Philip Xu
Software Engineer 2 at Microsoft
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolPhilip Xu is a software engineer at Microsoft specializing in computer vision systems integrated with large language models, with 11 years of ML and software development experience. Since 2022 he has been Software Engineer 2 at Microsoft in Redmond, applying deep learning to vision tasks and multimodal AI. His background spans facial recognition, tracking, and live media streaming, with internships at IBM, Samsung, and other research-forward organizations. He independently engineered a deep learning stock-technical-analysis project using CNNs and LSTMs, including candlestick visualization and PyBrain integration. As a Teaching Assistant for Brown University's CS1470 - Deep Learning, he developed curriculum and mentored students, blending education with practical research. He earned a Bachelor of Applied Science in Computer Science from Brown University and brings a rare mix of production ML, research curiosity, and teaching experience to cross-disciplinary AI work.
11 years of coding experience
2 years of employment as a software developer
High School/Secondary Diplomas and Certificates, High School/Secondary Diplomas and Certificates at Lord Byng Secondary SchoolBachelor of Applied Science (B.A.Sc.), Computer Science, Bachelor of Applied Science (B.A.Sc.), Computer Science at Brown UniversityChoate Rosemary Hall
English, Chinese
Github Skills (15)
tensorflow210
lstm10
deep-learning10
tensorflow10
python10
neural-network9
technicalindicators9
analyse9
convolutional-neural-networks9
technical-indicator9
machine-learning8
pandas8
data-analysis8
numpy8
plotly7
Programming languages (3)
Github contributions (5)
philipxjm/Deep-Convolution-Stock-Technical-Analysis
Mar 2016 - Jul 2017
Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.
Role in this project:
ML Engineer
ML EngineerContributions:25 commits, 23 pushes, 1 branch in 1 year 3 months
Contributions summary:Philip primarily contributed to building a deep learning model for stock market prediction. They developed and modified a neural network framework with Python, incorporated historical stock data, and added candlestick chart visualization. They integrated PyBrain to construct and train recurrent neural networks using LSTM layers and added supporting modules. This work involved implementing and refining core components for stock data analysis and prediction.
convolutional-neural-networksneural-networkstock-marketstock-price-prediction
philipxjm/PieMessengerBackend
Sep 2015 - Oct 2015
Contributions:16 commits, 11 pushes, 1 branch in 22 days
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