cv

Python Developer

Welltory Inc.

Welltory is a science-based app that helps you measure your stress & energy levels and improve productivity and health.

• Contributed to the development of a microservices platform (Django, PostgreSQL) for the wellness app with over 7 million users
• Enhanced the message generation pipeline to improve performance and speed (Celery, Redis, RabbitMQ)
• Implemented key performance metrics to facilitate consistent monitoring of system performance (Kibana/ElasticSearch, Grafana, Sentry, Kubernetes)
• Created an AI-based tool to assist the QA team in accelerating the development of test cases (OpenAI API).

Data Engineer

EPAM Systems

EPAM Systems, Inc. is a global provider of software engineering and IT consulting services headquartered in Newtown, Pennsylvania, United States. The company has software development centers and branch offices in North America, Europe, Asia and Australia.

• Developed ML model pipelines to recognise tabular data from medical articles and reports (Flask, paddleOCR)
• Implemented ML model evaluation metrics for order volume prediction
• Python, Flask, ML pipelines, NLP algorithms, OCR, AWS

Independent Researcher

Concordances 2.0

• Python
• web2py
• MySQL
• NLP algorithms
• word2vec
• sklearn

MA

University of Bologna, Bologna, Italy

Digital Humanities and Digital Knowledge

• Natural Language Processing
• Machine Learning
• Open Science
• Information Modeling
• Knowledge Management
• Network Analysis

PhD

Herzen State Pedagogical University of Russia

Russian Language and Literature

Computational Analysis of Literary Communities: Event-Based Social Network Study of St. Petersburg 1999-2019

Journal of Computational Literary Studies

Conference paper accepted for publication

Evaluating Named Entity Recognition Models for Russian Cultural News Texts: From BERT to LLM

arXiv preprint

Preprint available on arXiv

TEI Encoding as a Unified Structure for Multilingual Digital Editions: The LeggoManzoni Case Study

AIUCD-2025, Verona, Italy

Co-authored with Beatrice Nava and Ersilia Russo

AI-Supported Scaffolded Learning for Teaching Python in Digital Humanities Education

ADHO Digital Humanities Conference 2025

Accepted for presentation at ADHO DH 2025

Как живут Digital Humanities в Китае: рассказ очевидца

SysBlok.ru

Firsthand account of Digital Humanities development in China

Как можно улучшить ответы языковых моделей? Гайд по промтам

SysBlok.ru

Guide on prompt engineering techniques for improving language model responses

Commentare – Leggo Manzoni. Quaranta commenti alla Quarantana

Manzoni e Leopardi in digitale. Idee e proposte per la scuola, Clueb, Bologna

Co-authored with Giulia Menna and Beatrice Nava, pages 73-86

Mapping Literary Space: A Social Network from the Timeline of Cultural Events

DH2024. Book of Abstracts, Zenodo

Pages 423-424, George Mason University, Washington, DC, USA

Automatic Translation Alignment Pipeline for Multilingual Digital Editions of Literary Works

Proceedings of the Computational Humanities Research Conference 2024

Pages 1086-1104, Aarhus University, Denmark

Издательские стратегии в поле современной русской поэзии / Publishing Strategies in the Field of Contemporary Russian Poetry

Артикуляция / Artikuljacija, Volume 14

Analysis of contemporary Russian poetry publishing strategies and market dynamics

BertAlign API: Multilingual Sentence Alignment Service

FastAPI-based web service for multilingual sentence alignment using LaBSE embeddings, developed as part of DiScEPT (Digital Scholarly Editions Platform and aligned Translations). Supports 25 languages, TEI XML documents, and flexible alignment patterns for digital scholarly editions.

• Developed production-ready API service deployed on Google Cloud Run
• Implemented semantic alignment using LaBSE embeddings for 25 languages
• Created specialized TEI XML processing endpoints for digital humanities workflows
• FastAPI, sentence-transformers, Docker, Google Cloud Run, TEI XML

ALTO-to-TEI Converter: Document Processing Toolkit

Python toolkit for converting eScriptorium ALTO XML files to TEI format with Segmonto ontology support. Features page-level and book-level conversion modes, YAML-driven configuration, and advanced cross-page paragraph merging for historical document processing.

• Implemented Segmonto ontology compliance for document structure classification
• Developed YAML-driven configuration system for flexible document processing
• Created cross-page paragraph merging with hyphenation handling
• Built both page-level and book-level conversion modes for different use cases
• Python, XML/TEI, YAML, eScriptorium, Segmonto ontology

Computational Analysis of Literary Communities

Event-based social network analysis of Saint Petersburg's literary landscape from 1999-2019 using SPbLitGuide newsletter data. Processes 15,012 cultural events involving 11,777 participants across 862 venues to map literary community formation.

• Processed 20 years of cultural event data using Python data science stack
• Applied network analysis algorithms to identify 49 distinct literary communities
• Developed data processing pipeline with entity recognition and geospatial mapping
• Created interactive visualizations and statistical analysis of literary networks
• Python, NetworkX, pandas, NLP, geospatial analysis, data visualization

LeggoManzoni: Quaranta commenti alla Quarantana

Interactive web platform for teaching Italian literature, providing 40 critical comments on Alessandro Manzoni's 'The Betrothed'. Developed at University of Bologna with XML/TEI processing and multilingual digital edition capabilities.

• Developed interactive web platform for Italian literature education
• Implemented XML/TEI processing for text-commentary alignment
• Created segment-level text correlation algorithms
• Built responsive interface for educational use in secondary schools
• Node.js, XML/TEI, JavaScript, digital editions, educational technology

Zemelah.online: Soviet Jewish Egodocuments Archive

Digital archive with computational text analysis of Soviet-era Jewish egodocuments. Features topic modeling with LDA, GPT-powered chatbot interface, and AI-assisted indexing for historical document discovery and analysis.

• Implemented topic modeling analysis using Latent Dirichlet Allocation (LDA)
• Developed GPT-powered chatbot for thematic document queries
• Created AI-assisted markup editor with pre-annotation using Claude
• Applied sentiment analysis and word embeddings for semantic pattern mapping
• Python, LDA, OpenAI GPT, topic modeling, historical text analysis