جست‌وجوی استاد و پژوهشگر

Luca Danielمشاهده پروفایل

استاد

• Electrical Engineering
• Biomedical Engineering
• Computer Science
• +۵ مورد دیگر

Luca Daniel is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE) and the Department of Electrical Engineering and Computer Science. His research focuses on computational modeling, simulation, and design of complex systems, with applications in electronics, biomedical engineering, and machine learning. He leads the Computational Prototyping Group, which develops algorithms and software for model order reduction, integral equation solvers, and uncertainty quantification. Professor Daniel holds a Ph.D. from the University of California, Berkeley (2003), and has authored numerous influential papers in top conferences and journals. His work has been recognized with awards such as the APS Best Paper Award (2023), EPEPS Best Conference Paper Award (2020), and the ACM Outstanding Ph.D. Dissertation Award in EDA (2016). His research interests include electromagnetic simulation, biomedical systems, stochastic methods, and machine learning robustness. He has advised over 20 PhD students and developed open-source tools like FastMaxwell and CAPLET for electromagnetic analysis. His teaching spans courses on modeling, simulation, signals and systems, and numerical methods.

• Machine Learning
• Recommender Systems
• Data Privacy
• +۷ مورد دیگر

Mihaela Curmei is a researcher active in machine learning, recommender systems, and algorithmic fairness, with significant contributions to privacy-preserving methods, behavioral modeling, and optimization. Her collaborative work spans institutions and co-authors like Benjamin Recht, Georgina Hall, and Sarah Dean. Research Themes: Shape-constrained regression using polynomial optimization Privacy in matrix factorization with public features Dynamic preference modeling grounded in psychology Multi-learner systems under participation dynamics Temporal impacts of recommendation algorithms Technical Domains: Sum-of-squares programming Federated and distributed learning Stochastic reachability analysis Gradient flow-based dataset modeling Key Venues: Published in Operations Research , NeurIPS, ICML, RecSys, FAccT, and preprint archives. Her recent work (2023-2025) focuses on temporal effects in recommendations, private computation methods, and equilibrium analysis in multi-agent games. Earlier projects (2017-2021) include search engine indexing techniques (BitFunnel) and foundational studies on offline/online metric correlations in recommendation systems.

Terence Chi-Shen Taoمشاهده پروفایل

استاد

• Harmonic analysis
• Partial differential equations
• Algebraic combinatorics
• +۵ مورد دیگر

Terence Tao is an Australian-American mathematician and professor of mathematics at the University of California, Los Angeles (UCLA), where he holds the James and Carol Collins Chair in the College of Letters and Sciences. Widely regarded as one of the greatest living mathematicians, Tao has received numerous prestigious awards including the Fields Medal, the Breakthrough Prize in Mathematics, and the MacArthur Fellowship. Dr. Tao's educational background includes: Bachelor's and Master's degrees from Flinders University (1991) Ph.D. from Princeton University (1996) under Elias M. Stein Tao's research spans an extraordinary breadth of mathematical fields. He is particularly known for his work in harmonic analysis, partial differential equations, combinatorics, and analytic number theory. His research has included groundbreaking contributions to compressed sensing, the Green-Tao theorem on arithmetic progressions in prime numbers, and progress on the Navier-Stokes equations. Tao is renowned for his collaborative approach, having worked with over 60 co-authors throughout his career. Tao's publications demonstrate remarkable diversity across mathematical disciplines. His work shows strong trends in connecting seemingly disparate areas of mathematics, often bringing techniques from one field to solve problems in another. He has made significant contributions to both theoretical and applied mathematics, with applications ranging from signal processing to number theory. Among his numerous scientific achievements, Tao has received: Fields Medal (2006) Breakthrough Prize in Mathematics (2014) Royal Medal (2014) MacArthur Fellowship (2006) Crafoord Prize (2012) Princess of Asturias Award (2020) Tao has mentored numerous students throughout his career, with Monica Vișan among his doctoral students. He has secured significant research funding through prestigious awards including the Packard Fellowship, Sloan Fellowship, and Simons Investigator award. His collaborative research has been supported by multiple National Science Foundation grants. Tao maintains an active research group at UCLA and frequently collaborates with mathematicians worldwide. His blog and public lectures have made advanced mathematical concepts accessible to broader audiences, demonstrating his commitment to mathematical education and outreach.

Denny Wuمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Neural Networks
• High-Dimensional Statistics
• +۲ مورد دیگر

Denny Wu is a Faculty Fellow at the New York University Center for Data Science and affiliated with the Flatiron Institute 's Center for Computational Mathematics. He completed his PhD in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence , advised by Assistant Professors Jimmy Ba and Murat A. Erdogdu . Prior, he earned an undergraduate degree in Computational Biology from Carnegie Mellon University as a research assistant under Ruslan Salakhutdinov . His research focuses on Theoretical Machine Learning , particularly Neural Network Optimization , Generalization Performance , and High-Dimensional Statistics . His work has been presented at top conferences like NeurIPS , ICML , and AISTATS , alongside publications in journals such as Nature Protocols and Journal of Statistical Mechanics: Theory and Experiment . Collaborative efforts include partnerships with RIKEN AIP 's Deep Learning Theory Team and Microsoft's Deep Learning Group. Borealis AI Fellowship 2023 UChicago Rising Star in Data Science Denny actively collaborates with institutions like the Vector Institute , RIKEN AIP , and Flatiron Institute , focusing on mathematical frameworks for understanding deep learning systems.

• Computational Methods
• Numerical Analysis
• Control Theory
• +۹ مورد دیگر

Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.

Dave Sullivanمشاهده پروفایل

مدرس ارشد

• Human Resource Management
• Organizational Behavior
• Performance Management
• +۵ مورد دیگر

Dr. Dave Sullivan is a Senior Lecturer in Human Resources at the UNSW Business School, specifically within the School of Management and Governance. His academic journey includes earning a Ph.D. in Management and Organizational Behavior from the Mays Business School at Texas A&M University. Education: Ph.D. in Management and Organizational Behavior from Texas A&M University His research explores the social, psychological, and cognitive dimensions of human resource management, focusing on performance management, person-organization fit and misfit, job search dynamics, and social evaluation processes. Notably, his work addresses gender bias in recruitment, resource allocation, and social evaluations of CEOs, bridging gaps between organizational behavior and broader societal challenges like the Great Resignation and crowdfunding equity. Recent publications highlight intersections of technology funding, innovation strategies, and gender disparities. Key themes include Crowdfunding Models , Performance Evaluation , and Organizational Culture . Despite no explicit mention of awards, his work appears in top-tier journals such as the Journal of Applied Psychology and Human Resource Management .

Manolis Zampetakisمشاهده پروفایل

استادیار

• Theoretical Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoc at UC Berkeley's EECS Department working with Michael Jordan, and earned his PhD from MIT's EECS Department under Constantinos Daskalakis. His research spans Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, and Mechanism Design. He has received the ACM SIGEcom Doctoral Dissertation Award and a Google PhD Fellowship. Current affiliation: Yale University (Assistant Professor) Prior affiliations: UC Berkeley (Postdoc), MIT (PhD student), NTUA (Undergraduate) His research focuses on algorithmic game theory, robust statistics, and optimization challenges in machine learning. He explores computational complexity in multi-player games, truncated linear regression, and strategy-proof mechanisms. Recent work includes backdoor attacks in neural networks and jailbreaking black-box LLMs, with publications in top venues like NeurIPS, COLT, FOCS, and STOC. Notable scientific contributions have been recognized through awards and special issues. He co-organized workshops at FOCS 2018, WALE 2019, and WALE 2022. His students include Anay Mehrotra, Jane Lee, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis, often co-advised with prominent researchers like Amin Karbasi and Tuomas Sandholm.

Carlos A. Coello Coelloمشاهده پروفایل

استاد مهمان

• Evolutionary Computation
• Multi-objective Optimization
• Genetic Algorithms
• +۵ مورد دیگر

Dr. Carlos A. Coello Coello is a distinguished professor (CINVESTAV 3F Researcher) at the Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN) in Mexico City. He also serves as a Distinguished Visiting Professor in Computer Science and Computational Intelligence at Tecnológico de Monterrey's School of Engineering and Science, and as a visiting professor at the Basque Center for Applied Mathematics (BCAM) in Spain. Elected to El Colegio Nacional in 2023, he is the first computer scientist to join this prestigious Mexican institution, which groups the country's most important scientists, artists, and intellectuals. Dr. Coello's research focuses on evolutionary computation, particularly multi-objective optimization algorithms. His work spans genetic algorithms, evolutionary strategies, and bio-inspired optimization techniques with applications in engineering. He has made pioneering contributions to evolutionary multi-objective optimization, including developing the first genetic microalgorithm for multi-objective optimization, which has been applied in real-world contexts such as aerospace design. His research bridges computer science, applied mathematics, and operations research. Dr. Coello has published extensively since the 1990s, with recent publications focusing on advanced evolutionary algorithms, surrogate modeling for expensive optimization, and dynamic multi-objective optimization. His work shows a clear progression from foundational evolutionary computation techniques to sophisticated multi-objective and many-objective optimization approaches with practical engineering applications. MCDM Edgeworth-Pareto Prize (2024) Crónica Prize in Science and Technology (2023) SIGEVO Outstanding Contribution Award (2023) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2021) 2013 IEEE Kiyo Tomiyasu Award 2012 National Medal of Science and Arts IEEE Fellow (since 2011) Dr. Coello has successfully secured significant research funding through Mexico's CONACyT, with projects ranging from foundational algorithm development to advanced multi-objective optimization techniques. He has supervised PhD students, including Dr. Luis Vicente Santana-Quintero who received the Arturo Rosenblueth PhD Thesis Award. As Associate Editor for prestigious journals including IEEE Transactions on Evolutionary Computation, Dr. Coello plays an active role in shaping the field. His work has earned him international recognition, including being ranked 289th worldwide and 1st in Mexico among the 1000 most outstanding computer scientists in the 2022 Guide2Research Ranking.

• Statistical Network Analysis
• Non-parametric Statistics
• Causal Inference
• +۷ مورد دیگر

Dr. Alexander Fuchs-Kreiß is a Junior Professor for Statistics at the Institute of Mathematics, Faculty of Mathematics and Computer Science at Leipzig University. He joined the university in April 2022 as a member of the Stochastics research group. He is also a scientific member of the International Max Planck Research School for Mathematics in the Sciences and affiliated with the HKMetrics Network. His research focuses on the statistical analysis of network data, particularly semi- and non-parametric methods for dynamic interaction networks. He has developed methodologies for relational event models using counting processes like Hawkes processes, and has contributed to causal inference, high-dimensional statistics, and quantile regression. His work often bridges multiple statistical areas, creating innovative approaches to complex data structures. His recent publications show a strong trend toward developing statistical methods for network data with applications in economics, epidemiology, and social sciences. His research combines theoretical developments with practical implementations, as evidenced by the R packages accompanying his publications. As an educator, he teaches Mathematical Statistics, Statistical Network Analysis, and Causal Inference at Leipzig University. He supervises PhD students working on network models and advises diploma theses in mathematics and business mathematics. Dr. Fuchs-Kreiß maintains active research collaborations with institutions including Heidelberg University, UC Davis, KU Leuven, and LSE. He co-organizes academic seminars and participates in international conferences on statistical learning and probability.

Jeffrey Grahamمشاهده پروفایل

دانشیار

• Applied Mathematics
• Computational Mathematics
• Numerical Analysis
• +۶ مورد دیگر

Jeffrey Graham is an Associate Professor in the School of Math and Computer Sciences at Susquehanna University, where he has served as Head of Mathematical Sciences since 2007. He previously held roles as Acting Head of Physics (2009–2014) and Assistant Professor (2001–2007), with earlier experience at Western Carolina University (1992–2001). PhD in Mathematics (1992), Rensselaer Polytechnic Institute MS in Mathematics (1986), Texas Tech University BS in Computer Science and Mathematics (1984), Iowa State University His research bridges mathematics and computer science, focusing on inverse problems, differential equations, and computational methods. He has pioneered educational technologies, including MATLAB GUIs and Excel-based tools, and explored intersections with music theory via group theory applications. Jeffrey’s recent publications span quantum computing (2010), financial mathematics (2014), and leadership strategies (2011), reflecting his interdisciplinary approach. His work emphasizes pedagogical innovation and computational modeling across fields. Jeffrey has secured multiple grants, including NSF travel funding (1993), Microsoft instructional lab support (1997), and NSF REU grants (2008–2010). He has served on editorial boards, curriculum committees, and search committees, demonstrating institutional leadership.

• Environmental Science
• Climate Change
• Wetland Ecology
• +۴ مورد دیگر

Wenxin Zhang serves as a Guest Researcher at the Department of Geosciences and Natural Resource Management, University of Copenhagen, where he investigates critical environmental processes across diverse ecosystems. His work bridges field observations with modeling approaches to address climate change impacts, particularly in vulnerable high-latitude and agricultural regions. Dr. Zhang's research centers on biogeochemical cycles and climate-ecosystem interactions, with focused expertise in greenhouse gas dynamics (CO 2 and CH 4 ) within wetlands, permafrost degradation mechanisms, and hydrological responses to environmental stressors. His methodologies integrate remote sensing, in-situ measurements, and climate scenario modeling to quantify seasonal and long-term trends in ecosystem functioning. Analysis of his recent publications reveals a strategic focus on northern high-latitude regions (Arctic/subarctic) and agricultural systems in China, examining how climate variables drive carbon fluxes, soil processes, and crop productivity. This work demonstrates consistent interdisciplinary collaboration across hydrology, ecology, and climate science to address pressing environmental challenges. His research portfolio includes significant contributions to understanding snow-carbon interactions in tundra ecosystems, aeolian impacts on permafrost stability, drought-agriculture linkages, and precision irrigation techniques. These studies collectively advance predictive capabilities for ecosystem responses under future climate scenarios while informing sustainable land management practices.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +۵ مورد دیگر

Rafael Rumí Rodríguez is a Professor in the Mathematics Department at the University of Almería, Spain, with extensive experience in probabilistic modeling and data analysis. He leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple research projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' (2023-2026). Dr. Rumí Rodríguez's research focuses on Bayesian networks and probabilistic graphical models , with applications spanning environmental science, ecology, agriculture, and education. His work includes developing methods for continuous distributions in hybrid Bayesian networks, flood risk assessment in Mediterranean regions, climate change impact modeling for Andalusian agriculture, and educational assessment analytics. He has created practical tools like the 'hcrBN' R package for hybrid Bayesian network applications. His publication record includes 43 articles , 2 books or chapters , 3 theses , and 31 conference papers , with an h-index of 16 (Scopus) and 15 (Web of Science). Many publications appear in Q1 journals, reflecting significant research impact across computer science, environmental science, and statistics. Scopus h-index: 16 Web of Science h-index: 15 Scopus i10-index: 27 Web of Science i10-index: 23 Dr. Rumí Rodríguez has secured substantial research funding throughout his career, including as Principal Investigator for the current TOPAI-UAL project (€120,100) and previous projects like the European AMIDST initiative (€323,490). His research group provides opportunities for students to engage in both theoretical advances and practical applications of probabilistic modeling, with strong connections to environmental and agricultural domains in the Mediterranean region.

Lili Yuمشاهده پروفایل

استاد

• Biostatistics
• Survival Analysis
• Categorical Data Analysis
• +۵ مورد دیگر

Lili Yu is the Karl E. Peace Endowed Chair of Biostatistics and Professor in the Department of Biostatistics, Epidemiology & Environmental Health Sciences at Georgia Southern University, where she has been faculty since 2007. Her academic appointments include serving as Principal Investigator for the Office of International Chinese Statistical Association (2007-2017) and as Co-Principal Investigator for current research projects including 'Hierarchical Bayes Regression Models for Geospatial Inquiries' at Georgia Southern University. Dr. Yu's educational background is highly interdisciplinary, with degrees spanning medicine and statistics: PhD in Biostatistics from Ohio State University (2007) MS in Statistics from Ohio State University (2004) MS in Neuroscience from Capital Medical University (2001) MD in Clinical Medicine from Tianjin Medical University (1995) Her research interests focus on biostatistical methodology development and application, particularly in survival analysis, categorical data analysis, and multivariate data analysis. Dr. Yu has made significant contributions to accelerated failure time models, Bayesian statistics, and spatiotemporal modeling. Her work often addresses important public health questions related to disease modeling, mortality risk factors, and health outcomes assessment. Analysis of her publication trends shows a consistent trajectory from methodological development in survival analysis to applied studies addressing significant health issues. Dr. Yu has received recognition for her scholarly contributions, with an h-index of 11 according to Scopus data, and 1,876 citations across her publications. Her work has appeared in numerous high-impact journals across biostatistics, epidemiology, and public health disciplines. Her research funding includes multiple successful grant awards, demonstrating her ability to secure competitive research support. Notable projects include 'Hierarchical Bayes Regression Models for Geospatial Inquiries' (2022-2023), 'Efficient statistical methods in AFT model for cancer data' (2009-2010), and leadership of the Office of International Chinese Statistical Association (2007-2017). Dr. Yu's collaborative network is extensive, with co-authors from multiple institutions across the United States and internationally. Her work bridges methodological statistics with practical applications in public health and medicine, contributing to United Nations Sustainable Development Goals related to health and well-being.

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
• +۵ مورد دیگر

Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.