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I explore two questions in this thesis: how can computer science be used tofight human trafficking? And how can computer vision create a sense of touch?  I use natural language processing (NLP) to monitor the United States illicitmassage industry (IMI), a multi-billion dollar industry that offers not justtherapeutic massages but also commercial sexual services. Employees of thisindustry are often immigrant women with few job opportunities, leaving themvulnerable to fraud, coercion, and other facets of human trafficking.Monitoring spatiotemporal trends helps prevent trafficking in the IMI. Bycreating datasets with three publicly-accessible websites: Google Places,Rubmaps, and AMPReviews, combined with NLP techniques such as bag-of-words andWord2Vec, I show how to derive insights into the labor pressures and languagebarriers that employees face, as well as the income, demographics, and societalpressures affecting sex buyers. I include a call-to-action to other researchersgiven these datasets. I also consider how to creating synthetic financial data,which can aid with counter-trafficking in the banking sector. I use anagent-based model to create both tabular and payee-recipient graph data.  I then consider the role of computer vision in making tactile sensors. Ireport on a novel sensor, the Digger Finger, that adapts the Gelsight sensor tofinding objects in granular media. Changes include using a wedge shape tofacilitate digging, replacing the internal lighting LEDs with fluorescentpaint, and adding a vibrator motor to counteract jamming. Finally, I also showhow to use a webcam and a printed reference marker, or fiducial, to create alow-cost six-axis force-torque sensor. This sensor is up to a hundred timesless expensive than commercial sensors, allowing for a wider range ofapplications. For this and earlier chapters I release design files and code asopen source.

Machine Learning for Tangible Effects: Natural Language Processing for Uncovering the Illicit Massage Industry & Computer Vision for Tactile Sensing