Michael Wang
Founding AI Engineer at CarbonAI
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Wang is a Founding AI Engineer in Chicago with 9 years of experience building production ML systems across HFT, enterprise SaaS, and scrappy startups. He excels at 0-to-1 product delivery and leading teams through ambiguous product cycles to turn messy data into revenue-driving features. At Enigma he shipped transformer-based entity resolution that compared 4B+ business pairs and cleaned 200M+ raw business names, and he’s contributed to an open-source PyTorch time-series library (originally for flood forecasting) adding dropout sampling, early stopping, visualization with Plotly/Wandb, and interpretability tools. Currently at CarbonAI he focuses on automating commercial real estate workflows, combining hands-on code, model-driven product thinking, and talent development to deliver measurable impact.
10 years of coding experience
10 years of employment as a software developer
University of California San DiegoNanodegree, Machine Learning, Nanodegree, Machine Learning at UdacityUniversity of Illinois Urbana-Champaign
English, Chinese, Japanese
Github Skills (14)
forecasting10
pytorch10
machine-learning10
forecast10
time-series10
deep-learning10
anomaly-detection10
python10
testing10
wandb9
plotly9
shap9
lstm8
transformers8
Programming languages (2)
Github contributions (5)
AIStream-Peelout/flow-forecast
May 2020 - Jul 2020
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
Role in this project:
ML Engineer
ML EngineerContributions:38 commits, 4 PRs, 21 pushes in 1 month
Contributions summary:Michael primarily contributed to the evaluation and enhancement of a PyTorch-based time series forecasting library. Their work involved refactoring prediction functions, generating prediction samples for dropout layers, and adding comprehensive unit tests for evaluating model performance. They also made improvements to the training loop by integrating early stopping and incorporated plotting functionalities using Plotly and Wandb to visualize model outputs, including confidence intervals and heatmaps of Shap values, to improve model interpretability and provide actionable insights.
forecastingtime-series-analysistime-seriesclassificationautoencoder
michaelwang1994/Main
Mar 2016 - Mar 2022
Contributions:133 pushes, 1 branch in 6 years 1 month
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