Darron Fuller, Developer in Washington, DC, United States
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Darron Fuller

Verified Expert  in Engineering

Machine Learning Engineer and Developer

Location
Washington, DC, United States
Toptal Member Since
July 29, 2019

Darron has 15 years experience as a client-facing lead engineer of infrastructure and mission-critical advanced analytics software for notable clients in the renewable energy industry and U.S. Department of Defense. He has degrees in data analytics and computer science, covering machine learning and decision optimization.

Data AnalyticsMachine LearningPythonBokehPandasNumPyPyCharmAgileJavaC++KerasTensorFlowJupyterDockerGit

Availability

Part-time

Preferred Environment

Amazon Web Services (AWS), Git, Docker, Jupyter, PyCharm, Python

The most amazing...

...project I've designed was an advanced energy market trading algorithm with multiple machine learning (predictive) and optimization (prescriptive) models.

Work Experience

Lead Machine Learning Engineer

2013 - PRESENT
StreamEdge Analytics, LLC
  • Designed and implemented specific components of the Xfinity Voice Assistant Platform that is used by millions of customers every day. Applied machine learning and cloud technologies to process billions of natural language queries.
  • Supported the $1.2 billion contract with the Centers for Medicare Services and worked as the lead machine learning engineer on the project. I focused on predictive modeling, machine learning, and data mining on a large healthcare dataset.
  • Developed predictive models to improve the effectiveness of child support collection activities and monitoring of the health and safety of children in foster care. I oversaw the project as the lead machine learning engineer.
  • Developed multi-terabyte automated vehicle location data sets for public transit systems. Applied data mining, statistical inference, and machine learning to identify unsafe operational practices in public transportation systems.
  • Supported the special activities for the Chairman of the Joint Chiefs of Staff, and Office of Secretary of Defense, and Defense Manpower Data Center. I engineered and managed mission-critical manpower analytics and logistics systems.
  • Collaborated as the teaching and technical assistant to the lead instructors for the Predictive Analytics for Healthcare workshop of the largest conference on Predictive Analytics.
  • Worked as a senior machine learning engineer on Comcast Labs' applied AI research and engineering team that owns the full stack and operationalization of voice and video analysis product offerings, as well as advanced analytics and ML pipelines.
  • Developed machine learning platform tools and infrastructure for Xfinity voice remote and hands-free device machine learning models.
  • Architected the MLOps infrastructure for the deployment of TensorFlow and OpenVINO-based object-detection models. Implemented with AWS EKS, Seldon, Prometheus, and Grafana and developed load tests using Locust and Artillery frameworks.
  • Develop for Xfinity Computer Vision (XCV) Anomaly Detection Machine Learning models and infrastructure for Xfinity Home Security projects.
Technologies: Amazon Web Services (AWS), NVIDIA Grid SDK, Graphics Processing Unit (GPU), Jupyter, Pandas, Python, GitHub, Git, Google Cloud Platform (GCP), TensorFlow, Keras, Scikit-learn

Machine Learning Software Engineer

2017 - 2018
Greensmith Energy (Acquired by Wärtsilä)
  • Developed the machine learning and analytics software for the simulation and optimized operation of advanced renewable-energy storage systems (Singularityhub.com/2019/07/21/machine-learning-vs-climate-change-ai-for-the-greener-good/).
  • Designed and developed energy market trading optimization algorithms and applications.
  • Designed and developed Greensmith Energy's first real-time energy market trading process that makes use of advanced optimization models. Ran the models through a 30-day operational test with Électricité de France, the world's largest producer of electricity (En.wikipedia.org/wiki/Électricité_de_France).
  • Designed and developed an energy analytics framework, including energy management optimization, machine learning-based forecasting, and simulation services.
  • Proposed and led design, acquisition and, implementation of Greensmith Energy's largest software acquisition for embedded enterprise and reseller licenses of Gurobi Optimization and Mathematical Solver into Greensmith's Energy Management and Control System (GEMS), CD/CI pipeline, and GEMS Cloud and remote customer deployments.
Technologies: Gurobi, NumPy, Pandas, Scikit-learn, Python

Senior Software Engineer | Business Intelligence Specialist

2005 - 2013
Independent Consultant
  • Developed for various federal, DoD, intelligence communities, and Fortune 500 companies as an enterprise software engineer and business intelligence consultant.
  • Provided expertise on a variety of data extraction methods that would facilitate the State of New York's compliance with Hearst's FOIA request. I was the expert technical witness for Hearst Publishing in Hearst vs. State of New York (Casetext.com/case/hearst-v-state-of-ny).
Technologies: Tableau, IBM Cognos, Python, Java

Active Learning Research: Department of Computer Sciences at George Mason University

Active learning is a special case of machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature, it is sometimes also called optimal experimental design. [Wikipedia: https://en.wikipedia.org/wiki/Active_learning_(machine_learning)]

Renewable Energy Market Trading Optimization

Designed and developed Greensmith Energy's first real-time energy market trading process that makes use of advanced optimization models. Successful 30-day operational test by the world's largest producer of electricity, Électricité de France (EDF: https://en.wikipedia.org/wiki/Électricité_de_France) in a CAISO five-minute real-time energy market simulator. [https://en.wikipedia.org/wiki/California_Independent_System_Operator]

Languages

Python, Java, C++

Paradigms

Linear Programming, Agile, Object-oriented Programming (OOP), Functional Programming

Other

Machine Learning, Amazon Machine Learning, Google Cloud Machine Learning, Predictive Analytics, Predictive Modeling, Optimization, Mixed-integer Linear Programming, Data Analytics, Model Validation, SOLID Principles, Bokeh, Graphics Processing Unit (GPU)

Libraries/APIs

Scikit-learn, Pandas, NumPy, Keras, TensorFlow

Tools

Gurobi, PyCharm, IntelliJ IDEA, Jupyter, Git, GitHub, NVIDIA Grid SDK, IBM Cognos, Tableau, CPLEX

Frameworks

MOA

Platforms

Docker, Google Cloud Platform (GCP), Amazon Web Services (AWS)

2014 - 2017

Master of Science Degree in Data Analytics Engineering

George Mason University - Fairfax County, Virginia, USA

2014 - 2015

Graduate Certificate in Predictive Analytics

University of California at Irvine - Irvine, California, USA

1987 - 1993

Bachelor of Science Degree in Computer Science

University of Maryland at College Park - College Park, Maryland, USA