Laura Villanova is a Research Fellow in the School of Mathematical Sciences at Monash University in Australia. She is undertaking research as part of the Monash Academy for Cross and Interdisciplinary Mathematical Applications (MAXIMA) program.
Laura had previously held research positions at Monash University and Ceramic Fuel Cells Ltd -CFCL- in Australia (2011-2015), at the European Center for Living Technology -ECLT- and at the Coordinamento Interuniversitario Veneto per le Nanotecnologie -CIVEN- in Italy (2006-2008). Laura holds a PhD in Statistical Sciences from University of Padova (Italy) and developed a significant part of her PhD research at Monash University (Australia). She also holds a B.Sc in Statistics and Informatics for Business Management and a M.Sc in Business Statistics and Information Systems, both from University Ca' Foscari of Venice (Italy). Laura has contributed to 12 scientific publications, 8 of which are peer-reviewed manuscripts. The significance of her contribution in applied mathematics is demonstrated by the publications in high-impact factor journals such as Nature Communications (IF = 10.742), Analytical Chemistry (IF = 5.825, ERA rank = A*), Journal of Materials Chemistry (IF = 6.626, ERA rank = A) and BMC Bioinformatics (IF = 2.672). Laura has shown promising capabilities in writing grant proposals (over $240,000 granted) and student supervision (4 students (co-)supervised at various levels ranging from postdoctoral to undergraduate).
My research interests involve multiple research fields ranging from Design of Experiments (DoE) to computational statistics, evolutionary optimisation and machine learning.
I have applied experimental design methodologies to various fields of research including bio-chemistry, materials science and engineering. Applied studies allowed me to see the power of DoE techniques but also their limitations and lack of flexibility. I have thus focused in empowering existing design methodologies with advances in other fields such as evolutionary optimisation, mathematical modelling and meta-learning. I am now interested to further progress research in this interdisciplinary field with additional flexibility and integration of statistical principles.
Design of Experiments (DoE); expensive black-box optimisation; computational statistics
Doherty, C.M., Knystautas, E., Buso, D., Villanova, L., Konstas, K., Hill, A., Takahashi, M., Falcaro, P., 2012, Magnetic framework composites for polycyclic aromatic hydrocarbon sequestration, Journal Of Materials Chemistry [P], vol 22, issue 23, RSC Publishing, Cambridge England, pp. 11470-11474. View Publication
Steponavice, I., Hyndman, R.J., Smith-Miles, K.A., Villanova, L., 2014, Efficient identification of the Pareto optimal set, Learning and Intelligent Optimization, 16 February 2014 to 21 February 2014, Springer, Cham Switzerland, pp. 341-352. View Publication
STA1010 - Statistical Methods for Science (tutor - year 2010)
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