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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)

American and Russian Sign Language Dactyl Recognition and Text2Sign Translation

2019 · CHAPTER · en

Sign language is the main way to communicate for people from deaf community. However, common people mostly do not know sign language. In this paper, we overview several real-time sign language dactyl recognition systems using deep convolutional neural networks. These systems are able to recognize dactylized words gestured by signs for each letter. We evaluate our approach on American (ASL) and Russian (RSL) sign languages. This solution may help fasten the process of communication for deaf people. On the contrary, we also present the algorithm for generating sign animation from text information using text-to-sign video vocabulary, which helps to integrate sign language in dubbed TV and combining with speech recognition tool provide full translation from natural language to sign language.

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American and Russian Sign Language Dactyl Recognition

2019 · CHAPTER · en

Sign languages are the main way for people from deaf community to communicate with other people. In this paper, we have compared several real-time sign language dactyl recognition systems using deep convolutional neural networks. Our system is able to recognize words from natural language gestured using signs for each letter. We evaluate our approach on American (ASL) and Russian (RSL) sign languages. For ASL, we trained on dataset prepared by Massey University, Institute of Information and Mathematical Sciences, for RSL we collect our own dataset, which we aim to enlarge together with RSL community in Russia. The results showed 100% accuracy for ASL Massey dataset, while RSL recognition quality is behind sufficient quality due to much more complex nature of real-world RSL dataset.

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Predicting Winning Team and Probabilistic Ratings in Dota 2 and Counter-Strike: Global Offensive Video Games

2018 · CHAPTER · en

In this paper, we present novel winning team predicting models and compare the accuracy of the obtained prediction with TrueSkill model of ranking individual players impact based on their impact in team victory for the two most popular online games: Dota 2 and Counter-Strike: Global Offensive.

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Scientific Matchmaker: Collaborator Recommender System

2018 · CHAPTER · en

Modern co-authorship networks contain hidden patterns of researchers interaction and publishing activities. We aim to provide a system for selecting a collaborator for joint research or an expert on a given list of topics. We have improved a recommender system for finding possible collaborator with respect to research interests and predicting quality and quantity of the anticipated publications. Our system is based on a co-authorship network derived from the bibliographic database, as well as content information on research papers obtained from SJR Scimago, staff information and the other features from the open data of researchers profiles. We formulate the recommendation problem as a weighted link prediction within the co-authorship network and evaluate its prediction for strong and weak ties in collaborative communities.

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Commercial Astroturfing Detection in Social Networks

2018 · CHAPTER · en

One of the major problem of recommendation services is commercial astroturfing. This work is devoted to constructing a model capable of detecting astroturfing based on network analysis. The main idea of the model is projecting a multipartite network to a unipartite and detecting communities in it representing actors with falsified opinions.

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Information Propagation Strategies in Online Social Networks

2018 · CHAPTER · en

Online social networks play major role in the spread of information on a very large scale. One of the major problems is to predict information propagation using social network interactions. The main purpose of this paper is to construct heuristic model of weighted graph based on empirical data that can outperform the existing models. We suggest a new approach of constructing the model of information based on matching specific weights to a given network.

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A Novel Autonomous Taxi Model for Smart Cities

2018 · CHAPTER · en

Autonomous taxies are in high demand for smart city scenario. Such taxies have a well specified path to travel. Therefore, these vehicles only required two important parameters. One is detection parameter and other is control parameter. Further, detection parameters require turn detection and obstacle detection. The control parameters contain steering control and speed control. In this paper a novel autonomous taxi model has been proposed for smart city scenario. Deep learning has been used to model the human driver capabilities for the autonomous taxi. A hierarchical Deep Neural Network (DNN) architecture has been utilized to train various driving aspects. In first level, the proposed DNN architecture classifies the straight and turning of road. A parallel DNN is used to detect obstacle at level one. In second level, the DNN discriminates the turning i.e. left or right for steering and speed controls. Two multi layered DNNs have been used on Nvidia Tesla K 40 GPU based system with Core i-7 processor. The mean squared error (MSE) for the detection parameters viz. speed and steering angle were 0.018 and 0.0248 percent, respectively, with 15 milli seconds of realtime response delay.

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Realistic post-processing of rendered 3D scenes

2018 · CHAPTER · en

In this talk, we show a realistic post-processing rendering based on generative adversarial network CycleWGAN. We propose to use CycleGAN architecture and Wasserstein loss function with additional identity component in order to transfer graphics from Grand Theft Auto V to the older version of GTA video-game, Grand Theft Auto: San Andreas. We aim to present the application of modern art style transfer and unpaired image-to-image translations methods for graphics improvement using deep neural networks with adversarial loss.

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Sparse Depth Map Interpolation using Deep Convolutional Neural Networks

2018 · CHAPTER · en

The problem of dense depth map inference from sparse depth values is considered in this paper. We address this issue in situation when one has low-cost sensor data and limited computational resources. We propose a method that performs interpolation and then super-resolution while comparing our approach with the state-of-the-art direct RGB-to-Dense reconstruction solutions. In particular, we use an encoder-decoder model of CNN with loss consisting of standard mean squared error and perceptual loss function. Futhermore, it has been shown that the described approach could be adopted to estimate rough depth map in real-time.

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Improving Picture Quality with Photo-Realistic Style Transfer

2018 · CHAPTER · en

In this paper, we study style transfer applications for the photo-realistic image processing tasks. First, we present the results on image quality improvement based with photo style transfer. Second, we describe the problems of learning style transfer under geometrical constraints for processing portrait images and multi-style transfer. Finally, we give a short glimpse on application of image-to-image translation methods for updating realistic graphics for video games.

DOI ↗ PDF ↗

Курсы (7)