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<!doctype html>
<html>
<head>
<link rel="stylesheet" href="styles/style.css">
<title>Anastasiia Iurshina</title>
<meta property="title" content="Anastasiia Iurshina" />
<meta property="site_name" content="Anastasiia Iurshina" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="google-site-verification" content="yBC6-9vKI64bkvivcdYKNlbnnZS1q5oRtCaOd-RwDvg" />
<meta name="msvalidate.01" content="53F64115049526A007902F6B07EF4CDB" />
</head>
<body>
<div class="sidebar">
<a href="#about">About</a>
<a href="#experience">Work Experience</a>
<a href="#skills">Skills</a>
<a href="#projects">Projects</a>
<a href="#education">Education</a>
<a href="#publications">Publications</a>
<a href="#volunteering">Volunteering</a>
<hr>
<a href="#books">Books</a>
<a href="#pictures">Pictures</a>
<!-- <a href="play.html">Play</a> -->
<hr>
<a href="https://www.linkedin.com/in/anastasiia-iurshina">LinkedIn</a>
<a href="https://github.com/iurshina">GitHub</a>
<a href="https://scholar.google.com/citations?user=3PUgA9kAAAAJ&hl=ru">Google Scholar</a>
</div>
<div class="content">
<h1>Ana(stasiia) Iurshina</h1>
<br>
<h2 id="about">About</h2>
Hi, I'm Ana, a backend developer and machine learning engineer. I have over 7 years of experience software
development in the industry and around 3.5 years of research experience, primarily in applying deep
learning methods to natural language processing tasks such as entity linking, named entity recognition,
automatic text summarization, and more. Most recently, I spent a year as a machine learning engineer in the
industry.
<br>
This is pretty much a clickable version of my CV, plus a tiny addition of some personal info (Books and Pictures
sections).
<br>
Apart from pure coding, debugging (I 💛 debugging!) and training models, I'm interested in the following topics:
<ul>
<li>Open source</li>
<!-- <li>Data visualization</li> -->
<li>Human rights and equal opportunities for all</li>
<li>Cybersecurity, OSINT</li>
<!-- <li>Data analysis for social/cultural issues</li> -->
<li>Art (involving computers or not)</li>
</ul>
If you happen to have any ideas related to these topics and you need technical help or just someone to
collaborate with, feel free to contact me (by email: anastasiia.iurshina @ gmail.com or on linkedin).
<h2 id="experience">Work Experience</h2>
<table class="paddingBetweenCols">
<tr class="spaceUnder">
<td><strong>09.2023 <br>now</strong></td>
<td>Senior Machine Learning Engineer at <strong>GFT</strong><br>
<em>Tasks:</em> made significant contribution into development and design of a chat application
which is successfully used in production by more than 3000 users; <br>
developed a testing framework for a RAG pipeline; <br>
mentored junior developers; <br>
<em>Environment:</em> Python, LLMs, Azure, FastAPI, Git
</td>
</tr>
<tr class="spaceUnder">
<td><strong>07.2023 <br>09.2023</strong></td>
<td>Senior Machine Learning Engineer at <strong>Insaas</strong><br>
<em>Tasks:</em> prototyping a question answering system with a vector database, LLM, FastAPI and
streamlit; <br>
using an LLM to solve a business task: aspect-based sentiment analysis of product reviews <br>
<em>Environment:</em> Python, LLMs, GCP, Git
</td>
</tr>
<tr class="spaceUnder">
<td><strong>07.2020 <br> 06.2023</strong></td>
<td>Researcher at <strong>University of Stuttgart</strong><br>
<em>Tasks:</em> research in the area of neural entity linking; <br>deploying and supporting a
website with a demo (nginx, docker compose, SSL); <br> teaching assistant for "Intro to AI" course;
<br> student theses supervision (e.g. "Extracting and Segmenting High-Variance References from PDF
Documents with BERT")<br>
<em>Environment:</em> Python, Pytorch, Git, Docker
</td>
</tr>
<tr class="spaceUnder">
<td><strong>01.2020 <br> 03.2020</strong></td>
<td>Research Intern at <strong>Bosch Center for AI</strong><br>
<em>Tasks:</em> research project on the topic of task-specific named entity recognition in a
multi-lingual setting<br>
<em>Environment:</em> Python, Pytorch, Git
</td>
</tr>
<tr class="spaceUnder">
<td><strong>09.2019 <br> 12.2019</strong></td>
<td>Research Science Intern at <strong>Amazon</strong><br>
<em>Tasks:</em> research project in the field of automatic summarization<br>
<em>Environment:</em> Python, Tensorflow, Pandas, Git, AWS
</td>
</tr>
<tr class="spaceUnder">
<td><strong>04.2018 <br> 09.2019</strong></td>
<td>Software Developer for NLP at <strong>Sony, Speech and Sound group</strong><br>
<em>Tasks:</em> investigation and implementation of different tasks in the area of text processing
(tokenization, sentence segmentation, POS tagging, etc)<br>
<em>Environment:</em> Java, Git, Python, C++
</td>
</tr>
<tr class="spaceUnder">
<td><strong>04.2018 <br> 09.2019</strong></td>
<td>Research assistant at <strong>Institut für Linguistik/Anglistik</strong><br>
<em>Tasks:</em> data prepossessing, extraction of sentences of a certain structure based on
dependency parsing results<br>
<em>Environment:</em> Python (nltk, pandas, spaCy etc), MaltParser, Java
</td>
</tr>
<tr class="spaceUnder">
<td><strong>07.2014 <br> 09.2017</strong></td>
<td>Senior Software Engineer, Team Lead at <strong>EPAM Systems</strong><br>
<em>Tasks:</em> implementing different design and coding tasks, leading a team of 3 developers<br>
<em>Environment:</em> Java, Python, Git, Spring, Jenkins, CXF, REST, Maven, Tomcat, Talend,
Virtuoso, PostgreSQL, MyBatis, Apache Ignite, Hadoop, Spark
</td>
</tr>
<tr class="spaceUnder">
<td><strong>07.2012 <br> 07.2014</strong></td>
<td>Senior Software Engineer at <strong>GGA Software Services</strong><br>
<em>Project:</em> a system for interacting with liquid handling robot devices and.<br>
<em>Tasks:</em> implemented robot manipulation logic, applied searching for connectivity components
in a graph to a business task<br>
<em>Environment:</em> Java, SVN, ORACLE, Spring, SOAP, Maven, Tomcat, Mule ESB, Apache Solr
</td>
</tr>
<tr class="spaceUnder">
<td><strong>07.2010 <br> 07.2012</strong></td>
<td>Software Engineer at <strong>GGA Software Services</strong><br>
<em>Tasks:</em> developing software for pharmaceutical companies.<br>
<em>Environment:</em> Java, SVN, ORACLE, Spring, SOAP, Maven, Tomcat, Quartz for job scheduling
</td>
</tr>
<tr class="spaceUnder">
<td><strong>01.2010 <br> 07.2010</strong></td>
<td>Junior Software Developer at <strong>GGA Software Services</strong><br>
<em>Tasks:</em>implementing web services, working with a database.<br>
<em>Environment:</em> Java, SVN, Swing, MySQL, Hibernate
</td>
</tr>
</table>
<h2 id="skills">Skills</h2>
<h3>Using (almost) every day:</h3>
<table>
<tr>
<td>Python</td>
<td>*****</td>
</tr>
<tr>
<td>Pytorch</td>
<td>*****</td>
</tr>
<tr>
<td>Natural Language Processing</td>
<td>*****</td>
</tr>
<tr>
<td>Machine Learning</td>
<td>****<FONT COLOR="grey">*</FONT>
</tr>
<tr>
<tr>
<td>Deep Learning</td>
<td>****<FONT COLOR="grey">*</FONT>
</tr>
<tr>
<td>Transformers package</td>
<td>*****</td>
</tr>
<tr>
<td>Git</td>
<td>****<FONT COLOR="grey">*</FONT>
</td>
</tr>
<tr>
<td>Docker</td>
<td>****<FONT COLOR="grey">*</FONT>
</td>
</tr>
<tr>
<td>Python ML/NLP stack (numpy, spacy, scikit-learn, etc)</td>
<td>****<FONT COLOR="grey">*</FONT>
</td>
</tr>
</table>
<h3>Significant experience in the past or using from time to time:</h3>
Java, SQL, Spring, AWS
<h3>Familiar with (used in different projects throughout my career and studies):</h3>
Keras, tensorflow, LaTex, bash scripting, HTML, CSS, no-SQL databases, Scala, Hadoop, Spark, elasticsearch,
Solr, Lucene, Kafka, C++
<h3>Languages</h3>
<table>
<tr>
<td>English</td>
<td>Fluent (C1/C2)</td>
</tr>
<tr>
<td>Russian</td>
<td>Native</td>
</tr>
<tr>
<td>German</td>
<td>Limited (B1+)</td>
</tr>
<tr>
<td>Arabic</td>
<td>Basic (A1/A2)</td>
</tr>
</table>
<h2 id="projects">Recent projects</h2>
<button type="button" class="collapsible">Entity Linking dataset (NILK)</button>
<div class="col_content">
<p>The NIL-linking task in Entity Linking deals with cases where the text mentions do not have a
corresponding entity in the associated knowledge base. We created a dataset for this task. <br />
To achieve this, we compared two snapshots of WikiData (from different timestamps), found entities that
are in the later snapshot but absent from the older one. For this entities we extracted corresponding
text from Wikipedia.
<br />
More information can be found in the <a
href="https://dl.acm.org/doi/abs/10.1145/3511808.3557659">paper</a>.
<br />
The dataset is available at <a href=" https://zenodo.org/record/6607514">zenodo</a>
</p>
</div>
<button type="button" class="collapsible">Producer-consumer app prototype</button>
<div class="col_content">
<p>
A prototype of a pipeline for processing of .tmx files (xml-like files used for translation annotation).
Relies on the producer-consumer architecture (implemented using Kafka).
<br />
<em>Stack</em>: Python, Kafka, Docker
<br />
<a href="https://github.com/iurshina/example_prod_consm_project">Code on github</a>
</p>
</div>
<button type="button" class="collapsible">Multilingual named entity recognition</button>
<div class="col_content">
<p>In this work, we explored multilingual methods for the extraction of temporal expressions
from text and investigated adversarial training for aligning embedding spaces to one common space. </br>
The work resulted in the <a href="https://aclanthology.org/2020.repl4nlp-1.14/">publication</a></p>
</div>
<button type="button" class="collapsible">Aspect-based automatic summarizaton</button>
<div class="col_content">
<p>During my internship at Amazon (2020) I worked on automatic summarization of user reviews. <br />
<em>Stack</em>: Python, Tensorflow, USE (universal sentence encoder), BERT
</p>
</div>
<button type="button" class="collapsible">Fiction summarizaton</button>
<div class="col_content">
<p>My master thesis (2020) addressed the problem of fiction summarization using transformers. <br /> I tried
different training and pre-training settings, and different architectures (vanilla transformer, GPT-2),
looked into the attention distribution and analyzed errors. The best solution (though still very prone
to hallucinations) consisted of a pre-processing step with extraction of "main" sentences followed by
summarization by GPT-2.
<br />
The pre-processing step relied on k-means clustering of sentences encoded with universal sentence
encoder. I took 2-3 sentences closest to the centroid of a cluster for each paragraph. <br />
<em>Stack</em>: Python, Pytorch, USE (universal sentence encoder), scikit-learn
</p>
</div>
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<h2 id="education">Education</h2>
<h3>University education</h3>
<table class="paddingBetweenCols">
<tr>
<td><strong>07.2020 <br> Now</strong></td>
<td>PhD Student in Computer Science at University of Stuttgart</td>
</tr>
<tr class="">
<td><strong>03.2020 </strong></td>
<td>Master Degree in Computational Linguistics at University of Stuttgart</br>
<em>Selected coursework:</em> Machine Learning, Reinforcement Learning, Deep Learning
for NLP, Advanced Computational Semantics, Advanced Semantics, Lexical Semantics
</td>
</tr>
<tr>
<td><strong>07.2010</strong></td>
<td>Undergraduate Degree in Computer Science at St. Petersburg State Polytechnic</td>
</tr>
<tr>
<td><strong>07.2008</strong></td>
<td>Undergraduate Degree in Innovation Management at St. Petersburg State Polytechnic University</td>
</tr>
</table>
<h3>Summer schools</h3>
<table class="paddingBetweenCols">
<tr>
<td><strong>06.2021</strong></td>
<td>Nordic Probabilistic AI School (ProbAI) <br>
<em>Topics:</em> Probabilistic programming, variational inference, GANs
</tr>
<tr>
<td><strong>04.2021</strong></td>
<td>Oxford ML School (OxML) <br>
<em>Topics:</em> Machine Learning for healthcare
</td>
</tr>
</table>
<h2 id="volunteering">Volunteering</h2>
<table class="paddingBetweenCols">
<tr>
<td><strong>08.2024-now</strong></td>
<td><a href="https://www.joinimagine.com/">Imagine Foundation e.V.</a><br>
<em>Tasks:</em> Providing career coaching for tech candidates from the MENA region
</tr>
<tr>
<td><strong>05.2023-05.2024</strong></td>
<td><a href="https://www.frauenloop.org/">FrauenLoop</a><br>
<em>Tasks:</em> Mentoring of web development for women who want to change their career
</tr>
</table>
<h2 id="publications">Publications</h2>
<a href="https://dl.acm.org/doi/abs/10.1145/3511808.3557659">"NILK: Entity Linking Dataset Targeting NIL-linking
Cases", Anastasiia Iurshina, Jiaxin Pan, Rafika Boutalbi, Steffen Staab, CIKM 2022</a>
<br>
<a href="https://dl.acm.org/doi/10.1145/3477495.3531834">"Tensor-based Graph Modularity for Text Data
Clustering", Rafika Boutalbi, Mira Ait-Saada, Anastasiia Iurshina, Steffen Staab, Mohamed Nadif, SIGIR
2022</a>
<br>
<a href="https://aclanthology.org/2020.repl4nlp-1.14/">"Adversarial Alignment of Multilingual Models for
Extracting Temporal Expressions from Text, Lukas Lange, Anastasiia Iurshina, Heike Adel, Jannik Strötgen,
RepL4NLP at ACL 2020</a>
<h2 id="books">Books</h2>
A list of books I read recently (or not so recently but like to bring up on every possible occasion).
<h3>Tech</h3>
<ul>
<li>"Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications", Chip Huyen
</li>
<li>"Machine Learning Design Patterns", V. Lakshmanan, S. Robinson, M. Munn</li>
<li>"The Hundred-Page Machine Learning Book", Andriy Burkov</li>
<li>"Pro Git", Scott Chacon and Ben Straub</li>
<li>"Rust", Steve Klabnik and Carol Nichols</li>
<li>"Introduction to Information Retrieval", Christopher D. Manning</li>
</ul>
<h3>Culture&Theory</h3>
<ul>
<li>"Cultural Studies. Theory and Practice", Chris Barker</li>
<li>"American Originality: Essays on Poetry", Louise Glück</li>
<li>"The Queer Art of Failure", J. Jack Halberstam</li>
<li>"Metamodernism: Historicity, Affect, and Depth After Postmodernism", Robin van den Akker</li>
<li>"Technofeminism", Judy Wajcman</li>
</ul>
<h3>Fiction</h3>
<ul>
<li>"Cold Enough for Snow", Jessica Au</li>
<li>"Light in August", William Faulkner</li>
<li>"Infinite Jest", David Foster Wallace</li>
<li>"White Noise", Don DeLillo</li>
<li>"The Sailor Who Fell from Grace with the Sea", Yukio Mishima</li>
</ul>
<h3>Poetry</h3>
<ul>
<li>"Winter Recipes from the Collective", Louise Glück</li>
<li>"All My Pretty Ones", Anne Sexton</li>
<li>"Dream Work", Mary Oliver</li>
<li>"Dancing in Odessa", Ilya Kaminsky</li>
</ul>
<h2 id="pictures">Pictures</h2>
Just to give you an idea what I'm doing when I'm not debugging
or reading.
<br>
<div class="image-grid">
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</div>
</div>
</body>
</html>