Haowen Xu
Software Development Engineer at Amazon
Bellevue, Washington, United States
Summary
👤
Senior🎓
Top SchoolHaowen Xu is a versatile research scientist and adjunct professor based in Oak Ridge, Tennessee, with 13 years in research and 5+ years post-PhD in data science, AI, and software engineering. He is currently Research Staff (Level B) at UNSW and Adjunct Associate Professor at the University of Tennessee, Knoxville, building on his prior roles at Oak Ridge National Laboratory. An award-winning U.S. government researcher with 50+ peer-reviewed publications and patents, his work spans Urban Informatics, Visual Analytics, and GIS, and he frequently leads or co-leads projects as PI or Co-PI. In addition to his academic and policy work, he contributes to open-source software, notably improving the zhusuan probabilistic programming library (TensorFlow-based Bayesian deep learning) by enhancing StochasticTensor usability, addressing bugs, and adding unit tests. His technical foundation is in Civil & Environmental Engineering and Hydro-informatics from the University of Iowa, reflecting a strong blend of theory, geospatial data, and scalable software engineering. He collaborates across academia, national labs, and government-funded programs to translate complex environmental and urban data into robust, data-driven decision support.
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 (13)
tensorflow210
bayesian10
probabilistic-programming10
tensorflow10
python10
bayesian-inference10
deep-learning9
generative-models8
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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