Traveling 2016

I flew about 75,000 miles this year. That’s a lot. For comparison, that’s about 1/3 of the way to the moon (238,855 miles on average). I went to:

  • LA (6 times – once for work, the rest for family stuff)
  • DC (3 times for work, including a memorable Snowpocalypse adventure)
  • New York, NY (for work, but managed to see Hamilton!)
  • Baltimore, MD (work conference)
  • Bloomington, Indiana (workshop)
  • Pittsburgh, PA (workshop)
  • Wichita, KS (work stuff)
  • Madison, WI (for a friend’s wedding!)
  • Arecibo, Puerto Rico (radio telescope!)
  • Carpinteria, CA (annual family Labor Day fun time)
  • Paris and Lyon, France (work conference)
  • Edinburgh, Scotland (work conference)
  • Singapore (work conference)
  • Hanoi and Ha Long Bay, Vietnam (vacation!)

So yeah, I’m a bit tired. That was probably too much. I’m going to try really hard not to repeat that in 2017, but who knows what will happen. I love traveling, but it is exhausting.

Here are some photo highlights from my year of travel.

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Multi-modal Learning Data Collection at (Small) Scale

subtitle: even the best-laid plans…

Last year (spring 2015) we collected a really nice set of data of students collaborating in groups of three. The data collection process wasn’t entirely smooth or perfect, but it generally went off without any major technical or logistical problems. We ended up with a really nice dataset of almost 150 students with high quality audio data (four channels per group), video recordings (one per group), and computer log files (ideally one per group, practically more than one). [NB: The annotated audio from this first phase of data collection will be made available soon to other researchers. You can read the paper about the data set (presented at Interspeech 2016) here.]

In the spring of 2016 we set off to do our second phase of data collection, in classrooms during a regular class period. So unlike the first phase where we had just two groups at a time with kids who had volunteered and were excited to try out some math problems (a.k.a. the best kids), we had up to 10 groups at once with varying levels of excitedness and/or willingness to follow directions. We mostly wanted to test out how well the audio capture worked with all of the background noise in a typical classroom environment and see if our speech models still held up.

Continue reading “Multi-modal Learning Data Collection at (Small) Scale”