Personal Portfolio

Showcase of my projects developed during my studies

Andrea Lazzari

M.Sc. Student in Physics of Data at the University of Padua

Master's degree study plan

First year courses

  • Laboratory of Computational Physics
    • MOD. A: high level programming in Python
    • MOD. B: modern tools for classifying data and machine learning techniques
  • Management and Analysis of Physics Dataset
    • MOD. A: hands-on laboratory of data management with FPGA (Xilinx Artix-7)
    • MOD. B: data management and data processing, with a focus on parallel processing and distributed computing
  • Machine Learning
  • Advanced Statistic for Physics Analysis (R programming)
  • Models of Theoretical Physics
  • Biological Datasets for Computational Physics
  • Artificial Intelligence
  • Statistical Mechanics

Second year courses

  • Information Theory and Inference
  • Neural Networks and Deep Learning
  • Physical Models of Living Systems
  • Human Data Analytics
  • Vision and Cognitive Systems

More information on the courses, such as the unit contents and the examination methods, can be found here.

Koln Traffic Regulator with Parallel Computing

Our work started from a project jointly developed by IBM and by the German city of Koln thought to be a first step towards traffic regulation and an efficient exploitation of transport’s resources. In particular, we analyzed a set of mobility data emulated with SUMO, consisting of 394 million records and 20 Gb in size.
To reach our goals, we set up a cluster on CloudVeneto made of 5 virtual machines (4 cores and 8 GB RAM each) and created a volume, shared across the instances using a NFS. Moreover, we used Dask to parallelize the tasks.

Microbial Scaling Laws

In this project, we want to combine methods from Statistical Physics and Bayesian Data Analysis to elucidate the principles behind cellular growth and division.
We will study various classes of individual-based growth-division models and infer individal-level processes (model structures and likely ranges of associated parameters) from sigle-cell observations. In the Bayesian framework, we formalize our process understanding the form of different rate functions, expressing the dependence of growth and division rates on variables characterizing a cell’s state (such as size and protein content), and calculate the Bayesian posteriors for the parameters of these functions.