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Global Optimization Laboratory "Gerardo Poggiali"

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Ingegneria Informatica (triennale)

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++

Fabio Schoen March 13, 2017September 29, 2021 Uncategorized Read more

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

Fabio Schoen February 16, 2017May 8, 2017 Theses: discussed Read more

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  

Fabio Schoen February 16, 2017February 16, 2017 Theses: discussed Read more

Machine Hearing

content-based feature engineering for automatic identification of musical genre Candidate: Tina Raissi

Fabio Schoen February 16, 2017 Theses: discussed Read more

Operations Research courses @ Florence rank #1

        In the main Computer Science Engineering  curricula at Florence University, Operations Research courses rank #1: Fondamenti di Ricerca Operativa (prof. Fabio Schoen): #1 course for the Laurea degree in Computer Science Engineering Optimization Methods (prof. Marco Sciandrone)

Fabio Schoen March 16, 2016March 16, 2016 Latest news from the lab Read more

Semi-supervised training via a lagrangean approach

Implementation of global optimization algorithms, preferably in python, to trai a SVM in which some of the data has no label Many algorithms exist for S3VM – exact (branch and bound) and heuristic (global optimization). We aim at implementing some

Fabio Schoen February 23, 2016February 16, 2017 Theses: discussed Read more

Forecasting time series with Support Vector Regression

A comparison between classical (ARIMA) forecasting methods for time series and regression based on Support Vector Machines. A huge set of economic time series is available to train and validate foreasting methods Skills required: basic computer science skills; python might

Fabio Schoen February 23, 2016February 16, 2017 Theses: discussed Read more

Do not waste past computations: Learning and Optimization for Optimal Circle Packing

A simple task: use machine learning in order to avoid useless local searches to be started. Try to find new putative optimal configurations learning from past trials. Skill required: just python – most numerical experiments in circle packing have already been

Fabio Schoen January 29, 2016February 16, 2017 Theses: discussed Read more

Semi supervised learning by continuous optimization methods

  Training an SVM when many (most) data are unlabeled. This thesis considers an approach based on a continuous differentiable formulation of the problem. Candidate: Andrea Boddi  Start: January 2016 Image credits: http://inverseprobability.com/ncnm/

Fabio Schoen January 19, 2016February 16, 2017 Theses: discussed Read more

Predictive Mainteinance

Changed to machine learning for  ephylectic seizure prediction candidate: Alberto Pitti Machine learning for fault prediction in mechanical engines

Fabio Schoen October 26, 2015February 16, 2017 Theses: discussed Read more
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Recent News from the Lab

  • 2022 Summer – Two International Events Organized by GOL

  • Optimization Models

  • Master Thesis Proposals / Information Engineering & Artificial Intelligence, Management Engineering et al.

  • Open Day Engineering School

  • Introduction to Operations Research

Theses (proposals)

  • Thesis Proposals (M. Lapucci)

  • Machine Learning & Optimization for Cancer Research

  • Global Optimization methods based on Clustering, Populations, Random Projections

  • Experiments in quantum computing and optimization

  • On the combination of Clustering and Multi-Objective Approaches

COVID-19 emergency

  • Crowded Zone – a Web App for social-distancing

  • Personnel scheduling in health care

  • Covering and localization problems

  • folding@home – GPU sharing initiative to fight the virus

  • Shift planning for emergency

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