PyData Yerevan 2022

Aghasi Tavadyan

Aghasi Tavadyan is the founder of the data science organization, which translates to "data" from Armenian. Tvyal is data analysis, modeling, and visualizations. It simplifies the complexity of any data-blobs to digestible pieces and forms the T-wings that can help your business fly more effectively. He is the head of "Economic Uncertainty Modeling" laboratory, ASUE, and the executive secretary of "Armenian Economic Journal," which is published by the National Academy of Sciences, Armenia.

Aghasi Tavadyan teaches Probability Theory, Mathematical Statistics, Econometrics, Mathematical Models in Financial Markets, Bayesian Statistics at the Armenian State University of Economics and Armenian-Russian Universities.

Please visit his websites for more info:,

  • The dangers of mindless forecasting
Aleksandr Patrushev

Aleksandr Patrushev is AI/ML Specialist Solutions Architect at AWS, based in Luxembourg. He is passionate about cloud and machine learning, and the way how they could change the world. Outside work, he enjoys hiking, sports and spending time with his family.

  • Use AutoML to create high-quality models
Alex Laptev

Aleksandr Laptev is a Ph.D. student at ITMO University and a senior research scientist as NVIDIA. His scientific interests are Automatic Speech Recognition, Speech Synthesis (TTS), and Natural Language Processing. He writes open-access scientific articles, contributes to open-source software, and participates in international speech recognition competitions. His current research area is differentiable Weighted Finite-State Transducers.

  • NVIDIA NeMo: toolkit for conversational AI
Andrey Manoshin

Andrey Manoshin got a specialist degree in control engineering in NRNU MEPHI (Moscow) in 2022. He worked for a few years as a Data Scientist with OCR and NLP tasks, and since late 2021 he has been working in Yandex.Research. His interests include but are not limited to fields of reinforcement learning, robotics, meta-learning, and representation learning.

  • EENLP: Cross-lingual Eastern European NLP Index
Anna Shahinyan

CEO, co-founder at FiveBrane
3 years CTO at 50+, multi regional, ISO certified company
15+ years in IT
20+ publications in quantum physics
President Prize 2011 in Physics

  • Cifar-10 Exploratory Data Analysis
Anush Tosunyan
  • Tiling & Parallel Processing of Large Images
Arpine Sahakyan

Arpine Sahakyan is a Machine learning engineer with a Mathematics background. Specializing in the Computer Vision branch herself, she works in SmartClick LLC. Arpine Sahakyan enjoys working with state of the art models and keeping up with the latest research. Her education background gives her a good foundation for statistical analysis and intuition about data.

  • How to start critical thinking in Data Science
Artem Terentyuk

Machine Learning Engineer / Data Science Team Lead at Quantori end-to-end digital
IT service provider for the life science and healthcare industries.

  • BERT Model for Real World Healthcare Data
Ashot Vardanian

Ashot was born with a keyboard in his hands, mastering the art of programming in the womb. Jokes aside, for the last ~15 years, he mainly was programming in C-like languages, OpenCL, CUDA, Assembly on x86 and ARM and a bit in Python.

He currently runs the C++ Armenia community, a deep-tech startup called Unum and has a mixed past, including a few years in Astrophysics and a few more - running a private wealth fund. He has lived in dozens of countries all around the planet, so feel free to ask him about anything from travel to extreme sports 🤗

  • Accelerated Data Science Libraries
Christopher Markosian

Christopher Markosian has been a student in the MD/Ph.D. program at Rutgers New Jersey Medical School since 2019. He is currently a researcher in the Arap/Pasqualini Laboratory, which is an established group studying phage display technology for various biomedical applications. His latest work centers on a protein structure-guided approach to design a phage-based immunization strategy against COVID-19. Prior to his graduate studies, he served as an instructor at Yerevan State Medical University through the Fulbright US Student Program. He completed his undergraduate studies in Molecular Biology and Biochemistry at Rutgers University–New Brunswick in 2018.

  • Visualizing and Analyzing Protein Structures with UCSF Chimera
Daniel Kornev

Daniel Kornev is a CPO of Previously, he consulted a number of international AI startups, shipped productivity scenarios for a cross-device AI assistant in Eastern Europe, and founded an award-winning AI startup in Boston. He also interned at Microsoft Research in Redmond, WA, USA, and worked as PM at Google and Microsoft in Eastern Europe.

  • Building your own Multiskill AI Assistant with DeepPavlov
Davit Abgaryan

Davit Abgaryan works as Sr. Data Scientist at DISQO and is Co-Founder and CEO at PrekogAI, an automated crypto asset management company. He has a Ph.D. in Economics and lectured Programming for Data Science at the American University of Armenia for 3 years.

  • Recommendation Systems in Market Research
Diyar Mohammadi

Diyar Mohammadi is a Data Scientist at (an online classified ad service in Iran). I've been using various machine learning approaches to develop valuable products or solve complex problems in Divar's business.
He is interested in learning and experiencing new approaches which lead to expanding my stack in machine learning and having a broader viewpoint toward problems.
Recently Diyar Mohammadi has been working on Multi-modal deep learning models for complex tasks (such as Near-Duplicate Ad Detection and Real-Time Ad Recommender Systems).
Also, he is currently an undergrad student at the University of Tehran studying Computer Engineering.

  • Near-Duplicate Ad Detection in Online Classified Ad Services
Dmitry Korobchenko

Dmitry graduated with honors from Moscow State University with a M.Sc degree in Computer Science. His main research in the university was focused on computer vision and image processing within Graphics and Media Lab. After graduation Dmitry worked as a software engineer at IBM before moving to Samsung Electronics for 5 years where he undertook various research and development in areas of deep learning, computer vision and signal processing, as both an engineer and project manager. Currently he is a Director of Artificial Intelligence at NVIDIA, continuing research, development and team leading in these areas. From time to time he gives talks at conferences, conducts intensive seminars, courses, gives pop-science lectures and runs a Youtube channel on introduction to deep learning and related topics.

  • PyTorch Geometric for Graph Neural Nets
Dmitry Mezhensky

Director of Data Engineering and ML at Grid Dynamics, responsible for sophisticated technical programs delivery, R&D and solutions development and mentoring architects.

  • ML Platform for Insurance Conglomerate
Dmitry Mironov

Dmitry Mironov is an AI Solutions Architect at NVIDIA. He helps customers use the GPUs efficiently and helps speed up various pipelines in CV, NLP, Conversational AI, and Data Science. Before NVIDIA, Dmitry served as a CTO and co-founder of a startup. He had been integrating Computer Vision into gold mining, transportation, energy, and other industries.

  • Accelerated Machine Learning Systems End-to-End: From Data Stream to App
Elina Israelyan

Elina Israelyan is a Data Scientist at EasyDMARC, where they develop AI solutions for email security and deliverability. She also studies at the American University of Armenia, majoring in Data Science. Her experience is mainly related to Natural Language Processing and Anomaly Detection. Elina Israelyan has been working on various projects such as URL and Website Phishing Detection, Email traffic Anomaly Detection, Spam Detection, etc., where she has developed end-to-end projects starting from the raw data preprocessing, exploratory data analysis, and finishing with the model deployment processes.

  • AI-Powered Solutions for Cybersecurity
Erik Harutyunyan

Erik Harutyunyan is a Machine Learning Engineer, mostly specialized and interested in the Computer Vision domain. He is currently working at SuperAnnotate AI as a Machine Learning Researcher and concurrently pursuing a Master's degree in Mathematics in Data Science at the Technical University of Munich.

  • Active Learning for 3D mesh semantic segmentation
Gevorg Soghomonyan

Co-creator of AimStack, co-founder at AimHub. Gev has been building AI infrastructure for the past 7 years.
Previously, 4th engineer at Altocloud (acqu. by Genesys).

  • The structure and interpretation of ML metadata
Hadi Abdi Khojasteh

Hadi is leading a software team as a chief engineer at the R&D department of TELIGHT, Czechia, France and a lecturer at the Institute for Advanced Studies in Basic Sciences (IASBS), Iran. He is a former researcher at the Institute of Formal and Applied Linguistics (ÚFAL) at Charles University, Prague and participated in several international projects in collaboration with the concentration of experts in the fields of CV/NLP/HLT/CL/ML/DL. His research focuses on multimodal learning inspired by neural models that are both linguistically motivated, and tailored to language and vision, visual reasoning and deep learning. His main research interests are Machine Learning, Deep Learning, Computer Vision, Multimodal Learning and Visual Reasoning while he is experienced in a wide variety of international projects on cutting-edge technologies. Currently, they are developing a new generation of the patented holographic microscope that utilises live-cell label-free imaging to turn invisible live cells into visible ones.

  • Sequential Attention-Based Neural Machine Translation
  • Large Scale Representation Learning In-the-wild
Hayk Aprikyan
  • Machine Learning Engineer at
  • Undergraduate student at Yerevan State University, Department of Mathematics
  • 2 years of experience in EDA, 1.5 years of experience in NLP
  • Writes tools and utilities to scrape, analyze and make use of data both for fun and self-development (and personal needs), most of them open sourced

  • Data Science enthusiast with a background in Mathematics

  • Founder of
  • What can your Telegram tell about you? (Answer: Everything)
Hossein Mortazavi

Hossein Mortazavi is a data scientist based in Estonia. During his career, he has worked in various industries, such as transportation and sales in Iran and Estonia.
He enjoys exploring the data science world to solve even a small part of business problems.
Hossein Mortazavi is experienced with Python and other packages, especially Pandas, for the purpose of analyzing data in order to bring value to data-driven businesses.
Besides work, he is also passionate about startups, following startup events, and building networks with startup experts.

  • How to use Pandas efficiently
Hovhannes Margaryan

Hovhannes Margaryan has a bachelor's degree in Computer Science from the American University of Armenia and is currently a master's student in Data Science at KU Leuven and ML Scientist at Picsart's Creative Intelligence team. Hovhannes is interested in different areas of computer vision and has experience with texture synthesis, style transfer, facial inpainting, image outpainting, full body generation, vector graphics, and physical simulation.

  • Classical Texture Synthesis and Beyond
Hrach Asatryan

Hrachya Asatryan was born in Yerevan on June 8, 2000. In 2002, he moved to San Diego, California, where he lived until 2006, upon which he returned to Armenia. Hrachya Asatryan graduated from Physmath school in 2017 and got admitted to YSU Faculty of Physics, where he is currently pursuing his Master's degree. In 2021 he got into the field of Machine Learning, starting his career at Intelinair, where he works to this day. His main interests are sports and gaming, and he is a big tech enthusiast.

  • Large scale field delineation
Karen Javadyan


  • Streamlit: A faster way to build and share data apps
Katherine Munro

Katherine is a Data Scientist and Data Science Ambassador in the e-commerce domain, conducting both research and corporate training in AI, machine learning, Natural Language Processing (NLP) and data science. She is a speaker, writer, teacher, and passionate workaholic.

With a background in computational linguistics and (deep) machine learning, Katherine has worked in R&D for Mercedes-Benz and the Fraunhofer Institute, specialising in user interfaces and Natural Language Understanding. In her 'free' time, she is education Lead for Women in AI Upper Austria, volunteer mentor at Female Coders Linz, a trainer for Linkedin Learning, and recently co-authored the textbook, 'The Handbook of Data Science and AI'.

  • Eating humble Py: From toy problem to real-world solution in predicting Customer Lifetime Value
Liana Minasyan

Liana Minasyan is an NLP researcher and Machine Learning engineer at Polixis. She is involved in various NLP projects, from classic problems to tackling currently unsolved ones. As part of her job, she is creating tools for transliteration, data mapping, target-based sentiment analysis, text summarization, etc. She was involved in sentiment and emotion recognition research for Krisp in collaboration with TUMO. As her capstone at AUA, with YerevaNN supervision, she has worked on the Western and Eastern Armenian Treebank project and was a contributor to Stanford's Universal Dependencies and their NLP toolkit.

  • Target based sentiment analysis with T5
Luka Chkhetiani

Luka Chkhetiani is a Deep Learning Research & Technology Lead with extensive experience in research, management, deployment, and optimization of end-to-end deep learning services in cloud and edge ecosystems.
He is responsible for Research & Technology Leadership for Unsupervised and Semi-Supervised multilingual ASR research, deployment, and optimization at AssemblyAI.

  • Scaling Semi-Supervised Production-Grade ASR on 200 Languages
Manana Hakobyan

Manana Hakobyan is a recent graduate of Harvard University with an MS in Data Science. Prior to Harvard, Manana studied at UC Berkeley, obtaining degrees in Economics and Data Science. Driven with the desire to see Armenia as a hub of data-driven activities, Manana cofounded DataPoint Armenia - a non-profit organization with a mission to accelerate the development of data science in Armenia.

  • Decoding Human Physiological Behaviors from Intracranial Field Potentials
Maria Sahakyan

Maria Sahakyan is a postdoctoral associate at New York University Abu Dhabi specializing in Explainable Artificial Intelligence. She earned her Ph.D. in Interdisciplinary Engineering from Khalifa University Abu Dhabi, specializing in Explainable Artificial Intelligence (XAI).

  • Explainable AI as a Conventional Data Analysis Tool
Marine Palyan

Marine Palyan works as a Data Scientist at IntelInAir Armenia. Her job mostly involves image processing, data analysis, and Python development. She is also interested in Data Engineering and Cloud Development. As for her education, she is currently a second year Masters student at Yerevan State University, majoring in Applied Statistics and Data Science. She has a Bachelor’s degree in Computer Science from YSU. In her free time, she likes playing the guitar and enjoys reading science fiction and thriller books.

  • Moving Inference to Triton Servers
Mark Hamazaspyan

Mark Hamazaspyan is a Machine Learning specialist with 3+ years of experience in computer vision, 4+ years of experience in traditional data science, and 2+ experience in ML engineering and MlOps. He has completed various projects, including computer vision, time series, and event data. Mark Hamazaspyan has a teaching experience in universities and private organizations.

2021-present - Applied Statistics and Data Science, Yerevan State University.
2017-2021 - BA in Business: Economics, American University of Armenia

  • Using Few Shot Object Detection for Utility Pole detection from Google Street View images.
Meirav Ben Izhak

Meirav Ben Izhak is a Senior AI Researcher at Salesforce. She holds an MSc in Bioinformatics which focused on the Microbiome but also included side projects in immunology and epidemiology.

During the past few years, she had the fortune to analyze various datasets as part of work and as side projects, including but not limited to election patterns, Cannabis DNA, cellular activity, and spontaneous conversations.

In her spare time, she promotes women in STEM, goes on hikes, and contributes to stock photography.

  • NetworkX - your unexcpected assistant for clustering analysis
Mher Khachatryan

2017-2021 - YSU, Faculty of Physics, theoretical physics.
My diploma work was "Classification of Blazars with Machine Learning Techniuqes"
2021-present - YSU, Faculty of Mathematics and Mechanics, applied statistics and data science

2018 - present - ICRANet-Armenia, research assistant
I was engaged in manual astrophysical data analysis and then co-wrote software or automation, various research of high energy astrophysics, and Machine Learning on those data
2020-2021 - EasyDMARC, Machine Learning Specialist
With the mentorship of a senior manager, I have to build an end-to-end machine learning solution for anomaly detection for an email cyber security platform.
2021-2022 - Krisp, Machine Learning Engineer, Computer Vision.
I was engaged in human segmentation task, to replace the background in virtual meetings. Collaborating with QA, PM, and other staff research team I contributed to the development of already created technology.

  • Best practices for coding in ML/DS - Techniques to construct your project
Nacho Aranguren

I believe in data’s power to identify solutions for the biggest challenges and as unique enabler for business stakeholders to achieve sustainable growth. I have rising experience in creating and leading high-performance teams in the engineering and fintech industries. I created and managed a Data team from scratch, designing and implementing the whole Business Intelligence and ML-ops cloud infrastructure.

As a data professional I am passionate about analysing and integrating data from different sources to design tools and specialised analytics to drive strategic decisions. To further leverage disruptive technologies such as AI and Machine Learning approaches to optimise the relationship with people and customers.

My mission is to be the ally of those who have the challenge to make an impact, and give them the tools to understand performance and what users love.

  • Modern Data Stack: Optimising and Scaling Data in a tech company
Nura Kawa

Nura Kawa is a Research Scientist at neurocat (Berlin, Germany), a startup that delivers innovation in the development of safe and secure AI systems. Her current research focus is on adversarial robustness of deep neural networks. Additionally, she is interested in privacy-preserving machine learning and in explainable AI. Nura holds an MSc in Statistics and Data Science from KU Leuven (Leuven, Belgium) and a BA in Statistics from UC Berkeley (Berkeley, USA).

  • The Explainability Problem: Towards Understanding Artificial Intelligence
Ricardas Ralys


  • Using Python for the PCR design or an easy way to data analysis in life science projects
Rudolf Eremyan

Rudolf Eremyan is a data scientist with six years of experience. In 2016 he joined the startup as an NLP/ML engineer and developed the first chatbot framework for the Georgian language. Starting from 2018, he works in the Toptal freelance network as a data engineer where created Amazon Web Services-based big data processing and visualization tools for various companies in the field of eCommerce, sport, and pharma. As an active community member, he was invited speaker and judge at the international conferences and hackathons like NASA's International Space Apps Challenge, Google DevFest, DataFest Georgia, Pecha Kucha.

  • Building Data Pipelines on AWS
Sergey Hayrapetyan


Yerevan, Armenia

Bachelors: State Engineering University of Armenia at the faculty of Applied Mathematics and Informatics

2014 – 2017

Karlsruhe, Germany

Masters: Karlsruhe Institute of Technology (KIT) at the faculty of Informatics.

Specialization: Cognitive Systems and Algorithm Design
Master Thesis: Deep Learning based Parking space segmentation
June 2015 - Aug. 2015

Karlsruhe, Germany

Research assistant at KIT, Computer Vision for human-machine interaction lab

Affective Impact of Movies and Violence scene detection
Oct. 2015 – April 2016

Karlsruhe, Germany

Research assistant at KIT, Forschungszentrum Informatik

Developing software for autonomous driving systems
Since 2017

Stuttgart, Germany

Computer Vision and Machine Learning Engineer/Function Developer in the automotive industry

  • Bachelor theses in Deep Learning: submitted to an Armenian University
Sona Hambaryan

I'm Sona, Data Scientist at ServiceTitan. I started my career as a big data analyst and then shifted toward data science. I love combining work and education thus I started teaching at the Armenian State University of Economics "Data analytics with Python" and "Into to machine learning" modules.

  • A/B testing in production
Sona Hunanyan

Sona Hunanyan is a scientist-statistician at Philip Morris Armenia. Apart from that, she is working on research projects in statistical analysis of machine learning algorithms within the framework of the FAST Advance STEM program.
Sona completed a Master's Degree in Applied Mathematics at EPFL Lausanne. Beside her studies, she gained practical experience while working as a quantitative analyst at Nuclear Power Plant Goesgen, Switzerland. Sona holds a PhD in Biostatistics from the University of Zurich. The main topic of her PhD thesis was Bayesian hierarchical modeling and software development in R.

  • Empirical determinacy of posterior location and scale in Bayesian hierarchical models
Tigran Sedrakyan

With a background in computer science and physics, I am currently following my academic journey in quantum computing. I have previously worked as a software engineer and gathered some hands-on experience in machine learning. Some of my recent insights include the applications of innovative techniques of quantum computing to machine learning, around which I am going to construct my talk.

  • Grover’s quantum search for data science and why should we care
Viacheslav Inozemtsev

I am a Software Engineer with 10 years of professional experience in data and backend engineering. During my career I have contributed to the design and development of various data systems, such as data lake, data mesh, lakehouse, batch and streaming data pipelines. My interests: software architecture principles, programming patterns, distributed data processing frameworks, distributed storage systems, file/table formats, queueing systems, databases, resource managers, schedulers, metastores, cloud services. I hold a Specialist degree in Applied Mathematics and Computer Science, and a Master's degree in Computer Science. I speak English, German, and Russian.

  • Building a Lakehouse data platform using Delta Lake, PySpark, and Trino
Vladimir Orshulevich

Vladimir Orshulevich is an NLP research engineer at Unum. He used to work as a Research Engineer in SberDevices.

  • Semantic Multimodal Multilingual Similarity engine
Yana Khalitova

I am an Applied Scientist, currently working in Zalando (Berlin, Germany). Throughout my career I have contributed to the design and development of several machine learning projects, spanning from product localisation to optimising onsite marketing campaigns. In the past I also conducted independent research in the areas of natural language processing and statistical modelling. I have strong interest in building complex systems using Python, Spark, as well as cloud technologies such as AWS. I am a career changer, having a background in humanities and music.

  • Exploratory Data Analysis and Feature Engineering with PySpark
Zohreh Jafari

I have studied Geometry in university, but programming and numerical solutions have never ceased to fascinate me. For now, I've come to conclusion that I like using Mathematics for obtaining more precise answers in data science.

Several years ago at age 38, I left my job as a lecturer/researcher in Computer Science department to face more challenges in data industry.
Although, the data industry is inherently interdisciplinary, broad and constantly evolving, and the ecosystem around it very noisy, I was very lucky to begin from classic BI and step by step found my way towards big data pipeline.

Currently I am head of data team at Avihang which is a software company finding solutions and producing enterprise products in e-health, mostly.

  • Building a Streaming (E-health) Data Pipeline: When and How?