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chore: add toloka item
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README.md

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@@ -51,6 +51,6 @@ The following commands can be used to generate the files locally:
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* Latex CV generation - first generate the `tex` file and then compile it
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```shell
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python github-profile/generate_tex.py --data=user-data.yml --output=./build/latex-cv/cv.tex
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python latex-cv/generate_tex.py --data=user-data.yml --output=./build/latex-cv/cv.tex
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pdflatex build/latex-cv/cv.tex
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```

user-data.yml

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experience:
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- role: Machine Learning Researcher
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company: Toloka.ai
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location: Germany
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url: https://toloka.ai
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start: Jun 2024
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end: Present
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description:
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- To be updated.
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tags: [PyTorch, HuggingFace, GenAi, Data Quality]
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# - role: NLP Intern
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# company: JetBrains Research
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# location: Netherlands
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- Designed and developed a solution for scanned document analysis, trained a high mAP (~0.94) CV model for tables, imgs, and stamps.
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tags: [Python, PyTorch, HuggingFace, SkLearn, PyTest, ONNX, Docker, Gitlab-CI]
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- role: Research Intern
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company: LATNA Lab at Higher School of Economics
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location: Russia
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url: https://nnov.hse.ru/en/latna/
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start: Apr 2019
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end: Jan 2021
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description:
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- Conducted research on Compressed Sensing with l1 and l0 norms, resulting in a near SoTA recovery algorithm with faster convergence;
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- Created Abstractive Summarization model using Knowledge Graphs.
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tags: [Statistics, Python, HuggingFace, CoreNLP, SkLearn, SciPy]
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# - role: Research Intern
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# company: LATNA Lab at Higher School of Economics
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# location: Russia
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# url: https://nnov.hse.ru/en/latna/
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# start: Apr 2019
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# end: Jan 2021
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# description:
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# - Conducted research on Compressed Sensing with l1 and l0 norms, resulting in a near SoTA recovery algorithm with faster convergence;
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# - Created Abstractive Summarization model using Knowledge Graphs.
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# tags: [Statistics, Python, HuggingFace, CoreNLP, SkLearn, SciPy]
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education:

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