Lucas Pili

Lucas Pili has worked in a variety of roles in the field of physics since 2015. Lucas began as a Research Intern at the Instituto de Física de La Plata (IFLP), CONICET-UNLP, where they were granted an Undergraduate Research Training Fellowship. From 2015 to 2021, they worked as a Teaching Assistant at the Universidad Nacional de La Plata, teaching Computer Science and General Physics I, General Physics I and III, and Experimental Physics II and IV. In 2017, they began a PhD in Physics at CONICET, with a thesis titled 'Studies in magnetic systems with geometric frustration: simulations and experiments'. In 2018, they were a Collaborating PhD student at the Max Planck Institute for Chemical Physics of Solids (MPI-CPfS), where they investigated the low temperature susceptibility in the spin ice materials Dy2TiO7 and Ho2TiO7 under uniaxial pressure. Since 2021, they have been working as a Machine Learning Engineer at ZoomAgri.

Lucas Pili has an extensive education history. Lucas completed a Técnico en Producción Agropecuaria at Escuela Agropecuaria de Tres Arroyos from 2004 to 2006. Lucas then attended Universidad Nacional de La Plata from 2011 to 2017, obtaining a Licenciado en Física (equivalent to M.Sc. in Physics). Lucas is currently enrolled in a Doctor en Física (PhD in Physics) program at Universidad Nacional de La Plata, which they are expected to finish in 2022. Additionally, Lucas has obtained numerous certifications from Coursera, LinkedIn, and Udemy, including Introduction to Machine Learning in Production, Deep Learning Specialization, Sequence Models, Convolutional Neural Networks, AWS Essential Training for Developers, Advanced Python: Working with Databases, Apache Spark Essential Training, Azure Essential Training for Developers, Big Data Analytics with Hadoop and Apache Spark, Google Cloud Platform (GCP) Essential Training for Developers, Learning MongoDB, Structuring Machine Learning Projects, Complete SQL + Databases Bootcamp, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, Neural Networks and Deep Learning, Probability and Statistics in Experimental Physics (Postgraduate course), Complete Machine Learning & Data Science, Complete Python Developer, Data Structures + Algorithms, and Introduction to the methods of scientific knowledge (Postgraduate course).