Sarah M Brown




Data Carpenty Certified Instructor


Now that I’ve received my certificate, it’s official, I’m a Data Carpentry instructor. To do this, I completed a two day training, submitted a pull request to a Data Carpentry lesson, and passed a teaching demonstration.

Now I’m ready to teach and working on planning one for #NSBE44

UC Berkeley Chancellor's Postdoctoral Fellow

Getting Unstuck in Writing for Research

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I was recently contacted by another graduate student for advice on how to deal with feeling bogged down by theoretical and mathematical detail while working on a journal paper.  This is a problem that I have a lot actually, I don’t think I’ve gotten it all solved, but I have developed a number of strategies for getting through it.

Learn Strategies

The most general one is somewhat circular, but more proactive.  I’ve put significant effort toward learning to be a better researcher, learner, writer, and generally productive person. I’ve read some books and countless articles on these types of matters. This of course isn’t the best in the moment before a deadline solution, to try to consume all of these materials at once and then magically be able to get your work done, but slowly working through these materials over the course of time has made me lose less time and get less worried when I face these struggles. I maybe face them less often or maybe with about the same frequency, but lose less time with each occurrence and I do more complex and more theoretical work than I did at the beginning of graduate school. This strategy can help a little immediately as well. When I get really stuck I pause and spend 20-30 minutes reading whatever’s next on my ‘get better at x’ list or the book I’m currently working through. After a few minutes of a productive feeling distraction, I often have a better idea of how to proceed.   This used to be my first strategy, I’d spend a few minutes reading and learning about things I could try until I found one that sounded like a good strategy to try.  Recently this has fallen lower on  my list, because the ones below get me back on track.  I think this is the most important strategy and that it should the first one I mention here, because even though the strategies below help me they may not help you so learning about as many strategies and trying them out until you settle into your own toolbox of strategies is the most important.

Start Typing. Don’t Stop.

I open a separate space (for me, which I’ve mentioned previously or writebox is the new one I’ve just started occasionally) somewhere where the project isn’t there, I have no context and zero pressure for formatting or even correct grammar or spelling. I start typing and don’t stop until I reach a predetermined goal.  I most often require myself to push through this exercise until I reach 750 words, sometimes though I go much longer, other times to get there I write some pretty redundant things (not literally repetitive though, that’s cheating), but having a minimum I must reach require me to reach some level of breadth or depth.  This strategy has a few different sub-components that I’ve pulled together from other places, but in general, getting whatever ideas, or stumbling blocks, that are on my mind out and in writing helps me move on. The separate space is important for me because there’s no pressure for what I write to fit into a project or distraction of the existing text.  I have no problem writing non stop with varying tone, audience or topic, when it’s not in a project, just what I need out of my way: a note on what I’m stuck on to my adviser, a new draft of how it could go, another version of the same paragraph from a different perspective, etc. Then when I’m done, I copy and paste anything useful into the project.

Most often, I start writing about what I’m having trouble writing, coding or figuring out and why, maybe with a friend or my adviser in mind as the audience. Sometimes that takes a while, but at the end I at least feel better and usually have an idea. Usually, after about a paragraph, I have an idea for how to fix the thing or explain whatever it is I’m having trouble with.  I start to then draft the writing I need, maybe halfway through a paragraph I have a better way to say something, so I just write the second option next. Later, I can pick and choose the best aspects of each way, or maybe after I can try to write out a justification for each and use those to decide.  This is nonstop writing, no revising; clean up and sorting is for after.

Other times I try to write out instructions to myself for what to do or the new questions I have to research in order to progress. When stuck with writing specifically, I’ve found that writing out the objectives for the section I’m having trouble with, “by the end of this section the reader should …” helps a lot.  One of my favorite get unstuck freewriting exercises it to write out the material as a script of a talk I might give if I were talking to children or other lay people.  This text of course, won’t be useful to directly copy and paste into a manuscript, but often helps me figure out how to write for the manuscript.

Structured Struggling

Sometimes, struggling through work just has to happen.  Staring at the problem, thinking about it every imaginable way, reading and rereading, writing and rewriting.  Doing this endlessly, however wouldn’t move anything progress, so I do a lot of my work using the pomodoro technique, I’ve mentioned it previously [](”>here. I set a timer for 25 minutes and have to stay on task for those 25 minutes, no e-mail, texts, calls, social media, anything. I also pick a single specific task on a specific project (ex: write section 1.4, reorganize chapter 2, etc) that I have to work on the whole time. If I truly finish in less time, I can move onto something that’s a natural succession, but if I’m stuck I have to stay and keep trying until that 25 minutes is up. Then 5 minute break and repeat. After 4 take a longer break. The 25 minutes is often long enough I get myself unstuck, but short enough I don’t feel like I’m wasting time and I also stop and move on before I actually mull too long.

Visualize: Digitally, Not Mentally

I’m a very visual learner, so writing is hard for me. Explaining and learning things with diagrams and tables is easier for me; when I’m troubleshooting code I generate figures at intermediate steps to see how the data changes to check that it’s working.  When I think through how ideas relate, I think of them that way.  Sometimes, I draft slides or paper figures for a section when I’m having trouble with or the whole thing if I’m having trouble with organization.  For equations, while a specific equation might be hard to understand and explain, an annotated plot of it might easier. Thinking about how to visualize content for quick understanding that’s necessary for good slides, helps me figure out how to explain it in text. Also, then I feel like I’m making progress, I’ve at least got the figures for the paper or the slides for later ready in advance.  The slides, I can zoom out on and see a story board of the whole project which helps provide perspective that is useful for organization.


For really abstract theory, try to think of an analogy that could work to explain the concept to a child. That process helped a lot with a large collaborative project. We needed to explain the mathematical (systems theory) definition of state for an audience of psychologists. We tried a few different analogies until we got one that was simple enough to cover what we needed. That is included in the paper as a box/sidebar, but also it helped us really refine the right words to use to provide the base definition in the main text of the paper.  We have a long list of failed candidate analogies we brainstormed in meetings, but each one got us closer to one that actually worked.  This can be worked into a free-writing exercise but I more often do it with a table.  I make a row for each aspect and a column for the analogy and then fill in the boxes with what about each analogy relates to that aspect of the concept.  I often add rows as I go and sometimes it takes some other figure-like form, but the idea is to relate the work to something that’s more broadly understandable.  Giving yourself new ways to think about it, will help you not only write it down for others to understand, but can even give you new ideas about how to approach the problem or extend your result.

Google Forms for Better Live Discussion

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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.

In Google forms, here’s how to set up a form for use in an activity like what I ran:

  • Set  the first question as a multiple choice question, “Breakout part” and set “after page 1” to “go to next page”.
  • For each sub-activity, add page breaks and name the pages.
  • Set the various choices to the “Breakout part” question to jump to the respective pages, by checking the box, “Go to page based on answer” and then set the “Go to page” field on the breakout choice question.
  • On the separate pages, add the questions necessary for each part of the activity and for each of those pages, set “after page x” to “Submit form”.
  • At the bottom, below the confirmation text, check the box for “Allow responders to edit responses after submitting.”

In testing, I added some dummy responses, to each phase. Then I created figures that displayed the results of each step in the different ways I wanted to have available for discussion. With the figures made, I published them and then made short urls for each published figure that I could put in my slides for use during the workshop. Before the workshop, I cleared my dummy data out of the spreadsheet.  In the first activity, I had the groups name their project with an identifier that could be used as a title in subsequent steps.  When I gave the instructions for the activity, I reminded them to save the “Edit my response” link from the confirmation page. Having the breakout questions on a form they could open on their own, also allowed me to go back to reference slides while they discussed and meant I didn’t need to print out any handouts.

During the workshop, as the attendees completed each activity, I clicked the link on the next slide and we could then use the plot of their results. This made it easy to compare the prevalence of various results and discuss trends.The first activity, the groups defined a problem they wanted to apply machine learning to, for this I had all of the groups share their idea. In the second, they rated how hard various steps of the design process would be for their problem. Since I had them submit the results in the form, I was able to call on groups based on being atypical or extreme to justify their choices instead of going around and having all of the groups explain their decision making since much of it would be similar. The third idea was a series of a or b choices about what types of machine learning they might want for their problem. Again, being able to discuss the trends and commonalities immediately after the participants finished their small group discussions, made for a better use of time and I was able to have groups who chose differently than the others explain their decision making.

In the end of the workshop, the attendees said they enjoyed the workshop format, had learned something new and that entering the responses via the form wasn’t too complicated.

I’ve published as a template a simplified version of the google form used for the activity.

A Gentle Technical Reading List for Big Data for Social Good

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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.

Model Based Machine Learning

In this paper he develops the storyline of what model based- as opposed to feature engineering based- machine learning looks like. Along the way, he describes the basics of probabilistic graphical models- a language for expressing statistical models - and concludes with a new type of programming language designed specifically for machine learning. This paper is helpful not only because that easy to read tutorial provides knowledge that will make reading many other papers easier, but because models are a widely understood idea- many other researchers use models of some form. For me, that makes model based machine learning especially important when working with data from any application domain, especially one where decision makers may not be as strong in math and computer science. There’s also a forthcoming book on the topic.

Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency

There have been a series of workshops lately on fairness, accountability, transparency in ML. This medium post by Hanna Wallach -she’s also a founder of WiML and pretty awesome in general- summarizes a talk she gave at the first FATML workshop.  She focuses on data, questions, models, and findings to highlight the state of the field and some of the key challenges in computational social science with respect to fairness accountability and transparency.  She begins with a few different definitions of big data- what makes the current trend in big data different from large and a pretty clear overview of some of the challenges facing machine learning if applied to social science problems.

Machine learning: Trends, perspectives, and prospects

This clearly written survey name-drops just about every sub area in machine learning and paints a pretty clear picture about how the areas relate or compare. This article will serve as a great launching point if you think you might be interested in machine learning but want to This concludes by highlighting some of the core challenges facing machine learning going forward.

Machine Learning that Matters

This paper reads more as a position paper, it’s again, not technical but this essentially argues that too many machine learning papers and journals focus on the incremental discovery of machine learning techniques without actually attending to the data on which the methods are applied. It highlights a systematic problem in machine learning: reusing the same data sets repeatedly for tasks that may or may not have actually been of interest to those who generated the dataset. Arbitrary measures of performance declare the author’s proposed new method better than another, but don’t declare clearly why.  There was also a follow up special issue of Machine Learning Journal on Machine Learning for Science and Society.

Why & How I Chose to Get a PhD

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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.

Prepare anyway

[Tweet “Thinking about what I would say if I was applying to different types of programs helped me decide”]

I had attended the GEM grad lab at the advice (maybe, insistence) of my school’s Minority Engineering Program director before, so I knew the components of a grad school application. I knew then and still know now, that writing doesn’t come easy for me and I change my mind a lot about how I want to organize things and create the story- so, before I was even sure exactly what I’d be applying for, I started thinking about the different essays.   I began searching grad programs, both MS and PhDs initially and even started working on the essays for the school applications and for two fellowships: GEM and NSF.  Thinking about what I would say if I was applying to different types of programs helped me decide exactly what I wanted to do and made my applications stronger: I was awarded the NSF fellowship and contacted by a GEM employer.  Starting early also left me plenty of time to think about different types of programs that could get me to where I wanted to go and understand the differences.  Learning about different programs and researching associated faculty, helped me reach a decision about what I wanted to do.

I was thinking about biomedical engineering programs because I liked the healthcare-related applications I had worked on and the challenges related to my current co-op working with physiological signals. I had a minor in biomedical engineering in undergrad, so I had taken Anatomy and Physiology, and earned the lowest grade of my undergraduate career in it.  Applying a lot of that same material through programming simulations in my biomedical physics course had been easy, but answering multiple choice questions or labeling points of a bone on a practical never worked out well for me.  Fortunately, since I had plenty of time to explore programs and look at faculty research and CVs,  realized I could do the same research in an EE or BioE program, but in EE I wouldn’t have to take biology courses.  I decided to apply to EE programs and express interest in biomed applications to avoid coursework that would be challenging in stressful, but unproductive ways.

How did they do it?

Before the interview for the position I was offered and accepted, I had met the Director of Education while on an informational interview my co-op adviser convinced me to go on (I’m pretty sure he knew I’d be getting a PhD before I did… but he let me figure it out, he just gave me the tools I needed to realize that).  In my meeting with her, I learned that because Draper originated as an MIT lab, they maintained the tradition to fund graduate students to work on real problems when it divested in the 70s through the Draper Lab Fellows program. Once I was settled into my job and it was time to follow up about the fellowship.   I set up a time to talk to her about the fellowship again- she gave me some details about how to apply and a list of people to talk about potential graduate research with.  Many of them referred me to more people and soon I had a long list of potential thesis advisers to consider.

I started these conversations still not 100% sure if I wanted a PhD or MS. As I spoke to candidate advisers and attended more project meetings and group meetings where others in my group talked about what they did, I thought about who’s jobs seemed interesting. Which of these people were doing things that I wanted to do? I already knew that being a professor was a possibility.  I had always found teaching appealing, but was hesitant to not get to continue to do engineering, but professors still do research. I was hesitant to go for a PhD though because I was worried about what if I don’t like academia… a PhD is a big commitment and I didn’t know what I could do with it other than be a professor. While on co-op, I realized that everyone who had jobs I wanted, had a PhD, even in industry. Then I realized that I to, needed a PhD to do the types of things I wanted to do.

Now or later?

I also realized that getting a PhD right away made sense for me, because I knew what I wanted to research at least roughly.  As my co-op continued, I kept thinking of related questions and new things I wanted to work on related to it.  I had a rough project idea and I was capable of writing a research proposal - my only challenge was narrowing so it sounded small enough - not deciding between conflicting ideas.  This level of clarity is admittedly due to hindsight, I realize now, that I’m four years into the program that if I hadn’t been certain about a problem to work on, I wouldn’t have been successful.  At the time, a part of my decision was, “if I want one, it makes sense to just stay in school all at once, I won’t want to be poor again later”.  I don’t really recommend that being your only reason, but it can be a factor.


Honestly, I’m much more certain of my decision and able to articulate why it was right for me now, years later.  When I started grad school, the fact that most people who had careers I thought I might want had a PhD was the main reason I was comfortable with my choice.  Now, however, I actually understand what a PhD is a lot more and I’m still happy with my choice.  It was totally OK that I wasn’t sure what it was, it wasn’t important to know everything before I started, but some things might have made the choice easier, had I known them in advance.

A PhD is training as a researcher - it makes you an independent scholar - even as a fresh PhD you lead the technical direction of projects. A PhD gives you the authority to make definitive statements and the training to properly discern them. I chose to get a PhD, because I knew that in industry or academia the jobs I wanted required one, at least that’s when I settled down with the ideas and began writing down that I wanted a PhD and told my recommenders that that’s what I was applying for so that I could get the recommendations I needed for my grad school applications. In reality, I’m excited to be getting my PhD because it lets me work on really hard problems- even the ones where we don’t know if there’s an answer in flexible, general ways. It gives me a lot of freedom to think about what I’m working on and how. I have to deliver, but there’s more flexibility in what the format of the deliverables I create than in many jobs. As an engineer, the fact that the primary medium is writing is sometimes a little painful, but I like the challenge; if something’s easy I get bored.

As I continued to think more and more about what I wanted to do, and why I wanted a PhD, and I learned more about what the PhD meant.  I realized I probably could have decided I wanted a PhD and to be a professor in the summer after my first year of undergrad.  I spent that summer doing research in a Research Experience for Undergraduates Program and serving as  a Teaching Assistant for a summer pre-calculus program on campus.  I enjoyed that a lot, but it took a few years for me to realized that it meant I should pursue an academic career.  The idea of being a professor was pretty foreign to me, going to college and moving (even if only an hour away) was different than either of my parents had done.  Explaining to family that I wanted to spend even more time in school, and then “just” be a professor was (and has remained, in some cases) hard.  Most people don’t know that a professor’s job is largely research or they only see the negative side of that in professors who are uninterested in the classroom or that engineering faculty aren’t underpaid as severely as k-12 teachers.

Tips for you

If you’re spending the fall on co-op, or if you still have contacts at your previous jobs, think about who you met that has a job you want someday and look at their educational path(LinkedIn is great for this). Ask them if they think that was the best way or if another way might have been better. You need to figure out the right path for you, but getting as many examples and anecdotes as possible can help you figure out what the options are or that things you thought weren’t possible actually were.

A master’s gives you more depth and specialization, a PhD involves learning everything there is to know about a subject and then creating more knowledge. What topic would you pick? Getting into the program you want matters more at the graduate level different specializations are often available in fewer locations.  If you’re not sure about the strength of your application, a MS first before a PhD or even working for a year or two (in a related area) can help strengthen your application.  Work won’t erase bad grades, but will give you more to put in the other components of the application.

Some questions to help you get started:

  • What degree?

  • What, if any, advanced degrees do people (10+ years into career) with careers you admire have? Examples are helpful in making decisions, but not the rule.
  • What do people in professional positions you admire recommend as an educational path?  Some people think the way they did it was harder and will advise an easier way.

  • Now or later?

  • Do you know what you want to research? If not, working can help you figure it out.
  • Are you proud of your current academic standing? If not, some work experience can strengthen your application.

Becoming a Better Writer: Building a Daily Writing Habit

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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.

Everything Starts with the Right Tool

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.

Early Success!

Writing is becoming a habit!  Yesterday I got a new badge: flamingo for 30 day streak it took be 72 days to reach this benchmark, but I’ve only missed two and come up shorter than the 750 words once.  In total, I’ve done over 50,000 words of free writing.  These 72 days include my last two ski trips of the season, NSBE Convention and while being home sick.  On the ski days, I had ambitions of waking up early enough to write first (I leave before 6:30am on ski days, teach kids to ski, herd them to & from the mountain and get home after 7pm), that didn’t materialize, but both times, I sat down and wrote after. This was a big achievement because often after those trips, I’m asleep for the night a little after I finish dinner.  During NSBE was the biggest challenge, I haven’t done work during convention in years, but I managed to only miss one day that week of writing.

At a minimum, this habit leaves me with something tangible that I’ve achieved every day- which is especially useful on the days where research feels impossible.  Most days,  however, I’ve gone back and excerpted at least some portion of it as a starting point toward more polished work.  Not all of the writing has been academic, some has been more toward a blog entry and some has just been to work through a bad mood as a sort of meditation almost. Some of the daily pages have been of e-mails I needed to write, that eventually went out much shorter, some days have been a few different topics as my mind wanders. Some have been on technical things or PhD related matters and some were for NSBE.  Some has been thinking through ideas in order to prepare, mentally, for a meeting the following day (or later that day).  In fact, this post is actually blended together revision of a few different pages from where I reflected on my progress so far and plan for going forward.

Writing is Getting Easier

Most importantly, I’ve had less trouble with the writing I need to do in general, even outside of the focused sessions where I force myself to just keep writing and thinking through the ideas until I hit at least 750 words, . The first few days I did this early and it kicked off a longer writing binge, where when I got into it I was then able to go to the next item and start writing things that I needed to work on for real. Lately, I’ve been doing the daily pages at the end of the day, I’ve been on a good streak with my research and getting straight to that in the morning.  During the day, I just add to an idea list off topic ideas I have and then at the end of the day, I come back to the list, pick one and write.

Free Writing Brings Clarity

Some days the 750 word requirement has forced me to keep going, putting my thoughts in words until I reach that goal. This has pushed me to take ideas that I was stuck on and think through them more creatively or deeply. Continuing even when I felt that my brain was empty, has pushed me to develop partial ideas into something actionable. Some days, I’ve written basically the same idea multiple times either in the same day or in subsequent days and that has helped me think through the idea more creatively and choose a better way of presenting it.  Writing and rewriting a summary version of a paper idea I was struggling on helped me clear sort it out. On days that I got stuck in my research, writing out my thoughts on why has helped me get past what I was stuck on and develop a plan for the next day.

A Path Forward

One of my committee members once said while discussing a paper that when you’re learning something new, the first step is to just write it out for yourself. After it’s written once, you can figure out what else there is to learn and then you can rewrite it to be what it needs to be. I hope to continue to use this exercise as a means of writing out new things I’m studying for myself.  I’ve been employing this strategy on a limited basis in a literature review section that I’m working on a limited basis, but my next step is to start pushing more of this in my daily writing.  The word requirement will help force me to reflect more and re-explain the things I’m working to learn in more detail.

Now that I’ve made writing a habit, my next step is creating more accountability, which starts with this post.  Writing every day has produced over 50,000 words in the past two and a half months.  My next goal is to start polishing and finishing more of it.

Organizing WiML 2014

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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.

In the end the workshop went smoothly- we even stayed on schedule-from an 7:00am volunteer orientation to a 5:30pm workshop end! Our biggest issue day of was mic batteries dying (but we had a backup) and a mystery projector problem(which the Convention Center staff fixed for us promptly).  We had phenomenal women (invited and contributed) in machine learning give technical talks, a poster session and small group discussions on variouscareer topics for the last session. We got great feedback from both participants and the sponsors on the whole day, especially the Career and Advice Session which was something new we tried in an attempt to balance the complaint of both too much industry focus and too much academic focus in the career panel that was held in past years.

The most rewarding part of organizing WiML, however, was throughout the rest of NIPS. Throughout the week, WiML attendees approached me to thank me for the workshop and say how much they enjoyed it. Also, several men asked how they could attend in the future. They heard about how good the talks were and were disappointed they had missed in.   I also had a number of conversations with men about how important it is for WiML to exist for the advancement of our field.  Their support is crucial in gaining ground in a field that’s about 90% men.  I went and visited the sponsors tables at NIPS and some asked how they else could support WiML.  One company that didn’t sponsor overheard me talking to another and asked for information for next year.  Machine learning is a rapidly growing field and the lack of women in the field is a broad concern- events like WiML are well received and appreciated by the community.

Our last official task as organizers is to find our replacements.  The call for organizers is currently open and it can be found here.  If  you’re a woman and a grad student or post doc working in machine learning I encourage you to apply.  Planning WiML isn’t too much work- I promise.  Over the years we’ve built an archive of what to expect and do and documented everything.

AAAI15 Paper Content Posted!

My Top 5 Academic Productivity Tools

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Keeping up with school can be tough.  Everyone has their own study/organizational habits, but having the right tools is important too.  Notebooks and pencils are great, but there are ways to use technology to stay on top of work, away from distractions and make painful tasks a little more pleasant.  Here are 5 tools I use every day to keep up that I would recommend trying to anyone.

#1. StayFocusd: This is one I’ve used for quite a long time, I think I installed it in my 3rd or 4th year of undergrad.  It’s an extension for chrome, that blocks any sites you add to it (Facebook and Twitter for me) after a certain amount of time spent on those sites.  Of course, there are work-arounds, that I do occasionally use, but the small extra step makes sure that I’m cognizant of how much time I’m at the computer but not working. This is free, but if you go over time, and then try to visit a blocked site, the “Shouldn’t you be working?” page has a donate button.

2. Clockwork Tomato

This tool is my newest tool and one that I’m most excited about.  I learned about the Pomodoro technique this summer while taking a Coursera course, “Learning How to Learn”.  Essentially you alternate 25 minutes of focused work with 5 minute breaks, after the fourth work session, that break is 15 minutes, you’ve earned a longer break.  This app is a timer that automatically counts them for your and gives some additional stats.  It also allows you to change the 3 times (work, short break, long beak).  For example, when I was finishing up a paper for submission in September I found 45 minutes of work, 5 minute break worked, long break after 3 to be a better rhythm.  For writing, the 25 minutes felt too short.  This is free and there are many other pomodoro timer apps.

3. ShareLaTeX

LaTeX is a computer language for typesetting documents.  I wish I had learned LaTeX in undergrad, writing lab reports would have been much less frustrating without MS Word in my life.  LaTeX is designed to make writing math very easy, but has lots of useful features.  I’ve written about it briefly before.  ShareLaTeX is a cloud-based editor; it is to LaTeX, what Google Docs is to Word.  It’s been especially useful for a paper I’m working on with 8 other people(story on that is to come). It’s a good way to experiment and try LaTeX, because you can get straight to the code without setting anything up (this is how I taught myself).  This is also helpful since I work in many different places, this way I always have the same compile environment (and I don’t have to maintain it!).  It’s even simple enough we’ve been able to get our non-technical collaborators in my lab to do revisions here instead of having to either use Word or manually enter their edits from a pdf.  I had a free account before my school bought licenses, the difference is that a paid account can have more collaborators per document.  As an academic resource, it’s pretty cheap, a professor in my research group was easily able to persuade the Dean of Engineering to buy a bunch of licenses for us.

4. Mendeley

Mendeley is a reference manager.  A reference manager is an absolute necessity for research.  Typing out bibliographies is painful.  With a referene manager you don’t have to.  Mendeley can be added as a plugin to Word, or if you use LaTeX, it will output a bibtex file so you can use it with that way.  It provides a simple user interface, and, when you point it at a folder full of .pdf papers, it will find the information on them on its own.  You do, of course, have to check them and review that it’s right and for really old papers it’s not great at automatically figuring it out, but it’s better than manual.  I have only a free account here, it limits how many collaborators on a ‘folder’ and the amount of cloud storage, but that hasn’t been a problem for me in almost 3.5 years of grad school.  It also has annotation and other tools, but the simple interface for accessing my references has been enough for me.

5. EZpdf

This is a .pdf reader app that I use.  I use the paid version of this, but it was well worth the $2.99.  I store all of the papers I need to read in a dropbox folder (which I also point Mendeley to).  With this, I can read them on my phone or tablet while on the go and highlight and mark them up as I would a printed version.  It maintains them synced across devices through dropbox too.   This also lets me easily fill out forms that are saved as a pdf but aren’t a real .pdf forms.  I have the full paid version of Acrobat from school, and this is still better.

And a bonus: Airplane Mode:

This isn’t exactly a tool, but it is something I use, just like StayFocusd blocks helps limit some distractions, airplane mode blocks a whole set of distractions.  I’ve used this for a while now whenever I need to focus on things (along with closing e-mail).  Lately, since I’ve started using the Pomodoro technique, I use this for the 25 minute bursts.  Then for the 5 minute break, I turn it off and check whatever I want, when the timer goes off again, I put airplane mode back on and continue to work.

Do you have any favorite apps or sites you use for studying?  Research?  Will you try any of these?