuses on approximation algorithms for combinatorial optimization problems, with recent emphasis on stochastic optimization in network design, scheduling, and caching systems. Previously, he held postdoctoral positions at the London School of Economics (Mathematics Department) and Google Research (Discrete Algorithms Group). Education: PhD and M.Math in Combinatorics and Optimization (University of Waterloo), B.Sc. in Applied Mathematics (IIT Roorkee) Advisor: Chaitanya Swamy (University of Waterloo) Research interests span network design , load balancing , caching , and stochastic optimization . His publications in venues like IPCO , ICALP , and MathProg demonstrate expertise in primal-dual methods, independent rounding, and uncrossable functions. Key collaborations include Vera Traub, László Végh, and Manish Purohit.

Kurt Anstreicherمشاهده پروفایل

استاد

• Mathematical Programming
• Optimization Expertise
• Interior-Point Algorithms
• +۳ مورد دیگر

Kurt Anstreicher is a Professor of Business Analytics at the University of Iowa's Tippie College of Business, with complementary appointments in the Department of Computer Science and Department of Industrial Engineering. He holds the Gary C. Fethke Chair in Leadership. His research focuses on mathematical programming, optimization, interior-point algorithms, and nonlinear programming. Anstreicher earned his Ph.D. from Stanford University in 1983 and a B.A. from Dartmouth College in 1978. Research interests include optimization theory and methods, particularly in nonconvex programming, convex relaxations, and algorithm design. His work addresses challenges in quadratic programming, semidefinite optimization, and global optimization techniques. Recent contributions explore applications in trust-region subproblems, copositive cones, and maximum-entropy sampling. Publications span topics like convex hull representations, Kronecker product constraints, and spherical codes. His work bridges theoretical advancements and practical computational methods, with implications for operations research, data analysis, and engineering systems.

Dominique de Werraمشاهده پروفایل

عضو هیئت علمی

• Operations Research
• Combinatorial Optimization
• Graph Theory
• +۵ مورد دیگر

Dominique de Werra is an Honorary Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the Department of Mathematics within the Faculty of Basic Sciences. He holds an Engineering degree and a Doctorate in Operations Research from EPFL. His career includes roles as Full Professor (1979–2008), Vice-President (1991–2000), and Dean of International Relations (2000–2004). He has served as President of IFORS (2010–2012) and EURO (1987–1988), and led academic networks like CLUSTER and EURECOM. His research focuses on Operations Research, Combinatorial Optimization, Graph Theory, and applications in scheduling, logistics, and multidisciplinary fields. He has authored over 200 technical publications, co-authored textbooks like Operations Research for Engineers , and edited numerous journal special issues. Awards include Honorary Doctorates from Paris-Dauphine, Poznan, and Fribourg, as well as the EURO Gold Medal (1995) and Distinguished Service Medal (2012). de Werra has supervised over 140 master’s theses and 350 student projects. He has held visiting professorships worldwide and served on editorial boards of journals like Discrete Applied Mathematics and European Journal of Operational Research . His contributions span academic leadership, conference organization, and interdisciplinary collaborations in archaeology, zoology, and engineering.

Yaakov Malinovskyمشاهده پروفایل

استاد

• Decision theory
• Stochastic ordering
• Sequential selection
• +۶ مورد دیگر

Yaakov Malinovsky is a Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on decision theory, stochastic ordering, sequential selection methods, group testing, and nonparametric methods. He has held editorial roles at journals including Enumerative Combinatorics and Applications , Methodology and Computing in Applied Probability , and The American Statistician . His work bridges theoretical probability and applied statistics, with contributions to group testing optimization, sequential analysis, and combinatorial probability. Malinovsky earned his Ph.D. in Statistics from The Hebrew University of Jerusalem in 2009. He has secured research funding from the United States-Israel Binational Science Foundation for projects on minimax online learning policies in stochastic sequential selection. His teaching portfolio includes courses such as Probability Theory, Mathematical Statistics, and specialized topics in stochastic processes and nonparametric methods. His recent research explores round-robin tournament models, prime number distribution via dice rolls, and optimal stopping rules. He has published extensively in top-tier journals like Statistica Sinica , Biometrics , and Sequential Analysis , demonstrating expertise in statistical inference and combinatorial problems. His work often addresses practical applications in epidemiology and algorithm design while advancing foundational probability theory.

Ellen Veomettمشاهده پروفایل

دانشیار

• Mathematical and computational techniques to detect gerrymandering
• Geometry of redistricting
• Discrete and computational geometry
• +۴ مورد دیگر

Ellen Veomett is an Associate Professor in the Department of Computer Science at the University of San Francisco. Her research focuses on mathematical and computational approaches to detecting gerrymandering, with significant contributions to the development of the GEO metric for evaluating redistricting maps. She has held roles at Saint Mary's College of California, serving as Chair of the Mathematics and Computer Science department and a Professor. Dr. Veomett earned a PhD in Mathematics from the University of Michigan (2007) and a BS in Mathematics from the University of Nebraska-Lincoln (2002). Her research explores the intersection of discrete geometry, graph theory, and political science, emphasizing the use of geometric and algorithmic methods to address issues of fairness in redistricting. Notable contributions include co-designing the GEO metric, which analyzes geographic and election data to identify potentially gerrymandered maps. She has also contributed to interdisciplinary projects like the 'Caminos a Las Ciencias' initiative funded by the US Department of Education, aimed at supporting underrepresented students in STEM fields. Dr. Veomett's work has been published in venues like the Election Law Journal, American Mathematical Monthly, and Simons Institute research programs. Her awards include the 2020 Saint Mary's College Outstanding Scholar Award. She serves on the editorial board of Math Horizons and participated in the Simons Laufer Mathematical Sciences Institute's Algorithms, Fairness, and Equity program. Her research highlights the societal impact of mathematical methodologies in addressing contemporary political challenges.

• Integer Programming
• Stochastic Programming
• High-Performance Computing
• +۳ مورد دیگر

Jeffrey Linderoth is a Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin-Madison, within the College of Engineering. His research focuses on large-scale optimization problems, including integer programming and stochastic programming, with an emphasis on high-performance algorithms and software development. He holds a PhD from Georgia Institute of Technology (1998) and has been recognized with numerous awards, including INFORMS Fellow (2016) and multiple best paper awards in optimization journals. Linderoth is actively involved in teaching optimization courses such as COMP SCI 524 and I SY E 524, and mentors pre-dissertation and thesis research. Education: PhD (1998), MS (1994) – Georgia Tech; BS (1992) – University of Illinois Research Interests: Linderoth specializes in high-performance computing for optimization, numerical methods, and distributed algorithms. His work addresses challenges in computational grids, combinatorial optimization, and applications in fields like energy systems and computational biology. He has developed influential software tools like Minotaur for mixed-integer nonlinear programming. Recent Achievements: His 2024 work on stochastic integer programming and dual decomposition methods, as well as contributions to autocatalytic networks in origin-of-life studies, highlight his interdisciplinary impact. Linderoth has also been honored multiple times for teaching excellence, including the University Housing Honored Instructor Award (2019, 2016, 2008). Awards: Over 20 awards spanning best papers, teaching excellence, and early career recognition Grants/Projects: Includes Department of Energy funding and collaborations on optimizing power systems and minimizing cascading failures Linderoth leads research teams and contributes to academic organizations, advancing both theoretical and applied aspects of optimization science.

Michael Tsatsomerosمشاهده پروفایل

استاد

• Linear Algebra
• Matrix Analysis
• Nonnegative Matrices
• +۸ مورد دیگر

Michael Tsatsomeros is a Professor of Mathematics and Statistics at Washington State University (WSU), Pullman, WA, where he has served since 2007. He holds a PhD in Mathematics from the University of Connecticut (1990) and has extensive academic experience, including roles at the University of Regina, Canada, and visiting positions at institutions like the Indian Institute of Technology-Madras. His research focuses on linear algebra, matrix analysis, nonnegative matrices, numerical range, and applications in dynamical systems, control theory, and machine learning. Education: PhD in Mathematics (1990, University of Connecticut), M.Sc. in Mathematics (1986, Concordia University), and a Diploma in Mathematics (1984, University of Patras, Greece). Research interests include matrix positivity, numerical linear algebra, and applications of matrix theory in interdisciplinary fields like AI and data science. He has authored/co-authored books such as Matrix Positivity (2020) and Linear Algebra and Matrix Methods (2024). His work has been recognized with awards including the 2024 Tom & Julie Lutz Teaching Excellence Award and the 2021 Excellence in Graduate Advising. Key research trends in his articles include P-matrices, Cayley transforms, and matrix positivity classes, with contributions to both theoretical and applied aspects of linear algebra. His grants include collaborations with the SPARC program and NSERC funding. He advises and mentors numerous graduate students, including current PhD candidates and over 20 completed students. His lab and collaborative work emphasize matrix analysis and its interdisciplinary applications.

• Approximation Algorithms
• Graph Theory
• Discrete Optimization
• +۳ مورد دیگر

Chandra Sekhar Chekuri is the Paul and Cynthia Saylor Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. His research focuses on approximation algorithms, graph theory, and combinatorial optimization with applications in computer science and discrete mathematics. He has contributed to topics such as directed Steiner forests, feedback vertex sets, and network design. Research interests include algorithm design for complex optimization problems, leveraging polyhedral combinatorics and approximation techniques. His recent work addresses challenges in planar digraphs, pseudoforests, and fair resource allocation. He has published influential papers in venues like SODA, AAAI, and ESA. Awards: ACM Fellow (2023) His articles explore advanced theoretical concepts while maintaining practical relevance to network design and algorithmic efficiency. Collaborative work spans international teams addressing computational problems in discrete mathematics and optimization.

Erin Moriarty Wolf Chambersمشاهده پروفایل

استاد

• Computational Topology
• Computational Geometry
• Combinatorics
• +۳ مورد دیگر

Erin Moriarty Wolf Chambers is the Snyder Family Mission Collegiate Professor of Computer Science at the University of Notre Dame's Department of Computer Science and Engineering, with a concurrent appointment in Applied and Computational Mathematics and Statistics. Her research is supported by the National Science Foundation, and she serves as editor for the Journal of Computational Geometry and Journal of Applied and Computational Topology. She is also active in committee work for SafeToC and the Society for Computational Geometry. Her research focuses on computational topology and geometry, combinatorics, and combinatorial algorithms, with additional work in improving STEM education and academic culture. Recent publications demonstrate strong emphasis on topological data analysis methods including Reeb graphs, interleaving distances, and mapper graphs, alongside geometric graph comparisons and curve algorithms on 3-manifolds. She maintains active NSF funding for her research program and teaches undergraduate algorithms courses. While no specific research lab is mentioned, she collaborates widely as evidenced by her service roles and multi-author publications.

Dana Randallمشاهده پروفایل

استاد

• Theoretical Computer Science
• Randomized Algorithms
• Statistical Physics
• +۴ مورد دیگر

Dana Randall is the ADVANCE Professor of Computing and an Adjunct Professor of Mathematics at Georgia Institute of Technology. She serves as the Associate Dean for Access and Advancement in the College of Computing, Co-Executive Director of the Institute for Data Engineering and Science (IDEaS), and previously directed the Algorithms and Randomness Center (ARC). Her research bridges theoretical computer science, discrete mathematics, and statistical physics, focusing on randomized algorithms and stochastic processes. Education: A.B. in Mathematics, Harvard University (1988) Ph.D. in Computer Science, University of California, Berkeley (1994) Research Interests: Randomized algorithms and Markov chain Monte Carlo methods Computational problems in statistical physics Combinatorics, distributed algorithms, and collective computation Awards & Honors: Fellow of the American Mathematical Society (AMS) National Associate of the National Academies Former Sloan Fellow and NSF Career Award recipient Grants & Leadership: Principal Investigator on a $6.25M ARO MURI grant on collective emergent behavior (2019–2024) Co-founder and Co-Executive Director of IDEaS (2016–2019) Directed the Algorithms and Randomness Center (2014–2016) Labs & Teams: Active in the Algorithms and Randomness Center (ARC) and Institute for Data Engineering and Science (IDEaS), focusing on interdisciplinary research in algorithms, data science, and computational theory.

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.

• Neuroanatomy
• Connectomics
• Neural circuits
• +۵ مورد دیگر

Professor Hong Wei Dong is a faculty member in the Department of Neurobiology at the David Geffen School of Medicine, University of California Los Angeles (UCLA). His research focuses on creating comprehensive connectome maps of the C57Bl/6 mouse brain to understand functional network organization and behavioral output mechanisms. He integrates Connectomics Genetics 3D high-resolution imaging Artificial intelligence to explore the fundamental architecture of the central nervous system. Key research directions include: Classification of mouse brain/spinal cord cell types through anatomic, molecular, and physiological properties Development of microscopy/histological technologies for human brain mapping at axonal resolution Application to neurodegenerative disease models (Alzheimer’s, Huntington’s) Recent publications highlight his work on: Visceromotor cortex networks High-resolution brain atlases Thalamic subnetworks Neuronal diversity analysis Advanced image processing tools Transsynaptic tracing methodologies Awardeeship highlights: Suzanne Eaton Memorial Prize Taylor M. Brown Memorial Award His lab develops scalable technologies like Gossamer for petabyte-scale image processing and Morphohub for multi-morphometry generation, while maintaining affiliations with UCLA Brain Research & Artificial Intelligence Nexus (B.R.A.I.N.) and NIH T32 training grants.

Abel Jansmaمشاهده پروفایل

پژوهشگر ارشد

• Complexity
• Information Theory
• Statistical Physics
• +۷ مورد دیگر

Abel Jansma is a Research Fellow at the Dutch Institute for Emergent Phenomena (DIEP), affiliated with the University of Amsterdam's Institute of Physics (IoP) and Institute of Logic, Language, and Computation (ILLC). His work bridges physics, mathematics, and biology to study emergence, higher-order interactions, and complexity. He holds a unique interdisciplinary background, transitioning from art school to scientific research while maintaining creative projects like origami design and AI culinary applications. Research interests include quantitative mereology, information theory, and causal modelling, with a focus on decomposing systems into parts and interactions. Notable contributions involve applying Möbius inversion techniques to study higher-order effects in complex systems. He explores topics like entropy decomposition, coarse-graining, and interdisciplinary frameworks linking category theory with physics. Additional projects span art-science interfaces: creating Penrose tiling visualizations, developing AI cooking assistants (Autolenghi), and collaborating on bio-design projects (mycorrhizal transplantations). His work often emphasizes visual and conceptual clarity, with outputs ranging from mathematical blogs to physical installations. Active on social media platforms including Twitter and GitHub, he engages in open science and public communication.

• Quantum Computing
• Bayesian Statistics
• Bioinformatics
• +۳ مورد دیگر

Kevin Chern is a researcher in the Department of Statistics at the University of British Columbia’s Faculty of Science. His work bridges quantum computing, Bayesian statistics, and computational biology. Key research interests include quantum simulation optimization, probabilistic programming frameworks (e.g., Blang), and bioinformatics applications such as spatial proteomics and cancer phylogenetics. His recent publications (2019–2025) focus on advancing quantum annealing calibration, Bayesian clustering methods for spatial data, and scalable inference of phylogenetic trees from single-cell genomic data. He has contributed to foundational work in quantum supremacy and probabilistic modeling of combinatorial spaces. While no explicit awards or grants are listed here, his involvement with the Applied Statistics and Data Science Group (ASDa) and participation in seminar series suggest active engagement with interdisciplinary research and education initiatives.