William Grisaitis
Verified Expert in Engineering
Machine Learning Developer
William is a data scientist with a diverse background and skill set, having worked on deep learning, time series forecasting, Bayesian modeling, and more. He has co-authored academic research applying deep learning to computational neuroscience and has worked in industry applying machine learning to financial markets and consumer finance. William graduated from Duke University where he studied physics, finance, and literature.
Portfolio
Experience
Availability
Preferred Environment
Jupyter Notebook, Python, MacOS, Linux
The most amazing...
...project I've worked on was mapping neurons in the fruit fly brain with convolutional neural networks.
Work Experience
Data Scientist/Quantitative Analyst
A Hedge Fund in the United States
- Developed predictive models of electricity prices in the United States. Developed trading strategies.
- Conducted exploratory analysis to understand market behavior and predictive variables.
- Incorporated external data sources to improve prediction accuracy, trading profitability.
- Developed a pipeline to streamline data ingestion.
- Built new predictive models on an ongoing basis.
- Analyzed time series with correlation analysis, k-means clustering, and other methods.
Senior Software Engineer
Howard Hughes Medical Institute Janelia Research Campus
- Trained convolutional neural networks that learn to map neurons in images of brain tissue.
- Developed complete data science pipeline, including data storage, support code for training, cluster deployment, and evaluating results (metrics, plots).
- Co-authored publications about work.
Senior Associate
Capital One
- Conducted financial and statistical analysis for business decision making in consumer/small business finance.
- Developed software for an internal data science application, using React and Python on AWS.
- Filed a patent for a self-driving car with a built-in ATM.
Experience
Used Deep Learning to Infer Brain Structure in Fruit Fly Brain Imagery
https://ieeexplore.ieee.org/document/8364622My responsibilities include all aspects of the project:
* deep learning - training and evaluating neural networks. Developing support code for automating and repeating this work.
* data engineering - storing large image data, developing access layers
* sys admin - maintaining a cluster of GPU machines. Deploying code with Docker and Mesos. General Linux/Ubuntu sysadmin.
* miscellaneous - data visualization, Jupyter, visualizing results, etc.
Forecast Electricity Prices in Wholesale Energy Markets for Financial Trading
I worked with domain experts to develop a predictive linear model that successfully explains about 20% of all price movement in the market. Beyond statistical modeling and analysis, I also prepared all data needed for the project, starting from the ground up. This included data about the market itself as well as other data relevant to the problem, such as weather data.
Skills
Languages
Python, Bash, SQL, JavaScript, R
Frameworks
Caffe
Libraries/APIs
Scikit-learn, Pandas, NumPy, HDF5, Matplotlib, TensorFlow
Platforms
Jupyter Notebook, Docker, Linux, MacOS, Amazon Web Services (AWS)
Other
Machine Learning, Optimization, Linear Algebra, Microsoft 365, Libraries, Bayesian Statistics
Tools
NVIDIA Grid SDK, Jupyter
Paradigms
Data Science
Education
Bachelor of Arts Degree in Physics (major), Economics and Literature (minors)
Duke University - Durham, North Carolina
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