جست‌وجوی استاد و پژوهشگر

• Combinatorics
• Graph Theory
• Asymptotic Enumeration
• +۷ مورد دیگر

Brendan McKay is a Professor in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of mathematics and computing. His work is deeply rooted in combinatorics, graph theory, and probabilistic methods in discrete structures. His research interests span a wide range of topics in discrete mathematics, including random graphs , asymptotic enumeration , Hamiltonian cycles , planar and bipartite graphs , matrix theory , and computational combinatorics . His work often combines theoretical depth with algorithmic applications, particularly in the analysis of complex networks and discrete systems. The recent publications (2015–2025) reflect a sustained focus on asymptotic enumeration techniques, structural properties of graphs, and algorithmic challenges in graph isomorphism and distance queries. Key themes include the use of probabilistic tools (e.g., cumulant expansions, martingales), enumeration under degree constraints, and symmetry-preserving operations on maps. His collaborations frequently involve researchers such as Mikhail Isaev, Catherine Greenhill, and Qing Wang. Scientific Awards and Recognitions: Fellow, Australian Mathematical Society (2000 → …) Fellow, Australian Academy of Science (1997 → …) McKay has led and co-led several major research projects, including Deep Learning for Graph Isomorphism and Hypergraph models for complex discrete systems , demonstrating leadership in both theoretical and applied directions. He has not explicitly listed advisees, but his role as Principal Investigator (PI) on multiple projects indicates active mentorship and grant leadership. His work often involves interdisciplinary applications in bioinformatics and network science. He is associated with research groups and projects focused on graph algorithms , random discrete structures , and computational combinatorics , often in collaboration with the mathematical sciences community at ANU and beyond.

Shayan Oveis Gharanمشاهده پروفایل

استاد

• Algorithm Design
• Applied Probability
• Spectral Graph Theory
• +۷ مورد دیگر

Shayan Oveis Gharan is a Professor in the University of Washington 's Computer Science and Engineering department. His work bridges algorithm design, applied probability, and spectral graph theory, with a focus on leveraging Markov Chains and polynomial paradigms in approximation algorithms. Education: PhD in Computer Science from Stanford University (2013) Research interests span theoretical computer science, combinatorics, and optimization. He explores how algebraic techniques, particularly log-concave and hyperbolic polynomials, can enhance spectral graph theory and counting/sampling algorithms. His studies on the Traveling Salesman Problem and matroid theory have redefined approximation bounds. Recent publications highlight advancements in trickle-down theorems, polynomial paradigms for graph sparsification, and high-dimensional random walks. These works intersect with fields like machine learning and computational complexity. Scientific awards include the 2025 Michael and Sheila Held Prize, 2022 Simons Investigator Award, and 2016 NSF Career Award. His students, such as Nathan Klein and Kuikui Liu, have transitioned to academic and industry roles.

• Fluid Mechanics
• Polymer Physics
• Soft Matter
• +۵ مورد دیگر

Vivek Narsimhan is the Michael Ott Associate Professor of Chemical Engineering at Purdue University's School of Chemical Engineering, part of the Davidson School of Chemical Engineering. His research focuses on soft matter systems including fluid mechanics, polymer physics, interfacial rheology, and microfluidics. He holds a B.S. from Caltech (2008), M.A.st. from Cambridge (2009), and M.S./Ph.D. from Stanford (2015). Education: B.S., Chemical Engineering, California Institute of Technology, 2008 M.A.st., Mathematics, University of Cambridge, 2009 M.S. and Ph.D., Chemical Engineering, Stanford University, 2015 Research interests span suspensions, complex interfaces, vesicle dynamics, and polymer physics. His lab integrates theory, simulations, and experiments to study phenomena such as droplet breakup, knotted polymer dynamics, and lyophilization processes. Key applications include pharmaceutical manufacturing, biofluid dynamics, and industrial fluid mechanics. Awards include the ACS Petroleum Research Fund Doctoral New Investigator Grant (2020) and recognition in Physics of Fluids as an Editor’s Pick (2020). His work on vesicle stability and interfacial rheology has been featured in AIChE Journal's Futures 2020 issue. Advising and grants include mentoring over a dozen graduate students and postdocs, with active projects on microfluidic separations and heat transfer modeling. His lab collaborates on lyophilization studies with industry partners and has developed novel methodologies for starch suspension dynamics. Labs/Teams: The Narsimhan Soft Matter Research Group at Purdue explores interdisciplinary challenges in soft matter, with ongoing collaborations in biophysics, materials science, and computational rheology. The group maintains a lab website with detailed research descriptions and publications.

Wojciech Szpankowskiمشاهده پروفایل

استاد

• Bioinformatics and Computational Biology
• Theory of Computing
• Algorithms
• +۵ مورد دیگر

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University, with a courtesy appointment in Electrical and Computer Engineering. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from Gdansk University of Technology (1980 and 1970, respectively). His career includes visiting roles at institutions such as McGill University, Stanford, and INRIA. He directs the NSF-funded Center for Science of Information, focusing on extending information theory to modern data challenges. Szpankowski is an IEEE Fellow, Erskine Fellow, and recipient of the Humboldt Research Award (2010). His research spans algorithms, information theory, bioinformatics, and analytic combinatorics. Key contributions include work on data compression, network analysis, and quantum computing. He authored *Average Case Analysis of Algorithms on Sequences* (2001) and co-launched the interdisciplinary Institute for Science of Information (2008). His recent work addresses fundamental limits in machine learning, quantum algorithms, and privacy-preserving systems. Research Interests: Bioinformatics, algorithm analysis, quantum computing, graph theory, and information-theoretic limits of learning. His work often bridges theory and applications, such as analyzing biological networks and developing efficient compression methods for dynamic data. Awards: IEEE Fellow Erskine Fellowship Humboldt Research Award (2010) Grants & Labs: Director of the NSF Science & Technology Center for Science of Information. Active in interdisciplinary projects, including temporal network analysis and quantum information theory.

Sabre Kaisمشاهده پروفایل

استاد

• Quantum Chemistry
• Quantum Computing
• Quantum Information Theory
• +۴ مورد دیگر

Sabre Kais is a Distinguished Professor of Chemistry and Professor of Chemical Physics at Purdue University, with courtesy appointments in Computer Science and Physics. He joined Purdue in 1994 as an Assistant Professor and became a full professor in 2002. His research focuses on quantum information, quantum computing for complex systems, and quantum machine learning applications in chemistry and materials science. He holds leadership roles such as Research Director of the theory group at Qatar Environment and Energy Research Institute and External Research Professor at the Santa Fe Institute. Education: BS, MS, and PhD in Chemical Physics from the Hebrew University (1983–1989), followed by a postdoc at Harvard University (1989–1994). Research Interests: Quantum algorithms for quantum chemistry, entanglement in chemical reactions, quantum machine learning for drug discovery, and quantum simulation of spin liquids. His work bridges quantum information science with practical applications in chemistry and physics. Notable contributions include quantum annealing for factorization, entanglement-based chemical reaction analysis, and quantum computing with polar molecules. Publications: Over 230 peer-reviewed articles, including influential works on quantum machine learning for electronic structure calculations and quantum simulation of avian compass dynamics. Recent focus areas include quantum algorithms for many-body systems and quantum state tomography techniques. Awards: NSF Career Award (1997), Guggenheim Fellowship (2005), APS Fellow (2007), and the Herbert Newby McCoy Award (2019). His work has been recognized for advancing quantum technologies in chemistry. Labs/Teams: Leads research groups at Purdue and QEERI, collaborating on projects like quantum information for quantum chemistry and quantum computing applications in energy research.

Stefan Steinerbergerمشاهده پروفایل

استاد

• Analysis
• Applied Mathematics
• Combinatorics
• +۵ مورد دیگر

Stefan Steinerberger is a Professor in the Department of Mathematics at the University of Washington, Seattle. He holds a PhD from the University of Bonn (2013) and specializes in Analysis, with research interests spanning Partial Differential Equations, Spectral Theory, Harmonic Analysis, and applications to Data Science and Mathematical Physics. His work often bridges theoretical and applied mathematics, addressing problems in geometry, combinatorics, and numerical methods. Education: PhD in Mathematics, University of Bonn, 2013 Research Interests: Steinerberger’s research focuses on Analysis, with emphasis on PDEs, Spectral Theory, and Harmonic Analysis. He explores interdisciplinary connections, such as applying analytic techniques to graph theory, optimization, and data science. Specific themes include eigenvalue estimates, dynamical systems, and the interplay between geometry and combinatorial structures. Awards & Recognition: While specific awards are not listed, his extensive publication record and affiliations with prestigious journals indicate significant contributions to the field. His work frequently appears in top-tier journals like Advances in Mathematics , Journal of Functional Analysis , and SIAM Journal on Discrete Mathematics . Teaching & Outreach: He teaches courses in the Department of Mathematics and is involved in outreach initiatives such as the Washington Experimental Mathematics Lab (WXML) and the UW Math Circle. His courses and research often emphasize problem-solving and real-world applications. Labs/Teams: Active in collaborations across mathematical disciplines, including work with researchers in combinatorics, probability, and numerical analysis. He maintains a personal website with resources on his research, teaching, and open problems.

Fan Liمشاهده پروفایل

استاد

• Causal Inference
• Biostatistics
• Machine Learning
• +۳ مورد دیگر

Fan Li is Professor of Statistical Science and Biostatistics & Bioinformatics at Duke University. His research focuses on causal inference applications in health, policy, and social science, with expertise in propensity scores, clinical trials, and machine learning interfaces. He develops methods for difference-in-differences, regression discontinuity, and representation learning. Li earned his PhD from Johns Hopkins University and BS from Peking University. Currently serving as PI on NIH/NIGMS grants developing statistical methods for pharmacological research, he previously received NSF funding for hyperpolarization techniques in chemical analysis.

Robert Garlickمشاهده پروفایل

پژوهشگر

• Labor Economics
• Development Economics
• Public Policy
• +۳ مورد دیگر

Robert Garlick is a Researcher in the Economics Department at Duke University, affiliated with Trinity College of Arts & Sciences. He served as Assistant Professor of Economics at Duke from 2014–2024 and previously held roles at the World Bank's Development Research Group. Garlick holds a PhD in Economics and Public Policy from the University of Michigan, with prior studies in economics, mathematics, and philosophy at the University of Cape Town. His research focuses on labor markets in developing economies, particularly search frictions, human capital, and policy impacts. He explores topics like cash transfer efficiency, gender labor supply dynamics, and skill mismatch using experimental and quasi-experimental methods. His work spans global regions including Kenya, Nigeria, and South Africa. Garlick has secured grants from institutions like the National Science Foundation and the London School of Economics, investigating issues such as job search barriers, digital labor platforms, and misreported qualifications in hiring. He teaches advanced courses in development economics and applied microeconomics, emphasizing policy-relevant empirical analysis. Key grants include studies on gendered job search (2024–2026), digital service export jobs in Kenya (2023–2025), and labor market frictions in Pakistan. His research often bridges theoretical models with field experiments, contributing to policy frameworks for poverty reduction and labor market efficiency.

Varun Kanadeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Algorithms
• +۴ مورد دیگر

Varun Kanade is an Associate Professor in the Department of Computer Science at the University of Oxford. He holds a Tutorial Fellowship at Lady Margaret Hall, where he contributes to undergraduate teaching and academic governance. His research focuses on theoretical computer science and machine learning, with particular emphasis on algorithms, computational learning theory, and the theoretical foundations of deep learning. Key research areas include: Artificial Intelligence and Machine Learning, Algorithms and Complexity Theory, and their intersections with topics like transformers, robust learning, and algorithmic fairness. He actively explores foundational issues such as in-context learning in transformers, representational capabilities of neural architectures, and statistical learning theory. Varun’s work has been published in top venues across machine learning and theoretical computer science. His recent research trends emphasize understanding the theoretical underpinnings of modern AI systems, including their generalization properties, robustness to adversarial attacks, and fairness considerations. He has also contributed to algorithmic advancements in areas like matrix completion, online clustering, and distributed learning. Varun currently advises PhD students Satwik Bhattamishra and Charles London, building on a track record with past students such as Bryn Elesedy and Pascale Gourdeau. His academic contributions span over two decades, with publications ranging from foundational studies in computational learning to applications in distributed systems and evolutionary algorithms.

Elizabeth Tiptonمشاهده پروفایل

استاد

• Statistical Methodology
• Meta-Analysis
• Causal Inference
• +۳ مورد دیگر

Elizabeth Tipton is a Professor of Statistics and Data Science at Northwestern University, affiliated with the Department of Education and Social Policy (by courtesy). She serves as Co-Director of the Statistics for Evidence-Based Policy and Practice (STEPP) Center and is an Institute for Policy Research (IPR) Fellow. Her work focuses on developing statistical methods for improving causal generalization in randomized trials and meta-analysis, with applications in education, psychology, and health sciences. Education: 2011: Ph.D. in Statistics, Northwestern University (Certificate in Education Science) 2005: M.A. in Sociology, University of Chicago 2001: B.A. in Mathematics, Transylvania University Research Interests: Elizabeth’s research emphasizes enhancing the generalizability of randomized trials and synthesizing evidence from meta-analyses. She develops methodologies for handling dependent effect sizes, propensity score techniques, and visualization tools (e.g., Meta-Analytic Rain Cloud plots) to aid decision-making. Her work bridges statistical rigor with practical applications in education policy, behavioral science, and health. Grants & Funding: Her research has been supported by the National Science Foundation, Institute of Education Sciences, Spencer Foundation, Raikes Foundation, and others. Labs & Collaborations: Co-Director of the STEPP Center, which focuses on methodological advancements for evidence-based policy. Active collaborations include projects on intervention design, heterogeneity analysis, and translational research.

Radu Herbeiمشاهده پروفایل

استاد

• Statistical Inference for Stochastic Processes
• Monte Carlo and MCMC Methods
• Inverse Problems
• +۳ مورد دیگر

Radu Herbei is a Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He joined the faculty in 2006 and has been funded by NSF and ONR. His research focuses on statistical inference for stochastic processes, including stochastic differential equations and stochastic partial differential equations, with an emphasis on 'exact' inference methods that avoid user-selected grids or approximations. He develops exact Markov chain Monte Carlo (MCMC) techniques using approximations of intractable probability density functions and explores high-performance GPU computing to address computational challenges. His education includes a PhD in Statistics from Florida State University (2006). Research areas include Bayesian statistics, Monte Carlo methods, inverse problems, and uncertainty quantification. Notable contributions include work on the Bernoulli factory algorithm for exact Bernoulli random variates and applications in phylogenetics and environmental modeling. Collaborations span computational biology, oceanography, and ecological systems. Key publications include developments in taxicab MCMC samplers for discrete spaces, Bayesian function registration, and statistical inference for stochastic differential equations. His work bridges theoretical advancements with computational tools, addressing complex modeling challenges in diverse scientific domains.

Leo Duanمشاهده پروفایل

دانشیار

• Statistical Methodology
• Bayesian Statistics
• Combinatorial Optimization
• +۳ مورد دیگر

Leo Duan is an Associate Professor with Tenure in the Department of Statistics at the University of Florida since 2018. His research focuses on developing statistical methods, computational tools, and Bayesian frameworks to address challenges in neuroscience, engineering, and transportation science. He specializes in combinatorial structures like tree graphs, clustering, and signal pathways, integrating optimization and probabilistic techniques. Dr. Duan’s recent work includes advancements in Bayesian inference using optimization, such as bridged posteriors and gradient-based methods. He co-teaches a short course on optimization in Bayesian inference at JSM 2025. His funded projects include NSF-ATD grants for geospatial modeling and hurricane risk mitigation, alongside other institutional awards. His advising spans doctoral students in optimization and diffusion models. Past trainees include Edric Tam (Stanford Postdoc) and Maoran Xu (Indiana University faculty). Current students Zeyu Yuwen and Yu Zheng are nearing graduation. Dr. Duan’s lab actively recruits new graduate researchers. Key achievements include the 2018 NeurIPS Bayesian Non-parametrics Award and a series of high-impact publications in JASA, JMLR, and Biometrika. His research bridges theory and application, with contributions to spanning trees, graphical models, and Bayesian vector autoregression.

Cong Maمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Reinforcement Learning
• Transfer Learning
• +۳ مورد دیگر

Cong Ma is an Assistant Professor in the Department of Statistics at the University of Chicago. He holds a PhD from Princeton University (2020), advised by Yuxin Chen and Jianqing Fan, and a BEng from Tsinghua University (2015). Previously, he was a postdoctoral researcher at UC Berkeley under Martin Wainwright. His research focuses on the mathematics of data science, emphasizing reinforcement learning, transfer learning, multi-modal learning, high-dimensional statistics, and nonconvex optimization. Key areas include developing computationally and statistically efficient methods for large-scale data problems, with recent work on contrastive learning, contextual bandits, and robust matrix completion. Education: PhD in Operations Research & Financial Engineering, Princeton University (2020) BEng in Electrical Engineering, Tsinghua University (2015) Awards: SIAM Activity Group on Imaging Science Best Paper Prize (2024) Teaching: STAT 253/317: Introduction to Probability Models (Winter 2025) STAT 28000: Optimization (Winter 2025) Machine Learning (Autumn 2022) His work bridges theoretical insights with practical applications, often addressing challenges in statistical guarantees for optimization algorithms and distributionally robust learning. Recent highlights include contributions to multi-modal contrastive learning and batched contextual bandits.

Bryan Wilderمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Causal Inference
• +۵ مورد دیگر

Bryan Wilder is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University (CMU). He directs the Lab for AI and Social Impact (LASI), focusing on AI methods for equitable decision-making in public health, medicine, and human services. Previously, he was a postdoctoral Schmidt Science Fellow at Harvard School of Public Health and earned his PhD in Computer Science from Harvard under Milind Tambe. His work spans algorithmic fairness, optimization, and causal inference, with funding from NSF, NIH, CDC, and Schmidt Futures. Education: PhD in Computer Science (Harvard, 2021), MSc from USC (2019), BS from UCF (2015). Key Roles: Chair of EAAMO Board, Co-PI of CDC Innovation Center at CMU, Guest Lecturer at multiple institutions. Research Interests: Integrating ML, optimization, and causal inference for high-stakes social systems. Current projects include maternal/child health, infectious disease modeling, and fair resource allocation. His lab emphasizes real-world impact through partnerships with public health organizations. Articles Trends: Recent work addresses federated epidemic surveillance, reinforcement learning for coupled systems, and auditing fairness in AI. Earlier papers focus on HIV prevention interventions, risk-sensitive optimization, and cultural evolution modeling. Awards: Includes the Schmidt AI2050 Fellowship, IFAAMAS Dissertation Award, and Siebel Scholarship. Over 30+ peer-reviewed papers in top venues (NeurIPS, ICML, AAAI). Advising & Grants: Supervises PhD students at CMU and USC. Leads projects funded by NSF, NIH, CDC, and Schmidt Futures. Collaborates with global health institutions. Labs/Teams: LASI lab develops AI tools for equitable decision-making, with applied work in maternal health, HIV prevention, and pandemic response.

Maria K. Cameronمشاهده پروفایل

استاد

• Numerical Analysis
• Stochastic Systems
• Rare Event Quantification
• +۴ مورد دیگر

Maria K. Cameron is a Professor in the Department of Mathematics and an Affiliate Professor in the Department of Computer Science at the University of Maryland, College Park. Her research focuses on numerical methods for solving mathematical problems in natural sciences, including quantification of rare events in complex systems, Hamilton-Jacobi solvers, and data-driven approaches using diffusion maps and neural networks. She leads the REU program in mathematics, mentoring undergraduate researchers in cutting-edge topics like stochastic systems and geometric measure theory. Cameron holds a Master’s from the Moscow Institute of Physics and Technology and a Ph.D. from UC Berkeley (2007). She has advised numerous graduate students and postdocs, contributing to advancements in computational science and stochastic analysis. Her academic roles include directing the NSF-funded REU program since 2022, organizing workshops on Scientific Machine Learning and Rare Events Analysis, and teaching advanced courses in scientific computing and numerical analysis. Notable collaborations include work on nonlinear oscillators, polymer network fracture models, and machine learning applications in committor problems.