Isadora Antoniano Villalobos

Isadora Antoniano Villalobos

Decision Sciences and Business Analytics

isadora.antoniano@unibocconi.it

Via Roentgen 1 (3rd floor)
Tel.+39 02 5836 5366 Fax.+39 02 5836 5634

Download v-card

Curriculum Vitae

PhD in Statistics at SMSAS, University of Kent, UK (2008-2012). Thesis: Bayesian Inference for Models with Infinite-Dimensionally Generated Intractable Components. Advisor: Stephen G. Waker.

MSc in Mathematical Sciences at IIMAS, UNAM, Mexico (2006-2008). Thesis: The neutral mutation Fleming-Viot process from a Bayesian perspective (in Spanish). Advisor: Ramses H. Mena 

Applied Mathematics degree at ITAM, Mexico (2000-2005). Thesis: Simulated annealing applied to the electoral districting problem (in Spanish). Advisor: José Luis Farah Ibáñez

 

Academic position and/or Professional activities

Assistant Professor of Decision Sciences

2012-2014Department of Decision Sciences

Bocconi University, Milan, Italy

External Research collaborator

Lecturer: Statistics (Bachelor's degree level)

Teaching assistant: Statistics for Economics and Business (Master's level)

2010- 2012Unit for Enhanced Learning and Teaching (UELT)

University of Kent, Canterbury, UK

Tutor for the Stats Clinic, providing support on statistics related subjects for all University of Kent students, as part of the Student Learning Advisory Service.

 2009- 2012School of Mathematics, Statistics and Actuarial Science (SMSAS)

University of Kent

Tutor for undergraduate courses (Psychology Satistics; Probability and Statistics for Actuarial Science)

Marking for undergraduate courses (Psychology Satistics; Probability and Statistics for Actuarial Science; Linear Algebra; Groups, Rings and Fields;Geometry; Analysis)

2003 – 2004Instituto Luis Vives (high school)

Probability and Statistics teacher for senior year students.

2002- 2003Instituto Tecnológico Autónomo de México (ITAM)

Mathematics, Probability and Statistics tutor for undergraduate students.

 

Research Interests

  • Bayesian Nonparametric models and methods for inference in the presence of intractable likelihoods and high-dimensional parameter spaces
  • Regression models, time series models Markov random fields and the relation between them
  • Bayesian consistency and convergence properties.