Category: Technology

My Dissertation Defense Announcement

Crowdsourcing Health Discoveries: from Anecdotes to Aggregated Self-Experiments

Time & Location 

July 26th, 1:00 PM, E14-244

Thesis Supervisor

Frank Moss, Professor of the Practice of Media Arts and Sciences, MIT


Henry Lieberman, Principle Research Scientist, MIT
Peter Szolovits, Professor of Computer Science and Engineering, MIT


Nearly one quarter of US adults read patient-generated health information found on blogs, forums and social media; many say they use this information to influence everyday health decisions. Topics of discussion in online forums are often poorly-addressed by existing, clinical research, so patient’s reported experiences are the only  evidence. No rigorous methods exist to help patients leverage anecdotal evidence to make better decisions.

This dissertation reports on multiple prototype systems that help patients augment anecdote with data to improve individual decision making, optimize healthcare delivery, and accelerate research.  The web-based systems were developed through a multi-year collaboration with individuals, advocacy organizations, healthcare providers, and biomedical researchers. The result of this work is a new scientific model for crowdsourcing health insights: Aggregated Self-Experiments.

The self-experiment, a type of single-subject (n-of-1) trial, validates the effectiveness of an intervention on a single person. Aggregating the outcomes of multiple trials can improve the efficiency of future trials and enable users to prioritize the sequencing of trials for a given condition. Successful outcomes from many patients will yield evidence to motivate future clinical research.  Aggregated Personal Experiments enables user communities to replace anecdotes with repeatable trials that can be run in the context of their daily life.  The properties and viability of the model were evaluated through user studies, secondary data analyses, and experience with real-world deployments.

You can check it out at


Clojure Debugging ’13: Emacs, nREPL, and Ritz

[NOTE: The release of cider deprecates much of the content here.  I will post an update on Clojure Debugging ’14 early in the near year]

I’m ramping up for a new set of development projects in 2013 and 2014.  My 2010 era setup with slime and swank-clojure is unlikely to remain a viable approach throughout the project.  I’ve decided it is time to join the nREPL community as well as take advantage of some of architecture innovations there which may make it easier to debug the distributed systems I’m going to be working on.

Features I’m accustomed to from common lisp slime/swank:

  • Code navigation via Meta-. and Meta-,
  • Fuzzy completion in editor windows and the repl
  • Documentation help in mini-buffer
  • Object inspector.  Ability to walk any value in the system
  • Walkable backtraces with one-key navigation to offending source
  • Evaluate an expression in a specific frame, inspect result
  • Easy tracing of functions to the repl or a trace buffer (in emacs)
  • Trigger a continuable backtrace via watchpoint or breakpoint

Only the first three of these features is available in the stock nrepl.  The rest of this post will discuss how to setup a reasonable approximation to this feature set in Emacs using nREPL middleware providers as of May 2013.

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Agile Software Metrics

The rise of dynamic software development methodologies such as Extreme Programming or Agile Programming, reflect the inherent dynamism of modern software design.  The malleability of software, the rapid evolution of consumer and technology driven requirements, the difficulty of writing accurate specifications given all the unknowns, and the sheer complexity of the software ecosystem itself makes the ancient development waterfall from specification through execution and QA to release a hazardous and mostly futile affair.

Most software developments fail.  While the situation has improved over the last decade, this remains mostly true today.   Less than a third of all software projects meet their objectives in approximately the time expected.  Over 10% of all projects fail without deliver anything, and most of the rest under-deliver, are terribly late, or way over budget.

This blog post is a thinking-out-loud exploration of how modern Agile methods address these problems and how my thinking is evolving with regards to how success is defined and the probability of success maximized.

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What can we learn from our E-Mail logs?

Recently I’ve been observing via RescueTime that I spend 3 hours or more hours in my e-mail application most days.  However, I don’t have a good breakdown of how much of this is scheduling, looking up information, commenting on something substantive or social discourse.  There is a tremendous amount of information locked up in the time-series of e-mail’s sent and received that can provide insight into aspects of my behavior such as focus of attention (time of day e-mail is sent), social relationships (what organizations I interact with in a given week), the density of idea generation, etc.  E-mail logs contains a wealth of raw data that can be instrumented to uncover important information about our life.

Our E-mail logs are also rich archive of useful information such as phone numbers, addresses, what we said to someone, when we said something to someone, edits to papers, attachments, etc. With a proper set of tools, many of which have been built for analyzing social media, we can turn this archive into a database of useful information that can significantly enhance e-mail-based  instrumentation.

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Schema support for Clojure HBase Client now called clojure-hbase-schemas

My github fork of the Clojure library for HBase, clojure-hbase is now deprecated.  I’ve extracted the functionality from David Santiago’s original library (with permission) along with a duplicate of his admin functions to create a parallel repository with the schema-oriented API I developed.

The new repository is owned by Compass Labs and can be found here.  The library can also be referenced via Maven / Leiningen:

com.compasslabs/clojure-hbase-schemas "0.90.4"

Writing Java plugins for Flume in Clojure

I recently wrote a plugin in Clojure to add to the Cloudera Flume framework.  As it was my first time writing a full java class interface I had to learn about the proper use of both proxy and gen-class.  Given the poor error reporting at the java-clojure boundary, figuring out what you did wrong if you don’t get every detail exactly right (particularly when loading a class in the plugin’s final environment) can be difficult.

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New Abstractions for Clojure-HBase

I just pushed Compass Lab’s HBase Client API to my fork of the clojure-hbase library.  The API includes support for table schemas (to auto-encode inputs and outputs) and a constraint language that generates filters and calls for Get and Scan operations.  We decided to integrate with the existing fork to retain access to the excellent admin functionality already implemented there.  We’ll be talking with the original author and see if we’ll merge or split these two API development paths.  In the meantime, you can use our fork of clojure-hbase.