Technical Lead & Deep Learning Research Engineer


Aug 2020 – Present Boston, MA
  • Technical lead for building out an experimentation platform for deep learning model benchmarking & analysis

    • Comparing results between NVIDIA GPUs and Lightelligence Optical Processing Units (OPUs)

    • Variety of other tools for interdisciplinary teams

  • Contributed to development of initial OPU SDK, particularly in API design and refactoring

  • Conducted feasibility studies of different model architectures for deployment onto OPUs


Staff Computer Vision and Deep Learning Research Engineer

IBM Research

May 2019 – Jul 2020 Cambridge, MA

Computer Vision and Deep Learning Research Engineer

IBM Research

Jul 2018 – May 2019 Cambridge, MA
  • Trained and tested activity detection models for spatio-temporally localizing and recognizing activities in surveillance video for the IARPA DIVA Program

  • Collaborated with researchers from MIT and Purdue University to build a state of the art activity detection system and publish papers to top conferences

  • Designed and developed an end to end activity detection system for running in a deployed environment

  • First place in the TRECVID Activity in Extended Video 2018 competition

    • Second place overall in DIVA Phase 1B evaluations

Software Engineering Intern


May 2017 – Aug 2017 Durham, NC
  • Developed Tensorflow and Scikit-Learn machine learning models to detect and predict container failures in Kubernetes clusters.

  • Designed and architected machine learning pipeline for data cleaning, feature extraction, normalization, learning, and prediction

  • Containerized Go and Python applications into Docker images to be deployed into a Kubernetes cluster

  • Worked on an Agile team and practiced continuous integration and continuous development using GitHub and TravisCI

  • Developed a 4-minute pitch every week with project team to present to IBM executives


Deep Learning Research Intern


Jun 2016 – Feb 2017 Gainesville, FL
  • Researched an aligned Point Cloud and Building Information Model object recognition model using 3D CNNs
  • Researched depth upsampling methods to generate better depth information from low resolution depth images and high resolution color images of the same scene using CNNs.