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Job description: In order to discern the depths of potential cysts and lesions inside the breast using ultrasound, it is important to know the speed of sound as is passes through the medium. In our case, this was water. The speed of sound in water is heavily dependent on the temperature of the water. It was therefore my job to calibrate and compensate temperature sensors to build an accurate temperature model of the water.

Our system contained two high-accuracy sensors and 157 low-accuracy sensors. Using signal processing techniques, the first step was to reduce drift in measurement of the low-accuracy sensors. The next step was to compensate the low-accuracy sensors for device parameters obtained as an average of values close to the high-accuracy sensors. The final step was to develop a model of the real temperature seen by the low-accuracy sensors using a low-order polynomial fit.

Comments: The personally rewarding thing for me was to discover CS beyond what is taught at Stanford. Most students at Stanford, myself included, are overexposed to AI and underexposed to good engineering practices. This internship helped to remind me of those good engineering practices.

I enjoyed meeting people from all around Europe and even the world. My team was very diverse.