جستجوی استاد راهنمای دانشگاهی

Marius Zimand is a Professor in the Department of Computer and Information Sciences at Towson University. His academic expertise spans computational complexity , algorithmic information theory , and cryptography . Holding Ph.D.s in both Computer Science (University of Rochester) and Mathematics (University of Bucharest), he contributes to the advancement of randomness extraction and theoretical computer science. Education: Ph.D. in Computer Science (University of Rochester), Ph.D. in Mathematics (University of Bucharest) Zimand's research focuses on transforming low-quality randomness into high-quality randomness through efficient algorithms. His work, supported by NSF grants 0634830 (2006-2009) and 1016158 (2010-2014) , explores randomness extractors' applications in cryptography, error-correcting codes, and data structures, with mathematical implications in Kolmogorov complexity and constructive Hausdorff dimension. His recent publications analyze topics such as time-bounded Kolmogorov complexity , dynamic matching in expanders , and universal coding theorems , reflecting a consistent emphasis on randomness extraction and complexity theory. Awards include Best Paper at CSR'2008 and Best Paper at ICALP 2005, Track C . Professional Service: Editorial Board Member of Journal of Universal Computer Science Grant Reviewer for NSF, National Research Council Canada, ANR France, and US-Israel BSF Zimand's contributions to theoretical computer science bridge mathematical rigor with practical applications, maintaining active research and service roles within the academic community.

• computational complexity
• algorithmic information theory
• cryptography

Tiantian Chen is an Assistant Professor in the Department of Mathematics & Computer Science at Santa Clara University, part of the College of Arts and Sciences. Their research focuses on artificial intelligence, machine learning, and social network analysis with applications to cybersecurity, privacy preservation, and algorithmic optimization. Educational background includes B.S. and M.S. from Ocean University of China, followed by M.S. and Ph.D. from the University of Texas, Dallas. Their work emphasizes interdisciplinary approaches to computational problems in social networks and data-driven systems. Recent research trends include deep reinforcement learning for influence maximization, blockchain-based privacy frameworks, and combating cyberbullying using AI. Their publications span topics such as graph representation learning, rumor blocking mechanisms, and budgeted profit maximization algorithms. No scientific awards have been explicitly mentioned in the provided information. Advising activities and grant details are not listed here. Tiantian Chen contributes to the department's academic programs and computational research initiatives.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Michael Sekula is an Assistant Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences. He joined the department in 2020. His research focuses on Bayesian inference, genomics, and statistical computing, particularly developing methodologies for analyzing single-cell RNA sequencing data. Education: Ph.D. in Biostatistics (2020), University of Louisville M.S. in Biostatistics (2015), University of Louisville B.S. in Mathematics Education (2010), Saginaw Valley State University His work emphasizes bioinformatics applications, including gene co-expression network construction and differential network analysis using Bayesian approaches. He has contributed to open-source tools like the optCluster R package for clustering algorithm selection. Publications highlight advancements in single-cell data analysis and genomic methodologies, reflecting his expertise in integrating statistical theory with computational biology.

• Bioinformatics
• Bayesian inference
• Genomics
• +۱ مورد دیگر

Dr. Li Chou is an Assistant Professor of Computer Science in the College of Engineering at West Texas A&M University. He holds a B.S. in Computer Science and an M.B.A. from Southern Methodist University, and an M.S. and Ph.D. in Computer Science from The University of Texas at Dallas. Prior to joining WTAMU, he was a Postdoctoral Research Associate at Rice University's Department of Computer Science. His research focuses on artificial intelligence , machine learning , data science , and scientific computing , with applications in recommendation systems, genomic data analysis, and federated learning. His work emphasizes efficient algorithms, model compression, and optimization for large-scale data processing. Notable contributions include the Random Offset Block Embedding (ROBE) technique for recommendation systems and diversified sampling methods for genomic streams. Dr. Chou teaches courses such as Programming Principles II, Data Structures and Algorithms, Computer Organization, and Artificial Intelligence. He is affiliated with the university's ECORE Lab and Human-Machine Teaming Laboratory , contributing to interdisciplinary research initiatives in engineering and computer science. His publications span topics like neural network optimization, federated learning frameworks, and bioinformatics applications, demonstrating a commitment to advancing both theoretical and applied computing.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Katherine Brumberg is an Assistant Professor of Statistics at the University of Michigan's Department of Statistics, part of the College of Literature, Science, and the Arts (LSA). Her office is located in 445C West Hall. Her research focuses on statistical methodologies in observational studies, including covariate balancing, stratification techniques, and applications in biostatistics and epidemiology. She has published extensively on topics such as optimal strata refinement, unmeasured bias analysis, and differential gene expression studies. Her recent work (2023-2024) emphasizes advancements in observational study design, leveraging statistical optimization and linear programming to address covariate imbalances. Earlier research includes studies on prostate tissue gene expression (2018). No scientific awards are explicitly listed in the provided materials. She contributes to the academic community through courses like Introductory Statistics for Data Analysis and maintains an active research profile within the Department of Statistics.

• Statistics
• Biostatistics
• Epidemiology
• +۳ مورد دیگر

Alex Lombardi is an Assistant Professor of Computer Science at Princeton University, specializing in cryptography and theoretical computer science. He joined Princeton in 2023 after a Simons-Berkeley postdoctoral fellowship hosted by Shafi Goldwasser. His PhD in computer science from MIT (2022) was advised by Vinod Vaikuntanathan. His research focuses on the theoretical foundations of classical and quantum cryptography, with a strong emphasis on cryptographic protocols, complexity theory, and post-quantum security. Education: PhD in Computer Science, MIT (2022); Postdoctoral Fellowship at Simons Institute for the Theory of Computing (Berkeley). Prior academic roles include teaching at MIT (2018–2019) and serving as a teaching assistant for Cryptography and Cryptanalysis courses. Research interests span cryptographic proof systems (e.g., succinct arguments, zero-knowledge proofs), lattice-based cryptography, quantum-resistant algorithms, and complexity-theoretic hardness assumptions. His work often bridges cryptography with computational complexity, exploring topics like PPAD hardness, indistinguishability obfuscation, and the Fiat-Shamir heuristic. Recent publications highlight contributions to SNARGs (succinct non-interactive arguments), post-quantum security analyses, and foundational studies of cryptographic protocols under advanced assumptions (e.g., LWE, Decisional Diffie-Hellman). Key trends include advancing succinct verification techniques, analyzing quantum computational threats, and formalizing cryptographic assumptions via complexity reductions. Awards: No explicit awards listed, though his prolific publication record in top venues (STOC, CRYPTO, EUROCRYPT) reflects academic recognition. Teaching: Taught COS 533 (Advanced Cryptography) in 2023 and COS 433/533 (Cryptography) in 2024. Previously, led courses at MIT on cryptographic proofs and foundations. Service: Served on program committees for STOC 2025, EUROCRYPT 2025, and multiple CRYPTO and TCC events. Active in reviewing for leading cryptography and theoretical computer science conferences.

• Cryptography
• Quantum Cryptography
• Theoretical Computer Science
• +۳ مورد دیگر

Roman Vershynin is a Professor in the Department of Mathematics at the University of California, Irvine (UCI). His research focuses on high-dimensional probability, random matrices, and their applications in data science, machine learning, and privacy-preserving technologies. He is particularly known for his work on the theoretical foundations of differential privacy, synthetic data generation, and the mathematical underpinnings of neural networks. Vershynin’s contributions bridge probability theory with practical challenges in high-dimensional data analysis. His research interests include random matrix theory, geometric functional analysis, and the interplay between probability and computational complexity. Notable works include developing frameworks for differentially private synthetic data and analyzing the capacity of neural networks. He authored the influential textbook High-Dimensional Probability: An Introduction with Applications in Data Science , which is a key resource in the field. Vershynin’s recent work emphasizes the privacy-utility tradeoff in synthetic data generation, leveraging tools from metric geometry and stochastic processes. He collaborates on projects funded by the National Science Foundation, focusing on mathematical frameworks for high-dimensional data analysis and algorithmic privacy. His research has implications for secure machine learning, compressed sensing, and robust statistical estimation.

• High-Dimensional Probability
• Random Matrices
• Applied Mathematics
• +۴ مورد دیگر

Jingjing Meng is a Senior Scientist affiliated with the Computer Science and Engineering Department at the University at Buffalo, SUNY, and Amazon. She holds a Ph.D. from Nanyang Technological University (NTU, Singapore), advised by Prof. Yap-Peng Tan, along with an M.S. from Vanderbilt University and a B.E. from Huazhong University of Science & Technology, China. Her research focuses on multimedia, large multimodal models, product recommendation/search, and computer vision applications. Notable contributions include work on surgical triplet recognition, 3D object representation, and video summarization. She has received the 2016 IEEE Transactions on Multimedia Best Paper Award. Service Roles: Technical Program Co-Chair (ICME 2024), Tutorial Co-Chair (ACM MM 2024), Area Chair (AAAI 2021-2025), and Associate Editor for IEEE TMM, Signal Processing: Image Communication, and others. Leadership: Member of IEEE IVMSP TC, VSPC TC, and MSA TC committees, and a Senior Member of IEEE. Teaching includes courses like Multimedia Systems (CSE 534), Computer Graphics (CSE 410/580), and Discrete Structures (CSE 191). Her work bridges theoretical advancements and practical applications in multimedia and AI.

• Multimedia
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dr. Quefeng Li is an Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), part of the Gillings School of Global Public Health. He earned his Ph.D. in Statistics from the University of Wisconsin-Madison (2013) and completed a postdoctoral fellowship at Princeton University (2013–2015). His research focuses on high-dimensional statistics and its applications in biomedical and neuroimaging data analysis, emphasizing robust methodologies and integrative data analysis techniques. Dr. Li's work has contributed to advancements in variable selection, data integration, and statistical methods for complex biomedical datasets. He has held editorial roles at journals like Biometrics and Biostatistics , and serves on multiple professional committees. His research has been supported by grants from the NIH, including R01AG073259 and R01CA282648, and he collaborates extensively with clinical researchers across disciplines. His research interests span high-dimensional statistics, neuroimaging analysis, and robust statistical methods. Notable contributions include developing the ARTdeConv algorithm for cell-type deconvolution and the glmmPen package for penalized generalized linear mixed models. He has advised numerous PhD students, many of whom now work in industry and academia. Awardees include the Gillings Research Excellence Award (2023), Junior Faculty Development Award (2018), and University Research Council Award (2016). His work bridges theoretical statistics with practical biomedical applications, addressing challenges in data heterogeneity and reproducibility.

• High-dimensional statistics
• Biomedical research
• Omics data integration
• +۵ مورد دیگر

Mahlet G. Tadesse is a Professor and Chair of the Department of Mathematics & Statistics at Georgetown University. She previously held an Assistant Professor position at the University of Pennsylvania. She earned her Ph.D. and M.Sc. in Biostatistics from Harvard University and a B.S. in Mathematics from the University of the District of Columbia. Her research focuses on developing statistical and computational tools for analyzing genomic data, with emphasis on Bayesian methods, variable selection, and integrative approaches to genomic data analysis. Her expertise spans Bayesian statistics, biostatistics, mixture models, and omics-based bioinformatics. She investigates high-dimensional datasets to identify biological markers, predict clinical outcomes, and integrate biological knowledge into genomic studies. Current projects include biomarker discovery for hepatocellular carcinoma, analysis of maternal-fetal genetic interactions, and modeling species dynamics in biodiversity-rich ecosystems. Dr. Tadesse has published extensively on topics such as Bayesian variable selection, integrative genomics, and computational methods in proteomics and metabolomics. Her work bridges statistical theory and application, addressing challenges in precision medicine, cancer biology, and ecological dynamics. Scientific Awards: Elected Member of the International Statistical Institute (2006), Fellow of the American Statistical Association (2013) Her contributions include advancing alignment methods for LC-MS data, developing hierarchical models for multi-omics integration, and exploring racial disparities in cancer outcomes. She leads Georgetown’s Mathematics & Statistics Department, fostering interdisciplinary research and education in data science.

• Bayesian Statistics
• Biostatistics
• Mixture Models
• +۶ مورد دیگر

Chao Chen is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State), where he has been since 2023. His research focuses on developing efficient algorithms for matrix computations, with applications ranging from solving partial differential equations to analyzing large high-dimensional datasets. Prior to this, he was a postdoctoral researcher at The University of Texas at Austin’s Oden Institute, working with George Biros and Gunnar Martinsson. He earned his PhD in Computational and Mathematical Engineering (ICME) from Stanford University in 2018, advised by Eric Darve. He also holds an MS from Stanford’s ICME (2014) and a BS in Information and Computational Mathematics from Nankai University (2012). Education: PhD in Computational and Mathematical Engineering, Stanford University, 2018 MS in Computational and Mathematical Engineering, Stanford University, 2014 BS in Information and Computational Mathematics, Nankai University, 2012 Research Interests: Chen’s work emphasizes high-performance computing, numerical linear algebra, and randomized algorithms. He develops methods to reduce computational complexity and enhance performance on modern hardware. His applications span computational physics, data science, and machine learning. Notably, he has contributed to fast multipole methods, hierarchical solvers, and preconditioners for large-scale systems. Teaching: Chen teaches courses such as Linear Analysis (MA 303), Mathematics of Scientific Computing (MA 402), Linear Transformations and Matrix Theory (MA 523), and Theory of Matrices (MA 723). His courses emphasize theoretical foundations and practical applications, often incorporating computational tools like MATLAB and LaTeX. Presentations: He has presented at conferences worldwide, including SIAM Annual Meetings, Supercomputing Conferences, and specialized workshops on numerical linear algebra and fast solvers.

• High performance computing
• Numerical linear algebra
• Numerical PDE
• +۳ مورد دیگر

Pierre Tarrès is a Professor of Mathematics at NYU Shanghai and serves as the Associate Provost for Strategic Initiatives and Co-Director of the NYU-ECNU Institute of Mathematical Sciences . He is also a Global Network Professor at the Courant Institute of Mathematical Sciences, New York University. Education PhD in Mathematics, École Normale Supérieure Paris-Saclay Master’s in Mathematics, École Normale Supérieure de Paris Master’s in Mathematics of Artificial Intelligence, École Normale Supérieure Paris-Saclay Research Interests Prof. Tarrès specializes in self-interacting random processes, particularly reinforced random walks, and their applications to stochastic algorithms and learning processes in game theory. His work bridges probability theory, mathematical physics, and computational methods, with a focus on understanding the dynamics of reinforcement and their implications for optimization and statistical mechanics. Articles Trends His publications span topics such as reinforced random walks, stochastic algorithms, Brownian motion, and bandit problems. Recent works emphasize the interplay between probability theory and mathematical physics, particularly through supersymmetric models and Schr"odinger operators. Scientific Awards Leverhulme Prize (2006) Prix des Annales de l'Institut Henri Poincaré (2008) Advising and Grants While student advising details are not provided, his research has been supported by grants linked to his work on stochastic processes and learning algorithms. His editorial role at the Annals of Applied Probability since 2019 highlights his contributions to academic leadership. Labs and Teams As Co-Director of the NYU-ECNU Institute of Mathematical Sciences, he leads collaborative research initiatives, fostering interdisciplinary work between mathematics, computer science, and theoretical physics.

• Probability Theory
• Stochastic Processes
• Game Theory
• +۵ مورد دیگر

Lu Ting is a Professor at the Department of Mathematics within the Courant Institute of Mathematical Sciences, New York University. Their research integrates mathematical statistics and biostatistics with applications in oncology, environmental health, and precision medicine. Key focuses include molecular pathway analysis in glioma, biomarker development for drug resistance, and statistical methods in translational research. Research interests span mathematical modeling of cancer therapies, environmental exposure impacts on human health, and optimization algorithms. Notable projects include longitudinal studies on World Trade Center (WTC) exposure effects and sphericity testing in high-dimensional covariance matrices. Publications from 2015–2024 reflect interdisciplinary work combining mathematical rigor with biomedical challenges, particularly in cancer treatment optimization and environmental health surveillance. No scientific awards or grants are explicitly documented in the provided text.

• Mathematical Statistics
• Biostatistics
• Medical Research
• +۳ مورد دیگر

Avanti Athreya is an Associate Research Professor at the Department of Applied Mathematics and Statistics, Johns Hopkins University, within the Whiting School of Engineering. Her research focuses on probability, stochastic processes, and network inference methodologies. She holds a BS from Iowa State University (1997), an MS from the University of Washington (2000), and a PhD from the University of Maryland College Park (2009). Her work emphasizes spectral analysis of random matrices, stochastic blockmodel graphs, and dynamic network inference. Recent publications explore Gaussian mixture models for language networks, mirror distance-based clustering, and change-point detection in organoid networks. She develops algorithms for community detection, anomaly identification, and geometric approaches to network dynamics. Key research trends include advancing methodologies for analyzing time-series networks, integrating vertex covariates into spectral algorithms, and applying Euclidean geometric principles to network structures. Her contributions bridge theoretical probability frameworks with practical applications in biology and machine learning.

• Probability
• Stochastic Processes
• Network Inference
• +۳ مورد دیگر