Parton distribution functions (PDFs) encode the non-perturbative structure of hadrons and their knowledge is essential for our ability to predict experimentally measured cross sections. They play an important role in discovering new physics at high energy collider experiments as well as in our understanding of the internal structure of hadrons. However, up until recently, PDFs could only be determined from experimental data while first principles theoretical computations were not possible. Novel ideas that came along have opened new avenues for ab initio parton distribution function computations. In this talk, I am presenting the basic ideas that are underlying these computations and review recent numerical results. Furthermore, I am discussing the future of such computations and their potential impact to phenomenology.

The TALENT initiative, Training in Advanced Low Energy Nuclear Theory, aims at providing advanced and comprehensive training to graduate students and young researchers in all aspects of low-energy nuclear theory. TALENT offers intensive three-week courses on a rotating set of topics. General information on TALENT and past courses can be found at http://www.nucleartalent.org. This year, due to the COVID-19 pandemic we offer a fully online variant of the course on: Machine Learning and Data Analysis for Nuclear Physics It will be held via the ECT* in Trento, Italy, from June 22 to July 3, 2020. The principal instructors will be 1. Daniel Bazin (MSU/NSCL/FRIB), 2. Morten Hjorth-Jensen (MSU/NSCL/FRIB), 3. Michelle Kuchera (Davidson College), 4. Sean Liddick (MSU/NSCL/FRIB), and 5. Raghuram Ramanujan (Davidson College). Videos of the learning material, programs, and all digital learning material will be made available to participants before the lectures start in a dedicated Classroom on Microsoft Teams. During the week of June 22-26 and June 29 to July 3, we will have online live lecture sessions from 2 pm to 4 pm Central European Time (CET), with questions and answers sessions from 5 pm to 6 pm CET. The standard variant of this course, with in-person attendance, is postponed to the summer of 2021. For applications and more information for the online TALENT course, please go to the website of the European Center for Theoretical Studies in Nuclear Physics and Related Areas, ECT*. The link with more information and registration is at https://www.ectstar.eu/node/4472 All material with schedule etc. will be available at https://github.com/NuclearTalent/MachineLearningECT For more information: Contact: Morten Hjorth-Jensen (hjensen@msu.edu)

The school will focus on dense matter in astrophysics, such as the matter in the interior of neutron stars and the one created in supernova explosions and neutron star mergers, along with comparisons with the matter created in laboratories. There will be lectures from the following experts, together with interactive activities.