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Regular version of the site

School-conference “Approximation and Data Analysis”

  1. Laboratory of High-Dimensional Approximation and Applications of the Lomonosov Moscow State University (MSU)
  2. Laboratory of Algorithms and Technologies for Network Analysis of National Research University Higher School of Economics (LATNA)
  3. Keldysh Institute of Applied Mathematics of Russian Academy of Science (IPM)

Sponsors: EPAM Systems

Partners: INTEL, Huawei

: September 30 - October 4, 2019 (arrival - September 29, departure – October 05)
Place: Nizhny Novgorod, Bolshaya Pecherskaya street 25 (Higher School of Economics), room 125

Target groups:
The target groups of the school are students and young researchers interested in modern theoretical and practical trends in data analysis related with high-dimensional approximation, compressed sensing, big data processing and new machine learning and data mining techniques.

Speakers at the conference and 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-conference is a continuation of the successful


Registration open: June 15, 2019
School registration and Conference registration
Registration deadline: September 27, 2019
Notification of acceptance:

Practical information: accommodation in Nizhny Novgorod

School schedule:
Morning, school part of the event – 3 lectures (50 min + 10 min for questions) designed for graduate students and young researchers
Afternoon, conference part of the event – scientific talks (30 min +5 min for questions


School Lecturers:

Andrew Barron, Department of Statistics and Data Science, Yale University, USA

Approximation, complexity and risk properties of learning networks (PDF, 50 Kb)

Approximation and complexity foundations of deep nets (PDF, 50 Kb) 

Information theory foundations of learning nets (PDF, 50 Kb)  

Evgeny Burnaev
, Center for Computational and Data-Intensive Science and Engineering, Skoltech Moscow

Latent Convolutional Models for Image Re-storation (PDF, 51 Kb) 

Sergey Nikolenko, Laboratory of Mathematical Logic of the St.-Petersburg Department of the Steklov Mathematical Institute, Russian Academy of Science

Synthetic data in deep learning (PDF, 98 Kb) 

Peter Richtarik, University of Edinburgh and King Abdullah University of Science and Technology

Stochastic Gradient Descent (mini cours) (PDF, 49 Kb) 

Nikolai Zolotykh, Laboratory of advanced methods for high-dimensional data analysis, Nizhny Novgorod Lobachevski State University

Machine Learning in Electrocardiogram Diagnosis (PDF, 50 Kb) 

Alexey Aleshin, Data Scientist, Ph.D., EPAM Systems

Deep Learning appliance for sensitive data (PDF, 49 Kb) 

Denis Grachev, Data Scientist, Ph.D. EPAM Systems

Predictive maintenance (PDF, 49 Kb) 

Daniil Osokin, INTEL Corporation

Human pose estimation (PDF, 49 Kb) 

Alexey Sidnev, Ph.D., Nizhny Novgorod Research Center, Huawei Technologies Co., Ltd., Lobachevsky State University of Nizhny Novgorod, Russia

Fashion AI: One-Shot Clothing Detection (PDF, 61 Kb) 


Program ADA_2019_Schedule (PDF, 230 Kb) 

Book of Abstracts ADA_2019_Abstracts (PDF, 496 Kb) 

Co-Chairs of the school-conference:
Vladimir Temlyakov, University of South Carolina, USA and MSU
Panos Pardalos, University of Florida, USA and HSE
Alexander Aptekarev, Keldysh Institute of Applied Mathematics of RAS

Organizing Committee:
Chair of the organizing committee:
Boris Kashin, Russian Academy of Science, Steklov Mathematical Institute and MSU

Sergey Konyagin, MSU
Valery Kalyagin, HSE Nizhny Novgorod
Yury Orlov, Keldysh Institute,
Timur Medvedev, HSE Nizhny Novgorod
Konstantin Rjutin, MSU
Alexander Kuleshov, MSU


Local Organizing Committee:
Staffs of the Lab LATNA
E-mail: vkalyagin@hse.ru


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