Overview

This project aims at investigating the prevalence of microbial motility in Earth’s most extreme environments with the hopes of one day searching for life in extreme extraterrestrial environments in our solar system. This project has also served as the basis for my PhD project at Caltech’s department of Medical Engineering. Digital Holographic Microscopy is a three dimensional imaging technique that is able to capture the information of the full electric field of an object with a single image capture. DHM is capable of doing this because of its use of off-axis interferometry and numerical reconstruction. I was brought onto this project as a summer intern in 2014. At the end of the summer, I secured a permanent position as a Research Engineer. A few years later, I was admitted to the Andrew and Peggy Cherng Department of Medical Engineering at the California Institute of Technology in order to further develop DHM instrument. Throughout the course of my work as well as thesis research, I have contributed to the project in a very interdisciplinary manner. For more information regarding the project, please feel free to contact me, or see the Publications Page.

Hardware

My initial contributions to this project included hardware developments. Due to the unique optical constraints of this instrument, custom microfluidic sample chambers are required. It was my responsibility to design and fabricated these microfluidic devices. This involved extensive optical analysis of various sample chamber substrates as well. For more regarding microfluidic developments for this project see, my peer-reviewed article in PLOS One from 2016.

Recently, I designed and built a modified Mach-Zehnder DHM with the intention of designing an instrument that was capable of supporting multiple types of objective lenses with various magnifications and numerical apertures. An instrument with the capability of interchangeable objective lenses is a highly desirable instrument to many microbiologists. This instrument was successfully delivered to the Kenneth Nealson Group at the University of Southern California and is being used to investigate extracellular electron transport (EET).

Software

Due to holography’s compressive sensing abilities, data analysis is not only cumbersome, but resource hungry. A 4 MB hologram, once decompressed expands to over 2.5 GB. To deal with this large amount of data, I developed a supervised machine learning based analysis tool to autonomously analyze and track data from holographic data sets. This work was published in AIMS Biophysics.

This machine learning tracking algorithm is capable of tracking diffraction limited sized particles through four dimensional space with sub-micron accuracy, but is still computationally heavy. In an effort to detect particles in a computationally light manner, I developed a novel ‘low-level’ detection algorithm that is capable of detecting the lateral position of particles directly from a hologram, with no numerical reconstruction necessary. At the current time, our DHM instrument has a temporally resolution of 15 fps. This low level detection scheme is capable of analyzing holograms at a speed of ~300 ms. Current development is aimed at decreasing the computational time in order for this detection scheme to operate in real time at the highest temporal resolution of the instrument.

Future Work