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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)

Real-Time 3D Model Reconstruction and Mapping for Fashion

2020 · CHAPTER · en

Recent developments in the apparel market created new tasks for computer vision application. One such challenge is designing a system for trying on clothes virtually in real-time. Proposed solutions require setting up a special stationary system for body capture and mostly generate animated avatar instead of overlaying the garments in real-time. We propose a framework that utilizes state-of-the-art monocular-based 3D skeleton reconstruction and parametric body generation techniques allowing to operate under constrained resources, such as smartphones. In addition, we also consider the problem of dealing with visual artifacts as a result of 3D projection on a real-time image and design a solution to reduce them based on iterative closest point method. © 2020 IEEE.

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Online supervised attention-based recurrent depth estimation from monocular video

2020 · ARTICLE · en

Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general. One of the way to improve quality of existing methods is to utilize temporal information from frame sequences. In this paper, we study intelligent ways of integrating recurrent block in common supervised depth estimation pipeline. We propose a novel method, which takes advantage of the convolutional gated recurrent unit (convGRU) and convolutional long short-term memory (convLSTM). We compare use of convGRU and convLSTM blocks and determine the best model for real-time depth estimation task. We carefully study training strategy and provide new deep neural networks architectures for the task of depth estimation from monocular video using information from past frames based on attention mechanism. We demonstrate the efficiency of exploiting temporal information by comparing our best recurrent method with existing image-based and video-based solutions for monocular depth reconstruction.

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Real-Time Vehicle Type Detection and Counting from Road Camera Video

2020 · CHAPTER · en

In this paper, we study automatic recognition and counting of vehicles in the wild. For this problem, we tested several object detection models for car type recognition among five classes: Bicycle, Bus, Car, Motorcycle, Truck, Van. We extend existing dataset in order to balance classes and achieve classification quality for detected cars with 92% mAP

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Content Based Video Retrieval System for Distorted Video Queries

2020 · CHAPTER · en

We consider the task of content-based video retrieval (CBVR) given a query video, which is expected to match if it is a distorted short subsequence of a reference video from a database. In this paper, we present a CBVR system architecture that is both robust and scalable. We use a modified rHash frame fingerprint generation method. It is both, extremely robust to distortions and fast to compute. We utilize the Faiss library, developed by Facebook Research, to index fingerprint binary vectors. The VCDB dataset is used for benchmarking.

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Named Entity Recognition from Chernobyl Documentaries

2020 · CHAPTER · en

The paper describes a system that extracts facts and opinions from documentary texts to create a domain ontology of a controversial topic for Chernobyl disaster. The pipeline of the system is based on RNNbased NER module, which was tested on an annotated text corpora.

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Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

2019 · ARTICLE · en

We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.

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Link Prediction Regression for Weighted Co-authorship Networks

2019 · CHAPTER · en

In this paper, we study the problem of predicting quantity of collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, formed by authors writing papers in co-authorship represented by edges between authors in the network. Our task is formulated as regression for edge weights, for which we use node2vec network embedding and new family of edge embedding operators. We evaluate our model on AMiner co-authorship network and showed that our model of network edge representation has better performance for stated regression link prediction task.

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Generative Models for Fashion Industry using Deep Neural Networks

2019 · CHAPTER · en

The progress of deep learning models in image and video processing leads to new artificial intelligence applications in Fashion industry. We consider the application of Generative Adversarial Networks and Neural Style Transfer for Digital Fashion presented as Virtual fashion for trying new clothes. Our model generate humans in clothes with respect to different fashion preferences, color layouts and fashion style. We propose that the virtual fashion industry will be highly impacted by accuracy of generating personalized human model taking into account different aspects of product and human preferences. We compare our model with state-of-art VITON model and show that using new perceptual loss in deep neural network architecture lead to better qualitative results in generating humans in clothes.

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Higher School of Economics Co-Authorship Network Study

2019 · CHAPTER · en

Co-authorship networks represent a graph, in which vertices are authors, and edges represent research papers written in co-authorship. Every paper could generate several edges in such a graph, if a number of coauthors is greater than two. Co-authorship networks play important role in understanding the structure of research collaborations usually resulted in joint research papers. Moreover, when analyzing university ranking and research staff publishing activity, coauthorship network may help identifying both, efficient research communities and also people, who lack proper collaborators while having poor research results. Our paper is devoted to the visualization and interpretation of the former sets using as an example co-authorship network of National Research University Higher School of Economics (HSE), Moscow, Russia, while we also discuss the possible solutions for recommending collaborators for the latter set of researchers with low academic profile. Our paper is a case study for our university, which can be extended to larger co-authorship networks using research indexing services.

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Russian Freight Flights Time Prediction

2019 · CHAPTER · en

We present a model for freight train time prediction based on station network analysis and specific feature engineering. We discuss the first pipeline to improve the freight flight duration prediction in Russia. While every freight company use only reference book made by RZD (Russian Railways) based on railroad distances with accuracy measured in days, we argue that one could predict the flight duration with error less than twenty hours while decreasing error to twelve hours for certain type of freight trains.

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Курсы (7)