Art Wild
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolArt Wild is a PhD candidate in Computer Science based in New York with 13 years of engineering experience focused on machine learning, high-dimensional data analysis, and model-based learning. He combines academic rigor with hands-on backend and test automation work, contributing serialization tests to the core Julia language and improving literate-programming tooling in Weave.jl. Art has practical experience integrating financial data pipelines—implementing an IEX data downloader and history provider for QuantConnect’s Lean algorithmic trading engine—bridging research interests in computational finance with production systems. Comfortable across languages and testing frameworks, he specializes in making complex data workflows reliable and reproducible, an angle reflected in both his open-source contributions and doctoral research.
13 years of coding experience
MS, MS at Moskovskij Ekonomico-Analiticheskij InstitutMS, MS at Moskovskij Inženerno-Fiziceskij Institut (Gosudarstvennyj Universitet)PhD, PhD at City University of New York Graduate Center
Stackoverflow
107reputation
28kreached
3answers
0questions
Github Skills (23)
reproducible-research10
programming-language10
trading-algorithms10
trading10
testing10
finance10
data-serialization10
tradingview10
serialization10
dotnet-core10
literate-programming10
csharp10
algorithmic-trading10
julia10
data-structure9
Programming languages (15)
Github contributions (5)
JuliaLang/julia
Jan 2015 - Sep 2016
The Julia Programming Language
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:137 commits, 40 PRs, 94 pushes in 1 year 8 months
Contributions summary:Art primarily focused on adding serialization tests to the Julia Programming Language. They implemented comprehensive testing for various data types and functions to ensure proper serialization and deserialization, including booleans, integers, tuples, symbols, and expressions. The code changes involved creating serialization streams and verifying the integrity of serialized data. These tests significantly contributed to the robustness of the serialization features within the Julia programming language.
sciencejulia-languagemachine-learningjulialangpetsc
QuantConnect/Lean
Jul 2018 - Jul 2018
Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
Role in this project:Back-end Developer
Back-end DeveloperContributions:7 commits, 2 PRs, 26 comments in 2 days
Contributions summary:Art implemented an IEX data downloader, integrating IEX data into the Lean algorithmic trading engine. This involved creating a data queue handler to manage live data, as well as a history provider for historical data retrieval. The user also added tests for the history provider functionality. Further contributions include corrected CLI prompts and missing documentation references.
algorithmic-trading-enginepythonpinescriptbacktesting-trading-strategiesc-sharp
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