First paper(s) accepted, and thesis proposal proposal proposed!

It's an exciting time here for this mid-level PhD Student.

First! I've had a paper accepted to CHI, the biggest Human-Computer Interaction conference. Paper publishing is nice. It means that people can read about what you're doing, it means four people think your work is worthwhile and well done, and it's a tangible mark of success.

It's called "Getting Users' Attention in Web Apps in Likable, Minimally Annoying Ways", the title is pretty self explanatory, and it is but one more bit of sand in the large pile of research that's been done on notification systems. Still, web sites are still not very good at this, and our paper offers one potential piece of a solution, so for that reason I am pretty happy to have done it. Baby steps.

I have Anupriya Ankolekar and especially Josh Hailpern to thank for the opportunity to intern at HP Labs, the guidance in running the study and writing the paper, and the moral support throughout the tumultuous process. Thanks, you two, for helping out a research neophyte.

Second! I've had another paper, that I helped with, accepted to CHI, and this one with so many good friends and my fiancee Tatiana, so that's cool. "Mailing Archived Emails As Postcards: Probing the Value of Virtual Collections." This one's almost four years in the making, so thanks to Tati, Dave, and Jenny for gathering and analyzing data with me; Jason, Will, and John for advice along the way, and Beka, Will, and Dave for doing the writing heavy lifting. This was pretty epic, and I'm excited we can talk about it. Finally.

(I'll post both of the papers when stuff's more officially published.)

Third! Sorry if you are feeling down about your work and this feels like rubbing it in; I have been that student very many times (and will probably continue to be in the future) and I feel ya. I don't really mean to humblebrag, or even straight-up brag, so I've resisted facebooking or twittering any of this, but I'm really pretty relieved and excited to hit this milestone, and nobody reads this blog anyway. Plus, another data point for "hang in there, keep resubmitting your drafts, you'll win this game someday" I guess?

Fourth! I'm going to graduate someday. I've got my thesis proposal proposal done (meaning, I haven't done the proposal, but I've talked with most of my committee and figured out what my thesis proposal will be). Now all I have to do is run a study or two to plan the thing, do some background research, build a giant web app, do a lot more studies, etc etc etc, but you know, it's on the right track. I've got some great profs behind me, I'm stoked about the work, and I can do most of it while I'm with Tati. So.

Roads Greenery Buildings

What is your neighborhood made of?

We don't interact with zoning or construction in our everyday lives. We just know that some places are more pleasant than others. We don't really see the effects of dedicating half our space to parking lots and roads. We sort of know that New York is denser than suburban Ohio, but how dense is it?

More pragmatically, you may be looking for a place to live in a new city. You like your neighborhood now, so you wouldn't mind a place that "feels like" it. Obviously, midtown Manhattan won't feel like Squirrel Hill, Pittsburgh, but what neighborhood would?

Roads Greenery Buildings is an attempt to partially answer that question.

Give it an address, it will look up the place on Google Maps and Google Earth, and tell you the approximate amount of that place's nearby area that's taken up with roads, green space, and buildings. You can look up a few places to compare. Here we see that my neighborhood (the third one) has more roads and buildings than Carnegie Mellon (the first one) - which makes sense; CMU is a college campus with some big lawns. My neighborhood is also a little greener than nearby Oakland.

Here's a comparison of some neighborhoods in San Francisco, based on some coffee shops I like. Haus Coffee (the first) is in the greenest area (24th st. in the Mission is full of trees) but greenery is in short supply all around. This is to be expected; it's a big city. I was surprised to find the Ritual Hayes Valley branch (#4) to have so many roads nearby, but on reflection, there are a couple of big boulevards right there. Meanwhile, the area around Four Barrel (#2) and Saint Frank (#5) look the densest in terms of buildings.

This doesn't tell you everything, of course. The space calculations are imperfect, and there's no description of what the green space is (a highway median is less good than a nice park) or what the buildings are (a parking garage, a house, and an office skyscraper all get the same weight). But it's a start. I think of this (or, you know, the platonic ideal of this) as a peer to Walkscore: by no means the only tool that helps you understand a place, but one of many.

What's good? Depends on you, I guess, but I think this tool shows how places with more buildings tend to be more approachable and interesting, while green space often just makes things farther apart.

Try it out! (disclaimer: link worked as of May 2015; apologies if it's rotted since then.)

Hat tip to Andrew Alexander Price for the blog post that inspired this work. (More details.)

Thinking about metrics

Reading about effective altruism, the Open Philanthropy Project, GiveWell, etc, and thinking "good lord, how can they possibly hope to put a number on what's The Best Thing to do with your money?" It feels like they're taking a (to use the one design concept I sort of understand) wicked problem and trying to make it tame. Usually this doesn't go well; as the Vox article above hinted, you often end up only representing a couple of viewpoints, or making it worse by playing whack-a-mole by iteratively solving whatever problem you're thinking about at the moment.

But, I've got reason to assume, based on what GiveWell's done so far, that at least some of the Open Phil people are thinking about it in this broad sense.

And metrics aren't all bad! I think about WalkScore, which is limited and flawed, but is still a pretty solid and useful indicator of how nice it is to live in a place. And really, the thing is, we're often making decisions by metrics anyway, and often those metrics are suuuper flawed. Like GDP. So if I'm reading about a Social Progress Index on TED, sure, it might be a TED blowhard with another half-assed idea, but it doesn't have to be that good. I'd love to start talking about SPI instead of GDP, not because it's great, but because it's better.

CHI 2015: some particularly interesting things

It was great. CHI, the biggest conference in Human-Computer Interaction, has revitalized me a bit; feeling grumpy and worn down, it helps to see a bunch of things that are actually exciting and realize that this "research" thing is actually ok and kind of worth doing sometimes. Spending a week in Seoul doesn't hurt. More on that later.

I ... want to write about so many things here. I'll try to just write about 10ish papers.

Talks I Enjoyed Because They Describe Interesting Phenomena Well

Your Money's No Good Here: The Elimination of Cash Payment on London Buses, Gary Pritchard, John Vines, Patrick Olivier. No cash means more work for customers, different work for drivers, drivers have to decide whether someone "might be vulnerable", loss of some social interaction, and the inability to gift. Among other ups and downs. But there is a curious side point: money is a technology too; why is that the assumed baseline?

A Muddle of Models of Motivation For Using Peer to Peer Economy Systems, Victoria Bellotti, Alexander Ambard, Daniel Turner, Christina Gassmann, Kamila Demkova, John M. Carroll. First of all, this was a top notch talk, flying through these theories but in a way I could still follow. Second, I like how they integrate a lot of these theories together. Or at least reference them all in the same place.

From Third to Surveilled Place: The Mobile in Irish Pubs, Norman Makoto Su, Lulu Wang. Phones change the pub dynamic in a few ways: you can look up quick facts instead of bantering about what the answer is (which is more fun anyway), you can use it for entertainment, but also everyone knows that if they do something silly (that is typically safe in a pub) you might be photographed and put on Facebook and that silly thing out of context will be embarrassing.

Talks I Enjoyed Because They Might Relate More Directly To My Work On Social Media, Urban Technology, Connecting People, and Maps

Modeling Ideology and Predicting Policy Change with Social Media: Case of Same-Sex Marriage, Amy X. Zhang, Scott Counts. They took tweets about same sex marriage in each state, and also compiled a list of same sex marriage bills that they were trying to pass in the same time. They could see correlations between words that indicate certain values (like "authority" or "fairness") and bills passing or failing. They could predict whether the laws would pass up to 87% accurately, where polls (which are slow etc) could only predict 70%.

"Everyone is Talking about it!": A Distributed Approach to Urban Voting Technology and Visualizations, Lisa Koeman, Vaiva Kalnikaite, Yvonne Rogers. They wanted to democratize data gathering and surface the data they gathered in multiple places. So they put a little thing with 3 buttons in shops along this road, and asked questions each day like "do you feel safe around here?", and then spray-chalked the % of people who said each thing on the chalk outside each store. Lots of super interesting results. Made me rethink the form of whatever I end up doing; not necessarily a thing-on-a-screen.

Making social matching context-aware - Design Concepts and Open Challenges, Julia M. Mayer, Starr Roxanne Hiltz, Quentin Jones. Sometimes you want to say "hey, you and you should meet." But there are a lot of factors that determine whether you should or not. This paper lays them all out pretty well. Some are individual, like maybe you are really busy right now. Some are temporal or spatial: in a train station, you're likely running for your train, not meeting randos. Some depend on how similar you are, relative to everyone else around.

An Evaluation of Interactive Map Comparison Techniques, Maria-Jesus Lobo. So if you have two layers on a map and you want to be able to see them both (to compare or whatever) what do you do? Answer: use a blended lens or a semi-transparent layer, not a slider or two maps next to each other.

Talks I Enjoyed Because Some Of My Friends Do Amazing Design Research

Making Multiple Uses of the Obscura 1C Digital Camera: Reflecting on the Design, Production, Packaging, and Distribution of a Counterfunctional Device, James Pierce, Eric Paulos. It's a camera encased in concrete except the button and the lens. So you can take thousands of photos, but if you ever want to get them out, you have to break the thing with a hammer. This is awesome: like Twitter or Snapchat, it puts a limit on a digital thing, and comes up with something really compelling in the process. Also, James packaged and sold it as a consumer device, on craigslist and in stores. This makes us think about it differently than a typical HCI study with an IRB etc., so it can generate new forms of knowledge. This paper and talk are pretty much the best.

Understanding Long-Term Interactions with a Slow Technology: An Investigation of Experiences with FutureMe, Will Odom. First, it's an interesting tool with a neat bunch of users: they send emails to their future selves. (or to someone else in the future.) Up to 60 years. And it's really deep; people send heavy stuff through this. So it's cool to hear about it, and it's cool to see "slow technology" actually deployed in action.

Talks I Wish I Saw But I Like The Papers

Can An Algorithm Know the "Real You": Understanding People's Reactions to Hyper-personal Analytics Systems. Jeffrey Warshaw, Tara Matthews, Steve Whittaker, Chris Kau, Mateo Bengualid, Barton A. Smith. They draw a line  between personalized (you like basketball) and hyper-personalized (your big 5 personality profile is ...) People find these hyper-personalized systems creepily accurate, more than humans, and they won't even correct them when they "make mistakes".

How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy, Matt Kay, Shwetak Patel, Julie Kientz. This paper addresses maybe my number one gripe with basically every ubicomp system, which is that it starts to recognize some thing with 85% accuracy, and then everyone says "ok, publish it" regardless of whether that accuracy (or precision or recall or whatever) is actually good enough to do anything in the real world. (if my self-driving car could see stop signs with 94% recall, I would not be in that car.)

Oh And Can We Talk About:

How good PSY was? Just the humblest dude, telling us about how he doesn't know social media, how one of his managers was like "we should upload Gangnam Style to Youtube" and he said "why?" Or how right now he's just trying not to be trying so hard, or about how when he performs everyone is just waiting for him to go "oppa Gangnam style" and that's ok? It was a simple talk, fun, and inspiring. Felt like the perfect end of the week.

How good Seoul was? Lots of fun sightseeing including a market of weird fish, temples all over, wonderful neighborhoods and cafes (my other blog will soon have a post about exactly that), super clean and safe, super easy, friendly people, lots of fun, and pretty cheap too, really. I... would hurry back to Korea, and encourage others to do the same, for work or fun.

PhD Grind part 0.8 of N: picking a school

Man, we just had a bunch of students come through for open house, and I wish I could offer them some helpful advice, but we had a brief time to actually get to know them at all.

Sometimes people ask, why did you pick CMU? In order, it was 1. students, 2. profs, 3. outcomes, 4. everything else was fine. I think that decision order is pretty ok, but maybe profs should be tied for #1, not sure.

But! In more detail:
1. Students: I just felt pretty well at home with the students here. Hard to say why. Ok, not much more detail here, other than to say: this is important! Both the existing students, and the other incoming ones, will be working with you for many years. Maybe hanging out outside school too, depending on the place. (we're pretty social at CMU). You'll probably spend more time with them than the professors. You get a half a minute to get to know them at open houses, so that's frustrating, but if you find you really like (or dislike) the students at one place in particular, take that into account.

2. Professors: best case, you find an advisor. Second best case, you find a group of potential advisors where you'd be happy to be advised by any of them. (more info on this decision) If you don't have either of those, don't go to that place. Your advisor relationship is suuuuper important. More important than a typical boss. They will not only be your boss (so they can make your life easy or hard) but they will also be some inspiration, introduce you to other people, etc. So make sure you have an advisor idea before you sign the form.

One side note: if one place has your #1 top choice advisor, and another place has your #2 choice, go with #1, obviously. But if one place has your #1 top choice, and the other place has #2, #3, and #4, and you've never worked directly with any of them before, I'd say go with the 2-3-4 place. There's a lot of randomness that you don't figure out until you work with someone, and someone who seems perfect might just for whatever reason end up not a perfect match. So in that second case, go with the #2 advisor, and you've always got 3 and 4 if 2 doesn't work out.

3. Outcomes: notice what the graduating students are doing. If they're all getting the kind of sweet jobs you want to do, that's a good sign. If half are doing startups, and you want to do a startup, great! If they're all going to industry jobs, and you want to be a prof, not so great. If 2/3 of them are dropping out and sitting at home wallowing in depression, probably not so great either.

4. Everything else: these will probably not factor into your decision, but look out in case they do. City, stipend, classes, quals, teaching, office space, etc. Usually these will all be fine: the city is ok, the stipend is about the same, you have to take ~8-10 classes, you have to pass through some hazing ritual such as quals or comm talks or extra classes, you have to teach 2 classes, your office is a desk in some depressing basement, etc. These are all fine. But this is an area where there can be red flags: if you don't have a guaranteed stipend, if you have to teach every semester, if the city is super expensive and difficult to live in (lookin at you, Stanford) or in the middle of nowhere and depressing to you, the hazing/quals makes half the students quit, etc. If there are any red flags, they should factor into your decision as much as the above 3 points. Otherwise, don't worry about these things, they're fine.

So! If you've considered all these, go with your gut then, and you'll do fine. Also, if any current prospective students end up reading this and want to talk more, find my email (or twitter), drop me a line.

Not really research, but fun: Swot Perderder

I remember seeing stuff like this and this and thinking it was the funniest thing ever. I guess Know Your Meme categorizes it as "wurds", but to me, this will always be Swot Perderder, because this one in particular always made me crack up:

So I made a twitter bot that generates these:

Tweets by @swot_perderder

I got a list of ~500 foods (harder than it may seem), used the CMU pronouncing dictionary to translate word -> phonemes, then mapped each phoneme to a randomly chosen letter that either matched it closely (s -> s) or not so closely (s -> zh).

That's all for now. It'd be neat to make it interactive someday, or make it a reddit bot or something, because the world needs more of these misspelled foods.

Also, it'd be neat to see which of these are favorited/retweeted/etc more, because then we could refine the rules to make them even funnier. Yes.

Edit: code's here: https://github.com/dantasse/swot_perderder

Pittsburgh Tweets

Heyo. Here's where (the geotagged tweeters in) Pittsburgh tweeted in 2014.

CheeseHoods

CheeseHoods: a block of Swiss cheese as dense as your neighborhood.

CheeseHoods are blocks that look like Pittsburgh neighborhoods. They have holes in them like Swiss cheese. The denser the neighborhood is, in terms of dwellings per acre, the fewer holes it has.

Why neighborhood blocks? I thought it'd be fun to have a jigsaw puzzle of Pittsburgh, really. Plus, just as playing with world maps helps kids learn country names, playing with your city might help you learn more about it.

Why the holes? Density is important. Jane Jacobs wrote about it as one of the four most important characteristics in creating vibrant neighborhoods. Dwelling density, in particular, is quite important; human density can just indicate overcrowding, but dwelling density indicates vitality. I was interested to explore what density looks and feels like in 3D. Putting holes through a neighborhood seemed like an easy way to do so. Plus, it gives the least dense neighborhoods a rather icky pock-marked feel, while thriving denser ones are pleasantly solid, so this gives a really visceral feel to "density is good".

Pictures just Bloomfield, above. Below, all of Pittsburgh. Central Oakland is a shining example of density, but you can see right through Greenfield (below). Code's on github. Not embedding it here because there's a lot. I used some tools from another repo (get_dwelling_densities.py) plus some census data to calculate dwelling density for each neighborhood, then computed a json file of all the borders of each neighborhood and random hole locations (nghd_to_shape.py), then finally slurped those into a last script that created objects in Rhino (rhino_script.py). Play with 'em:

Bloomfield by dantasse on Sketchfab

Pittsburgh by dantasse on Sketchfab