Haowen Xu
Software Development Engineer at Amazon
Bellevue, Washington, United States
Summary
👤
Senior🎓
Top SchoolHaowen Xu is a Software Development Engineer with 13 years of experience building scalable web and cloud-native systems, currently at Amazon after earning an MS in Computer Science from Columbia and a BA in CS & Economics from UVA. He designs end-to-end production pipelines—streaming audio ingestion, real-time transcription, and ML-driven feedback loops—having engineered systems that handle thousands of concurrent streams and optimized inference workloads to cut costs by 60%. Comfortable across FastAPI, Google Cloud Dataflow/Pub/Sub, AWS ECS/Inferentia, and BigQuery, he blends strong foundations in algorithms and databases with hands-on deployment and observability practices. An open-source contributor to probabilistic ML tooling, Haowen improved StochasticTensor behavior in the zhusuan library, highlighting his attention to correctness and testing in Bayesian deep learning tools. Based in Bellevue, WA, he gravitates toward applied AI and distributed systems, often translating research ideas into robust, low-latency production services.
13 years of coding experience
1 year of employment as a software developer
Exchange Student, Computer Science, Exchange Student, Computer Science at Fudan UniversityHigh School Diploma, High School Diploma at Wakefield SchoolMaster of Science - MS, Computer Science, 3.91/4.0, Master of Science - MS, Computer Science, 3.91/4.0 at Columbia UniversityBachelor of Arts - BA, Computer Science and Economic, 3.95/4.0, Bachelor of Arts - BA, Computer Science and Economic, 3.95/4.0 at University of Virginia
Chinese, English, Spanish
Stackoverflow
1reputation
1kreached
0answers
1question
Github Skills (11)
bayesian10
probabilistic-programming10
tensorflow10
python10
bayesian-inference10
deep-learning9
generative-model8
unit-testing8
graphical-models7
video-processing6
ffmpeg6
Programming languages (4)
Github contributions (5)
thu-ml/zhusuan
May 2017 - Dec 2017
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 3 PRs, 2 comments in 7 months
Contributions summary:Haowen primarily focused on improving the `StochasticTensor` functionality within the `zhusuan` probabilistic programming library. Their contributions included adding an "observed" argument for broader usability, fixing operator override issues, and incorporating unit tests to ensure the correct behavior of `StochasticTensor`. They addressed a specific bug related to the handling of `StochasticTensor` within the BayesianNet context, enhancing the library's robustness.
information-theorydeep-learningbayesian-inferencegraphical-modelsmachine-learning
korepwx/railgun
Jul 2014 - Dec 2014
Contributions:436 commits, 1 comment in 5 months
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