7th Winter School on Data Analytics (DA 2022)
Organizers:
- Laboratory of Algorithms and Technologies for Network Analysis of National Research University Higher School of Economics (LATNA)
- Keldysh Institute of Applied Mathematics of Russian Academy of Science (IPM)
- Moscow Center for Fundamental and Applied Mathematics
Place: Zoom
Target Groups: The target groups of the school are students and young researchers interested in modern trends in data analytics, including big data processing and new machine learning and data mining techniques. Lecturers at the school are internationally recognized experts in the field. Young researchers will have an opportunity to learn and understand a modern theoretical approaches and practical techniques in data analysis. The school is a continuation of the successful Winter Schools on Data Analytics DA 2016, DA 2017, DA 2018, DA 2019, DA 2020, DA 2021
Registration: The school is organized for last-year bachelor, master and PhD students. To attend the school as a participant you must register before November 13, 2021. Students are also encouraged to present the recent results of their research related to the topics of the school.
Notification of acceptance: ad hoc
School Schedule: Every day 4 lectures from 15:00 to 19:00
Program : Winter School 2022 Program
Presentations(TBA)
School Lecturers:
Roberto Battiti (University of Trento and HotelInCloud.com, Italy)
Lecture: Tourism and Hospitality: Relevant Problems for Machine Learning and Intelligent Optimization
Dmitry Kiselev (Faculty of Computer Science, HSE Moscow)
Lecture: Graph-based recommender systems
Andrey Kuznetsov (Sber AI)
Lecture: Text2Image Generation using Diffusion Models
(диффузионные модели для генерации изображений по текстовым описаниям)
Pierre Miasnikof (University of Toronto, Canada)
Lecture 1: Graph clustering quality
Lecture 2: Statistical testing of clusterability
Panos Pardalos (University of Florida, USA and HSE)
Lecture: AI and Data Analytics in Economics and Finance
Alex Shestopaloff (Queen Mary University of London and a Fellow of the Alan Turing Institute, UK),
Lecture 1. Bayesian methods for reduced rank regression models.
Lecture 2. Developments in principal component regression.
Co-Chairs of the school:
Panos Pardalos, University of Florida and HSE
Alexander Aptekarev, Keldysh Institute of Applied Mathematics
Program Committee:
Fuad Aleskerov, NRU HSE
Mikhail Batsyn, Huawei and NRU HSE
Fedor Fomin, Bergen University, Norway and St Petersburg department of Steklov Mathematical Institute
Valery Kalyagin, NRU HSE
Yury Kochetov, Russian Academy of Sciences, Novosibirsk
Alexander Koldanov, NRU HSE
Dmitriy Malyshev, NRU HSE
Andrey Raigorodskii, Moscow Institute of Physics and Technology, Moscow State University, Yandex
Nikolay Zolotykh, Lobachevsky State University, Nizhny Novgorod
Andrey Savchenko, NRU HSE
Local Organizing Committee:
Staff of the lab LATNA
Contact: vkalyagin@hse.ru
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