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Machine Learning Engineer
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Assessing your cultural and operational fit
EE masters + CS minor graduate from Georgia Tech with an emphasis on machine learning, machine vision and perception. Working with machine learning algorithms, including deep neural networks. Also experienced with signal processing, control theory, software design & architecture.
Georgia Institute of Technology
Masters, Electrical Engineering
January 1, 2014 – January 1, 2016
Georgia Institute of Technology
Bachelor's degree, Electrical Engineering
January 1, 2009 – January 1, 2013
Self-employed
Independent Researcher
July 1, 2024 – Present
Niantic, Inc.
Machine Learning Engineer
June 1, 2017 – July 1, 2024
San Francisco, California
SwarmX
CTO
October 1, 2016 – March 1, 2017
San Francisco, California
Bigstream
Machine Learning Engineer (Contractor)
July 1, 2016 – October 1, 2016
Sunnyvale, California
Thermo Fisher Scientific
Data Analytics and Machine Learning Intern
July 1, 2015 – December 1, 2015
South San Francisco, CA
Georgia Tech Research Institute: Aerospace, Transportation and Advanced Systems Laboratory
Graduate Researcher
August 1, 2014 – May 1, 2016
Georgia Tech Research Institute: Aerospace, Transportation and Advanced Systems Laboratory
Graduate Research Assistant
March 1, 2014 – September 1, 2014
Ubiquitous Computing Lab
Research Assistant
December 1, 2013 – August 1, 2014
Atlanta, Georgia
Intelligent Devices Inc.
Electrical Engineering Co-Op
May 1, 2010 – September 1, 2012
Suwanee, Georgia
Phalangee Project: Wrist-mounted mobile computer interface
December 1, 2013 – Present
- Collaborator on the Phalangee project: designing a wrist–mounted inertial sensing device to enable new mobile computer interface modes. Also coauthor on Phalangee paper, not yet published. - Development of drivers to interface with I2C sensors on an Arduino platform. - Analysis of analog designs for electromyographic (EMG) signal acquisition and conditioning. - Investigation of statistical learning techniques for inertial signal classification. - Design of Java classes to process and segment sensor data for machine classification. - Preparation of plots and confusion matrices for paper publication.
Electrical Engineering Senior Design Project
October 1, 2013 – Present
Multidisciplinary (electrical and biomedical engineering) effort to design and build an EMG sensing and control system for an assistive hand orthosis. - Designed software architecture for threshold state machine control in Labview. - Compiled literature review on machine learning techniques for EMG signal classification. - Presented poster at senior design expo.
Computer Vision Project
April 1, 2013 – Present
Team project to design a system for real-time 3D scene reconstruction in C# with the Microsoft Kinect. - Developed framework for real-time image acquisition and processing in C#. - Implemented iterative closest point algorithm using math libraries from Iridium.NET. - Composed report on design process and implementation.
Cultural Fit Analysis
The candidate's background is heavily skewed towards Machine Learning Engineering and Research, with significant experience in academic and R&D settings. While there's strong technical depth, the direct alignment with a 'Data Analyst' role, which often emphasizes business intelligence, reporting, and specific data visualization tools, is not explicitly demonstrated. The projects show a strong inclination towards algorithm development and system design rather than pure data analysis for business insights. The diversity of projects (biomedical, robotics, genomics, defense) indicates adaptability and a broad interest in applying technical skills, but the focus remains on engineering and research rather than traditional data analysis.
Soft Skills & Operational Fit
The candidate's project descriptions highlight collaboration in multidisciplinary teams and research environments, suggesting good teamwork and communication skills. Experience as a CTO and independent researcher indicates leadership, initiative, and problem-solving abilities. The detailed descriptions of technical challenges and solutions imply a strong operational fit for roles requiring deep technical engagement and independent work.