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Iliyan Zarov, Python Developer in London, United Kingdom
Iliyan Zarov

Python Developer in London, United Kingdom

Member since January 4, 2017
Iliyan is a machine learning engineer and full-stack developer with a decade of experience. He is a co-founder of an AI health-tech company backed by Europe's most prestigious accelerator. Iliyan was previously lead research engineer in the AI Research team at Babylon Health, building and scaling up the AI doctor to millions of users. Before that, he built a massively scalable distributed computing platform and worked as a quant at a hedge fund.
Iliyan is now available for hire

Portfolio

  • myLevels
    Python, NumPy, Pandas, PyTorch, JavaScript, React Native, Flask, Docker, AWS
  • Babylon Health
    Python, TensorFlow, PyTorch, Docker, AWS
  • Evoqus
    Clojure, Go, Python, Julia, AWS, Docker, OpenStack

Experience

  • Python, 7 years
  • Machine Learning, 7 years
  • Deep Learning, 4 years
  • Distributed Computing, 3 years
  • TensorFlow, 3 years
  • PyTorch, 2 years
  • Bayesian Inference & Modeling, 2 years
  • Computer Vision, 1 year
London, United Kingdom

Availability

Part-time

Preferred Environment

Linux, Emacs, Git

The most amazing...

...thing I've built is an app that uses machine learning to estimate the impact of food on our bodies in real time.

Employment

  • Co-founder | CTO

    2018 - PRESENT
    myLevels
    • Deconstructed blood glucose levels time series into constituent signals using Bayesian machine learning techniques. We can then estimate and assign a score to the individual impact of each food. This appears to have never been done before and we filed a patent on this approach.
    • Built an NLP model to classify foods into food groups (e.g., grains, dairy, meat, etc.).
    • Developed a regression model to estimate the contribution of each food group to a food score.
    • Reverse-engineered and implemented the protocol for communicating with a continuous glucose monitor over NFC using Kotlin and Swift.
    • Built our offline-first mobile app in React Native with the help of a front end engineer who I supervised. Designed a custom synchronization protocol to guarantee that personally identifiable data never leaves the data center in the UK (as opposed to, for example, Google's Firebase).
    • Developed a stateless back-end exposing a RESTful API using Python and Flask.
    • Created an automatically generated personalized report using LaTeX and Python's Jinja.
    Technologies: Python, NumPy, Pandas, PyTorch, JavaScript, React Native, Flask, Docker, AWS
  • AI Research Engineer

    2017 - 2018
    Babylon Health
    • Built Dr Deep, making it up to 10x cheaper to run medical diagnosis at the same level of accuracy. Dr Deep is a neural network based universal marginalizer for amortized inference in generative models.
    • Published and presented Dr Deep at the NeurIPS Workshop on Advances in Approximate Bayesian Inference.
    • Designed a massively scalable cloud architecture and productionised the AI doctor inference engine. This was used to scale Babylon's AI doctor to millions of people.
    • Wrote a new inference engine from scratch in ~100 lines of PyTorch code (vs ~5,000 LOC for the TensorFlow implementation), making it much easier to use in our research.
    • Introduced PyTorch and Pyro at Babylon.
    Technologies: Python, TensorFlow, PyTorch, Docker, AWS
  • Founder

    2015 - 2017
    Evoqus
    • Created a cloud-based distributed computing platform from the ground up. Code can be executed with a single command across hundreds of CPUs and GPUs in the cloud.
    • Developed RESTful API back-end services with a custom orchestration layer in Clojure.
    • Developed a Go client for batch and fully interactive jobs. The Go client includes support for attaching console with raw terminal mode, tunneling, and large data files transfer.
    • Designed the back-end services to be stateless and resilient with automatic scale-out.
    • Developed end-to-end tests against AWS and a local OpenStack deployment.
    Technologies: Clojure, Go, Python, Julia, AWS, Docker, OpenStack
  • Quantitative Developer

    2012 - 2016
    Markham Rae
    • Researched and validated volatility quant trading and portfolio allocation strategies.
    • Built swaptions and exchange-traded options pricing tools.
    • Built tools for scenario analysis of yield curves, volatility cubes and model parameters.
    • Built an exotic FX option pricing & scenario analysis tool.
    • Transitioned all interest rate applications to a new multi-curve framework.
    Technologies: Python, SciPy, C++, C#, MATLAB
  • Senior Developer

    2008 - 2012
    Trayport
    • Built the aggregated liquidity pool platform and trading front-end.
    • Played a leading role in multiple projects on order routing and consolidation of hybrid inter-dealer broker and exchange venues for cross-asset and cross-venue trading.
    • Spearheaded the development of indirect order aggression features for implied spread trading.
    • Led the development of a latency and availability platform enhancement project.
    • Worked in a lead role of the architecture and development of a new exchange connectivity and a new clearing platform.
    • Prototyped a distributed data storage and processing engine on top of Apache Cassandra.
    • Developed an F#-based, domain-specific language and contract specification for a commodity product registry and standardization service.
    Technologies: C++, C#, F#

Experience

  • myLevels (Development)
    https://mylevels.com

    myLevels uses data from continuous glucose monitors and Bayesian machine learning to model the very personalized impact that food has on people’s bodies. We help you see and understand what foods are best for your unique metabolism. People using myLevels for weight loss have dropped more than 10 kilograms.

  • A Universal Marginalizer for Amortized Inference in Generative Models (Other amazing things)
    https://arxiv.org/abs/1711.00695

    We consider the problem of inference in a causal generative model where the set of available observations differs between data instances. We show how combining samples drawn from the graphical model with an appropriate masking function makes it possible to train a single neural network to approximate all the corresponding conditional marginal distributions and thus amortize the cost of inference. We further demonstrate that the efficiency of importance sampling may be improved by basing proposals on the output of the neural network. We also outline how the same network can be used to generate samples from an approximate joint posterior via a chain decomposition of the graph.

  • Evoqus (Development)

    Evoqus is a cloud platform delivering effortless scalability for technical computing in Julia. Code can be executed with a single command across hundreds of CPUs and GPUs in the cloud.

    The stateless back end services are written in Clojure. The platform can be accessed using the CLI, written in Go, or the web app built on React/Om and ClojureScript.

    Evoqus grew out of a side project aiming to make it extremely easy to move the computing from my laptop to the cloud on demand. The details of the underlying cloud instances, storage, and network are transparent to the end user while retaining full control over the workers and the environment.

Skills

  • Languages

    Go, Julia, C, C++, Clojure, Python, JavaScript, Kotlin, Swift, Java, F#, Scala, C#
  • Frameworks

    Machine Learning, React Native, Flask, ClojureScript, Boost
  • Libraries/APIs

    Pandas, SciPy, TensorFlow, NumPy, Compojure, AWS EC2 API, PyTorch, Standard Template Library (STL), SpaCy
  • Tools

    AWS ELB, Amazon Virtual Private Cloud (VPC), Emacs, LaTeX, Git, Amazon SQS, AWS IAM, MATLAB, AWS SES
  • Paradigms

    Functional Programming, Asynchronous Programming, REST, Concurrent Programming, Parallel & Distributed Computing, High-performance Computing, Agile
  • Platforms

    Docker, AWS EC2, Amazon Web Services (AWS), Linux, Ubuntu, CUDA, OpenStack
  • Storage

    AWS S3, PostgreSQL, AWS RDS, Cassandra, MySQL, Microsoft SQL Server
  • Other

    Financial Modeling, Financial Engineering, Recurrent Neural Networks, Neural Networks, Convolutional Neural Networks, Deep Neural Networks, Deep Learning, API Design, Data Analysis, Data Analytics, Quantitative Analysis, Financial Markets, Bayesian Inference & Modeling, Elastic Beanstalk, Probabilistic Graphical Models, Algorithms, Distributed Computing, Evolutionary Algorithms, GPU Computing, Optimization, AWS Route 53, Elastic Load Balancers, Mathematical Modeling, Containers, Container Orchestration, Probability theory, Quantitative Finance, High Availability, Networking, Statistical Modeling, Time Series Analysis, Data Structures, Integration Testing, PaaS, Scalability, Network Programming, Natural Language Processing (NLP), Medical Diagnostics, Computer Vision, Wireless Sensor Networks, log4j

Education

  • Master of Science degree in Mathematical Trading and Finance
    2009 - 2011
    Cass Business School - London, UK
  • Bachelor of Science degree in Electrical Engineering and Computer Science
    2004 - 2007
    Jacobs University Bremen - Bremen, Germany
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