GOL will participate to the virtual open day of the Engineering School of Università degli Studi di Firenze Connect February 18th, 2021, at 17:00 CET at https://meet.google.com/jaq-hkkd-ugs Info: Virtual meeting: an introduction to the research activities at the Global Optimization
Optimal decision trees
Based on a paper by D. Bertsimas, we experimented on discrete optimization algorithms for the optimal training of a decision tree. Assigned: July 2018, discussed December 2018 Student: Francesca Del Lungo
Scheduling staff in an hospital ward
This thesis will be devoted to developing an optimization algorithm for health care staff planning, based on a real ward requirements. Required skills: linear programming, possibly python, Java or C++ Assigned (October 2019) to Giulia Forasassi (based on an initial
Predicting drug consumption
Hospital wards are faced with day-to-day necessity to accurately forecast the consumption of drugs in order not to under stock nor to have too large inventories. This thesis explores the capabilities of some time series and some machine learning tools
Optimization of pattern identification in RNA
Starting from the paper “A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications”, by Marco Pietrosanto, Eugenio Mattei, Manuela Helmer-Citterich,Fabrizio Ferrè, Nucleic Acids Research, 2016 1, doi: 10.1093/nar/gkw750, we wish to explore
Data mining on students careers
We have got some data on students’ careers in the Engineerring school. We would like to apply some machine learning technique to find useful information inside. E.g., which part of the pre-admission test is correlated with the career? How to
Multi-modal, multi-objective shortest paths
Developing efficient shortest path algorithm in large urban graphs, with multiple, conflicting, objectives and different modes of transportation. Skills required: C++
Python Global optimization for space trajectories
Numerical experiments with global optimization for space trajectory planning, using a library developed at ESA Candidate: Tommaso Aldinucci Graduated: april 2017
Global Optimization methods for semi-supervised learning
A quasi-Newton based approach to train a semi-supervised Support Vector Machine. Candidate: Lorenzo Norcini – graduated Feb 2017
Machine Hearing
content-based feature engineering for automatic identification of musical genre Candidate: Tina Raissi