Starting Up

Hey friends! I really try not to use this blog for shameless self-promotion, but today I just can’t resist.

Nick (CTO) and I digging into some data

Back in August, I joined a startup in Boston called ecovent Systems as the eighth full-time employee. We’re building a system of wirelessly controlled vents and sensors that lets you set each room in your home to a different temperature. I’m leading the cloud-based software development and data science efforts, while also leveraging my background in electronics design and embedded computing to help build the engineering team. It’s a really exciting and unique opportunity, as I get to work with some old friends from my one of my first jobs.

Today is a big day for us. First of all, we’re announcing that we have raised $2.2M in seed funding to get our great prototype product ready for production. On top of that, we’re also now accepting pre-orders for our system, which will ship in August 2015.

To help explain how the system works, I wrote up a short blog post at Check it out. And if you think this could help you in your home, please consider pre-ordering.

The Biggest Pond

What I learned taking one of MIT’s hardest graduate computer science classes (besides lots of math)

typical Tuesday morning

This past fall, I had the rare opportunity to take a graduate engineering course at MIT without being enrolled in a degree program. I knew a class would be hard, but being a student at the best technical school in the world, if only for a couple months, was something I had to experience. I talked to some students and recent grads, picked a class, and registered.

It’s hard to explain what the class was about. It was called “Algorithms for Inference”, and it focused on probabilistic graphical models, a framework for dealing with complicated probability distributions. There are no analogies I can use to explain what that means that would make sense to someone who hasn’t taken a graduate level probability and statistics course. Essentially, the course covered a lot of the mathematical techniques that underlie cutting edge artificial intelligence algorithms. Despite being an esoteric topic, it has become pretty trendy in certain computer science communities. It’s also known by students to be one of the hardest graduate subjects offered by MIT’s elite Electrical Engineering & Computer Science department.

The first lecture was fun. The professor was great and rather brilliantly outlined the course and its significance in the larger field of artificial intelligence and machine learning. It wasn’t until I sat down with the first problem set that I realized just how much trouble I was in. I remember spending an entire Saturday on the first question and barely making progress. My old college textbooks and Google searches were all but useless. After hitting the library and getting help from the TAs and my PhD co-workers, I finally started grasping the fundamentals. But things were going painfully slowly. If you’ve ever wondered how smart MIT students are, they’re crazy smart. Scary smart. One-in-a-thousand smart. I wouldn’t be surprised at all if the average IQ in that classroom was 140. Being so thoroughly outmatched was a new experience for me.

A few problem sets into the semester, I started to get the hang of things. Still, I had to spend about 20 hours per week outside of the classroom reading and working on problems. I came to appreciate that struggling with the workload is a fundamental learning tool. The expectations on an MIT graduate student are intense. This course packed far more content into a semester than my bachelor’s and master’s courses had. You can’t learn material at this depth by simply attending lectures and flipping through slides. Only by working through the problems, which always seem impossible at first, can you develop a profound understanding of the mathematical nuances that make these algorithms and models work. My classmates knew this and rarely complained. I found that the students weren’t just intellectually gifted but also ridiculously hard-working. I struggled at times to motivate myself, but I put in the work, managed roughly average performance, and made it through the class.

So what did I learn (besides probabilistic graphical models)?

When it comes to intellectual pursuits, striving to be the best is an impossible goal. This might sound like blasphemy to the over-achiever culture, but let’s be real. There will always be someone better than you in some way: smarter, faster, or harder working. Rather than worrying about being the biggest fish in your pond, focus instead on finding the right pond. Lots of people dream about getting a PhD at MIT because we’ve all heard stories about amazing people conducting impactful, innovative research. No one aspires to “just get by” at an elite institution. Find a place in the world where you’re challenged and need to put forth your best, but also where you’re good enough to make a real difference. And if you keep working hard and learning, who knows where you’ll end up.

Photos from the 2014 Boston Marathon

We had another great year watching the marathon in our usual spot near the Newton firehouse. We arrived just in time to see the elite women.


Although it wasn’t as obvious on TV, the security presence was noticeably larger.


Twenty minutes after the women, Meb Keflezighi runs by, way out in front of the pack.


…and looks back.


The elite men’s pack isn’t too far behind.


This year, the crowd on Commonwealth Avenue is the largest I’ve ever seen.


Despite the increased security, fans still cheer on the runners in creative ways.


And the crowd absolutely erupts for Team Hoyt, possibly running their last marathon.


Over-the-Air HDTV Looks Good

My latest move in the on-going battle to keep my cable bill down was to ditch the set-top box on my second TV. Now, I’m using an inexpensive antenna and over-the-air DVR. The picture quality is actually amazing. It turns out the over-the-air standard is 19.39 mbps, while cable companies allegedly compress the video down to the 8-13 mbps range.

NBC screen shot

I’d post a full-resolution screen shot, but I don’t want to get sued. Take my word for it: it looks better than cable.

If you’re in range of broadcast HDTV (you can determine what channels you’ll get here), I highly recommend grabbing an HD antenna (like this one) and trying it out. I’ve also been using this over-the-air DVR with a USB hard drive. It’s got a clunky interface, but works just fine.

The new equipment will pay for itself in a couple months. Plus, the video content I record is completely DRM-free, which means I can watch it on any device without any subscriptions.

Expected Payout for Football Squares

Ever wonder which squares you want to get in your Football Squares pool? I did a quick analysis using NFL data, looking at the last digit of the score after each quarter of every game since 2002.

Assume a $100 pot ($1 per square) with these payouts:

  • 1st quarter: $12.50
  • Halftime: $25
  • 3rd quarter: $12.50
  • Final: $50

Here’s the expected payout of each square:

1 2 3 4 5 6 7 8 9 0
1 $0.90 $0.17 $0.95 $1.40 $0.25 $0.45 $1.36 $0.51 $0.35 $1.24
2 $0.17 $0.07 $0.18 $0.33 $0.14 $0.14 $0.46 $0.09 $0.12 $0.49
3 $0.95 $0.18 $2.86 $1.63 $0.19 $1.18 $2.98 $0.54 $0.47 $4.27
4 $1.40 $0.33 $1.63 $2.17 $0.25 $0.72 $2.82 $0.53 $0.45 $2.96
5 $0.25 $0.14 $0.19 $0.25 $0.15 $0.13 $0.47 $0.22 $0.10 $0.46
6 $0.45 $0.14 $1.18 $0.72 $0.13 $0.55 $1.13 $0.24 $0.24 $1.52
7 $1.36 $0.46 $2.98 $2.82 $0.47 $1.13 $4.22 $0.55 $0.56 $5.67
8 $0.51 $0.09 $0.54 $0.53 $0.22 $0.24 $0.55 $0.46 $0.13 $0.88
9 $0.35 $0.12 $0.47 $0.45 $0.10 $0.24 $0.56 $0.13 $0.16 $0.57
0 $1.24 $0.49 $4.27 $2.96 $0.46 $1.52 $5.67 $0.88 $0.57 $7.45

Any expected payout greater than $1 (shown in bold) is a great square!

(Disclaimer: This database is derived from the play-by-play dataset, which contains play data through Week 12 of the 2013 regular season. All subsequent processing is automated and has not been verified for accuracy. I make no guarantee that these stats are accurate — use at your own risk!)