Posted in 2015

Google Forms for Better Live Discussion

During a workshop I hosted Friday, I was asked how I designed the activity we did. Here’s a quick writeup on how that worked. First, a little context. I presented an 80 minute workshop at the Region 1 FRC. I’ve attended NSBE conferences enough times to know that, no matter how interested I was in a workshop, lack of sleep influences my ability to focus, so I wanted to ensure the workshop was engaging and active. The conference theme for this year is engineering a cultural change; my take on this as a machine learning researcher is big data for social change. My objective for the workshop was that the attendees both learn about the core ideas of machine learning and big data to understand context if following up further and realize how it’s an exciting field with lots of room for exploration and discussion.  The workshop was formatted with the information loaded more at the front, but that we quickly worked into shaping the conversation around the attendees’ interests. I wanted to make sure that the activities were challenging and prompted discussion, but that they were also accessible, so I made it group activities.

However, in my own experience, too many groups reporting out and sharing their responses to the same questions, can get repetitive and boring.  To be able to let all groups share and give myself the ability to select the best groups to share for each different portion of the activity, I used Google forms and had the groups submit their answers to each step. Even without wifi, having participants complete the activity by submitting responses on their smart phones it worked great. I wanted the activity to be completed in stages: after some introduction from me, we’d break out, report back, discuss add new material, and repeat a few times. I also didn’t want the groups to have to type any information repeatedly while I could still match responses from one breakout part to the next. To achieve this, I set it up for them to “edit their response” and for separate pages of questions for each stage of the activity.

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A Gentle Technical Reading List for Big Data for Social Good

  • 30 October 2015

As a machine learning researcher, Big Data for Social Good is my take on this year’s NSBE conference theme of Engineering a Cultural Change.  Today, I’m presenting a workshop at the NSBE Region 1 Fall Regional Conference on that topic, but there’s so much to share, this is mostly intended as additional information for the attendees, but I think this could be useful more broadly.  My research, isn’t exactly on Big Data for Social Good, but I do applied machine learning research and I think there’s some important commonalities.  I begin from a real problem and design smart algorithms to help domain experts make sense of their data- this is exactly what a Data Scientist working at or with a nonprofit would do.  In my graduate work my collaborators have been psychologists who want to ask categorically different questions- questions so novel that traditional experimental designs and analysis techniques don’t get the job done. Since I’ve spent so much of my time outside of the classroom and lab dedicated to social impact through NSBE so Big Data for Social Good is a personal interest and possible future direction for me.

Machine learning and big data appear all over lately but there are a number of key resources that I think anyone interested in data driven methods for decision making, even if outside of the technical realm should consider and support making sense of. These are, however, challenging problems . There is additional research that must be done toward this end. Here I provide a list of some of my favorite (mostly) accessible machine learning papers that I think are good reading material for someone broadly interested in machine learning for social good but is not an expert in machine learning yet.  These will help you begin to get some perspective on the relevant technical matters and research questions without being bogged down by details.

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Why & How I Chose to Get a PhD

Getting a PhD is a major commitment; deciding to do it isn’t easy. Now I’m in the final stretch of my PhD: my qualifying exam is passed, coursework is complete, proposal stage is passed, just a dissertation left.  I think it’s a good time to share how I got started in grad school.  I’m completely happy with my choice to get a PhD, even though at the start of my final year of undergrad, I wasn’t sure.  Hopefully the way I made the choice and why I’m glad I’m getting a PhD helps you make the choice yourself.

At this time five years ago, I was a few months into my third and final co-op, at Draper Laboratory. My senior year had just started and even with my bonus year of delay by entering a five year program, I finally had to figure out what to do after graduation.  I wasn’t entirely certain what I wanted to do as a career. I did know I wanted to be an engineer- I was still happy with my major and had enjoyed my experiences through undergrad, but I also know I could be an engineer, and use the skills I had acquired through the degree in a lot of different ways. I had done research on campus since the spring of freshman year and I had done two research-related coops. In the second one, most of the people I had worked with had Master’s degrees and the first one was at a hospital, with mostly doctors of one sort or another.  Working around so many people with graduate degrees had convinced me I’d probably need one too, but I wasn’t sure what kind.  I liked doing research; I was continually drawn to research positions after reviewing hundreds of posted co-op positions.  I wasn’t sure, however, if I wanted to work in industry or academia and I didn’t see a lot of PhDs in industry.  I used my time at my third co-op to figure out which graduate degree would be the best choice for me and what I was going to do in grad school, at least roughly.

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Becoming a Better Writer: Building a Daily Writing Habit

Writing has always been hard for me.  For a while, as an engineer, I thought I was safe.  Then came writing in grad school.  My MS thesis was a painful process in summer 2013 and I vowed I would learn from that.  Then last summer, I struggled through my next paper again.  In both cases, the process of writing about my work had revealed gaps I wasn’t comfortable with leaving.  To overcome that, with my next project, I started writing it out as I worked on it.  Even before I had all the results figured out, I started writing it out and working on explaining it.  My new problem was just that writing felt like something to avoid.  I would decide I needed to write, but start with staring at the blank screen, wandering the internet, or answering e-mail to feel productive, while not accomplishing the important things. Writing is going to be a critical part of my career, so I need it to come more naturally. My plan to reach that, is to form a daily writing habit.

In the past, I’ve made plans to try to make writing come more naturally, but never managed to follow through.  I came across the site and started using it in the beginning of March.  The site was created by Buster who was working to make a daily writing habit, had tried numerous media and not succeed.  To help himself, he created a private location for the daily brain dumps.  It provides a clean interface to write in each day and statistics on your writing.  The site runs a monthly challenge and posts a leader board based on points earned for writing, reaching 750 words, and for streaks.  There are also badges for streaks of different lengths and other behaviors.  After the first 30 days, it does cost $5/month, but I think the idea that I paid for it helps me hold myself accountable even a little more.   Using has helped me hold myself accountable. It’s been helpful that I keep this in my mind as owing myself 750 words of text, on any topic from research to just a reflection on my day, every day.

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Organizing WiML 2014

Since April, I’ve served as a co-organizer with Marzyeh Ghassmi Jessica Thompson, and Allison Chaney for the 9th Annual Women in Machine Learning Workshop (WiML) co-located with the Neural Information Processing Systems (NIPS) conference, in in Montréal, QC, Canada in December. This year we had record attendance and sponsorship.  As Finance and Sponsorship Chair, I’m especially proud of our sponsorship accomplishments: we had 3 Gold Sponsors, 2 Silver and 6 Bronze and 2 Supporter Sponsors, which in total, doubled sponsorship over the previous year- a new WiML record.

As an organizing team, we met for an hour biweekly by Skype over the months leading up to the workshop. We worked fairly independently, but got great support from the WiML board as well. Organizing WiML is like running a small conference so there are a lot of things to keep track of, but past organizers have done a great job, with the support of the board at archiving everything.  For a lot of tasks we were able to copy & edit what was done the previous year, so even though the organizing team changes completely every year it’s not that hard. Organizing WiML was was of the best event-planning experiences I’ve had.  We really didn’t hit any major bumps or have painfully long meetings to avoid that.  In the days leading up to the conference, one of the last minute “problems” we has was that we had more people offer to volunteer than we had imagined jobs for.  That really speaks to the community of WiML, it’s a very supportive group of women.

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AAAI15 Paper Content Posted!

I’ll be attending AAAI-15 in Austin, TX next week to present my paper “A Sparse Combined Regression-Classification Formulation for Learning a Physiological Alternative to Clinical Post-Traumatic Stress Disorder Scores.”  The paper & related content are up and available on a project page now.

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