  I explore two questions in this thesis: how can computer science be used to
fight human trafficking? And how can computer vision create a sense of touch?
  I use natural language processing (NLP) to monitor the United States illicit
massage industry (IMI), a multi-billion dollar industry that offers not just
therapeutic massages but also commercial sexual services. Employees of this
industry are often immigrant women with few job opportunities, leaving them
vulnerable to fraud, coercion, and other facets of human trafficking.
Monitoring spatiotemporal trends helps prevent trafficking in the IMI. By
creating datasets with three publicly-accessible websites: Google Places,
Rubmaps, and AMPReviews, combined with NLP techniques such as bag-of-words and
Word2Vec, I show how to derive insights into the labor pressures and language
barriers that employees face, as well as the income, demographics, and societal
pressures affecting sex buyers. I include a call-to-action to other researchers
given these datasets. I also consider how to creating synthetic financial data,
which can aid with counter-trafficking in the banking sector. I use an
agent-based model to create both tabular and payee-recipient graph data.
  I then consider the role of computer vision in making tactile sensors. I
report on a novel sensor, the Digger Finger, that adapts the Gelsight sensor to
finding objects in granular media. Changes include using a wedge shape to
facilitate digging, replacing the internal lighting LEDs with fluorescent
paint, and adding a vibrator motor to counteract jamming. Finally, I also show
how to use a webcam and a printed reference marker, or fiducial, to create a
low-cost six-axis force-torque sensor. This sensor is up to a hundred times
less expensive than commercial sensors, allowing for a wider range of
applications. For this and earlier chapters I release design files and code as
open source.
