cv

This is my long resume in digital form, click on the icon for the .pdf 1-page version.

Table of contents

General Info

Full Name Simone Maria Giancola
Date of Birth 6th September 1999
email simonegiancola09 dot gmail dot com
summary Research intern. Passionate about using theoretical methodologies to solve real world problems.
Languages English (proficient), Italian (mothertongue), Spanish (basic)

Education

  • Aug 2021- Apr 2024
    Master of Science in Data Science, Data Science major
    Bocconi University, Milan, Italy
    • Grade
    • GPA 29/30, Final grade 110 cum Laude/ 110
    • Thesis "A roadmap to Message Passing methods for inference on Mixed Generalized Linear Models, with emphasis on Mixed Noiseless Phase Retrieval"
    • Advisor Prof. Carlo Lucibello
    • relevant coursework
      • Ph.D. Real Analysis (Optimal Transport), Graph Theory, Statistical Physics
      • Advanced Machine Learning, Optimization, Stochastic Processes
  • Jan 2021 - May 2021
    Undergraduate Exchange Program
    Arizona State University, Tempe, AZ, USA
    • GPA 4.17/4.00
    • Graduate relevant coursework Quantum Computation, Modeling with Game Theory
  • Aug 2018- Jul 2021
    Bachelor of Science in Economics and Computer Science
    Bocconi University, Milan, Italy
    • Grade 110 Cum Laude / 110
    • Thesis “Value of Information in a Support Vector Machine, an exploration”
    • GPA 29.18 / 30 , STEM GPA 30.14 / 30
    • Relevant coursework Advanced Statistical Methods, Applied Mathematics, Machine Learning

Experience

  • Feb 2024 - May 2024
    Research Intern
    King Abdullah University of Science and Technology (KAUST), KSA
    • Advisor: Prof. Peter Richtárik
    • Convex Optimization, Conditioned Gradient Descent methods
  • Oct 2023 - Dec 2023
    Research Intern
    Ecole Normale Superieure (ENS), Paris, FRA
    • Advisor: Bruno Loureiro
    • Theory of Neural Networks, Stochastic Gradient Descent
  • Jun 2023 - Aug 2023
    Research Intern
    Institute of Science and Technology Austria (ISTA) Wien, AUT
    • Advisor: Prof. Marco Mondelli
    • Statistical to Computational Gaps, Information Theory, Algorithms, Message Passing
    • ISTernship Summer Programme, ref. num. MPC-2023-01128, financed by ISTA, awarded by the OeAD
  • Mar 2022 - Jun 2023
    Visiting Student
    Bocconi Institute for Data Science, Milan, ITA
    • Advisors Prof. Carlo Lucibello and Prof. Luca Saglietti, Computing Sciences Department
    • Reading, self-study, writing
  • Jan 2022 - Mar 2022
    Data Science Intern
    Santagostino Clinic Milan, ITA
  • Jan 2021 - May 2021
    Research Assistant
    Bocconi University, Milan, ITA
    • Advisor Prof. Emanuele Borgonovo
    • Value of Information, Support Vector Machines

Portfolio and Talks

  • Mar 2023
    Compositional RBMs, a Birds Eye view
    • Restricted Boltzmann Machines, Statistical Physics, Unsupervised Learning
  • Mar 2023
    A view on Percolation and Spin Systems
    • Percolation, Potts Model, Spin Systems, Random Cluster Model
  • Jan 2023
    Notes on the Neural Tangent Kernel
    • Deep Learning Theory, Neural Networks, Kernel Methods
  • Jan 2023
    Notions in Optimal Transport for Sigmoid Neural Networks
    • Deep Learning Theory, Optimal Transport, Neural Networks
  • Jun 2022
    Bipartite Matching & extensions
    • Linear Algebra, Graph Theory, Duality, Hungarian Algorithm, admissible transformation theory, Pfaffian orientations
  • Jan 2022
    Advanced Session, Harvard mini-course on Computation
    • Simulated Annealing, Statistical Mechanics, TSP

Other self study / extracurriculars

  • Summer 2023
    Lectures on linearized Neural Networks
    • Kernels, Ridge Regression, High Dimensional Probability
  • Sep 2022 - Jan 2023
    Probability Theory
    • Measure Theory, Stochastic Processes
  • Jun 2022 - Ongoing
    Statistical Physics
    • Machine Learning, Optimization, Physics, Neural Networks
  • Jun 2022 - Dec 2022
    Geometric Deep Learning
    • Group Theory, Statistical Learning, Neural Networks, Topology
  • Dec 2021
    Bayesian MCMC probit analysis | co-authored
    • Gibbs Instrumental Algorithm, Metropolis Random Walk Algorithm

Academic Interests in Keywords

  • Mathematics, Statistics, Computer Science
  • Information Theory, Statistical Physics, Machine Learning
  • theoretical results, theory of learning, neural networks

Skills

  • Advanced Python, Latex, Sklearn, Numpy, Matplotlib, Scipy;
  • Certifications Deep Learning and AI for Medicine by DeepLearning.AI;
  • Intermediate Julia, Git, Unix, R, SQL, Matlab, C++, Keras, Tensorflow

Awards

  • First Italian School in Geometric Deep Learning, Funded MS student.
  • OeAD Scholarship, ref. num. MPC-2023-01128, financed by ISTA, awarded by the OeAD

Interests

  • rugby
  • podcasts
  • running
  • motorbike trips