Research Groups for SLT students
SLT students have unique opportunity to join one of the existing Research Groups leaded by outstanding researchers of HSE and Skoltech.
The International Laboratory of Stochastic Algorithms and High-Dimensional Inference was created in April 2018 and is part of the Faculty of Computer Science at HSE. Russian and international researchers work at the laboratory at the intersection of numerous mathematical disciplines, including modern statistics, optimisation, probability theory and theory of computation. Its main aim is to develop new probability and statistical approaches to current problems in the field of data analysis.
Centre of Deep Learning and Bayesian Methods is established on the basis of Bayesian Methods Research Group. The group is one of the strongest scientific groups in Russia in the area of machine learning and probabilistic modeling. The laboratory researches the neurobayesian models that combine the advantages of the two most successful machine learning approaches, namely neural networks and Bayesian methods.
LAMBDA currently focuses on using machine-learning and data analysis methods to solve issues in fundamental sciences such as particle physics and astrophysics. The Laboratory’s main developmental direction is to work with leading scientists from these fields to search for answers to the universe’s mysteries. Specifically, the Laboratory cooperates with the European Organization for Nuclear Research (CERN), researching the events of the Large Hadron Collider and raising the efficiency of data analysis.
Our research focuses on developing breakthrough numerical techniques for solving a broad range of high-dimensional problems. The key ingredient is the effective decomposition of multidimensional arrays (tensors). Applications include: Solution of multidimensional integral and differential equations on fine grids (multiscale problems) Ab initio computations in quantum chemistry Solution of multiparametric problems Data mining and compression.
Our research is about designing computer systems that can extract, organize, and quantify information contained in images of various types and origin. For this purpose, we develop new machine learning techniques (deep learning in particular) and optimization techniques that are robust and flexible enough to handle and to adapt to the diversity of image data in the modern world.
ADASE is a group of research enthusiasts pushing the state of the art at the intersection of kernel methods, prediction methods for 3D data, deep learning, online data, and machine learning. Our research mission is to construct data-driven (surrogate) models capable of predicting behavior, performing model-based control and recommend future actions, optimizing design and performance and detecting anomalies and predict failures. Aside from traditional convex and non-convex optimization techniques, we see great potential in modern artificial intelligence, mainly deep learning, in order to achieve these goals.