Gloria Cecchini received both her bachelor and master’s degrees in Mathematics at University of Firenze. Both theses focus on probability theory, and were completed under the supervision of Prof. Alberto Gandolfi. Her BSc thesis was a study of a scale-free stochastic model for epidemics, and her MSc thesis was entitled “mathematical analysis of a stochastic dielectric breakdown model for the phenomenological description of a lightning step leader discharge”. This research was on physical phenomenon; studied with mathematical tools like theoretical models, advanced probability, large deviation theory, Brownian motions and computer simulations. In October 2015 she started her PhD research at Aberdeen University on “inferring the high dimension network structure in a multitude of coupled oscillators“, under the supervision of Prof. Björn Schelter.
Gloria Cecchini, Marco Thiel, Björn Schelter, Linda Sommerlade “Improving network inference: The impact of false positive and false negative conclusions about the presence or absence of links” Journal of Neuroscience Methods 307, 31–36 (2018) LINK
Gloria Cecchini and Björn Schelter “Analytical approach to network inference: Investigating degree distribution” Phys. Rev. E 98, 022311 (2018) LINK
Irene Malvestio, Marc Grau, Eero Satuvuori, Gloria Cecchini, Rok Cestnik: ” Inferring network connectivity: open questions and some answers”. 2nd COSMOS Workshop, Amsterdam, Dec. 2016 (Talk)
Cecchini G., Thiel M., Schelter B., Sommerlade L., “Improving Brain Networks Inference” Bernstein Conference 2016, Berlin, Germany, Sep 2016. Poster
Cecchini G., Thiel M., Schelter B. “Improving Network Inference of Oscillatory Systems: A Novel Framework to Reliably Identify the Correct Class of Network” Dynamic Days, Corfu, Greece (2016). Talk
Cecchini G., Thiel M., Schelter B. “Improving Network Inference of Oscillatory Systems: A Novel Framework to Reliably Identify the Correct Class of Network” SUPA 2016 Annual Gathering. Glasgow, UK, May 2016. Poster
Cecchini G., Thiel M., Schelter B. “Improving Network Inference of Oscillatory Systems: A Novel Framework to Reliably Identify the Correct Class of Network” International conference on biological oscillations (ESGCO 2016). Lancaster, UK, Apr 2016 Poster