PyCon Tutorials (Days 1 & 2)

In a flash, the PyCon 2016 tutorials are over!

My session on network analysis was on the first day, in the morning. Overall, things went smoothly, and because of the competency level of the class, I was able to cover all of the material, including the ones that we usually don’t have enough time to get to (computational statistical inference on graphs, and bipartite graphs).

Most of the time, at the end of the workshop, I hear feedback on how to improve specific material, and details on what was new or useful for the participants. However, this time round, there was little of it. I was initially a bit disappointed, as I usually find ways to use the feedback to decide what to tweak for the next iteration. Later, over lunches and coffees (or in other tutorials), some participants did share their thoughts and feedback, and it was overall positive. Last night, I also shared some of these thoughts with David Baumgold (who led a Git tutorial) over a group trip to Powells, and that was a nice cheer-up as well.

I learned a bit about Lektor from David. Seriously thinking about moving my personal site out of WordPress and into Lektor, and off from BlueHost and onto DigitalOcean. Speed, cost, and customizability what I’m really thinking about right now.

Speaking of Powells: that bookstore is big! I had to ask for a bit of help to find the “science” section:

Me: “Hi! I’m looking for books in the sciences. Where should I go?”
Staff: “Hmm, did you mean ‘science fiction’ or the ‘hard sciences’?”
Me: “Ah yes, I meant the ‘hard sciences’. I’m a ‘hard scientist’ myself.”

They had books from conference proceedings, “open problems in computational sciences”, deep physics books… I was wowed, but didn’t buy anything; I ended up getting two books on minecraft instead. 😛 (They’re not for me, they’re for a colleague’s son.)

I also saw Panic’s sign – you can actually control the colour of their sign through a web app! Totally agree with David – that corner of Portland is one ‘magical’ corner.

On the second day, I decided to help out Prof. Allen Downey with his tutorials. I know Allen through the Boston Python User Group and from being a PyCon tutorial instructor before. His tutorials are always fun, hands-on, entertaining, and most importantly, a chance to learn something new. I like his philosophy too – leveraging the very practical skill of computation to learn more abstract things like statistics. He led two tutorials, one on Bayesian statistics and one on Computational statistics. Highly recommend attending his tutorials at PyCon!

It just so happened that my allergies flared up today as well. Two people, one an attendee and one an AV staff member (Jacob), offered ibuprofen to help deal with the general discomfort. Much kindness shown here.

Looking forward to the next few days of talks. Keeping the learning going!

Portland, OR

Now, my first thoughts on Portland… It’s a lovely city, not unlike Austin but without Austin’s heat and humidity. Very biker friendly, and the public transit beats Boston’s hands-down. The TriMet, as they call it, is modern, clean, efficient, and cost-effective. The residents here did a great job investing in communal infrastructure early on. It’s a sprawling city too; according to some who drive around here, it takes about 30 min to drive from one corner of the city’s quadrants to the opposite corner, and that’s taking the highway. Tattoos, alternative music, dark symbolism all seem to be vogue out here. People are laid back, very friendly. There are lots of independent businesses; I don’t see the symptoms of massive commercialization that I see in other big American cities. From talking with locals who are helping with PyCon, there’s a general focus on “the good life”, rather than the focus on “achievement” on the East Coast.

No doubt it’s an attractive for many, but I’m not sure that, given my own personal life history, I would be able to be sane in Portland. Without intending to devalue ‘the good life’, there are some for whom a mission/purpose-driven life matter more than the ‘good life’ that Portland offers. People in Boston give the city a different vibe, one that is nerdier and health-oriented. Knowing that I’m in a place wth lots of really smart and articulate people keeps my natural ego in check as well, something I think can only be beneficial in the long-run. I’m a person excited by the possibilities offered by ideas, and Boston is brimming with them.

Life’s all about tradeoffs, I guess. 🙂

PyCon 2016

Stage 2 of the conference tour starts today. I am at Logan right now, waiting for the JetBlue flight to Portland, OR, for PyCon 2016. There, I will be delivering a tutorial on network analysis, as well as help Allen Downey TA his Computational Statistics tutorial (assuming enough people join in).

I hope to see Portland as well; in my mind it’s always been one of those cities whose ‘green’ culture is worth experiencing.

Boston, I’ll be back in a week. ‘Till then!


I had a ton of fun delivering a workshop on network analysis fundamentals at ODSC East yesterday! This is my bullet-point journal version of my thoughts over ODSC East.

  1. Learned a ton from Bang Wong (of the Broad Institute) and Mark Schindler (of GroupVisual) about DataViz & User Experience (DVUX).
  2. Didn’t expect that the workshop would be over-subscribed! I was expecting the topic to be a bit more niche. Lots of kind tweets and feedback. Material are all available on GitHub.
  3. Invited to contribute content to DataCamp on network analysis. Timeline approximately Fall 2016 or Spring 2017. Strongly considering it.
  4. Talked one-on-one with a manager in the Facebook infrastructure data science team. FB gets a lot of stick for privacy reasons; after this talk, I realize they have bigger, altruistic plans that rarely get talked about. The short story is that there always some degree of tradeoff, and it sometimes takes a company amassing resources in order to do things that require a big jump rather than incremental improvements.
  5. I like Dask, great talk by Matthew Rocklin (slides). Time to try it out.
  6. Great to see biological applications featured at ODSC, especially on Sunday. Neglected tropical diseases and big microscopy analysis.

the tweets

The tweets are archived here. It’ll serve as my “feel good” memory stack if I ever need to return to it.

New funding from the Broad Next10!

(As is now become somewhat habitual, I’m reporting a week late to get some clarity in thought.)

Really humbling and yet exciting week last week. With my colleagues Tony and Jared (Blainey lab) at the Broad, we won a $40,000 Broad Next10 (Bn10) grant to conduct exploratory and hopefully “catalytic” experiments to develop influenza polymerase phenotyping assays that can be done at scale and at low cost, with the stretch goal of making it plug-and-play for other viral polymerases. We also won another $40,000 Bn10 grant scale the phenotyping of influenza neuraminidase drug resistance to oseltamivir (a.k.a. tamiflu).

It’s humbling because finally there’s a team of people who think these ideas are worth taking a risk on, and are willing to take a quantifiable $80,000 (total) gamble on it. It’s also an exciting time, because I have been working on the (cheaper) computational side of things for a while, and I have become convinced that endless optimization of the computation cannot beat simply having better data measured, and this funding enables us to run some experiments towards scalably generating that data. We have one year to accomplish this goal, and we are planning to treat this money as “accelerator” money to get a minimum viable prototype out and ready.

PyFlatten: A package for flattening nested data structures

Yesterday, I released PyFlatten to PyPI – it’s a utility that can flatten nested data structures (e.g. list of lists; dictionaries of lists of tuples) into a single 1-by-N vector, while also returning an ‘unflattener’ function that can restore the original data structure from the flattened version.

The source code are available on GitHub, where I make clear that I can’t take credit for writing the code – I can only credit myself for factoring it out of autograd for others to use. The real heroes are David Duvenaud, Dougal Maclaurin, and Matt Johnson of the Harvard Intelligent & Probabilistic Systems group. With David’s permission I am releasing it for public use.

Hope it comes in handy for whatever your project is!