Veronica Vinciotti

1999 -2002: Ph.D in Statistics, Imperial College London, UK

1995- 1999: First degree in Mathematics, University of Perugia, Italy

2020 - : Associate Professor, Department of Mathematics, University of Trento

2017-2020: Reader in Statistics, Department of Mathematics, Brunel University London

2012-2017: Senior Lecturer in Statistics, Department of Mathematics, Brunel University London

2007-2012: Lecturer in Statistics, Department of Mathematics, Brunel University London

2003-2007: Research Fellow in Bioinformatics, Brunel University London

Research Interests:

• Network inference and network models with applications in biology, health and finance
• Regularized approaches for high-dimensional data
• Regression models for count data

Publications from Google Scholar: https://scholar.google.co.uk/citations?user=KX-0T3oAAAAJ&hl=en

R Packages:

1. cglasso (conditional glasso under missing and censoring): https://CRAN.R-project.org/package=cglasso
2. sglasso (glasso under structural equality constraints): https://CRAN.R-project.org/package=sglasso
3. neat (network enrichment analysis): https://CRAN.R-project.org/package=neat
4. enRich (ChIP-seq binding identification): https://CRAN.R-project.org/package=enRich

Guest editor of the JRSS-A special issue on Networks and Society (January 2022)

Guest editor of the Statistica Neerlandica special issue on Statistical Network Science (August 2020)

Guest editor of the JRSS-C special issue Statistical Network Science and its Applications (April 2017)

Short-Term Scientific Mission (STSM) coordinator of COSTNET, the COST Action CA15109 (2016-2020)

PI on the BBSRC grant Micro-quantitative and macro-qualitative gene network models from ChIP-sequencing and microarray data (2011-2013). Postdoc: Yanchun Bao

PhD students:

• Matteo Framba (2021 -) "Modelling dynamic networks with differential equations"
• Paolo Berta (2015-2018) "Statistical evaluation of quality in healthcare"
• Alina Peluso (2014-2017) "Novel Regression Models for Discrete Response"
• Hamed Haselimashhadi (2013-2016) "Novel Regression Models for Dynamic and Discrete Response Data Under L1 and Differentiable Penalties"
• Hussein Hashem (2010-2014) "Regularized and Robust Regression Methods for High-dimensional Data"

Selected Talks:

• Cultures as networks of cultural traits: A unifying framework for measuring culture and cultural distances. FIW Research Conference 2023, Wien (AT)
• Hierarchical graphical modelling of count metagenomic data, CMStatistics 2022, London (UK)
• Hierarchical graphical modelling of microbiome interactions in related environments, IWSM 2022, Trieste (IT)
• Bayesian graphical modelling of counts via a discrete Weibull distribution, ISBA 2021 (online)
• Accounting for network effects in credit risk modelling, STOR-i 2020, Lancaster (UK), 2020
• Identifying potentially overlapping communities from two-mode networks, RSS2019, Belfast (UK), 2019
• Network inference in genomics under censoring, Women in Networks Workshop, Leeds (UK), 2019.
• Sparse graphical models in genomics: an application to censored qPCR data, CIBB2018, Lisbon (Portugal), 2018.
• Identifying overlapping terrorist cells from the Noordin Top actor-event network, NetSci2018, Paris (France), 2018.
• Credit risk modelling via efficient network-based inference, CMStatistics2017, London (UK), 2017.
• Mixture model under overlapping clusters: an application to network data, CLADAG2017, Milan (Italy), 2017.
• Structured graphical lasso with applications in biology and finance, ISI2017, Marrakesh (Marocco), 2017.
• Sparse Gaussian graphical models for dynamic gene regulatory networks, Dynamic Networks, Isaac Newton Institute, Cambridge (UK), 2016.
• Credit Risk Model with Network Effects for a Large Panel of Companies, CMStatistics2016, Seville (Spain), 2016.
• Making sense of large genomic networks, CMStatistics2015, London (UK), 2015.
• Model selection for dynamic regulatory networks under l1 penalty and structural constraints, SSB2014, Warwick (UK), 2014.
• Discovery of protein binding patterns by joint modelling of ChIP-seq data, SCO 2013, Milan (Italy), 2013.
• A Bayesian one-dimensional random field model for ChIP-seq data, RSS2012, Telford (UK), 2012.
• Identifying genes with high confidence from small samples, ECAI04, Valencia (Spain), 2004.
• Looking under the lamppost: matching models to the problem, RSS2002, Plymouth (UK), 2002.