QLS

Europe/Rome 2021-06-15 08:00:00 2021-06-18 22:00:00 Youth in High-Dimensions | (smr 3602) An ICTP virtual meeting   The aim of the conference is to give young researchers the opportunity to present their results related to high-dimensional statistical problems, as arising in machine learning, inference or statistical physics.   Typical problems in high-dimensions include the detection and estimation of noisy signals (compressed sensing, PCA and tensor decomposition etc), the analysis of (deep) neural-networks and their learning and generalization dynamics, the detection of communities in large networks, or disordered spin systems. While these problems may seem of different natures, they actually share many similarities in their common phenomenology and the tools used to analyze them. The core of the field is made of a very active, diverse and quickly expanding community of physicists, computer scientists, mathematicians, information theorists and engineers, with the common desire to tackle increasingly challenging problems at the forefront of data science. This conference aims at reinforcing the links among this interdisciplinary community, and in particular of its youngest theory-oriented members, and to bring forward the latest development happening in the high-dimensional world. Topics: Machine learning High-dimensional statistics Applied maths Computer sciences Participants interested to present a poster are kindly requested to submit their title and a short abstract in the application form. Speakers: L. BENIGNI, University of Chicago, USA M. CELENTANO, Stanford University, USA L. CHIZAT, University of Paris-Saclay SY. CHUNG, Massachusetts Institute of Technology, USA Z. FAN, Yale University, USA C. GERBELOT, ENS Paris, France A. GLIELMO, SISSA, Italy A. INGROSSO, ICTP, Italy A. JACOT, EPFL, Switzerland F. MASTROGIUSEPPE, University College London, UK F. MIGNACCO, IPHT Saclay, France T. MISIAKIEWICZ, Stanford University, USA P. NAKKIRAN, Harvard University, USA M. REFINETTI, ENS Paris, France C. RUSH, Columbia University, USA T. SCHRAMM, Stanford University, USA S. VILLAR, Johns Hopkins University, USA I. ZADIK, New York University, USA J. ZARKA, ENS Paris, France   Registration: There is no registration fee.   Online - ICTP pio@ictp.it 15 Jun 2021 - 18 Jun 2021

Europe/Rome 2021-07-05 08:00:00 2021-07-30 22:00:00 London Mathematical Laboratory Summer School | (smr 3598) An ICTP virtual meeting The London Mathematical Laboratory (LML) is pleased to announce its 2021 Summer School. This will be held as an ICTP Virtual school from 5 to 30 July 2021. The school engages postgraduate students for a period of four weeks on research projects under the supervision of active research scientists. Each project addresses one or more open questions in order to expose students to the frontier of scientific cross-disciplinary research in several fields (statistical physics, systems ecology, quantitative economics, dynamical systems, information and large deviation theory, and neural networks). This provides a first hand experience of “research in action”, where asking the right questions is at least as important as finding the right answers. Many previous students have found this a life changing experience that transformed their view of science as an activity and as a possible career. The list of projects: 1. Stability of ecosystems with a core-periphery structure 2. The evolution of income or wealth distribution with higher order autoregressive processes 3. Exploring the Connections between Information Theory and Ergodicity Economics Through the Lens of Inequality Measurement 4. Growth incidence curves in reallocating geometric Brownian motion 5. Optimal allocation and risk parity 6. Time-average based saving versus Ensemble-average based saving and the evolution of wealth distribution 7. Learning random dynamical systems from data 8. Explainability and rationalization in decision theory: a coding theory approach 9. Beyond Rational Herding 10. Verifying interaction among different types of discrete events by using the multivariate Hawkes process 11. Anomalous diffusion in random dynamical systems 12. The trade-off between profit and time constraints and market impact 13. Large deviation theory in diluted random matrices 14. Tackling Quantum Many-Body Systems with Artificial Neural Networks 15. Experiments in ergodicity 16. Epidemic spreading models in the presence of feedback   Supervisors include LML Fellows and colleagues from the ICTP and the International School for Advanced Studies (SISSA). Students will also attend a series of scientific lectures and a scientific writing course, and the School will culminate with student presentations of their research. Project Supervisors: A. ADAMOU, London Mathematical Laboratory, UK Y. BERMAN, London Mathematical Laboratory, UK J. BHASEEN, King’s College London, UK F. CACCIOLI, University College London, UK I.P. CASTILLO, Universidad Autónoma Metropolitana, Mexico D. DE MARTINO, Biofisika Institute - Ikerbasque, Spain J. GRILLI, ICTP, Italy O. HULME, Copenhagen University Hospital Hvidovre, Denmark R. KANBUR, Cornell University, USA M. KIRSTEIN, London Mathematical Laboratory, UK R. KLAGES, Queen Mary University of London, UK J. LAMB, Imperial College, UK S. LUZZATTO, ICTP, Italy M. MARSILI, ICTP, Italy L.F. METZ, Federal University of Rio Grande do Sul, Brazil O. PETERS, London Mathematical Laboratory, UK S. RUFFO, SISSA, Italy Y. SATO, Hokkaido University, Japan M. WERNER University of Bristol, UK J. ZHUANG ISM, Japan Grants: London Mathematical Laboratory intends to provide a grant of 500 EUR to all selected participants. Further information will be provided in due course. Registration: There is no registration fee.   Online - ICTP pio@ictp.it 5 Jul 2021 - 30 Jul 2021

Europe/Rome 2021-12-01 07:00:00 2021-12-17 21:00:00 ICTP-ICTS Winter School on Quantitative Systems Biology 2021 | (smr 3616) The series of Winter Schools on Quantitative Systems Biology (QSB) under the aegis of the ICTP-ICTS Programme in Biology is intended to help researchers from the theoretical sciences transition into life sciences research and experimental biologists with a quantitative background to assimilate theoretical perspectives and methodologies in their research. How organisms sense the world and generate behaviours is an exciting question that has motivated neuroscientists over more than a century. Neural command for generating behavioural output arises from operations at multiple scales, ranging from the flip-flops of ion channels to dynamics in circuits comprising ensembles of neurons. New tools to genetically manipulate organisms, monitor and perturb neural activity, and advanced microscopy that enables large scale imaging of neurons in vivo have yielded a hitherto unprecedented quantum of data with high resolution. Quantitative approaches are needed to mine these data sets for generating testable hypotheses regarding nervous system function. The 2021 QSB Winter School aims at exposing students from different backgrounds to the latest research in systems neuroscience, with an emphasis on quantitative methods and theory. Topics: introduction to neuroscience, including electrical properties of neuronal membranes and single neuronal biophysics; how circuit dynamics emerge in diverse circuits using invertebrate and vertebrate model organisms as examples; population coding, variability and stochasticity, and plasticity; applications of quantitative tools to neuroscience data sets such as whole-brain imaging data sets or behavioural clustering data sets. Throughout, we will also explore how theory can contribute to a normative understanding of various phenomena, and motivate future experiments. Participants in this quantitative school in systems neuroscience and behaviour will have a broad understanding of exciting questions in these fields and how to address them with modern computational tools. Bengaluru - India ICTP pio@ictp.it 1 Dec 2021 - 17 Dec 2021