Topics in the Statistical Analysis of Network Data by Professor Eric Kolaczyk, Boston University
Topics in the Statistical Analysis of Network Data by Professor Eric Kolaczyk, Professor of Statistics; Founding Member, Faculty of Computing and Data Sciences; and Director, Hariri Institute for Computing; Boston University.
Please note this Masterclass is split over two days and will be delivered over Zoom. One registration will cover both afternoons. Enterprise Alliance employees, please note each afternoon is 0.5 of a training day deducted from your overall CRT training allowance.
Day 1: Monday 9th November, 2-5pm
Day 2: Tuesday 10th November, 3-5pm
This series of lectures will serve as an introduction to select topics in the statistical analysis of network data, and consist of four lectures and a tutorial. The lectures will cover (i) background concepts and terminology; (ii) basic elements of network visualization and characterization; (iii) fundamentals of kernel machine learning on networks; and (iv) recent results on estimation in noisy networks, with an illustration in the context of epidemic reproduction numbers. The tutorial will provide an introduction to the manipulation, visualization, and characterization of network data in R. No background in networks is necessary.
Participants will require access to a computer, the R statistical computing software (www.r-project.org) and RStudio (rstudio.com). There will be a hands-on element to the lectures.
The majority of the material covered will mirror the presentation in Kolaczyk, E.D. and Csardi, G. (2020). Statistical Analysis of Network Data in R, 2nd Edition. Springer.
Speaker biography: Eric Kolaczyk is a Professor of Statistics, in the Department of Mathematics and Statistics, a founding member of the Faculty of Computing and Data Sciences, and Director of the Hariri Institute for Computing at Boston University. He is also affiliated with the Division of Systems Engineering, the Programs in Bioinformatics and in Computational Neuroscience, and the BU URBAN program. His research is focused at the point where statistical theory and methods support human endeavors enabled by computing and engineered systems, frequently from a network-based perspective of systems science. In general, he develops novel methodologies for design, representation, modeling, inference, prediction, or uncertainty quantification foundational to new paradigms for data measurement and analysis. He has published nearly 100 articles, including several books on the topic of network analysis. As an associate editor, he has served on the boards of JASA and JRSS-B in statistics, IEEE IP and TNSE in engineering, and SIMODS in mathematics. He formerly served as co-chair of the NAS Roundtable on Data Science Education. He is an elected fellow of the AAAS, ASA, and IMS, an elected senior member of the IEEE, and an elected member of the ISI.
The SFI Centre for Research Training in Foundations of Data Science will train a cohort of PhD students with world-class foundational understanding in the horizontal themes of Applied Mathematics, Statistics, and Machine Learning.
Our #walktober competition is heating up between the 3 universities @UCDMathStat @MU_Hamilton @MACSIMaths Two weeks to go to see which team will come out on top. #MentalHealthAwareness #smartertravel #StayConnected Photo: #Silvermines, #Tipperary https://t.co/XEAWHH5RhZ
For perfomance reasons we use Cloudflare as a CDN network. This saves a cookie "__cfduid" to apply security settings on a per-client basis. This cookie is strictly necessary for Cloudflare's security features and cannot be turned off.