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About me
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November 2020 was the lowest time for me during the COVID-19 pandemic. My boyfriend left to spend a month with his faimly across the country, while I stayed home in our apartment. Aside from a few days visiting my sister, I spent the month without human contact.
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Hello readers! It’s been a long time since I posted on this blog. Years, actually. During that time, I finished my PhD at UC Berkeley and started working at Pinterest. It was an adjustment, to say the least. I traded my 10 minute walk to campus for an hourlong bus ride across the Bay Bridge; freedom to work from anywhere with Wifi for butt-in-chair from nine to five; the pursuit of knowledge for the pursuit of measurable business impact.
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If you follow me on social media, you might’ve seen that I’ve been traveling a ton this past year, and most of it has been related to my grad school work. In my five years as a PhD student, I’ve visited five states and five countries for conferences and other events. As someone who didn’t travel much as a kid, I’ve been loving these opportunities!
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I gave a lightning talk at the SF R Ladies meet-up about a problem with R’s sampling algorithm. Check out my slides here!
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Last week, I attended my first voting conference: E-VOTE-ID. I’ve presented at statistics conferences before but never an interdisciplinary one like E-VOTE-ID. It brought together people working on electronic voting issues from a whole range of disciplines: legal studies, sociology, cryptography and security, voting systems developers, former election officials, and one statistician. This guy!
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I gave a talk about a short book I’m writing at the 4th Conference of the International Society of Nonparametric Statistics. Please check out my slides!
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A few years ago, I was pretty unhappy in my PhD program.
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I ALWAYS forget to put a license on my work until someone reminds me. I’ve learned over and over that it’s important, but I think the reason why it hasn’t stuck is that I was never taught why it’s important.
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I’ve really been gotten on the crunchy bandwagon this year – buying high quality grassfed meats, organic produce, paraben-free beauty products, and swapping out plastic food storage containers for glass ones. Up until recently, I was skeptical about the evidence that these choices really make a difference for your health.
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I participated in my first hackathon two weekends ago. I use code to do data analysis most of the time, not write apps or websites. For me, it was more of a fun learning experience and I got to see what kinds of work are expected and rewarded.
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Fewer grad students are on the job market for faculty positions – is it because they realize that there are fewer jobs or because they are genuinely more interested in other career paths? Roach and Sauermann studied interest in academic careers in a way that has never been done before: longitudinal surveys of current graduate students. By giving people the survey twice, once in their first or second year of the PhD and then again three years later, they are able to measure changes in interest. Previously, people have only looked at cross-sectional data and compared two groups at different points in their PhD.
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I gave a talk about p-values and hypothesis testing at BIDS. Please check out my slides!
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This week I had the privilege of participating in two workshops: I was a participant at a train-the-trainer workshop to become a Software Carpentry instructor and an instructor at the R Bootcamp put on by the Statistics Department and D-Lab. It was a unique opportunity to spend two days learning how to teach one of these bootcamps, and then to put my skills to the test a few days later.
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A lightweight markup language is a simple, human-readable language for formatting text. It’s easy to read and compatible with most text editors. Documents written in lightweight markup are usually then converted to things that are harder for people, but easier for computers, to read, like HTML. The most common ones that I’ve heard of people using are Markdown, R Markdown, and reStructured Text. I imagine that most people who do data analysis/exploratory visualization/data science use a markup language more often than they write in raw HTML.
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This question is related to my last blog post about what people consider when choosing which Python package to use. Say I want to use some statistical method. I have a few options. I could code it up from scratch myself, knowing that this might have undetected bugs and be pretty slow. I could Google what I’m looking for and use the first thing I find; similarly, there are no guarantees. Or, I could do my research, find all the packages that seem to offer what I’m looking for, and decide which looks best based on how thoroughly they’ve documented and tested their code.
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I’m in the early stages of creating several Python packages right now (shameless self plug – see permute, cryptorandom, and pscore_match). I want people to actually use them when they’re ready. They have potential for wide use, but they have narrow functionality compared to big packages like numpy
or scipy
. I could imagine that somebody looking to do a particular task in Python, like propensity score matching, would do a Google search and stumble upon my package.
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This blog was originally posted on the BIDS blog, here.
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This blog was originally posted on the BIDS blog, here, and was written with Rebecca Barter, Ryan Giordano, and Sara Stoudt.
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This blog post originally appeared on the BIDS blog.
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We showed that student evaluations of teaching are biased against female instructors, and it is not possible to adjust for the bias due to its dependence on many other factors.
Download here
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permute is a Python package for permutation testing.
Download here
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We propose several best practices for researchers using PRNGs for simulations, including the wide adoption of hash function based PRNGs.
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We explain a two-step process for partitioning the risk of projected returns into contributions from latent factors using nonparametric regression methods.
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We compared traditional ANCOVA to permutation approaches in the analysis of randomized experiments.
Download here
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I contributed two case studies of my data science workflow.
Download here
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We proposed a strategy for Colorado to conduct risk-limiting post-election audits of contests spanning jurisdictions that use heterogeneous voting systems.
Download here
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Faulty algorithms in R’s random sampling functions.
Download here
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I presented this work at the E-VOTE-ID 2018 PhD Colloquium.
Download here
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We’ve developed a new statistical method for risk-limiting post-election audits of stratified samples of ballots.
Download here
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Ballot polling RLAs using Bernoulli sampling, rather than simple random sampling, can bypass current logistical challenges.
Download here
Published in UC San Francisco, Department of Testing, 2012
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Published in UC-Berkeley Institute for Testing Science, 2013
Published in London School of Testing, 2014
Published in Testing Institute of America 2014 Annual Conference, 2014
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Published in UC Berkeley, Department of Statistics, 2015
I was the graduate student instructor for Stat 20, taught by Hank Ibser.
Published in UC Berkeley, Department of Statistics, 2016
I was the graduate student instructor for Stat 215B, taught by Jon McAuliffe.
Published in UC Berkeley, Department of Statistics, 2016
I designed and taught the lesson on creating graphics in R. Read the slides or watch a screencast of the presentation.
Published in UC Berkeley, Department of Statistics, 2016
I gave a guest lecture on hypothesis testing in Stat 215A, taught by Philip Stark. Slides are here.
Published in UC Berkeley, Department of Statistics, 2017
I designed and taught a lesson on using git and GitHub for collaborative reproducible workflows for the Statistics Graduate Student Association. The presentation materials are here.