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Sam is a software engineer at Google Research - Machine Perception, where he researches robotic localization and computational photography. Before that he worked in Google X, and during academia interned with SpaceX in Avionics Flight Software/GNC, Amazon Music in cloud database services, and GFDL NOAA working in climate modelling.

He finished his Masters at Carnegie Mellon's Robotics Institute in Robotics Systems Development, focusing on Motion Planning. Finished undergraduate studies at Georgia Tech with two bachelors in Mechanical Engineering and Computer Science, specializing in modeling/simulation and artificial intelligence.

He enjoys building random prototype robots, designing their minds, and developing large-scale distributed simulations and services, especially on the cloud. It's a safe bet that the future of robotics and drone technologies will involve cloud-based intelligence augmenting the physical robot much as the internet and smartphones augment humans.

Popular posts from this blog

Visualizing TLE Orbital Elements with Python and matplotlib

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A Two-Line Element Set (TLE) is a way of encoding orbital elements of Earth-orbiting objects at a given time. This is the format NORAD provides data on many satellites in space right now. It'd be nice to see that as a 3d visualization. We use python to parse text files pulled from NORAD, and plot with matplotlib . Military satellite orbits For example the TLE for SpaceX's Dragon spacecraft during the CRS-2 mission was dragon = """DRAGON CRS-2 1 39115U 13010A 13062.62492353 .00008823 00000-0 14845-3 0 188 2 39115 51.6441 272.5899 0012056 334.2535 68.5574 15.52501943 306""" orbit.py  contains the code to parse the TLE into components as well as call graphics to draw it. print "Satellite Name = %s " % title print "Satellite number = %g ( %s )" % (satellite_number, "Unclassified" if classification =
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Strawberry DNA extraction and viewing with a cheap USB Microscope

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I had recently bought a cheap USB microscope  (10/60/200x magnification) and thought a fun use for it was to try and see DNA! DNA is really small though, so we can't expect to see the components of it with a cheap microscope, however it is very long, and it turns out strawberry DNA is the easiest to see and extract. Microscope with DNA on a slide I followed this DNA extraction lab by Steve Spangler , which was very easy and fun to understand. I took an album of videos and photos during the process. We can clearly see strands  10x Magnification - DNA strands clearly visible within slide As we zoom in further, we can see the clumps of DNA strands start to show up. 60x Magnification - we can see some foreign debris in the slide, the strands look like spiderwebs. One thing to note, the microscope puts out color images and default camera settings, which with a backlight makes it pretty hard to see anything. I added a light source to the side as well as pushed
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Relearning web development by writing a clicking game using React, Npm and Browserify

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I had some free time this vacation, and came across this interesting little sandbox game called  Pokemon or BigData by pixelastic . The aesthetics of the code are clean and simple. It uses React , which seems to be used for real-time web UIs. It also uses  npm  as a package manager. My previous forays into async calls in web dev were not nearly as pretty, so this seems like a good opportunity to learn something new. I decided a fun first start would be a simple game, and the simplest game I could think of was clicking a button to make a count go up. To give it some real-time feedback, I added some upgrade buttons that automatically click for you, a common incremental game technique. The buttons don't show up until there are enough clicks, and are grayed out if there's not enough to further upgrade. I set it to run at 50Hz to feel responsive. The heart of the code is basically a state machine that contains the current number of clicks and the number of upgrades
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