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Макаров Илья Андреевич

Факультет компьютерных наук

Профиль на hse.ru ↗ тел.: +7(495) 772-95-90*27282 | +7(915)152-4532
Публикаций
117
Языков
1
Наград
7
Конференций
10
Профиль Публикации (117) Курсы (7)

Профессиональные интересы

27.03.19 Математическая логика27.15.00 Теория чисел28.23.00 Искусственный интеллект28.17.33 Компьютерное моделирование реальности. Виртуальная реальность

Должности

  • Доцент — Факультет компьютерных наук, Департамент анализа данных и искусственного интеллекта

Био

  • · Начал работать в НИУ ВШЭ в 2011 году.
  • · Научно-педагогический стаж: 14 лет.

Образование

  • 2021 · PhD: Университет Любляны
  • 2015 · Аспирантура: Московский государственный университет им. М.В. Ломоносова, факультет: Механико-математический
  • 2011 · Специалитет: Московский государственный университет им. М.В. Ломоносова, факультет: Механико-математический, специальность «Математика», квалификация «Математик»

Опыт работы

  • · 2011: НИУ ВШЭ, Департамент анализа данных и искусственного интеллекта – старший преподаватель, научный сотрудник ( настоящее время), заместитель руководителя (2012-2017)

Награды и поощрения

  • · Номинант на "Лучшие преподаватели 2014" (июль 2014)
  • · Надбавка за академическую работу (2017–2018)
  • · Надбавка за публикацию в журнале из Списка А (и приравненном к нему научном издании) (2025–2026, 2024–2025, 2023–2024)
  • · Надбавка за публикацию в международном рецензируемом научном издании (2022–2023, 2021–2022, 2018–2020)
  • · Надбавка за статью в зарубежном рецензируемом научном издании (2016–2017)
  • · Лучший преподаватель — 2022, 2017–2018
  • · Группа высокого профессионального потенциала (кадровый резерв НИУ ВШЭ)Категория "Новые преподаватели" (2013–2014)Категория "Будущие преподаватели" (2012)

Гранты и проекты

  • — · Грант президента РФ МК-5016.2012.1 "Многомерные диофантовы приближения" (2012) - исполнитель
  • — · Грант РНФ 17-11-01294 "Представление, обнаружение и обработка знаний: логический подход"

Конференции (10)

Показать все
  • · 2016: The 5th international conference on Analysis of Images, Social Networks, and Texts (AIST) (Екатеринбург). Доклад: Smoothing Voronoi-based Path with Minimized Length and Visibility using Composite Bezier Curves
  • · 2016: Third International Workshop on Experimental Economics and Machine Learning (EEML 2016) (Москва). Доклад: Modelling Human-like Behavior through Reward-based Approach in a First-Person Shooter Game
  • · 2016: The 6th International Conference on Network Analysis (Nizhny Novgorod). Доклад: Co-author Recommender System
  • · 2016: ACM Multimedia 2016 (Амстердам). Доклад: First-Person Shooter Game for Virtual Reality Headset with Advanced Multi-Agent Intelligent System
  • · 2015: The 4th international conference on Analysis of Images, Social Networks, and Texts (AIST) (Екатеринбург). Доклад: Imitation of human behavior in 3D-shooter game
  • · 2015: 10th Panhellenic Logic Symposium (Karlovasi, Samos). Доклад: Total Equivalence Systems for Classes of 3-valued Projection Logic whose Projections Equal to the Class of Linear Boolean Functions
  • · 2015: 10th Panhellenic Logic Symposium (Karlovasi, Samos). Доклад: Logical Generalized Continued Fractions
  • · 2015: 5th World Congress on Universal Logic (Istanbul). Доклад: Separator Method for Constructing Canonical Types of Formulas
  • · 2014: Конференция научно-педагогических работников Национального исследовательского университета «Высшая школа экономики» (Москва). Доклад: Выборы Ученого Совета НИУ ВШЭ
  • · 2012: Ломоносовские чтения - 2012 (Москва). Доклад: О некоторых свойствах внутренних полиэдров Клейна

Идентификаторы исследователя

Публикации (117)

Logic of Existentialism in Fiction

2017 · CHAPTER · en

We have considered core approaches to the problem of fictional objects. For each model authors covered the problem whether everything fictional exists or not in terms of evaluation, separating groups of objects, quantifying or existing in modal worlds. The article contains brief overview of the approaches for dealing with fictional objects and evaluating statements containing fictional objects as their part.

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Learning to Play Pong Video Game via Deep Reinforcement Learning: Tweaking Deep Q-Networks versus Episodic Control

2017 · CHAPTER · en

We consider deep reinforcement learning algorithms for playing a game based on video input. We compare choosing proper hyper-parameters in deep Q-network model and model-free episodic control focused on reusing of successful strategies. The evaluation was made based on Pong video game implemented in Unreal Engine 4.

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Quantum Logic and Natural Language Processing

2017 · CHAPTER · en

The paper presents a short summary on the applications of the quantum logic categorical constructions to the natural language processing. We give a brief overview on the topic of quantum logic in general, and in natural language processing, in particular. As a result, we discuss comparison of sentences and their representation in quantum logic formalism. The examples of using quantum diagrams are considered in order to understand text analysis in terms of quantum logic techniques.

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Semi-Dense Depth Interpolation using Deep Convolutional Neural Networks

2017 · CHAPTER · en

With advances of recent technologies, augmented reality systems and autonomous vehicles gained a lot of interest from academics and industry. Both these areas rely on scene geometry understanding, which usually requires depth map estimation. However, in case of systems with limited computational resources, such as smartphones or autonomous robots, high resolution dense depth map estimation may be challenging. In this paper, we study the problem of semi-dense depth map interpolation along with low resolution depth map upsampling. We present an end-to-end learnable residual convolutional neural network architecture that achieves fast interpolation of semi-dense depth maps with different sparse depth distributions: uniform, sparse grid and along intensity image gradient. We also propose a loss function combining classical mean squared error with perceptual loss widely used in intensity image super-resolution and style transfer tasks. We show that with some modifications, this architecture can be used for depth map super-resolution. Finally, we evaluate our results on both synthetic and real data, and consider applications for autonomous vehicles and creating AR/MR video games.

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Depth Map Interpolation using Perceptual Loss

2017 · CHAPTER · en

In this paper, we discuss a semi-dense depth map interpolation method based on convolutional neural network. We propose a compact neural network architecture with loss function defined as Euclidean distance in the feature space of VGG-16 neural network used for deep visual recognition. The suggested solution shows state-of-art performance on synthetic and real datasets. Together with LSD-SLAM, the method could be used to provide a dense depth map for interaction purposes, such as creating a first person game in AR/MR or perception module for autonomous vehicle.

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Predicting Psychology Attributes of a Social Network User

2017 · CHAPTER · en

Nowadays, the number of people using social network site increases every day. The social networking sites, such as Facebook or Twitter, are sources of human interaction, where users are allowed to create and share their activities, thoughts and place different information about themselves. However, most of this information remains unnoticed. In this work, we propose a machine learning approach to predict Big-Five personality using information from users’ accounts from the social network. The predictions can be used in different areas such as psychology, business, marketing.

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Smoothing Voronoi-based Path with Minimized Length and Visibility using Composite Bezier Curves

2016 · CHAPTER · en

We present an obstacle avoiding path planning method based on a Voronoi diagram. We use a tactical visibility measure to obtain the shortest path length with the lowest local probability to be discovered based on the map topology. A Voronoi-based navigation mesh for finding the shortest smooth path with the lowest visibility along the path is used. The piecewise linear rough path in the Voronoi diagram is compared with collision free composite Bezier curves with shortest curve length. Whether we use visibility component or not, the smooth path length does not differ more than 12\%. This allows us to use tactical information from map geometry without significant loss in path length.

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Modelling Human-like Behavior through Reward-based Approach in a First-Person Shooter Game

2016 · CHAPTER · en

We present two examples of how human-like behavior can be implemented in a model of computer player to improve its characteristics and decision-making patterns in video game. At first, we describe a reinforcement learning model, which helps to choose the best weapon depending on reward values obtained from shooting combat situations. Secondly, we consider an obstacle avoiding path planning adapted to the tactical visibility measure. We describe an implementation of a smoothing path model, which allows the use of penalties (negative rewards) for walking through ``bad'' tactical positions. We also study algorithms of path finding such as improved I-ARA* search algorithm for dynamic graph by copying human discrete decision-making model of reconsidering goals similar to Page-Rank algorithm. All the approaches demonstrate how human behavior can be modeled in applications with significant perception of intellectual agent actions.

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First-Person Shooter Game for Virtual Reality Headset with Advanced Multi-Agent Intelligent System

2016 · CHAPTER · en

We present a multiplayer first-person shooter (FPS) game with advanced intelligent non-playable characters (NPC) under computer control. The game is specially adapted for playing in VR headset so the simulator sickness symptoms are significantly reduced. The demo allows users to play with the other human and NPC players in a shooter game made in Unreal Engine 4. User can verify his/her game skills versus evolving human-like NPCs with a level adjusting model. The humanness of NPC was verified with Alan Turing game test beating 52\% record from BotPrize'12 competition.

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Учебно-методический комплекс дисциплины "Дискретная математика"

2015 · BOOK · ru

Учебно-методический комплекс предназначен для слушателей подготовительного отделения магистратуры по направлению «Математика и информатика» профиля подготовки «Прикладная математика и информатика» и может быть использован только в рамках образовательной программы подготовительного отделения магистратуры НИУ ВШЭ. УМК содержит программу дисциплины, методические рекомендации преподавателю и студентам, календарно-тематический план, а также обширные материалы для самостоятельной подготовки. Программа написана в соответствии с программой вступительных испытаний по направлению 010402.68 «Прикладная математика и информатика» подготовки магистра. Материалы для самостоятельной подготовки сформированы по темам и разделам в соответствии с рабочей программой дисциплины, к каждому разделу указан список использованной литературы. В рамках курса «Дискретная математика» рассматриваются базовые понятия теории множеств, алгебры логики и логических исчислений, теории графов и комбинаторики, теории формальных языков и теории алгоритмов.

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Курсы (7)