resume in plain text
Morgan Bryant
email: mrbryant@alumni.stanford.edu/ github: github.com/taoketao/ personal site: https://taoketao.github.io
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Stanford Computer Science B.S. Recent Graduate Seeking Software Engineering Engineering Job
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Core Skills and Experiences
Back-End Software Engineering: Proficient in Python, SQL databases, AWS server remote computing
Front-End Web Development: Proficient in modern tools (such as Typescript, React, Node) to build web apps
Data Science and Machine Learning: Building ML-driven data collection and analysis pipelines in industry and university
Deep Learning: Experienced deep learning researcher and engineer, specialties in RL and Attention
Scientific Research: Comfortable with statistical, technical, human behavioral experimentation
Stanford University Student: Symbolic Systems Graduate; Computer Science Undergraduate
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Highlighted Projects
Front-end database portal providing access nontechnical employees via a custom web application
Back-end machine learning suite for analyzing and optimizing interactions between users and web media content
Deep machine learning undergraduate thesis
- Frames of Reference for Neural Pathfinding Navigation: Do artificial neural networks model human spatial cognition?
- Developed and trained deep reinforcement learning networks using Tensorflow
Deep learning research investigations in frontier DeepRL models, LSTM RNNs, CNN, and Attentional systems, lab-affiliated
Research in learning psychology with PDP lab
- Build and deployed web-based experiments and automatic data analysis pipeline to better understand human behavior
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Technical Positions
Computer Science and Programming subject Tutor, Teacher, Curriculum Designer // 2021
Lab Researcher // Parallel Distributed Processing Lab // Stanford Department of Psychology // 2016—2020
Master’s Student // Symbolic Systems Program // Stanford University // 2017—2020
- Computational Track. Coursework in computer science, psychology, linguistics, philosophy, cognitive science
Undergraduate Student // Computer Science // Stanford University // Conferred June 2019
- Artificial Intelligence concentration. Coursework in computer science, data science, statistics, applied math, machine learning, liberal arts
Computer Vision Intern // Magic Leap // Mountain View, CA // 2017
Machine Learning Intern // Trove // San Francisco, CA // 2016
Software Developer and Full-Stack Engineer at many angel-stage tech startups since 2014
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Specific Competencies
- I am most fluent in Python (7 years regular usage) and Javascript (4 years regular usage)
- Deep learning: backpropagation, artificial neural network, RNN, LSTM, GRU, CNN, GraphConv, RL Reinforcement Q-learning, A3C, AlphaGo, GANs, RAM, NTM, ResNet, GTP-3
- Machine learning: supervised and unsupervised learning, regression, classification, prediction, clustering, image segmentation, natural language processing, computer vision, robotics, modeling, data analysis
- Python 2.7 and 3.4-3.6, pip, IPython, Jupyter, Numpy, Scipy, Sci-Kit Learn, Matplotlib, Tensorflow, Keras, PyTorch
- Web programming: HTML, CSS, PHP, JavaScript, Typescript, RESTful APIs, JSON, XML, Ajax, Node.js, React
- Databases: SQL, AWS Amazon Web Services, big data computing, distributed remote cluster computing
- Other tools: Linux, Windows, Mac OSX, unix; bash, Git, Docker; C++, C#, Java, Matlab
- 3D printing, VR Virtual Reality, AR Augmented Reality
- Computational cognitive science, theoretical neuroscience, human psychology, linguistics, and 3D spatial cognition
- In free time, I enjoy studying Tae Kwon Do (1st degree black belt), being an avid musician, and learning abstract math