Erik Barry Erhardt, PhD

Applying for an academic job is serious work.  I ended up lucky (though, luck favors the prepared (Louis Pasteur)).  I received two job offers this season and took my first-choice job.  But I worked hard to get those offers.  I kept a detailed CV my entire student career (starting as a BA student, not waiting until job season to start), wrote an extensive teaching dossier for the 20 courses I’ve taught and ugrad tutoring experience, and developed a research statement as that vision became clearer to me.  Clearly, self-investment and personal excellence are the most important ingredients.  Next is to find people who want to hire you.

Two sites and one magazine basically covers the bases for statistics.

1.  If you’re a statistics student, you’re already a member of the ASA, right?  If so, the back of the AmStat News magazine has many jobs listed.

http://magazine.amstat.org/

2. Many jobs are posted at the American Statistical Association (ASA) jobs website.   Subscribe to their feed in your RSS reader:

http://jobs.amstat.org/search/results/index.cfm?SN=25&ss=1&display=rss

While I have had my CV posted on the site for years, I’ve never received any contact because of it.  I think the more direct approach of networking or replying to specific jobs is more effective.

3. The University of Florida statistics website lists many jobs, too.  My impression is that this site is even more comprehensive than jobs.amstat sometimes.

http://www.stat.ufl.edu/vlib/Index.html

I recommend being subscribed to the jobs.amstat.org in your RSS reader, because then most of the jobs will come to you.  You can follow-up at the UFlorida website to make sure you’re not missing anything.  Start looking in Sept/Oct and work on cover letters through Nov/Dec for the Dec/Jan/Feb deadlines.  Ask for letters of recommendation early (maybe even late summer while your professors are not busy with the semester).  Ask your advisor to look over your CV, cover letter, and other submission materials (scan a pdf of your unofficial transcript).  They’ve reviewed many applications hiring in their department before and will have good advice.  Send your application materials (all in pdf format — not doc!) as soon as you are ready to help yours be near the top of their review pile.  And while your application is in the hands of many hiring committees, try not to sweat — you’ve done all you can and it’s largely out of your control until they ask for an interview (or send you a form rejection letter, or never respond to you at all).  Feel free to send a follow-up email to request status if it’s a week or so after their self-predicted decision deadline, if it will help calm your nerves, but try not to hassle them.  It’s a very challenging market and positions regularly get 80-300 applications, so everything you can do to rise to the top of that deep stack can make the difference between getting a toe in the door and the alternative.

Interviewing is next step.  Here are some pages with questions to prepare for.  Write your questions down just as you’d say them and practice saying them aloud, maybe to a friend who will listen.  You want to clarify your answers to yourself and get them to flow smoothly out of your mouth.
10 tough interview questions
General advice

The job talk is the last step.
You’re a grown up, use Mac’s iWork Keynote — it’s the best presentation software available.
BBP was a great resource, provided you can ignore all the MSPP BS.  First five slides.  Template. Video.
Matt Might’s presentation tips and job hunt advice.
CS Berkeley

Negotiating for your salary, start-up, teaching reduction, and more — ask your advisor for advice.  If you have a second offer, all of this becomes much, much easier!

Research, Statistics

For the second issue of Brain Connectivity, a new journal, we were invited to provide a response to a “controversial article” about issues of analysis and interpretation in fMRI.  In a fun paper, Elena, Eswar, Vince, and I provide our perspective and some better practices to continue the dialogue.

On network derivation, classification, and visualization: a response to Habeck and Moeller
Erik B. Erhardt, Elena A. Allen, Eswar Damaraju, Vince D. Calhoun.
Brain Connectivity 1(2), 2011.

Abstract
In the decade and a half since Biswal’s fortuitous discovery of spontaneous correlations in functional imaging data, the field of functional connectivity (FC) has seen exponential growth resulting in the identification of widely-replicated intrinsic networks and the innovation of novel analytic methods with the promise of diagnostic application.  As such a young field undergoing rapid change, we have yet to converge upon a desired and needed set of standards.  In this issue, Habeck and Moeller begin a dialogue for developing best practices by providing four criticisms with respect to FC estimation methods, interpretation of FC networks, assessment of FC network features in classifying sub-populations, and network visualization.  Here, we respond to Habeck and Moeller and provide our own perspective on the concerns raised in the hope that the neuroimaging field will benefit from this discussion.

MIND, Research

Together with David Hanson, I developed R package tdllicor which reads TDL and Licor files, aligns them, and calculates quantities of interest with bootstrap intervals.  It is currently private as it is specialized and not of general interest.  It has already been important for a number of conference publications and is used for active research:

Conference Publications

DT Pater, EB Erhardt, and DT Hanson. Photorespiratory and respiratory carbon
isotope fractionation in leaves. In Proceedings of the Biophysical Society 55th
Annual Meeting, Baltimore, MD, Mar 2010. Biophysical Society.

DT Pater, EB Erhardt, and DT Hanson. Isotopic signature of photorespiration.
In Joint Annual Meetings of the American Society of Plant Biologists and the
Canadian Society of Plant Physiologists, Montreal, CA, August 2010.

Research, Statistics

Working with Aaftab Jain, we developed a estimator for total number of bird and bat carcasses at a windfarm called “mortest” and implemented it as an R package.  We are interested in estimating c, the total number of carcasses (mortalities) in a period (year). The total number of carcasses is the sum of carcasses over size classes, c = sum_s=1^S c_s. If carcasses are retained (that is, not scavenged) and searcher efficiency is perfect (every carcass is found) and every tower is searched, then each c_s would be counted perfectly. Yet, carcass scavenging by predators and searchers overlooking carcasses are a reality, making observed counts an underestimate. Furthermore, tower sampling rather than censusing is a cost-saving convenience. Our estimator of total mortality, c, weighs the estimates from different search intervals and adjusts the observed counts for scavenging, search efficiency, searchable area of each tower, and proportion of towers searched, accounting for uncertainty in these estimates using a bootstrap.

The software was written by Erik Erhardt and is currently private.  Contact Aaftab Jain <aaftabj+gmail.com> for more information for using the software.

Research, Statistics