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WiDS Cambridge Women of Color in Data Science Panel

  • 11 March 2022

I spoke on a panel at the 2022 WiDS Cambridge on Women of Color in Data Science

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TD STEM Academy Workshop

  • 23 August 2021

Professor Brown and incoming Graduate student Emmely Trejo Alvarez are hosting a workshop for incoming students this week to learn about AI biases by replicating the COMPAS analysis. Resources are available online

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New Paper Accepted to EAAMO'21

  • 16 August 2021

A paper with the Boykin lab at Brown University was accepted to the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. It’s an extension of a previous workshop paper, “Opportunities for a More Interdisciplinary Approach to Perceptions of Fairness in Machine Learning”

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New Funding from IBM

  • 06 August 2021

I Received a 2021 IBM Global University Program Academic Award to develop an Information Theoretic Framework for Evaluating Fairness in AI Applications.

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New Paper accepted to Teachml 21

  • 24 July 2021

I have a paper accepted to ECMLPKDD Teach ML workshop on how I designed and taught CSC/DSP 310 last fall.

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I will serve as chair of the Carpentries Trainer Leadership Panel

  • 15 March 2021

I will serve as chair of the Carpentries Instructor Trainer Leadership Panel.
Read about the leadership panel in a blog post or check out our public facing work in our repository.

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I was featured in the Northeastern Engineering News

  • 02 March 2021

See the article on the NU COE website

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New preprint on measuring relative bias with optimal transport on arChiv!

  • 22 February 2021

Preprint on Archiv

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I was featured on the TWiML Podcast

  • 15 February 2021

See the episode here

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Paper Accepted to NeurIPS Workshop on ML Retrospectives, Surveys, and Meta Analyses

  • 03 November 2020

Our paper with Malik Boykin’s lab at Brown University, “Opportunities for a More Interdisciplinary Approach to Perceptions of Fairness in Machine Learning” has been accepted to the NeurIPS 2020 Workshop: ML Retrospectives, Surveys & Meta-Analyses.

The paper is on the workshop website

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Paper Accepted to NeurIPS Workshop Dataset Curation and Security

  • 02 November 2020

Kweku Kwekir-Aggrey’s paper “Measuring Bias with Wasserstein Distance” was accepted to the NeurIPS 2020 Workshop on Workshop on Dataset Curation and Security!

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Paper Accepted to NeurIPS Workshop on Consequential Decisions in Dynamic Environments

  • 31 October 2020

Jessica Dai’s paper “Label Bias, Label Shift: Fair Machine Learning with Unreliable Labels” was accepted to the NeurIPS 2020 Workshop on Consequential Decisions in Dynamic Environments! She will also present this work at the Women in Machine Learning Workshop.

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I joined FAccT Organizing Committee as Virtual Experience Chair

  • 08 October 2020

I joined FAccT Organizing Committee as Virtual Experience Chair

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  • 16 August 2020

I have joined the Department of Computer Science and Statistics at University of Rhode Island and started a new lab website.

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UMich AI Lab Symposium

  • 19 October 2019

On October 19, I will give a spotlight talk and present a poster at the University of Michigan AI Lab Symposium

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Carpentries Instructor Training

  • 10 September 2019

I’m Teaching the Carpentries Instructor Training at Carnegie Mellon University Library on September 10-11. More Info

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Harvard Big Data Conference

  • 19 August 2019

I will give a talk at the Harvard Center of Mathematical Sciences and Applications Conference on Big Data on August 19-20.

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Funded UTRA

  • 09 August 2019

Two Brown undergraduates, Jessica Dai and Pazia Bermudez-Silverman will work with me this fall funded through an Undergraduate Teaching and Research Award on our project, “Examining Fairness Interventions in Machine Learning: Empirical Validation and Social Grounding”

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Carpentries Instructor Training

  • 03 June 2019

I’m teaching my first Carpentries Instructor Training online with Danielle Quinn.

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NSBE-BAI Pathways into AI

  • 28 March 2019

I’m speaking on a panel hosted by Black in AI on Pathways into AI at the National Society of Black Engineers 45th Annual Convention in Detroit, MI on March 28. Join us to learn about how to get into the feild of AI and see how many different ways fellow NSBE members have made our way there.

More Info

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NSBE45 Python

  • 27 March 2019

I am teaching Data Carpentry at NSBE 45 on Wednesday as an all day workshop. Details are available on the workshop website

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Certified Instructor Trainer

  • 14 March 2019

I have completed the training and checkout process (observe a training and teaching demo) to become a certified trainer. I’m excited to schedule my first teaching opportunity soon.

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Carpentries Trainer Training

  • 14 January 2019

I have been accepted to become an instructor trainer with the Carpentries. We start training now and will certify in the spring.

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My first Software Carpentry Workshop

  • 10 April 2018

Last month, I spent a weekend across the bay at UCSF teaching introductory python with the SWC Python Gapminder in a workshop hosted by the UCSF Library. My first experience teaching with the carpentries went well, I think. It was fun and exhausting. After the second day, I turned down meeting friends in SF doing tourist things and instead went home and sat. In the dark. In silence. Despite the fact that I find teaching fun, I also identify strongly as an introvert and find interacting with people for long periods of time exhausting. I recharge by being alone.

Now that I’ve had some time to recover, and to recover from the things I had booked too many back to back, I’ve sorted through my thoughts and want to share about my first teaching experience.

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Getting Unstuck in Writing for Research

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.

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.

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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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My Top 5 Academic Productivity Tools

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.

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.

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