The Winds, They Are A-Turbulently Flowing

This may just my bubble talking, but it feels like something shifted after Trump’s attacks on scientists today.

That rather heaping disdain on the already-marginalized, he began to assault people most Americans aspire to be.

That we always accept heroic scientists’ politics because we see them as scientists first.

That politics has always been guilty entertainment where lies and violence make for great drama, but realizing The Apprentice shenanigans and Walking Dead villains are creeping into the impenetrable — but important — work being done in those labs you see on Big Bang Theory or hear on Radiolab.

That your co-worker’s brilliant, but too busy to keep up with your woke politics, friend is suddenly and severely worried that she can’t do her like *real, actual, Pasteur quadrant science* about cancer treatment or weather forecasting because our “isn’t that just awful, but there isn’t anything I can do about it so I might as well look” political circus is spilling into sacred secular spaces. Once the 94.9 million Americans who didn’t vote in this election expecting the serious scientists to bail us out at the end — like Scottie, R2D2, Lucius Fox, Tony Stark, Abby Sciuto, Samwell Tarly always do — instead hear those scientists saying “we won’t make it this time”… now people want to get off the ride.

Looking for Ph.D. Students

In case you missed the news via other channels, I am now an assistant professor at the University of Colorado’s Department of Information Science. This makes me a brand new professor in a brand new department in a brand new college. I’ll save my customary advice post for a later time, but suffice it to say that there’s a fractal nature to the perception of social order: if becoming a graduate student made you realize your undergraduate TAs were more-or-less faking it, then the same holds for the transition from graduate student to faculty member. If you want to see the results of my faking it in class, check out the repo that will contain all syllabus, slides, and lab notebooks for my Peer Production and Crowdsourcing seminar class.

One of the roles that I am most excited to fill as a professor is that of an advisor. I come out of a “lab” culture characterized by team collaboration and shared credit, informal peer mentorship, rapid iteration, promiscuous use of theories and methods, cumulative scholarly contributions, and common working space. This is different from other academic models that prioritize single authorship, one-to-one advising, detailed planning, methodological or theoretical purity, concentrated scholarly contributions, and more autonomous work. There are risks and benefits in either culture, but my experience with the former gives me the confidence to adopt the model and select applicants who want to collaborate in that context.

If you are not currently enrolled in a graduate program, applying for the NSF Graduate Research Fellowship program before the October 25 deadline this fall is an extremely compelling signal that you’re taking your application seriously enough to be planning ahead, applying for external funding, framing a research program, etc. If you are interested in applying to work with me and eligible for the GRFP, please get in contact at (change “4”s to “a”s) before October 11 so that I can give you feedback.

I have a few different projects in various stages of disarray I’m looking to admit Ph.D. students as funded research assistants and junior collaborators. There are several different Ph.D. programs at CU Boulder for which students can apply: the majority of our current students from Information Science, Computer Science, or the ATLAS program. The application deadlines for these are as early as December 7, 2016 to be admitted and start in the Fall 2017.

Applicants should have working knowledge and project experience working with programming/scripting for data analysis in R or Python. If you have worked through some O’Reilly data analysis books or taken some data science classes on Coursera and can point me to a blog post or GitHub repo where you’ve shared preliminary findings from your data analysis project (e.g., networks of songs in movie trailers), that’s exactly what I’m looking for! I primarily use methods from network science, machine learning, natural language processing, time series, and sequence analysis to understand the structure and dynamics of online collaboration. I am very open to junior collaborators mixing these methods with ethnography, interviews, critical design, surveys, and experiments.

  • Sequence analysis. A major new direction for my work is to understand how behavioral sequences can be a unit of analysis for understanding online collaboration. Applicants familiar with Markov chains, Viterbi reconstruction, finite state machines, sequence similarity and classification, or frequent pattern mining from streaming data, bioinformatics, or other domains would be amazing.
  • Social bots. How can social bots be used in conjunction with field experiment methods to rapidly and ethically conduct online experiments? I’m particularly interested in the role that social bots could be used for challenging online misinformation.
  • Forecasting and prediction. What features can be mined from online behavioral traces to forecast and predict economic, political, and cultural events? Identifying influential users, evidence of insider knowledge, and aggregating data across platforms are all exciting and productive areas for research.
  • Second screening. How does social behavior change when we are all watching the same event? My previous work has looked at changes in Twitter behavior during the 2012 U.S. Presidential election and 2014 World Cup.
  • Teamwork in online games. My early research examined cheaters in MMOGs and I have also become interested in analyzing online battle arenas like League of Legends to understand team assembly dynamics and non-verbal communication.
  • High-tempo online collaboration. How do people rapidly self-organize to collaborate and share knowledge? I’ve primarily examined this in the context of English Wikipedia, but there are many opportunities to extend this work into other linguistic and cultural contexts, across other platforms, and examine the consequences of participating in these collaborations for on-going community engagement.
  • Data-driven journalism. What lessons do Wikipedians writing about current events have for creating new models for journalism and public accountability? I am interested in developing tools and evaluating models for recruiting social media users to collaboratively surface and write-up information from large and complex data sets.

If you are interested or know of potential applicants that are interested in any of these topics as part of a future Ph.D. program, please encourage them to get in contact with me at (change “4”s to “a”s).