Projects

  • Airborne Collision Avoidance System X (ACAS X)
    • MIT Lincoln Laboratory - Group 42
    • Software for generating airborne collision avoidance advisories for manned and unmanned aircraft
      • Logic formulated as a Markov decision process
      • Computational strategies employed for handling large state space, including
        • Multilinear interpolation
        • Parallel computing
      • Contributed visualization tool for analysis of policy evolution through the course of the value iteration algorithm
    • C++, Julia, MATLAB
  • ClutteredEnvPathOpt.jl (repo)
    • Rice University - with Illya V. Hicks, Joey Huchette and Miles Olson
    • Package for solving optimal path planning problems in cluttered environments for robots and drones
      • Implemented the independent branching scheme to formulate obstacle avoidance disjunctive constraints, including an algorithm for obtaining the necessary biclique covers on a special class of graphs
      • Includes infrastructure for creating obstacles, generating obstacle-free regions, and constructing the associated graphs
    • Julia
  • Audubon_F21 (repo)
    • Rice University - Data to Science (D2K) Lab
    • Package for identifying and censusing various colonial waterbird species from UAV imagery of nesting islands
      • Sponsored by Houston Audubon for real-world deployment in their waterbird monitoring studies
      • Employs a Faster R-CNN object detector with a ResNet-50 feature pyramid network backbone, implemented with Detectron2
      • Capable of detecting 10+ species, with the 3 most populous (constituting over 70% of the population) being detected with an AP of over 90% (at an IoU of 0.5)
      • Led experimentation of a customized implementation of Faster R-CNN utilizing a DenseNet backbone
      • Bayesian hyperparameter optimization used for selection of learning rate and decay factor
      • Data augmentation techniques for minority species include horizontal and vertical mirrorings, 90 degree rotations, and random brightness and contrast adjustments
    • Python
  • Forecasting Yearly Battery Replacements
    • Rice University - Data to Science (D2K) Lab
    • Mentored a team of students on a capstone data science project focusing on forecasting yearly battery replacements for LivaNova‚Äôs vagus nerve stimulator medical device
    • Team employed survival analysis to generate battery durations for implants for both existing patients and new patients
      • Existing patients: Survival function modeled with a Log-normal Accelerated Failure Time model
      • New patients: Survival function modeled with a Kaplan-Meier estimator
    • 1st Place in the Fall 2022 D2K Showcase