Assistant Professor in Statistics
School of Mathematics and Statistics
University College Dublin, Ireland

Email address: isabella.gollini@ucd.ie

Links:


Recent/Upcoming Events

 16-18 Dec 2017 Invited Talk at CMStatistics 2017, Senate House, University of London, UK
 20 Oct 2017 Seminar at Department of Mathematics, Brunel University London, UK
 12-14 Jul 2017
Invited Talk at Data Science, Statistics & Visualisation, Lisbon, Portugal
 4-7 Jul 2017
Keynote Lecture at useR!2017, Brussels, Belgium. [Video and Slides]
 28-29 Jun 2017 Short Course on Statistical Social Network Analysis with R, University of Padua, Italy
 21-23 Jun 2017 Workshop on Statistical Social Network Analysis with R (SSNAR17), Birkbeck, University of London, UK
 14 Jun 2017 Seminar at Stochastic Processes Group, University College London, UK
 16-17 May 2017 Invited Talk at 6th International Workshop on Social Network Analysis, Naples, Italy
 11 May 2017 Seminar at Department of Mathematical Sciences, University of Essex, UK
 10-12 Apr 2017 Short Course on Statistical Social Network Analysis, University of Bologna, Italy
 14 Feb 2017 Seminar at School of Computer Science and Statistics, Trinity College Dublin, Ireland


My research activity aims to integrate statistical methods with various applications through cross-disciplinary collaborations. As an applied statistician I have been working closely with scientists with different expertise (e.g. engineers, geographers, computer scientists, etc.) in order to yield important practical results as well as developments of new statistical techniques.

During my research activity in UK and Ireland I worked on the development of original methodologies and gathered a broad experience in analyzing different types of data. 

At the University of Bristol, I worked on statistical methods for assessing uncertainty and risk in natural hazardsAt Maynooth University I worked on regression models for spatial data. During my PhD studies at the University College Dublin I developed latent variable models to summarize and visualize multiple information in biological and social networks and cluster complex categorical data. These methods find application in a wide range of research areas including social sciences, health sciences and marketing.

I am interested in teaching methods and tools to promote diversity in the Statistics, Data Science and R communities for this reason I am mentor at R-Ladies and leader of the teaching team at Forwards the R Foundation taskforce on women and other under-represented groups.