جستجوی استاد راهنمای دانشگاهی

Anderson Ye Zhangمشاهده پروفایل

استادیار

• Spectral Analysis
• Group Synchronization
• Ranking from Pairwise Comparisons
• +۴ مورد دیگر

Anderson Ye Zhang is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania, with a secondary appointment in the Department of Computer and Information Science. He holds a PhD from Yale University and previously served as a William H. Kruskal Instructor at the University of Chicago. His research focuses on the theoretical and applied aspects of statistics and machine learning, emphasizing spectral methods, synchronization problems, clustering, and network analysis. Education: PhD in Statistics and Data Science, Yale University (2018) Bachelor's degree from Zhejiang University (2012) Research Interests: His work addresses foundational challenges in spectral analysis, group synchronization (e.g., phase synchronization, permutation synchronization), ranking systems, and high-dimensional clustering. He develops algorithms with provable guarantees for problems such as Gaussian mixture models, stochastic block models, and item response theory. Recent Trends in Publications (2021–2025): His articles emphasize spectral methods for synchronization and clustering, with a focus on optimality in high-noise regimes, privacy-preserving learning, and efficient algorithms for complex data structures like human response models and anisotropic covariances. His work bridges statistical theory and computational practice. Awards & Grants: 2025: Sloan Research Fellowship, NSF CAREER Award 2019: New Researcher Award (ICSA) 2018: Francis J. Anscombe Award Grants: NSF DMS-2440180 (CAREER: Statistical Inference in Group Actions) NSF DMS-2112988 (Ranking from Comparisons) Academic Contributions: He has advised multiple collaborative research projects with postdocs and students, particularly on spectral methods and synchronization problems. His teaching spans advanced statistical theory and linear models at Wharton and the University of Chicago.

Christopher Harshawمشاهده پروفایل

استادیار

• Causal Inference
• Algorithm Design
• Randomized Experiments
• +۳ مورد دیگر

Christopher Harshaw is an Assistant Professor in the Statistics Department at Columbia University. His research focuses on causal inference and algorithm design, particularly in improving the design and analysis of randomized experiments, including experiments with interference and sequential experiments. He holds a PhD in Computer Science from Yale University and completed postdoctoral fellowships at MIT/UC Berkeley (via FODSI) and the Simons Institute for the Theory of Computing. Education: PhD in Computer Science (Yale University), Postdoctoral Fellowships at MIT/UC Berkeley and the Simons Institute. Research Interests: At the intersection of computation and statistics. Key areas include causal inference methodologies, algorithmic tools for experimental design, and submodular optimization. His work emphasizes balancing covariates, handling interference in experiments, and developing efficient experimental frameworks. Notable Achievements: Received the Best Paper Award at the CML4Impact NeurIPS 2022 Workshop and the Best Paper Award at CISRC 2016. His contributions include the Gram-Schmidt Walk Design for covariate balancing and the Conflict Graph Design for causal effect estimation under interference. Labs/Teams: Collaborations include work on software packages such as GSWDesign.jl and SubmodularGreedy.jl, advancing tools for statistical experimentation and submodular optimization.

• Artificial Intelligence
• Supply Chain Management
• Sustainability
• +۶ مورد دیگر

Evelyn Xiao-Yue Gong is an Assistant Professor at Carnegie Mellon University’s Tepper School of Business, where she holds the B P Junior Faculty Chair. Her research focuses on artificial intelligence for supply chain optimization and sustainability, with additional interests in assortment optimization, pure exploration, and data-driven decision-making. Education PhD in Operations Research, Massachusetts Institute of Technology (2023) BS in Honors Mathematics, New York University (2017) BS in Interactive Media Arts, New York University (2017) Research Interests Dr. Gong’s work integrates reinforcement learning, bandit algorithms, and AI to address sustainability challenges in supply chains. Her recent publications highlight applications in cloud server deployment, sustainable packaging, and inventory management under cyclic demands. Scientific Awards Best Dissertation Prize, Supply Chain Management in the Post-Pandemic and AI Age Conference (2023) Accenture Fellowship (2022-2023) Bayer Women in Operations Research Scholarship (2021) National Championship in Splendor (2024) Professional Services Dr. Gong serves as a reviewer for journals including Management Science and NeurIPS , and has held committee roles for INFORMS awards. She has chaired sessions at the INFORMS Annual Meeting multiple times.

Konstantin Geninمشاهده پروفایل

پژوهشگر

• Inductive Inference
• Machine Learning
• Algorithmic Fairness
• +۷ مورد دیگر

Konstantin Genin is the leader of the Epistemology and Ethics of Machine Learning Research Group within the Cluster of Excellence – Machine Learning for Science at the University of Tübingen , and a member of the Department of Computer Science. His research focuses on the intersection of formal epistemology, machine learning, and statistics, emphasizing topics like inductive inference, algorithmic fairness, causal discovery, and Ockham’s razor. He previously held a postdoctoral fellowship in Philosophy at the University of Toronto, supervised by Franz Huber, and completed his PhD in Logic, Computation, and Methodology at Carnegie Mellon University under Kevin T. Kelly. Genin’s work employs topological methods to address foundational questions in statistical and machine learning theory. Key contributions include non-circular epistemic justifications for Ockham’s razor, and applications to causal inference and algorithmic fairness. He has presented his research globally, including at institutions like Seoul National University, the University of Milan, and the University of Coimbra. His educational background includes a PhD (2018) and dual BAs in Math and Philosophy (2009) from Brown University. Genin’s academic contributions span workshops and conferences on philosophy of science, formal epistemology, and machine learning ethics. His research group explores the epistemological challenges and ethical implications arising from machine learning’s role in scientific inquiry and societal decision-making. He maintains active collaborations across disciplines, bridging philosophy, computer science, and statistics.

• Game Theory
• Search Theory
• Operational Research
• +۹ مورد دیگر

Professor Steve Alpern is a faculty member at the University of Warwick, affiliated with the ISM-Analytics (ISMA) Group within the Warwick Business School. He holds the academic rank of Professor of Operational Research. Prior to this role, he was Professor of Mathematics and Operational Research at the London School of Economics (LSE). His academic journey began in the United States, where he earned an AB from Princeton University (under Oskar Morgenstern) and a PhD from the Courant Institute at New York University (specializing in dynamical systems under Peter Lax). He has taught at NYU, UCLA, Bryn Mawr, and Yale before moving to the UK. Professor Alpern's research spans game theory and operational research, with a focus on search theory, rendezvous search, and decentralized matching theory. His recent work applies search theory to animal behavior (e.g., predator-prey dynamics and caching strategies) and group decision-making (e.g., jury voting and committee selection). He has also contributed to patrolling games, caching games, and network search strategies. His research bridges theoretical foundations with practical applications in ecology, economics, and security. He currently teaches modules in Mathematical Game Theory, Mathematical Programming, and related subjects. His scholarly contributions include co-authoring influential books like Search Theory: A Game Theoretic Perspective and The Theory of Search Games and Rendezvous . While no specific awards are listed in the text, his extensive publications and academic roles reflect his expertise in operational research and game theory. His advising and grant activities are not detailed here, but his research collaborations span institutions globally. He is actively involved in the ISMA Group, contributing to interdisciplinary research in analytics and operational decision-making.

Benjamin Moseleyمشاهده پروفایل

دانشیار

• Operations Research
• Theoretical Computer Science
• Machine Learning
• +۵ مورد دیگر

Benjamin Moseley is the Carnegie Bosch Associate Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University (CMU), with a courtesy appointment in the School of Computer Science's Machine Learning Department. He is also a consulting professor at the start-up Relational AI. Education: PhD in Computer Science from the University of Illinois, advised by Chandra Chekuri. Prior roles include Assistant Professor at Washington University in St. Louis (2014–2017), Research Assistant Professor at Toyota Technological Institute at Chicago (2012–2014), and visiting positions at Sandia National Laboratories and Yahoo Labs. Notable affiliations include the NSF-funded Algorithms, Combinatorics, and Optimization (ACO) PhD program. Research focuses on algorithm design, analysis, and evaluation, with emphasis on operations research, theoretical computer science, and machine learning. Current projects include algorithmic foundations of machine learning, distributed algorithm design, and scheduling/logistics optimization. His work bridges theoretical rigor and practical applications, addressing challenges in big data analysis, streaming algorithms, and approximation techniques. Recognition includes multiple best paper awards (IPDPS 2015, SPAA 2013, SODA 2010), top-tier NeurIPS presentations, and prestigious grants from NSF, ONR, Google, Bosch, and others. His research has been supported by a NSF CAREER Award, Google Faculty Research Awards, and the Carnegie-Bosch Chair. Teaching contributions include developing the Business Analytics and Optimization undergraduate minor at Tepper, courses for UBA/MBA/MSBA programs, and被评为“Top 50 Undergraduate Business School Professor” by Poets and Quants. He emphasizes pedagogy integrating advanced algorithms with business applications. Labs/Teams: Active in Relational AI collaborations and affiliated with CMU's machine learning and operations research research groups. His work frequently intersects with industrial partners like Google, Yahoo, and Bosch.

Wesley Pegdenمشاهده پروفایل

استاد

• Discrete Mathematics
• Probabilistic Combinatorics
• Combinatorial Game Theory
• +۷ مورد دیگر

Wesley Pegden is a Professor in the Department of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. He holds a Ph.D. from Rutgers University. His research focuses on Discrete Mathematics, including probabilistic combinatorics, combinatorial game theory, graph theory, and discrete geometry. A central theme of his work is the Abelian sandpile model, where he has explored fractal patterns and connections to Apollonian circle packings. Collaborations with Lionel Levine and Charles Smart have advanced understanding of the sandpile's geometric properties through novel constructions of integer superharmonic functions. His work extends to applications in network science, epidemiological modeling, and statistical sampling biases (e.g., analyzing the Bangladesh mask trial). Notably, he contributed to studies on gerrymandering and congressional districting, including an expert analysis of Pennsylvania's district map. Awards include the Sloan Research Fellowship. Research Highlights: Abelian sandpile fractal geometry, algorithmic local lemma extensions, and random graph dynamics. Key Collaborations: Lionel Levine, Charles Smart, Alan Frieze.

• Applied Mathematics
• Data Science
• Partial Differential Equations
• +۷ مورد دیگر

Dejan Slepčev is a Professor and Associate Dean for Faculty and Graduate Affairs at the Mellon College of Science, Department of Mathematical Sciences, Carnegie Mellon University. His research focuses on applied analysis, with applications to data science, collective behavior of particle systems, and energy-driven systems. He holds a Ph.D. from the University of Texas at Austin. His research interests include partial differential equations, calculus of variations, optimal transportation, and their applications to machine learning tasks such as clustering and classification. He develops mathematical frameworks for analyzing variational and PDE-based problems on random data samples, improving algorithms for data science. He also studies systems of interacting particles, particularly those modeling collective behavior in biological and physical systems, and energy-driven systems involving pattern formation and interface evolution. His work bridges theoretical analysis and computational methods, addressing challenges in data analysis, image processing, and the dynamics of complex systems. Key contributions include studies on spectral clustering, graph-based semi-supervised learning, and the continuum limits of discrete variational problems. Education: Ph.D., University of Texas at Austin.

Prasad Tetaliمشاهده پروفایل

استاد

• Discrete Mathematics
• Probability Theory
• Theory of Computing
• +۶ مورد دیگر

Prasad Tetali is the Alexander M. Knaster Professor and Department Head of the Department of Mathematical Sciences at Carnegie Mellon University (CMU), located in Pittsburgh, PA. He also holds adjunct professorships at Emory University (Math/CS) and the Georgia Institute of Technology (Math/CoC). His academic journey includes a Ph.D. from the Courant Institute of Mathematical Sciences, NYU, and postdoctoral research at AT&T Bell Labs. Education: Ph.D. (1991), Courant Institute of Mathematical Sciences, NYU M.S. (1987), Indian Institute of Science, Bangalore, India Postdoctoral Appointments: Mathematical Sciences Research Center, AT&T Bell Labs Research Interests: Dr. Tetali's work focuses on Discrete Mathematics, Probability Theory, and Theoretical Computing. Key areas include Markov chains, isoperimetry, combinatorics, computational number theory, and algorithm design. His contributions span foundational theory and applications in optimization, statistical physics, and network analysis. Publications & Trends: His research has led to influential papers on topics like mixing times of Markov chains, entropy inequalities, and combinatorial optimization. Notable works include foundational studies on the Potts model, the Swendsen-Wang algorithm, and sharp threshold phenomena in number theory. Awards & Recognition: AAAS Fellow SIAM Fellow (Inaugural Class, 2009) Fellow of the American Mathematical Society AMS Fellow (Inaugural Class, 2012) Georgia Tech’s Regents Professor Advising & Grants: Dr. Tetali has mentored 11 PhD students and numerous postdocs. His grants include leadership roles in initiatives such as the SIAM Activity Group on Discrete Mathematics and the ACO PhD Program at Georgia Tech. He has also held editorial roles at top journals like SIAM Journal on Discrete Mathematics and Random Structures & Algorithms. Labs & Collaborations: His work often intersects with interdisciplinary teams, including contributions to distributed algorithms, network science, and computational mathematics. He actively collaborates with researchers in computer science, physics, and applied mathematics.

David Offnerمشاهده پروفایل

استاد آموزشی

• Combinatorics
• Graph Theory
• Discrete Mathematics

David Offner is an Associate Teaching Professor and Director of Undergraduate Studies in the Department of Mathematical Sciences at Carnegie Mellon University's Mellon College of Science. He teaches combinatorics and mentors undergraduate researchers. His research focuses on combinatorial problems including packing/decomposition problems, polychromatic coloring problems, and pursuit-evasion games on graphs. His publications reflect a strong emphasis on discrete mathematics and graph theory applications. Offner has mentored numerous undergraduate researchers on accessible combinatorial problems. He serves as Director of Undergraduate Studies, overseeing academic programs and student advising.

Zelealem Yilmaمشاهده پروفایل

دانشگاهی

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

Zelealem Yilma is an Associate Teaching Professor in the Department of Mathematics at Carnegie Mellon University Qatar, part of the Mellon College of Science. They specialize in graph theory, combinatorics, and network analysis with a focus on extremal problems and structural graph properties. Their research often addresses spanning trees in regular graphs, network eccentricity, and extremal edge-colorings. Key research interests include exploring the minimum spanning trees in multigraphs, structural constraints in networks, and Ramsey-type problems in graph colorings. Yilma's work frequently intersects with hypergraph theory and algorithmic combinatorics. Publications highlight a consistent focus on combinatorial extremal problems, with recent work emphasizing 4-regular graphs (2024) and multigraph spanning tree enumeration (2022). Earlier contributions include studies on Erdős–Rothschild edge-coloring conjectures (2017) and antimagic graph properties (2013).

Elizabeth Holmمشاهده پروفایل

استاد

• Computational Materials Science
• Microstructural Evolution
• Machine Learning in Materials
• +۳ مورد دیگر

Elizabeth Holm is Professor and Department Chair of Materials Science and Engineering at the University of Michigan. Her research employs computational methods to study microstructural evolution, with expertise spanning atomic-scale molecular dynamics to continuum-scale modeling. Education includes a PhD in Materials Science and Scientific Computing from University of Michigan. Research focuses on computational materials science with emphasis on grain boundary dynamics, machine learning applications in materials, and additive manufacturing characterization. Recent publications develop AI methods for microstructure prediction, grain growth modeling, and automated analysis of material images. Work integrates machine vision with physics-based models to understand material behavior across scales. National Academy of Engineering Member (2025) ASEE Mike Ashby Outstanding Materials Educator Award (2022) AIME Honorary Member (2022) ASM Edward DeMille Campbell Memorial Lecturer (2021) The Minerals, Metals & Materials Society Fellow (2019) Professor Holm leads research on computational design of hierarchical materials and has held leadership positions including President of The Minerals, Metals & Materials Society (2013).

Tom Bohmanمشاهده پروفایل

استاد

• Extremal Combinatorics
• Probabilistic Combinatorics
• Discrete Mathematics
• +۳ مورد دیگر

Tom Bohman is a Professor of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. His research focuses on extremal and probabilistic combinatorics, exploring discrete structures inspired by mathematics, information theory, statistical physics, and computer science. He holds a Ph.D. from Rutgers University and has held postdoctoral positions at MIT and the Mathematical Sciences Research Institute (MSRI). Bohman's work includes studies on random graph processes, such as the triangle-free process and Hamilton cycles in random graphs, as well as hypergraph coloring and Ramsey numbers. His publications span over three decades, addressing topics like coprime matchings, lonely runner conjectures, and dynamic concentration phenomena in combinatorial systems. He teaches advanced courses in combinatorics and discrete mathematics, including Graph Theory and Random Structures & Algorithms. His research has contributed to understanding phase transitions in random processes and the interplay between combinatorial structures and probabilistic methods. While no awards are explicitly listed, his extensive publication record and editorial roles (e.g., with Random Structures & Algorithms ) highlight his academic impact. Bohman's work often bridges theoretical foundations with algorithmic applications, influencing both pure and applied combinatorics.

Boris Bukhمشاهده پروفایل

استاد

• Combinatorics
• Discrete Geometry
• Extremal Graph Theory
• +۷ مورد دیگر

Boris Bukh is a Professor of Mathematics at Carnegie Mellon University (CMU), affiliated with the Department of Mathematical Sciences within the Mellon College of Science. He holds a Ph.D. from Princeton University and has held postdoctoral positions at the University of Cambridge and Churchill College. His research focuses on combinatorics, discrete geometry, extremal graph theory, and geometric selection theorems. He has received prestigious awards such as the Sloan Research Fellowship and the NSF CAREER Award. His work spans topics like Turán problems, geometric configurations, and algebraic methods in combinatorics. Recent publications explore extremal graph structures, convex polytopes in restricted point sets, and applications of random algebraic constructions to computational complexity. Bukh organizes events like the Math Kangaroo competition, fostering mathematics engagement among students. Key contributions include advancements in Ramsey theory, coding theory, and the intersection of combinatorics with geometry. His research often bridges theoretical insights with computational techniques, addressing problems in graph density, geometric incidences, and discrete optimization.

• Computational Fabrication
• Computer Graphics
• Textile Design Automation
• +۳ مورد دیگر

Jenny Han Lin is an Assistant Professor at the University of Utah's Kahlert School of Computing. Her research focuses on computational fabrication, particularly leveraging knot theory and low-level computer representations to advance textile manufacturing and real-world phenomenon modeling. She completed her PhD in Computer Science at Carnegie Mellon University under James McCann, preceded by a B.E. in Computer Science and Molecular Biology from MIT. Her work bridges computer graphics, robotics, and mathematics, with notable contributions to machine knitting compilers, braid group theory applications, and crochet representation. She has been a postdoctoral scholar at the University of Utah and taught courses at CMU and the University of Washington. Lin actively promotes textile fabrication through initiatives like Knitout Office Hours and served as SIGBOVIK organizing committee chair (2019-2022), creating an award-winning conference website. Lin's research has been recognized with a finalist award at ICRA 2021. She collaborates widely, mentoring students in interdisciplinary projects at the intersection of computing and physical fabrication.