[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.


Publications

Pre-print

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)

Published

I like elephants


Software

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

Contributed

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.

Attended

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

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