[Gaetano Romano ~]$
about research teaching contacts
Over the last couple of years with Statscale, I focused on change-point detection for large data streams. I have been developing novel dynamical programming algorithms capable of dealing with applications where the usual normality assumptions fall; Lately, I have been looking at online anomaly detection algorithms. From that my interests span to other topics in data science, such as machine learning, MCMC, to general modelling to genetic admixtures.
Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise
Gaetano Romano, Guillem Rigaill, Vincent Runge, Paul Fearnhead (May 2020)
gfpop: an R Package for Univariate Graph-Constrained Change-point Detection
Vincent Runge, Toby Dylan Hocking, Gaetano Romano, Fatemeh Afghah, Paul Fearnhead, Guillem Rigaill (Feb 2020)
PublishedI like elephants
DeCAFS: Detecting Changes in Autocorrelated and Fluctuating Signals
Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process.
Conferences and seminars
CFE-CMStatistics - London (14-16 Dec 2019)
Given a talk about detecting abrupt changes in correlated time-series. Click here for abstract. Joint work in collaboration with Paul Fearnhead, Guillelm Rigaill and Vincent Runge from University Evry.
Changepoint and anomaly detection in big data settings - Paris
13-14 Nov 2019
APTS modules in Durham (8-12 Jul 2019)
Modules on Computer Intensive Statistics and on High-dimensional Statistics
APTS modules in Cambridge (10-14 Dec 2018)
Modules in Statistical Inference and Statistical Computing