Mikayel Samvelyan, Deep Learning Developer in Yerevan, Armenia
Mikayel Samvelyan

Deep Learning Developer in Yerevan, Armenia

Member since October 5, 2017
Mikayel is a machine learning expert and data scientist with a master's degree from the University of Oxford, specializing in natural language processing, reinforcement learning, and computer vision. He has substantial experience in research and development of deep learning solutions and has worked at such companies as Reddit and Mentor. Mikayel has co-authored several scientific publications at leading machine learning conferences.
Mikayel is now available for hire

Experience

  • Python, 6 years
  • C++, 5 years
  • Data Science, 4 years
  • Deep Learning, 4 years
  • Natural Language Processing (NLP), 3 years
  • PyTorch, 3 years
  • TensorFlow, 2 years

Location

Yerevan, Armenia

Availability

Full-time

Preferred Environment

Vim, PyCharm

The most amazing...

...project I've worked on is the development of a new AI algorithm that masters the game of StarCraft II.

Employment

  • Machine Learning Expert

    2019 - 2019
    Reddit (via Toptal)
    • Developed deep-learning solutions for large-scale natural language processing tasks using PyTorch.
    • Fine-tuned pre-trained NLP models, such as BERT, XLNet, and RoBERTa.
    • Conducted topic modeling experiments using methods such as LDA, NMF, and so on.
    • Built a modular pipeline for large-scale natural language classification tasks.
    • Analyzed large-scale datasets using BigQuery.
    Technologies: Python, PyTorch, BERT
  • Data Scientist

    2018 - 2019
    Highlander Technology, Inc. (via Toptal)
    • Developed and optimized deep learning solutions for large scale natural language processing tasks using PyTorch.
    • Implemented noise and novelty detection on a large corpus of data.
    • Performed rigorous data mining for raw and noisy data.
    • Created an API endpoint to use the model for multiclass classification.
    • Built a machine learning pipeline for large-scale multiclass classification.
    Technologies: Python, PyTorch
  • Machine Learning Consultant

    2018 - 2018
    Mission Ready Marketing, LLC (via Toptal)
    • Designed a pipeline for machine learning methods to be used in a recommendation engine.
    • Examined the methods for building a recommendation system with unsupervised learning techniques for settings without historical data.
    • Analyzed item-based, user-based, matrix factorization-based, and hybrid recommender systems.
    • Created a full technical approach documentation for connecting the learning component of the project with the database and back-end.
    Technologies: Python
  • Machine Learning Researcher

    2017 - 2018
    XIX.ai
    • Created a machine learning framework for predicting a user's intentions.
    • Designed and implemented a deep reinforcement learning algorithm for large-scale fleet management.
    • Created a simulator for a ride-hailing service using Python.
    Technologies: Python, PyTorch, Sacred
  • Research and Development Engineer

    2014 - 2016
    Mentor Graphics
    • Conducted research about algorithms for logic optimization, partitioning, placement, and routing.
    • Developed a unified environment for design capturing, simulation setup, verification, and analysis for a custom integrated circuit design platform.
    • Integrated the environment with third-party IDE software.
    • Created various kinds of reusable compiler-compilers using Lex and Yacc.
    Technologies: C++, Lex, Yacc
  • Software Engineer

    2012 - 2013
    Instigate Robotics
    • Built firm/software for robotic applications.
    • Developed 3D printing technologies.
    • Created an application's graphical user interface (GUI) with C++ and Qt.
    • Designed and implemented various embedded applications on STM32 and Arduino MCUs.
    • Built an educational development environment for robotics.
    Technologies: C, C++, Smalltalk, Qt
  • Software Engineering Intern

    2012 - 2012
    Instigate Design
    • Developed a hard/software independent environment for parallel computing on multiprocessor computers.
    • Supported the development of a compiler's front end.
    Technologies: C++, Clang

Experience

  • QMIX: Monotonic Value Function Factorization for Deep Multi-agent Reinforcement Learning (Other amazing things)
    http://proceedings.mlr.press/v80/rashid18a.html

    QMIX is a state-of-the-art value-based algorithm for collaborative deep multi-agent reinforcement learning for the settings of centralized training with decentralized execution.
    QMIX employs a neural network that estimates joint action-values as a complex nonlinear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximization of the joint action-value in off-policy learning, and guarantees consistency between the centralized and decentralized policies.

    QMIX was presented at ICML 2018, one of the leading conferences for machine learning research.

  • PyMARL: Python Multi-agent Reinforcement Learning (Development)
    https://github.com/oxwhirl/pymarl

    PyMARL is a research platform for deep multi-agent reinforcement learning which allows out-of-the-box experimentation and development.

    Written in PyTorch, PyMARL features implementations of several state-of-the-art methods such as QMIX, COMA, and independent Q-Learning.

    In collaboration with the Berkeley AI Research lab, some of the above algorithms have also been successfully ported to the scalable RLlib framework.

  • SMAC: The StarCraft Multi-agent Challenge (Development)
    https://github.com/oxwhirl/smac

    The StarCraft Multi-agent Challenge (SMAC) is a benchmark for cooperative multi-agent reinforcement learning (MARL) that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.

    Paper: https://arxiv.org/abs/1902.04043
    Blogpost: http://whirl.cs.ox.ac.uk/blog/smac/
    Blogpost by NVIDIA: https://nvda.ws/2I88F1I

    SMAC was presented at AAMAS 2019, the largest and most influential conference in the area of agents and multiagent systems.

Skills

  • Languages

    C++, C#, Python, Java, Lua, SQL, R, C, Assembler x86, Prolog, Lisp, JavaScript, Octave
  • Frameworks

    Flask, .NET, Spark
  • Libraries/APIs

    SciPy, NumPy, Pandas, PyTorch, TensorFlow, Sklearn
  • Tools

    Jupyter, LaTeX, Makefile, Vim Text Editor, Subversion (SVN), Git, PyCharm, CVS, MATLAB, Tmux, Tableau, BigQuery, Amazon SageMaker
  • Platforms

    Unix, Docker, MacOS, Linux, AWS EC2
  • Other

    System Programming, Deep Learning, Reinforcement Learning, Natural Language Processing (NLP), Custom BERT, Statistics, Robotics, Computer Vision, Recommendation Systems, Big Data
  • Paradigms

    Requirements Analysis, Design Patterns, Data Science
  • Storage

    MongoDB, PostgreSQL

Education

  • Master of Science degree in Computer Science
    2016 - 2017
    University of Oxford - Oxford, United Kingdom
  • Master of Science degree in Computer Science and Applied Mathematics
    2014 - 2016
    Yerevan State University - Yerevan, Armenia
  • Bachelor of Science degree in Computer Science and Applied Mathematics
    2010 - 2014
    Yerevan State University - Yerevan, Armenia
  • Undergraduate Exchange in Computer Science
    2013 - 2013
    Delta State University - Cleveland, MS, USA

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