Claudia Battistin
Background and activities
I'm developing algorithms for efficient inference of interactions from high-throughput data and I'm going to apply them to multi-electrode neural recordings.
PhD Project
My PhD project focuses on the inverse problem related to the kinetic Ising Model and to the Generalized Linear Model. With my group we aim to set the analitical framework and to test numerically the algorithms associated to:
- adaptive TAP equations;
- Bethe approximation;
Once studied the ability of such techniques in recostructing networks connectivity/ dynamics, we will start apply them to multi-neural data collected at the Kavli Institute to understand the circuitry that underlies spatial navigation in mammals.
Education
- 2012-dd PhD Candidate in Neuroscience / Early Stage Researcher in the NETADIS project;
- 2012 MSc in Theoretical Physics, University of Trieste (IT). Thesis on "Criticality of models inferred in Boltzmann learning", supervisor: Dr. Matteo Marsili;
- 2008 BSc in Physics, University of Trieste (IT). Thesis on "Caos ed Entanglement in condensati di Bose Einstein" (Chaos and Entanglement of Bose-Einstein condensates), supervisor: Dr. Fabio Benatti;
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Journal publications
- (2018) The Stochastic Complexity of Spin Models: Are Pairwise Models Really Simple?. Entropy. vol. 20 (10).
- (2017) Learning with unknowns: analyzing biological data in the presence of hidden variables. Current Opinion in Systems Biology.
- (2017) The appropriateness of ignorance in the inverse kinetic Ising model. Journal of Physics A: Mathematical and Theoretical. vol. 50 (12).
- (2016) Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model. Journal of Physics A: Mathematical and Theoretical. vol. 49:434003 (43).
- (2015) Belief propagation and replicas for inference and learning in a kinetic Ising model with hidden spins. Journal of Statistical Mechanics: Theory and Experiment. vol. 2015.
Report/dissertation
- (2018) Dynamics of randomly connected neural networks and inference in the presence of hidden nodes. 2018. ISBN 978-82-326-3548-1.