Yesterday was the end of the first half of the Lede Program, and the students presented their projects, which were really impressive. I am hoping some of them will be willing to put them up on a WordPress site or something like that in order to showcase them and so I can brag about them more explicitly. Since I didn’t get anyone’s permission yet, let me just say: wow.
During the second half of the program the students will do another project (or continue their first) as homework for my class. We’re going to start planning for that on the first day, so the fact that they’ve all dipped their toes into data projects is great. For example, during presentations yesterday I heard the following a number of times: “I spent most of my time cleaning my data” or “next time I will spend more time thinking about how to drill down in my data to find an interesting story”. These are key phrases for people learning lessons with data.
Since they are journalists (I’ve learned a thing or two about journalists and their mindset in the past few months) they love projects because they love deadlines and they want something they can add to their portfolio. Recently they’ve been learning lots of geocoding stuff, and coming up they’ll be learning lots of algorithms as well. So they’ll be well equipped to do some seriously cool shit for their final project. Yeah!
In addition to the guest lectures I’m having in The Platform, I’ll also be reviewing prerequisites for the classes many of them will be taking in the Computer Science department in the fall, so for example linear algebra, calculus, and basic statistics. I just bought them all a copy of How to Lie with Statistics as well as The Cartoon Guide to Statistics, both of which I adore. I’m also making them aware of Statistics Done Wrong, which is online. I am also considering The Cartoon Guide to Calculus, which I have but I haven’t read yet.
Keep an eye out for some of their amazing projects! I’ll definitely blog about them once they’re up.
My schedule nowadays is to go to the Lede Program classes every morning from 10am until 1pm, then office hours, when I can, from 2-4pm. The students are awesome and are learning a huge amount in a super short time.
So for instance, last time I mentioned we set up iPython notebooks on the cloud, on Amazon EC2 servers. After getting used to the various kinds of data structures in python like integers and strings and lists and dictionaries, and some simple for loops and list comprehensions, we started examining regular expressions and we played around with the old enron emails for things like social security numbers and words that had four or more vowels in a row (turns out that always means you’re really happy as in “woooooohooooooo!!!” or really sad as in “aaaaaaarghghgh”).
Then this week we installed git and started working in an editor and using the command line, which is exciting, and then we imported pandas and started to understand dataframes and series and boolean indexes. At some point we also plotted something in matplotlib. We had a nice discussion about unsupervised learning and how such techniques relate to surveillance.
My overall conclusion so far is that when you have a class of 20 people installing git, everything that can go wrong does (versus if you do it yourself, then just anything that could go wrong might), and also that there really should be a better viz tool than matplotlib. Plus my Lede students are awesome.
I get asked pretty often whether I “believe” in open data. I tend to murmur a response along the lines of “it depends,” which doesn’t seem too satisfying to me or to the person I’m talking about. But this morning, I’m happy to say, I’ve finally come up with a kind of rule, which isn’t universal. It focuses on power.
Namely, I like data that shines light on powerful people. Like the Sunlight Foundation tracks money and politicians, and that’s good. But I tend to want to protect powerless people, like people who are being surveilled with sensors and their phones. And the thing is, most of the open data focuses on the latter. How people ride the subway or how they use the public park or where they shop.
Something in the middle is crime data, where you have compilation of people being stopped by the police (powerless) and the police themselves (powerful). But here as well you’ll notice an asymmetry on identifying information. Looking at Stop and Frisk data, for example, there’s a precinct to identify the police officer, but no badge number, whereas there’s a bunch of identifying information about the person being stopped which is recorded.
A lot of the time you won’t even find data about powerful people. Worker bees get scored but the managers are somehow above scoring. Bloomberg never scored his lieutenants or himself even when he insisted that teachers should be scored. I like to keep an eye on who gets data collected about them. The power is where the data isn’t.
I guess my point is this. Data and data modeling are not magical tools. They are in fact crude tools, and so to focus on them is misleading and distracting from the real show, which is always about power (and/or money). It’s a boondoggle to think about data when we should be thinking about when and how a model is being wielded and who gets to decide.
One of the biggest problem we face is that all this data is being collected and saved now and the models haven’t even been invented yet. That’s why there’s so much urgency in getting reasonable laws in place to protect the powerless.
A few weeks ago I mentioned that I’m the Program Director for the new Lede Program at the Columbia Graduate School of Journalism. I’m super excited to announce that I’ve found amazing faculty for the summer part of the program, including:
- Jonathan Soma, who will be the primary instructor for Basic Computing and for Algorithms
- Dennis Tenen, who will be helping Soma in the first half of the summer with Basic Computing
- Chris Wiggins, who will be helping Soma in the second half of the summer with Algorithms
- An amazing primary instructor for Databases who I will announce soon,
- Matthew Jones, who will help that amazing yet-to-be-announced instructor in Data and Databases
- Three amazing TA’s: Charles Berret, Sophie Chou, and Josh Vekhter (who doesn’t have a website!).
I’m planning to teach The Platform with the help of a bunch of generous guest lecturers (please make suggestions or offer your services!).
Applications are open now, and we’re hoping to get amazing students to enjoy these amazing faculty and the truly innovative plan they have for the summer (and I don’t use the word “innovative” lightly!). We’ve already gotten some super strong applications and made a couple offers of admission.
Also, I was very pleased yesterday to see a blogpost I wrote about the genesis and the goals of the program be published in PBS’s MediaShift.
Finally, it turns out I’m a key influencer, according to The Big Roundtable.
By now most of you have read about the major bug that was found in OpenSSL, an open source security software toolkit. The bug itself is called the Heartbleed Bug, and there’s lots of information about it and how to fix it here. People are super upset about this, and lots of questions remain.
For example, was it intentionally undermined? Has the NSA deliberately inserted weaknesses into this as well? It seems like the jury is out right now, but if I’m the guy who put in the bug, I’m changing my name and going undercover just in case.
Next, how widely was the weakness exploited? If you’re super worried about stuff, or if you are a particular target of attack, the answer is probably “widely.” The frustrating thing is that there’s seemingly no way to measure or test that assumption, since the attackers would leave no trace.
Here’s what I find interesting the most interesting question: what will the long-term reaction be to open source software? People might think that open source code is a bust after this. They will complain that something like this should never have been allowed to happen – that the whole point of open software is that people should be checking this stuff as it comes in – and it never would have happened if there were people getting paid to test the software.
First of all, it did work as intended, even though it took two years instead of two days like people might have wanted. And maybe this shouldn’t have happened like it did, but I suspect that people will learn this particular lesson really well as of now.
But in general terms, bugs are everywhere. Think about Knight Capital’s trading debacle or the ObamaCare website, just two famous recent problems with large-scale coding projects that aren’t open source.
Even when people are paid to fix bugs, they fix the kind of bugs that cause the software to stop a lot sooner than the kind of bug that doesn’t make anything explode, lets people see information they shouldn’t see, and leaves no trace. So for every Knight’s Capital there are tons of other bugs in software that continue to exist.
In other words it’s more a question of who knows about the bugs and who can exploit them. And of course, whether those weaknesses will ever be exposed to the public at all.
It would be great to see the OpenSSL bug story become, over time, a success story. This would mean that, on the one hand the nerds becoming more vigilant in checking vitally important code, and learning to think like assholes, but also the public would need to acknowledge how freaking hard it is to program.
Today I’d like to mention two ideas I’ve been having recently on how to make being a research mathematician (even) more fun.
1) Mathematicians should consider holding public discussions about papers
First, math nerds, did you know that in statistics they have formal discussions about papers? It’s been a long-standing tradition by the Royal Statistical Society, whose motto is “Advancing the science and application of statistics, and promoting use and awareness for public benefit,” to choose papers by some criterion and then hold regular public discussions about those papers by a few experts who are not the author, about the paper. Then the author responds to their points and the whole conversation is published for posterity.
I think this is a cool idea for math papers too. One thing that kind of depressed me about math is how rarely you’d find people reading the same papers unless you specifically got a group of people together to do so, which was a lot of work. This way the work is done mostly by other people and more importantly the payoff is much better for them since everyone gets a view into the discussion.
Note I’m sidestepping who would organize this whole thing, and how the papers would be chosen exactly, but I’d expect it would improve the overall feeling that I had of being isolated in a tiny math community, especially if the conversations were meant to be penetrable.
2) There should be a good clustering method for papers around topics
This second idea may already be happening, but I’m going to say it anyway, and it could easily be a thesis for someone in CS.
Namely, the idea of using NLP and other such techniques to cluster math papers by topic. Right now the most obvious way to find a “nearby” paper is to look at the graph of papers by direct reference, but you’re probably missing out on lots of stuff that way. I think a different and possibly more interesting way would be to use the text in the title, abstract, and introduction to find papers with similar subjects.
This might be especially useful when you want to know the answer to a question like, “has anyone proved that such-and-such?” and you can do a text search for the statement of that theorem.
The good news here is that mathematicians are in love with terminology, and give weird names to things that make NLP techniques very happy. My favorite recent example which I hear Johan muttering under his breath from time to time is Flabby Sheaves. There’s no way that’s not a distinctive phrase.
The bad news is that such techniques won’t help at all in finding different fields who have come across the same idea but have different names for the relevant objects. But that’s OK, because it means there’s still lots of work for mathematicians.
By the way, back to the question of whether this has already been done. My buddy Max Lieblich has a website called MarXiv which is a wrapper over the math ArXiv and has a “similar” button. I have no idea what that button actually does though. In any case I totally dig the design of the similar button, and what I propose is just to have something like that work with NLP.
This is a guest post by Marc Joffe, the principal consultant at Public Sector Credit Solutions, an organization that provides data and analysis related to sovereign and municipal securities. Previously, Joffe was a Senior Director at Moody’s Analytics.
As Cathy has argued, open source models can bring much needed transparency to scientific research, finance, education and other fields plagued by biased, self-serving analytics. Models often need large volumes of data, and if the model is to be run on an ongoing basis, regular data updates are required.
Unfortunately, many data sets are not ready to be loaded into your analytical tool of choice; they arrive in an unstructured form and must be organized into a consistent set of rows and columns. This cleaning process can be quite costly. Since open source modeling efforts are usually low dollar operations, the costs of data cleaning may prove to be prohibitive. Hence no open model – distortion and bias continue their reign.
Much data comes to us in the form of PDFs. Say, for example, you want to model student loan securitizations. You will be confronted with a large number of PDF servicing reports that look like this. A corporation or well funded research institution can purchase an expensive, enterprise-level ETL (Extract-Transform-Load) tool to migrate data from the PDFs into a database. But this is not much help to insurgent modelers who want to produce open source work.
Data journalists face a similar challenge. They often need to extract bulk data from PDFs to support their reporting. Examples include IRS Form 990s filed by non-profits and budgets issued by governments at all levels.
The data journalism community has responded to this challenge by developing software to harvest usable information from PDFs. Examples include Tabula, a tool written by Knight-Mozilla OpenNews Fellow Manuel Aristarán, extracts data from PDF tables in a form that can be readily imported to a spreadsheet – if the PDF was “printed” from a computer application. Introduced earlier this year, Tabula continues to evolve thanks to the volunteer efforts of Manuel, with help from OpenNews Fellow Mike Tigas and New York Times interactive developer Jeremy Merrill. Meanwhile, DocHive, a tool whose continuing development is being funded by a Knight Foundation grant, addresses PDFs that were created by scanning paper documents. DocHive is a project of Raleigh Public Record and is led by Charles and Edward Duncan.
These open source tools join a number of commercial offerings such as Able2Extract and ABBYY Fine Reader that extract data from PDFs. A more comprehensive list of open source and commercial resources is available here.
Unfortunately, the free and low cost tools available to modelers, data journalists and transparency advocates have limitations that hinder their ability to handle large scale tasks. If, like me, you want to submit hundreds of PDFs to a software tool, press “Go” and see large volumes of cleanly formatted data, you are out of luck.
It is for this reason that I am working with The Sunlight Foundation and other sponsors to stage the PDF Liberation Hackathon from January 17-19, 2014. We’ll have hack sites at Sunlight’s Washington DC office and at RallyPad in San Francisco. Developers can also join remotely because we will publish a number of clearly specified PDF extraction challenges before the hackathon.
Participants can work on one of the pre-specified challenges or choose their own PDF extraction projects. Ideally, hackathon teams will use (and hopefully improve upon) open source tools to meet the hacking challenges, but they will also be allowed to embed commercial tools into their projects as long as their licensing cost is less than $1000 and an unlimited trial is available.
Prizes of up to $500 will be awarded to winning entries. To receive a prize, a team must publish their source code on a GitHub public repository. To join the hackathon in DC or remotely, please sign up at Eventbrite; to hack with us in SF, please sign up via this Meetup. Please also complete our Google Form survey. Also, if anyone reading this is associated with an organization in New York or Chicago that would like to organize an additional hack space, please contact me.
The PDF Liberation Hackathon is going to be a great opportunity to advance the state of the art when it comes to harvesting data from public documents. I hope you can join us.
I’m lucky to be working with a super fantastic python guy on this, and the details are under wraps, but let’s just say it’s exciting.
So I’m looking to showcase a few good models to start with, preferably in python, but the critical ingredient is that they’re open source. They don’t have to be great, because the point is to see their flaws and possible to improve them.
- For example, I put in a FOIA request a couple of days ago to get the current teacher value-added model from New York City.
- A friends of mine, Marc Joffe, has an open source municipal credit rating model. It’s not in python but I’m hopeful we can work with it anyway.
- I’m in search of an open source credit scoring model for individuals. Does anyone know of something like that?
- They don’t have to be creepy! How about a Nate Silver – style weather model?
- Or something that relies on open government data?
- Can we get the Reinhart-Rogoff model?
The idea here is to get the model, not necessarily the data (although even better if it can be attached to data and updated regularly). And once we get a model, we’d build interactives with the model (like this one), or at least the tools to do so, so other people could build them.
At its core, the point of open models is this: you don’t really know what a model does until you can interact with it. You don’t know if a model is robust unless you can fiddle with its parameters and check. And finally, you don’t know if a model is best possible unless you’ve let people try to improve it.
The idea is that we’re analyzing metadata around a texting hotline for teens in crisis. We’re trying to see if we can use the information we have on these texts (timestamps, character length, topic – which is most often suicide – and outcome reported by both the texter and the counselor) to help the counselors improve their responses.
For example, right now counselors can be in up to 5 conversations at a time – is that too many? Can we figure that out from the data? Is there too much waiting between texts? Other questions are listed here.
Our “hackpad” is located here, and will hopefully be updated like a wiki with results and visuals from the exploration of our group. It looks like we have a pretty amazing group of nerds over here looking into this (mostly python users!), and I’m hopeful that we will be helping the good people at Crisis Text Line.
I’m preparing for a short trip to D.C. this week to take part in a day-long event held by Americans for Financial Reform. You can get the announcement here online, but I’m not sure what the finalized schedule of the day is going to be. Also, I believe it will be recorded, but I don’t know the details yet.
In any case, I’m psyched to be joining this, and the AFR are great guys doing important work in the realm of financial reform.
Opening Wall Street’s Black Box: Pathways to Improved Financial Transparency
Sponsored By Americans for Financial Reform and Georgetown University Law Center
Keynote Speaker: Gary Gensler Chair, Commodity Futures Trading Commission
October 11, 2013 10 AM – 3 PM
Georgetown Law Center, Gewirz Student Center, 12th Floor
120 F Street NW, Washington, DC (Judiciary Square Metro) (Space is limited. Please RSVP to AFRtransparencyrsvp@gmail.com)
The 2008 financial crisis revealed that regulators and many sophisticated market participants were in the dark about major risks and exposures in our financial system. The lack of financial transparency enabled large-scale fraud and deception of investors, weakened the stability of the financial system, and contributed to the market failure after the collapse of Lehman Brothers. Five years later, despite regulatory efforts, it’s not clear how much the situation has improved.
Join regulators, market participants, and academic experts for an exploration of the progress made – and the work that remains to be done – toward meaningful transparency on Wall Street. How can better information and disclosure make the financial system both fairer and safer?
|Jesse Eisinger, Pulitzer Prize-winning reporter for the New York Times and Pro Publica|
|Zach Gast, Head of financial sector research, Center on Financial Research and Analysis|
|Amias Gerety, Deputy Assistant Secretary for the FSOC, United States Treasury|
|Henry Hu, Alan Shivers Chair in the Law of Banking and Finance, University of Texas Law School|
|Albert “Pete” Kyle, Charles E. Smith Professor of Finance, University of Maryland|
|Adam Levitan, Professor of Law, Georgetown University Law Center|
|Antoine Martin, Vice President, New York Federal Reserve Bank|
|Brad Miller, Former Representative from North Carolina; Of Counsel, Grais & Ellsworth|
|Cathy O’Neil, Senior Data Scientist, Johnson Research Labs; Occupy Alternative Banking|
|Gene Phillips, Director, PF2 Securities Evaluation|
|Greg Smith, Author of “Why I Left Goldman Sachs”; former Goldman Sachs Executive Director|
The 2013 PopTech & Rockefeller Foundation Bellagio Fellows - Kate Crawford, Patrick Meier, Claudia Perlich, Amy Luers, Gustavo Faleiros and Jer Thorp - yesterday published “Seven Principles for Big Data and Resilience Projects” on Patrick Meier’s blog iRevolution.
Although they claim that these principles are meant for “best practices for resilience building projects that leverage Big Data and Advanced Computing,” I think they’re more general than that (although I’m not sure exactly what a resilience building project is) I and I really like them. They are looking for public comments too. Go to the post for the full description of each, but here is a summary:
1. Open Source Data Tools
Wherever possible, data analytics and manipulation tools should be open source, architecture independent and broadly prevalent (R, python, etc.).
2. Transparent Data Infrastructure
Infrastructure for data collection and storage should operate based on transparent standards to maximize the number of users that can interact with the infrastructure.
3. Develop and Maintain Local Skills
Make “Data Literacy” more widespread. Leverage local data labor and build on existing skills.
4. Local Data Ownership
Use Creative Commons and licenses that state that data is not to be used for commercial purposes.
5. Ethical Data Sharing
Adopt existing data sharing protocols like the ICRC’s (2013). Permission for sharing is essential. How the data will be used should be clearly articulated. An opt in approach should be the preference wherever possible, and the ability for individuals to remove themselves from a data set after it has been collected must always be an option.
6. Right Not To Be Sensed
Local communities have a right not to be sensed. Large scale city sensing projects must have a clear framework for how people are able to be involved or choose not to participate.
7. Learning from Mistakes
Big Data and Resilience projects need to be open to face, report, and discuss failures.
There are lots of things I know nothing at all about. It annoys me not to understand a subject at all, because it often means I can’t follow a conversation that I care about. The list includes, just as a start: accounting, law, and politics.
Of those three, accounting seems like the easiest thing to tackle by far. This is partly because the space between what it’s theoretically supposed to be and how it’s practiced is smaller than with law or politics. Or maybe the kind of tricks accountants use seem closer to the kind of tricks I know about from being a quant, so that space seems easier to navigate for me personally.
Anyway, I might be wrong, but my impression is that my lack of understanding of accounting is mostly a language barrier, rather than a conceptual problem. There are expenses, and revenue, and lots of tax issues. There are categories. I’m working on the assumption that none of this stuff is exactly mathematical either, it’s all about knowing what things are called. And I don’t know any of it.
So I just signed up to learn at least some of it on a free Coursera course from the Wharton MBA Foundation Series. Here’s the introductory video, the professor seems super nerdy and goofy, which is a good start.
So in my copious free time I’ll be watching videos explaining the language of tax deferment and the like. Or at least that’s the fantasy – the thing about Coursera is that it’s free, so there’s not much incentive to keep up with the course. And the fact that all four Wharton 1st-year courses are being given away for free is proof of something, by the way – possibly that what you’re really paying for in business school is the connections you make while you’re there.
I want to bring up two quick topics this morning I’ve been mulling over lately which are both related to this recent post by Economist Rajiv Sethi from Barnard (h/t Suresh Naidu), who happened to be my assigned faculty mentor when I was an assistant prof there. I have mostly questions and few answers right now.
In his post, Sethi talks about former computer nerd for Goldman Sachs Sergey Aleynikov and his trial, which was chronicled by Michael Lewis recently. See also this related interview with Lewis, h/t Chris Wiggins.
I haven’t read Lewis’s piece yet, only his interview and Sethi’s reaction. But I can tell it’ll be juicy and fun, as Lewis usually is. He’s got a way with words and he’s bloodthirsty, always an entertaining combination.
So, the two topics.
First off, let’s talk a bit about high frequency trading, or HFT. My first two questions are, who does HFT benefit and what does HFT cost? For both of these, there’s the easy answer and then there’s the hard answer.
Easy answer for HFT benefitting someone: primarily the people who make loads of money off of it, including the hardware industry and the people who get paid to drill through mountains with cables to make connections between Chicago and New York faster.
Secondarily, market participants whose fees have been lowered because of the tight market-making brought about by HFT, although that savings may be partially undone by the way HFT’ers operate to pick off “dumb money” participants. After all, you say market making, I say arbing. Sorting out the winners, especially when you consider times of “extreme market conditions”, is where it gets hard.
Easy answer for the costs of HFT is for the companies that invest in IT and infrastructure and people to do the work, although to be sure they wouldn’t be willing to make that investment if they didn’t expect it to pay off.
A harder and more complete answer would involve how much risk we take on as a society when we build black boxes that we don’t understand and let them collide with each other with our money, as well as possibly a guess at what those people and resources now doing HFT might be doing otherwise.
And that brings me to my second topic, namely the interaction between the open source community and the finance community, but mostly the HFTers.
Sethi said it
well (Cathy: see bottom of this for an update) this way in his post:
Aleynikov relied routinely on open-source code, which he modified and improved to meet the needs of the company. It is customary, if
not mandatory(Cathy: see bottom of this for an update) for these improvements to be released back into the public domain for use by others. But his attempts to do so were blocked:
Serge quickly discovered, to his surprise, that Goldman had a one-way relationship with open source. They took huge amounts of free software off the Web, but they did not return it after he had modified it, even when his modifications were very slight and of general rather than financial use. “Once I took some open-source components, repackaged them to come up with a component that was not even used at Goldman Sachs,” he says. “It was basically a way to make two computers look like one, so if one went down the other could jump in and perform the task.” He described the pleasure of his innovation this way: “It created something out of chaos. When you create something out of chaos, essentially, you reduce the entropy in the world.” He went to his boss, a fellow named Adam Schlesinger, and asked if he could release it back into open source, as was his inclination. “He said it was now Goldman’s property,” recalls Serge. “He was quite tense. When I mentioned it, it was very close to bonus time. And he didn’t want any disturbances.”
This resonates with my experience at D.E. Shaw. We used lots of python stuff, and as a community were working at the edges of its capabilities (not me, I didn’t do fancy HFT stuff, my models worked at a much longer time frame of at least a few hours between trades).
The urge to give back to the OS community was largely thwarted, when it came up at all, because there was a fear, or at least an argument, that somehow our competition would use it against us, to eliminate our edge, even if it was an invention or tool completely sanitized from the actual financial algorithm at hand.
A few caveats: First, I do think that stuff, i.e. python technology and the like eventually gets out to the open source domain even if people are consistently thwarting it. But it’s incredibly slow compared to what you might expect.
Second, It might be the case that python developers working outside of finance are actually much better at developing good tools for python, especially if they have some interaction with finance but don’t work inside. I’m guessing this because, as a modeler, you have a very selfish outlook and only want to develop tools for your particular situation. In other words, you might have some really weird looking tools if you did see a bunch coming from finance.
Finally, I think I should mention that quite a few people I knew at D.E. Shaw have now left and are actively contributing to the open source community now. So it’s a lagged contribution but a contribution nonetheless, which is nice to see.
Update: from my Facebook page, a discussion of the “mandatoriness” of giving back to the OS community from my brother Eugene O’Neil, super nerd, and friend William Stein, other super nerd:
Eugene O’Neil: the GPL says that if you give someone a binary executable compiled with GPL source code, you also have to provide them free access to all the source code used to generate that binary, under the terms of the GPL. This makes the commercial sale of GPL binaries without source code illegal. However, if you DON’T give anyone outside your organization a binary, you are not legally required to give them the modified source code for the binary you didn’t give them. That being said, any company policy that tries to explicitly PROHIBIT employees from redistributing modified GPL code is in a legal gray area: the loophole works best if you completely trust everyone who has the modified code to simply not want to distribute it.
William Stein: Eugene — You are absolutely right. The “mandatory” part of the quote: “It is customary, if not mandatory, for these improvements to be released back into the public domain for use by others.” from Cathy’s article is misleading. I frequently get asked about this sort of thing (because of people using Sage (http://sagemath.org) for web backends, trading, etc.). I’m not aware of any popular open source license that make it mandatory to give back changes if you use a project internally in an organization (let alone the GPL, which definitely doesn’t). The closest is AGPL, which involves external use for a website. Cathy — you might consider changing “Sethi said it well…”, since I think his quote is misleading at best. I’m personally aware of quite a few people that do use Sage right now who wouldn’t otherwise if Sethi’s statement were correct.
Crossposted on Not Even Wrong.
Here’s a completely biased interview I did with my husband A. Johan de Jong, who has been working with Pieter Belmans on a very cool online math project using d3js. I even made up some of his answers (with his approval).
Q: What is the Stacks Project?
A: It’s an open source textbook and reference for my field, which is algebraic geometry. It builds foundations starting from elementary college algebra and going up to algebraic stacks. It’s a self-contained exposition of all the material there, which makes it different from a research textbook or the experience you’d have reading a bunch of papers.
We were quite neurotic setting it up – everything has a proof, other results are referenced explicitly, and it’s strictly linear, which is to say there’s a strict ordering of the text so that all references are always to earlier results.
Of course the field itself has different directions, some of which are represented in the stacks project, but we had to choose a way of presenting it which allowed for this idea of linearity (of course, any mathematician thinks we can do that for all of mathematics).
Q: How has the Stacks Project website changed?
A: It started out as just a place you could download the pdf and tex files, but then Pieter Belmans came on board and he added features such as full text search, tag look-up, and a commenting system. In this latest version, we’ve added a whole bunch of features, but the most interesting one is the dynamic generation of dependency graphs.
We’ve had some crude visualizations for a while, and we made t-shirts from those pictures. I even had this deal where, if people found mathematical mistakes in the Stacks Project, they’d get a free t-shirt, and I’m happy to report that I just last week gave away my last t-shirt. Here’s an old picture of me with my adorable son (who’s now huge).
Q: Talk a little bit about the new viz.
A: First a word about the tags, which we need to understand the viz.
Every mathematical result in the Stacks Project has a “tag”, which is a four letter code, and which is a permanent reference for that result, even as other results are added before or after that one (by the way, Cathy O’Neil figured this system out).
The graphs show the logical dependencies between these tags, represented by arrows between nodes. You can see this structure in the above picture already.
So for example, if tag ABCD refers to Zariski’s Main Theorem, and tag ADFG refers to Nakayama’s Lemma, then since Zariski depends on Nakayama, there’s a logical dependency, which means the node labeled ABCD points to the node labeled ADFG in the entire graph.
Of course, we don’t really look at the entire graph, we look at the subgraph of results which a given result depends on. And we don’t draw all the arrows either, we only draw the arrows corresponding to direct references in the proofs. Which is to say, in the subgraph for Zariski, there will be a path from node ABCD to node ADFG, but not necessarily a direct link.
Q: Can we see an example?
Let’s move to an example for result 01WC, which refers to the proof that “a locally projective morphism is proper”.
First, there are two kinds of heat maps. Here’s one that defines distance as the maximum (directed) distance from the root node. In other words, how far down in the proof is this result needed? In this case the main result 01WC is bright red with a black dotted border, and any result that 01WC depends on is represented as a node. The edges are directed, although the arrows aren’t drawn, but you can figure out the direction by how the color changes. The dark blue colors are the leaf nodes that are farthest away from the root.
Another way of saying this is that the redder results are the results that are closer to it in meaning and sophistication level.
Note if we had defined the distance as the minimum distance from the root node (to come soon hopefully), then we’d have a slightly different and also meaningful way of thinking about “redness” as “relevance” to the root node.
This is a screenshot but feel free to play with it directly here. For all of the graphs, hovering over a result will cause the statement of the result to appear, which is awesome.
Next, let’s look at another kind of heat map where the color is defined as maximum distance from some leaf note in the overall graph. So dark blue nodes are basic results in algebra, sheaves, sites, cohomology, simplicial methods, and other chapters. The link is the same, you can just toggle between the different metric.
Next we delved further into how results depend on those different topics. Here, again for the same result, we can see the extent to which that result depends on the different on results from the various chapters. If you scroll over the nodes you can see more details. This is just a screenshot but you can play with it yourself here and you can collapse it in various ways corresponding to the internal hierarchy of the project.
Finally, we have a way of looking at the logical dependency graph directly, where result node is labeled with a tag and colored by “type”: whether it’s a lemma, proposition, theorem, or something else, and it also annotates the results which have separate names. Again a screenshot but play with it here, it rotates!
Check out the whole project here, and feel free to leave comments using the comment feature!
Not much time because I’m giving a keynote talk at the PyData 2013 conference in Cambridge today, which is being held at the Microsoft NERD conference center.
It’s gonna be videotaped so I’ll link to that when it’s ready.
My title is “Storytelling With Data” but for whatever reason on the schedule handed out yesterday the name had been changed to “Scalable Storytelling With Data”. I’m thinking of addressing this name change in my talk – one of the points of the talk, in fact, is that with great tools, we don’t need to worry too much about the scale.
Plus since it’s Sunday morning I’m going to make an effort to tie my talk into an old testament story, which is totally bizarre since I’m not at all religious but for some reason it feels right. Please wish me luck.
Have you read this recent article in Slate about they canceled online courses at San Jose State University after more than half the students failed? The failure rate ranged from 56 to 76 percent for five basic undergrad classes with a student enrollment limit of 100 people.
Personally, I’m impressed that so many people passed them considering how light-weight the connection is in such course experiences. Maybe it’s because they weren’t free – they cost $150.
It all depends on what you were expecting, I guess. It begs the question of what college is for anyway.
I was talking to a business guy about the MOOC potential for disruption, and he mentioned that, as a Yale undergrad himself, he never learned a thing in classes, that in fact he skipped most of his classes to hang out with his buddies. He somehow thought MOOCs would be a fine replacement for that experience. However, when I asked him whether he still knew any of his buddies from college, he acknowledged that he does business with them all the time.
Personally, this confirms my theory that education is more about making connection than education per se, and although I learned a lot of math in college, I also made a friend who helped me get into grad school and even introduced me to my thesis advisor.
I’ve blogged before about how I find it outrageous that the credit scoring models are proprietary, considering the impact they have on so many lives.
The argument given for keeping them secret is that otherwise people would game the models, but that really doesn’t make sense.
After all, the models that the big banks have to deal with through regulation aren’t secret, and they game those models all the time. It’s one of the main functions of the banks, in fact, to figure out how to game the models. So either we don’t mind gaming or we don’t hold up our banks to the same standards as our citizens.
Plus, let’s say the models were open and people started gaming the credit score models – what would that look like? A bunch of people paying their electricity bill on time?
Let’s face it: the real reason the models are secret is that the companies who set them up make more money that way, pretending to have some kind of secret sauce. What they really have, of course, is a pretty simple model and access to an amazing network of up-to-date personal financial data, as well as lots of clients.
Their fear is that, if their model gets out, anyone could start a credit scoring agency, but actually it wouldn’t be so easy – if I wanted to do it, I’d have to get all that personal data on everyone. In fact, if I could get all that personal data on everyone, including the historical data, I could easily build a credit scoring model.
So anyhoo, it’s all about money, that and the fact that we’re living under the assumption that it’s appropriate for credit scoring companies to wield all this power over people’s lives, including their love lives.
It’s like we have a secondary system of secret laws where we don’t actually get to see the rules, nor do we get to point out mistakes or reasonably refute them. And if you’re thinking “free credit report,” let’s be clear that that only tells you what data goes in to the model, it doesn’t tell you how it’s used.
As it turns out, though, it’s now more than like a secondary system of laws – it’s become embedded in our actual laws. Somehow the proprietary credit scoring company Equifax is now explicitly part of our healthcare laws. From this New York Times article (hat tip Matt Stoller):
Federal officials said they would rely on Equifax — a company widely used by mortgage lenders, social service agencies and others — to verify income and employment and could extend the initial 12-month contract, bringing its potential value to $329.4 million over five years.
Contract documents show that Equifax must provide income information “in real time,” usually within a second of receiving a query from the federal government. Equifax says much of its information comes from data that is provided by employers and updated each payroll period.
Under the contract, Equifax can use sources like credit card applications but must develop a plan to indicate the accuracy of data and to reduce the risk of fraud.
Thanks Equifax, I guess we’ll just trust you on all of this.
This is a guest post by Peter Darche, an engineer at DataKind and recent graduate of NYU’s ITP program. At ITP he focused primarily on using personal data to improve personal social and environmental impact. Prior to graduate school he taught in NYC public schools with Teach for America and Uncommon Schools.
We all ‘know’ that money influences the way congressmen and women legislate; at least we certainly believe it does. According to poll conducted by law professor Larry Lessig for his book Republic Lost, 75% of respondents (Republican and Democrat) said that ‘money buys results in Congress.’
But what does that explanation really tell us? Yes, a congresswoman’s receiving millions dollars from an industry then voting with that industry’s interests reeks of corruption. But, when that industry is responsible for 80% of her constituents’ jobs the causation becomes much less clear and the explanation much less informative.
The real devil is in the details. It is in the ways that money has shaped her legislative worldview over time and in the small, particular actions that tilt her policy one way rather than another.
In the past finding these many and subtle ways would have taken a herculean effort: untold hours collecting campaign contributions, voting records, speeches, and so on. Today however, due to the efforts of organizations like the Sunlight Foundation and Center for Responsive Politics, this information is online and programmatically accessible; you can write a few lines of code and have a computer gather it all for you.
The last few months Cathy O’Neil, Lee Drutman (a Senior Fellow at the Sunlight Foundation), myself and others have been working on a project that leverages these data sources to attempt to unearth some of these particular facts. By connecting all the avenues by which influence is exerted on the legislative process to the actions taken by legislators, we’re hoping to find some of the detailed ways money changes behavior over time.
The ideas is this: first, find and aggregate what data exists related to the ways influence can be exerted on the legislative process (data on campaign contributions, lobbying contributions, etc), then find data that might track influence manifesting itself in the legislative process (bill sponsorships, co-sponsorships, speeches, votes, committee memberships, etc). Finally, connect the interest group or industry behind the influence to the policies and see how they change over time.
One immediate and attainable goal for this project, for example, is to create an affinity score between legislators and industries, or in other words a metric that would indicate the extent to which a given legislator is influenced by and acts in the interest of a given industry.
So far most of our efforts have focused on finding, collecting, and connecting the records of influence and legislative behavior. We’ve pulled in lobbying and campaign contribution data, as well as sponsored legislation, co-sponsored legislation, speeches and votes. We’ve connected the instances of influence to legislative actions for a given legislator and visualized it on a timeline showing the entirety of a legislator’s career.
Here’s an example of how one might use the timeline. The example below is of Nancy Pelosi’s career. Each green circle represents a campaign contribution she received, and is grouped within a larger circle by the month it was recorded by the FEC. Above are colored rectangles representing legislative actions she took during the time-period in focus (indigo are votes, orange speeches, red co-sponsored bills, blue sponsored bills). Some of the green circles are highlighted because the events have been filtered for connection to health professionals.
Changing the filter to Health Services/HMOs, we see different contributions coming from that industry as well as a co-sponsored bill related to that industry.
Mousing over the bill indicates its a proposal to amend the Social Security act to provide Medicaid coverage to low-income individuals with HIV. Further, looking around at speeches, one can see a relevant speech about the children’s health insurance. Clicking on the speech reveals the text.
By combining data about various events, and allowing users to filter and dive into them, we’re hoping to leverage our natural pattern-seeking capabilities to find specific hypotheses to test. Once an interesting pattern has been found, the tool would allow one to download the data and conduct analyses.
Again, It’s just start, and the timeline and other project related code are internal prototypes created to start seeing some of the connections. We wanted to open it up to you all though to see what you all think and get some feedback. So, with it’s pre-alphaness in mind, what do you think about the project generally and the timeline specifically? What works well – helps you gain insights or generate hypotheses about the connection between money and politics – and what other functionality would you like to see?
The demo version be found here with data for the following legislators:
- Nancy Pelosi
- John Boehner
- Cathy McMorris Rodgers
- John Boehner
- Eric Cantor
- James Lankford
- John Cornyn
- Nancy Pelosi
- James Clyburn
- Kevin McCarthy
- Steny Hoyer
Note: when the timeline is revealed, click and drag over content at the bottom of the timeline to reveal the focus events.
So here’s the thing about being a parent of benign neglect: it’s no walk in the park. I talk a big game, but the truth is I’ve have trouble getting to sleep from the anxiety. To distract myself I’ve been watching Law & Order episodes on Netflix until the wee hours of the night.
Two things about this plan suck. First, my husband is in Amsterdam, which means he’s 6 time zones away from our oldest son whereas I’m only 3, but somehow that means I’m shouldering 99.5% of the responsibility to worry (there’s some universal geographic law of parenting at work there but I don’t know how to formulate it). Second, half of the L&O episodes involve either children getting maimed or killed or child killers. Not restful but I freaking can’t stop!
In any case, not much extra energy to spring out of bed and write the blog, so apologies for a sparse period for mathbabe. For whatever reason I woke up this morning in time to blog, however, so as to not miss an opportunity it’s gonna be in list form:
- I’ve been invited to keynote at PyData in Cambridge, MA at the end of the month – me and Travis Oliphant! I’m still coming up with the title and abstract for my talk, but it’s going to be something about storytelling with data using the iPython Notebook. Please make suggestions!
- I was in a Wall Street Journal article about Larry Summers, talking about whether he’s got a good personality to take over from Ben Bernanke, i.e. should we trust our lives and our future with him. I say nope. What’s funny is that my uncle, economist Bob Hall, is also referred to in the same article. The journalist didn’t know we’re related until after the article came out and Uncle Bob informed him.
- Hey, can we give it up for Eliot Spitzer? The powers that be are down about that guy presumably for having sex with prostitutes but really because he’s a threat. I say legalize prostitution, unionize the prostitutes a la the dutch, and put Spitzer in charge of something involving money and corruption, he’s smart and fearless. Who’s with me?
- It looks like good news: the Consumer Financial Protection Bureau might be cracking down on illegal debt collector tactics. Update: wait, the fines are fractions of 1% of the revenue these guys made on their unfair practices. Can we please have a rule that when you get caught breaking the law, the fine will be large enough so it’s no longer profitable?
I’m psyched to see Suresh Naidu tonight in the first Data Skeptics Meetup. He’s talking about Political Uses and Abuses of Data and his abstract is this:
While a lot has been made of the use of technology for election campaigns, little discussion has focused on other political uses of data. From targeting dissidents and tax-evaders to organizing protests, the same datasets and analytics that let data scientists do prediction of consumer and voter behavior can also be used to forecast political opponents, mobilize likely leaders, solve collective problems and generally push people around. In this discussion, Suresh will put this in a 1000 year government data-collection perspective, and talk about how data science might be getting used in authoritarian countries, both by regimes and their opponents.
Given the recent articles highlighting this kind of stuff, I’m sure the topic will provoke a lively discussion – my favorite kind!
Unfortunately the Meetup is full but I’d love you guys to give suggestions for more speakers and/or more topics.