جست‌وجوی استاد و پژوهشگر

Jinbo Biمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine learning
• Data mining
• +۷ مورد دیگر

Jinbo Bi is the Frederick H. Leonhardt Professor of Computer Science and Associate Head in the Department of Computer Science & Engineering at the University of Connecticut's School of Engineering. He holds additional affiliations with the Department of Community Medicine and Health Care at UConn Health Center, Department of Mathematics, Department of Statistics in the College of Liberal Arts and Sciences, and the Department of Biomedical Engineering. Bi earned his Ph.D. from Rensselaer Polytechnic Institute and M.Sc. from Beijing Institute of Technology. His research spans artificial intelligence, machine learning, data mining, pattern recognition, computer vision, bioinformatics, medical informatics, and drug discovery. He directs two laboratories: the Laboratory of Machine Learning & Health Informatics and the Laboratory of Artificial Intelligence & Drug Discovery. His recent publications demonstrate a strong focus on applying machine learning to healthcare challenges, particularly in mental health monitoring through smartphone data, depression prediction, substance use disorder analysis, and drug discovery. His work combines theoretical advances in AI with practical healthcare applications, showing particular strength in multi-view learning, graph neural networks, and optimization techniques. Bi has received recognition including a best paper award at BIBM 2014 workshop and two highly accessed articles in BMC Genomics and BMC Genetics. His research is supported by numerous grants from NSF, NIH, VA, and industry partners including Travelers Insurance and Pfizer. As an educator, Bi teaches advanced courses in machine learning and reinforcement learning. His laboratory currently has multiple postdoctoral fellowship openings, reflecting his active research program and mentorship of numerous students who appear as co-authors on his publications.

Shakeel Gavioli-Akilagunمشاهده پروفایل

پژوهشگر ارشد

• Changepoints
• Feature Detection
• Multiscale Statistics
• +۴ مورد دیگر

Shakeel Gavioli-Akilagun is a Research Fellow in the Department of Statistics at the London School of Economics and Political Science (LSE), part of the Time Series and Statistical Learning research group. He holds a BSc in Economics and Econometrics from the University of York and an MSc in Statistics (Research) from LSE, where he completed an ESRC studentship. His primary research focuses on changepoints, feature detection, multiscale statistics, and causal inference, with applications in time series analysis and machine learning. His academic contributions include developing algorithms for non-standard change point problems and has published in journals like Electronic Journal of Statistics and Journal of the Royal Statistical Society Series B . He teaches courses such as Distributed Computing for Big Data and Graph Data Analytics at LSE. Recent invited talks include presentations at the Institute of Mathematical Statistics Asia Pacific Rim Meeting (2026) and the EcoSta conference on change point detection (2025).

Amir Reza Asadiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Differential Privacy
• Information Theory
• +۳ مورد دیگر

Dr. Amir Reza Asadi is a Leverhulme Early Career Fellow at the Statistical Laboratory, part of the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge (since September 2023). He is also a Fellow of the Isaac Newton Trust and a Postdoctoral Affiliate of Trinity College, Cambridge. Previously, he held a Postdoctoral Research Associate position at the same department (2021–2023) and completed his Ph.D. and M.A. in Electrical and Computer Engineering at Princeton University (2017–2021), following dual B.Sc. degrees in Mathematics and Electrical Engineering from Sharif University of Technology (2010–2015). His research focuses on machine learning, differential privacy, and information theory, with contributions to entropic regularization, generalization bounds, and privacy-preserving algorithms. His work bridges theoretical foundations and practical applications, such as neural network training and synthetic data generation. Education: B.Sc. in Mathematics & Electrical Engineering, Sharif University of Technology (2010–2015) M.A. in Electrical and Computer Engineering, Princeton University (2015–2017) Ph.D. in Electrical and Computer Engineering, Princeton University (2017–2021) His recent articles emphasize differential privacy in machine learning, entropy-based regularization techniques, and hierarchical learning models. He has organized workshops, including the 1st and 2nd Cambridge Information Theory Colloquia and the 8th London Symposium on Information Theory. His awards include the Leverhulme Early Career Fellowship, the Teaching Assistant Award from Princeton, and the Bronze Medal in the Iranian Mathematical Olympiad. Amir actively collaborates with institutions worldwide, including MIT, NYU, ETH Zürich, and the Institute for Advanced Study. His work reflects a commitment to advancing theoretical frameworks while addressing real-world challenges in data privacy and algorithmic fairness.

• Psychological Methods
• Causal Inference
• Developmental Psychology
• +۳ مورد دیگر

Felix Thoemmes is an Assistant Professor in the Department of Human Development at Cornell University, within the College of Human Ecology. His work focuses on advancing statistical methods for causal inference, mediation analysis, and longitudinal research design. He specializes in addressing methodological challenges in psychological and educational research, particularly regarding nonexperimental data and missing data problems. His research integrates advanced statistical techniques with substantive questions in developmental psychology, mental health, and education policy. Thoemmes' research interests include causal modeling, sensitivity analysis for unobserved confounding, and the application of structural equation modeling. He has contributed to understanding mechanisms linking socioeconomic status to child development, mental health trajectories in college students, and the effects of educational policies. His recent work emphasizes methodological rigor in addressing biases and improving reproducibility in psychological science. He is particularly known for advancing methodologies in mediation analysis, regression discontinuity designs, and inverse probability weighting. His work bridges theoretical causal frameworks with practical applications, providing researchers with tools to better understand complex human developmental processes. Thoemmes has collaborated on studies involving chronic pain management, fire department wellness programs, and the impact of military training on personality development. Thoemmes teaches courses on advanced statistical methods and has contributed to open-source software implementations of causal inference techniques. His research has been published in top journals across psychology, statistics, and education, demonstrating both methodological innovation and substantive scientific impact.

• Renewable Energy
• Energy Storage
• GIS Analysis
• +۳ مورد دیگر

Dr. Cheng Cheng is a Senior Research Officer at the Australian National University's School of Engineering. He holds a Bachelor of Engineering (Honours) and PhD from ANU. His research focuses on renewable energy integration, decarbonization pathways, energy system modeling, and GIS analysis. He specializes in optimizing off-river pumped hydro energy storage systems. Cheng leads multiple renewable energy projects funded by government and industry grants, and has developed methodologies for energy demand forecasting and least-cost optimization. His work bridges theoretical energy modeling with practical implementation through collaborative outreach activities. Education: B.Eng(Hons) & PhD (Australian National University) Research interests emphasize sustainable energy transitions, with particular attention to geospatial analysis for energy infrastructure planning. His recent projects include developing frameworks for integrating renewable generation with storage solutions. Over 150 publications span machine learning applications in energy systems, astrophysics, and bioinformatics. Awards include academic fellowships supporting his interdisciplinary work. Cheng collaborates with industry partners to translate research into policy and technology. He directs the ANU's Renewable Energy Integration Lab (RE100), advancing grid stability solutions through innovative storage technologies. His work has been recognized in special issues of journals focusing on robust machine learning applications.

Rachel Wangمشاهده پروفایل

دانشیار

• Statistical Network Modeling
• Statistical Machine Learning
• Genomic Data Analysis
• +۳ مورد دیگر

Dr. Rachel Wang is an Associate Professor in the School of Mathematics and Statistics at the University of Sydney. Her research focuses on statistical network modeling, machine learning, and applications to genomics. She holds a PhD from UC Berkeley and completed postdoctoral work at Stanford University. Previously, she was a Harrington Faculty Fellow at the University of Texas, Austin. Education: PhD in Statistics, UC Berkeley Postdoctoral Fellowship, Department of Statistics, Stanford University Research Interests: Statistical methods for biological networks High-dimensional data analysis Single-cell genomics and multimodal data integration Conformal prediction and robust classification Grants and Awards: Harrington Faculty Fellow (2021-2022) Australian Research Council Discovery Early Career Researcher Award (2017) Teaching: STAT3023/3923 Statistical Inference (University of Sydney) STAT2011 Probability and Estimation Theory (course coordinator) Past teaching roles at Stanford University Research Tools: Developed scTIE and scJoint methods for single-cell data integration, and contributed to TAD boundary detection algorithms in genome analysis.

Morgane Austernمشاهده پروفایل

استادیار

• Applied Probability
• Machine Learning Theory
• High Dimensional Statistics
• +۶ مورد دیگر

Morgane Austern is an Assistant Professor of Statistics at Harvard University and an affiliate of the Center of Mathematical Sciences and Applications (CMSA). She holds a PhD in Statistics from Columbia University (2019), where her research focused on limit theorems for dependent/structured data under advisors Peter Orbanz and Arian Maleki. From 2019-2021, she conducted postdoctoral research at Microsoft Research New England. She has been a long-term visitor at the Simons Institute for Theoretical Computing (2022) and was invited to speak at the National Academies of Science, Engineering, and Medicine on AI for mathematical reasoning (2023). Her research spans Probability theory (Stein method, Gaussian universality, concentration inequalities, ergodic theory, optimal transport) Machine learning theory (graph neural networks, data augmentation, deep learning theory) Statistical inference (resampling methods, causal inference, high-dimensional statistics for dependent data) Awards Kavli Fellow (National Academy of Sciences, 2022) NSF CAREER Award (2025) Her work bridges theoretical foundations across probability, statistics, and machine learning, with a focus on structured data analysis and algorithmic robustness.

Gonzalo Mateosمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Machine Learning
• Network Science
• +۴ مورد دیگر

Gonzalo Mateos is an Associate Professor in the Department of Electrical and Computer Engineering and holds a secondary appointment in the Department of Computer Science at the University of Rochester. He is also the Asaro Biggar Family Fellow in Data Science and a member of the Goergen Institute for Data Science and Artificial Intelligence. Additionally, he serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Transactions on Signal and Information Processing over Networks , and is part of the IEEE SigPort Editorial Board. Dr. Mateos earned his B.Sc. in Electrical Engineering from Universidad de la Republica, Uruguay in 2005. He then pursued graduate studies at the University of Minnesota, Twin Cities, where he received his M.Sc. in 2009 and Ph.D. in 2011. Prior to joining the University of Rochester in 2014, he worked as a Systems Engineer at Asea Brown Boveri (ABB) in Uruguay from 2004 to 2006 and served as a visiting scholar at Carnegie Mellon University's Computer Science Department during the 2013 academic year. His research interests focus on statistical learning from Big Data , network science , decentralized optimization , and graph signal processing , with applications in dynamic network health monitoring , social networks , power grid analysis , and large-scale data analytics . His work emphasizes blind deconvolution, graph topology inference, fairness-aware methodologies, and explainable AI for medical and engineering systems. Dr. Mateos has been honored with the NSF CAREER Award (2018), the IEEE Young Author Best Paper Award (2017), and multiple conference best paper awards including ICASSP 2018, SSP Workshop 2016, and SPAWC 2012. His doctoral research was recognized with the University of Minnesota's Best Dissertation Award (2013, Honorable Mention) in Physical Sciences and Engineering. In advising, he mentors graduate students in his research areas, though specific names are not listed here. His grants include the NSF CAREER Award, and he collaborates on projects like fairness-aware graph filter design and dynamic network monitoring. His research teams and labs operate within the Goergen Institute, focusing on interdisciplinary data science and AI applications. He actively contributes to the Hajim School of Engineering and the broader University of Rochester community, fostering innovation at the intersection of electrical engineering and computer science. His industry experience and academic roles reflect a commitment to both theoretical and practical advancements in networked systems and machine learning.

Carly Knightمشاهده پروفایل

استادیار

• Economic Sociology
• Cultural Sociology
• Social Theory
• +۵ مورد دیگر

Carly Knight is an Assistant Professor of Sociology at New York University's College of Arts & Science. Her research focuses on economic sociology, cultural sociology, and social theory, with particular emphasis on language, moral discourse, and computational text analysis. Ph.D., Sociology, Harvard University (2018) M.A., Sociology, Harvard University (2013) B.A., Economics & Political Science, Duke University (2007) Dr. Knight investigates the historical development of corporate personhood and metaphorical frameworks in legitimating corporate capitalism. Her ongoing projects use computational text analysis to examine cultural schemas in corporate responses to market transitions, alongside historical research on financial speculation cycles and asset bubbles. Her recent publications span topics including the sociological implications of "mechanism" in theory, discrimination lawsuits in organizational contexts, and gender bias in image recognition systems. She co-organizes NYU’s Sociology of Culture Workshop. Contact: carly.knight@nyu.edu | Office: 79 Washington Square E, Room 322, New York, NY 10003

Anna Plaksienkoمشاهده پروفایل

پژوهشگر ارشد

• High-dimensional statistics
• Omics data integration
• Multi-omics analysis
• +۴ مورد دیگر

Dr. Anna Plaksienko is a Research Fellow in High-dimensional Statistics at the University of Oslo's Institute of Basic Medical Sciences, Faculty of Medicine. Her work focuses on statistical methods for omics data integration, particularly in multi-source high-dimensional regression and graphical models. She develops computational tools like the methyLImp2 and jewel R packages for missing value imputation and joint network estimation. Education: PhD in Mathematics (2021) - Gran Sasso Science Institute, Italy BSc in Mathematics (2016) - Novosibirsk State University, Russia Research Interests: She explores differential network estimation, stability selection, and error control in omics data (RNA-Seq, methylation, lipidomics). Her methods aim to uncover biological insights from multi-omics integration and high-dimensional datasets. Labs/Teams: She is part of the High-dimensional Statistics research group at the Institute of Basic Medical Sciences, collaborating on projects involving statistical methodology and computational biology.

Raul Mondragonمشاهده پروفایل

مدرس ارشد

• Complex Networks
• Network Science
• Telecommunications
• +۳ مورد دیگر

Dr. Raul Mondragon is a Senior Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds an MSc and PhD, and is a Senior Member of the IEEE (SMIEEE). His research focuses on complex network analysis, including topics like network cores, rich-club phenomena, and applications in telecommunications and social media platforms. He has contributed to understanding internet topology, network robustness, and Web3 technologies. He teaches modules on analogue electronics and network security, and has supervised interdisciplinary PhD students in areas such as energy-efficient networks and dynamical modeling. His work includes grants like the EPSRC-funded RAVEN project (2010-2013) on complex energy networks. Mondragon is affiliated with the Centre for Networks, Communications and Systems, where he explores network science applications in diverse domains. Affiliations: School of Electronic Engineering and Computer Science, Centre for Networks, Communications and Systems Education: PhD and MSc (specific fields and institutions not explicitly stated) Research Interests: Dr. Mondragon’s work spans theoretical and applied network science. He investigates structural properties of networks (e.g., rich-club effects, core-periphery organization) and their implications for system resilience. His research applies to telecommunications, social media, and ecological systems. Notable contributions include analyzing cryptocurrency transaction networks, Web3 platform moderation, and drought impacts on food web structures. Teaching: Dr. Mondragon instructs undergraduate courses on analogue electronics and postgraduate/undergraduate modules on network security and authentication principles. His teaching emphasizes foundational circuit theory and cryptographic techniques for secure communications. Grants: Principal Investigator (PI) for the EPSRC RAVEN project (£355,446), focusing on complex energy networks’ resilience and adaptability. Students: Supervised PhD candidates include Mr. Iftekharul Mobin (energy-efficient ad-hoc networks), Mr. Sabri Zaman (functional topology), and interdisciplinary students like Mr. Gibson Ikoro (chat room dynamics) and Mr. Massimo Cavallaro (stochastic processes). Labs/Teams: Active in the Centre for Networks, Communications and Systems, collaborating on projects involving network modeling, social media analysis, and interdisciplinary applications.

• Econometrics
• Panel Data Analysis
• Stochastic Modeling
• +۲ مورد دیگر

Louise Laage is an Assistant Professor in the Department of Economics at Georgetown University, specializing in econometrics. Her research focuses on advanced econometric methodologies, including panel data analysis, stochastic modeling, and nonparametric techniques. She develops statistical tools for applied economics, such as the MFELOGIT Stata module for estimating marginal effects in fixed-effect logit models. Her recent work addresses topics like time-varying endogeneity in panel models, stochastic block models with covariates, and identification strategies in fixed-effect frameworks. These contributions bridge theoretical econometrics and practical applications in policy analysis and social sciences. Laage's publications span journals and modules since 2018, reflecting her expertise in both methodological innovation and software implementation. She holds no listed awards or advisees, but her work demonstrates a strong focus on advancing quantitative research tools in economics. Contact: louise.laage@georgetown.edu

• Category Theory
• Compositional Game Theory
• Categorical Cybernetics
• +۷ مورد دیگر

Jules Hedges is a mathematics and computer science researcher affiliated with the Institute for Categorical Cybernetics and the Mathematically Structured Programming group at the University of Strathclyde. As of September 2024, he is working 90% for the CyberCat Institute and 10% for the University of Strathclyde, focusing on applying category theory across various interdisciplinary domains. Hedges completed his PhD at Queen Mary University of London in 2016 with a thesis titled "Towards compositional game theory," which established foundational work for his ongoing research program. His academic journey spans from early work on game theory to current developments in categorical cybernetics. His primary research involves applying category theory in economics, machine learning, and control theory, with a view towards what was historically called cybernetics. Hedges is particularly known for developing compositional game theory, which provides a categorical framework for understanding strategic interactions through concepts like "open games" and "selection functions." His other research interests include graphical calculi for visual representation of categorical structures, functional programming paradigms, proof theory, and the semantics of natural language. He has made significant contributions to understanding how complex systems can be built from simpler components while preserving mathematical properties. Hedges' publication record reveals a consistent trajectory toward developing compositional frameworks for complex systems, with recent work increasingly focused on categorical cybernetics—the application of category theory to systems with feedback loops and goal-directed behavior. His research often bridges abstract mathematical concepts with practical applications in economics and artificial intelligence, particularly in reinforcement learning where he has demonstrated connections between value iteration algorithms and categorical optic composition. Hedges is a signatory of the "No Free View? No Review!" pledge, indicating his strong commitment to open access publishing. He has publicly stated he will not peer review for closed access or hybrid venues, reflecting his principled stance on making academic research freely available. While specific details about his advising activities are not documented in the provided text, his extensive publication record with numerous co-authors suggests active research collaboration and likely mentorship of junior researchers. His work spans multiple institutions and involves collaborators across mathematics, computer science, and economics. Hedges is a founding member of the Institute for Categorical Cybernetics, formally incorporated in October 2023, which represents the primary focus of his current work (90% of his professional time as of September 2024). This institute serves as a hub for researchers working at the intersection of category theory, cybernetics, and their applications to complex adaptive systems, potentially supporting collaborative projects, workshops, and publications in this emerging field.

Van Vuمشاهده پروفایل

استاد

• Additive Number Theory
• Combinatorics
• Probability
• +۳ مورد دیگر

Van Vu is the Percey F. Smith Professor of Mathematics and Professor of Statistics & Data Science at Yale University. He holds a Ph.D. from Yale University (1998). His research focuses on additive number theory, combinatorics, probability, and random matrices, with significant contributions to probabilistic combinatorics and random matrix theory. He co-leads the combinatorics group at Yale, which hosts regular seminars and explores topics like random graphs, hypergraphs, and additive combinatorics. Dr. Vu has been recognized with prestigious awards, including the Polya Prize (2008) and Fulkerson Prize (2012). His work bridges theoretical mathematics and applied domains, addressing challenges in spectral analysis, network consensus dynamics, and statistical inference. He actively participates in academic service, including grant-funded research projects and collaborations on interdisciplinary topics like biocontrol agents derived from plant extracts. Education: Ph.D., Yale University, 1998 Affiliations: Department of Mathematics, Yale University Key Research Themes: Random matrix perturbation, sparse network dynamics, additive combinatorics, and applications in data science His research group emphasizes collaborative efforts, with ongoing projects on eigenvalue distribution universality, matrix completion algorithms, and probabilistic methods in combinatorics. Dr. Vu also maintains an active blog and contributes to academic conferences, reflecting his commitment to advancing both theoretical and applied mathematical research.

• Bioinformatics
• High-Performance Computing
• Parallel Algorithms
• +۳ مورد دیگر

Lukas Hübner is a Doctoral Researcher at the Karlsruhe Institute of Technology (KIT) and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS) . He holds a B.Sc. in Molecular Biotechnology from Heidelberg University and an M.Sc. in Computer Science from KIT. His research focuses on integrating computational methods with biological data analysis, particularly in phylogenetic inference, high-performance computing, and parallel algorithm design. He explores fault-tolerant systems, MPI optimizations, and reproducibility in computational workflows. Key research interests include: Parallel algorithms for large-scale phylogenetic analysis Optimization of MPI-based distributed systems Reproducibility in high-performance computing Fault-tolerance mechanisms in parallel algorithms His work often bridges computer science and bioinformatics, addressing challenges in genetic data processing and algorithm scalability. Recent publications emphasize techniques like memoization for genealogical forests and zero-overhead C++ bindings for MPI. He has collaborated on tools like RAxML-NG and ReStore, advancing robust computational methods for phylogenetic studies.