convert to sphinx design and begin updates

conf.py

@@ -44,9 +44,9 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', "*import_posts*",
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', "*import_posts*", 
         "**/pandoc_ipynb/inputs/*", ".nox/*", "README.md", '_bibliography',
-        '_data','_pages','_people','_projects']
+        '_data','_pages','_people','_projects', '_drafts','_speaking']
 
 
 # -- Options for HTML output -------------------------------------------------

requirements.txt

@@ -4,7 +4,7 @@ myst_nb
 MyST-Parser
 pydata-sphinx-theme
 sphinx
-sphinx-panels
+sphinx-design
 sphinx-autobuild
 sphinxcontrib-bibtex
 sphinxext-opengraph

research.md

@@ -5,19 +5,10 @@ I am making AI more fair by enabling it to use subjective human expertise. I do
 
 More active projects are on the [lab website](https://ml4sts.com/projects/)
 
-````{panels}
-:card: mb-4
-:body:
-:column: pl-0
-:container:
-
----
-:column: col-6
-
-Detecting Simpson's Paradox
-
-^^^
+## Past Projects
 
+::::{grid} 3
+:::{grid-item-card}  Detecting Simpson's Paradox
 
 Simpson’s paradox is the phenomenon that a trend of an association
 in the whole population reverses within the subpopulations
@@ -28,37 +19,36 @@ binary variables, but studies for the exploration of it for continuous
 variables are relatively rare. This paper describes a
 method to discover Simpson’s paradox for the trend of the
 pair of continuous variables
+:::
+
+
 
----
-:column: col-6
+:::{grid-item-card}  Racial Profiling and Marijuana Reform"
 
-Racial Profiling and Marijuana Reform"
 
-^^^
+In collaboration with economist Terry-Ann Craigie, I am studied the impact of marijuana reform on racial profiling as measured through traffic stops.
 
-In collaboration with economist Terry-Ann Craigie, I am studying the impact of marijuana reform on racial profiling as measured through traffic stops.
 
+:::
 
----
-:column: col-6
 
-A Sparse Combined Regression-Classification Formulation for Learning a Physiological
 
-^^^
+:::{grid-item-card} A Sparse Combined Regression-Classification Formulation for Learning a Physiological
+
+
 
 Current diagnostic methods for mental pathologies, including Post-Traumatic Stress Disorder (PTSD), involve a clinician-coded interview, which can be subjective. Heart rate and skin conductance, as well as other peripheral physiology measures, have previously shown utility in predicting binary diagnostic decisions. The binary decision problem is easier, but misses important information on the severity of the patient’s condition. This work utilizes a novel experimental set-up that exploits virtual reality videos and peripheral physiology for PTSD diagnosis. In pursuit of an automated physiology-based objective diagnostic method, we propose a learning formulation that integrates the description of the experimental data and expert knowledge on desirable properties of a physiological diagnostic score. From a list of desired criteria, we derive a new cost function that combines regression and classification while learning the salient features for predicting physiological score. The physiological score produced by Sparse Combined Regression-Classification (SCRC) is assessed with respect to three sets of criteria chosen to reflect design goals for an objective, physiological PTSD score: parsimony and context of selected features, diagnostic score validity, and learning generalizability. For these criteria, we demonstrate that Sparse Combined Regression-Classification performs better than more generic learning approaches.
 
 
----
-:column: col-6
+:::
 
-Machine Learning Analysis of Peripheral Physiology for Emotion Detection
-^^^
 
 
+:::{grid-item-card} Machine Learning Analysis of Peripheral Physiology for Emotion Detection
+
 Peripheral physiological signals have shown promise as a measure of a person's emotional state. There are many applications where a more quantitative evaluation of an individual's mental state would be beneficial. For example, in PTSD or depression diagnosis, a quantitative measure to assist and compliment the qualitative assessments conducted by clinicians could reduce the time involved in treatment planning. A better understanding of the underlying mechanism is necessary for building systems that use these signals to assist in critical decision making.
 
 Previous work in emotion research has relied upon averaging in time and then applying standard significance tests and simple classifiers to analyze psychophysiological data. In this work, we extend analysis beyond traditional hypothesis testing and simple classifiers to better understand previous results and design an appropriate computational model. Under the hypothesis that modeling dynamics is important, we design and apply an Input-Output Hidden Markov Model (IOHMM). Through exploration of the learned IOHMM model parameters, we demonstrate the promise that more descriptive, generative machine learning models provide over the more task-specific discriminative models and traditional statistical hypothesis testing. Incorporating time provides an improvement over simple static classifiers in single trial (without averaging in time) prediction accuracy, but does not provide significant improvement over the time averaged results found in literature. To address this, we employ exploratory data analysis methods and examine properties of the algorithms applied to better understand the results and consider improvements. Mutual information computation and clustering provide insight as to the challenges in modeling this data. By applying concepts from learning theory, we show that these seemingly weaker results are actually consistent with previous results. We conclude with insights as to how an alternative approach could elicit more positive results out of this dataset and key theoretical contributions to machine learning that are of value for applying these techniques in scientific research.
+:::
 
-
-````
+::::

service.md

@@ -4,54 +4,42 @@ Outside of the lab, I am a passionate advocate for underrepresented STEM engagem
 
 
 
-````{panels}
-:card: mb-4
-:body:
-:column: pl-0
-:container:
+::::{grid} 3
+:::{grid-item-card} CS @ URI Talent Development
 
----
-:column: col-6
+URI's [Talent Development Program](https://web.uri.edu/talentdevelopment/) recruits and serves Rhode Island high school graduates with college potential who come from historically disadvantaged backgrounds, a majority of whom are Scholars of color.
 
-ACM Fairness, Accountability, Transparency
+Since 2022, I have lead the Computer Science & Statistics Department's Participation in their Summer Success Program. 
+In this capacity, I plan our component for Computer Science and Data Science majors during the Exploration
+Academy. 
+:::
 
-^^^
+:::{grid-item-card} ACM Fairness, Accountability, Transparency
 
-I served as the 2021 FAccT Virtual Experience Chair.
-+++
 
+I served as the 2021 FAccT Virtual Experience Chair for the fully virtual conference
+ and 2022-2024 Diversity and Inclusion co-Chair.
+:::
 
----
-:column: col-6
+:::{grid-item-card} the Carpentries
 
-the Carpentries
 
-^^^
 
 I became a certified Carpentries instructor in November 2017 and in 2018-2020 I served on the Lesson Infrastructure Committee.  I became a trainer in 2019 and served on an ad hoc committee to develop a leadership structure by invitation starting in 2020.  In 2021 and 2022 I was elected to the Trainers Leadership Committee, where I served as the first chair.
 
-+++
 
+:::
 
----
-:column: col-6
+:::{grid-item-card} Women in Machine Learning
 
-Women in Machine Learning
 
-^^^
 
 In 2016 I joined the WiML Board, I served as the Treasurer from 2016 until March 2019. In this capacity, I formalized our financial practices, built a finance committee, and established a direct funding for general administrative support.
-I served as a co-organizer for the 2014 [Women in Machine Learning Workshop](http://www.wimlworkshop.org/).  The 9th Annual WiML was again co-located with [NeurIPS](http://nips.cc).  As the Finance and Sponsorship chair, I lead our efforts to secure corporate sponsorship and our sponsor relations.
+I served as a co-organizer for the 2014 [Women in Machine Learning Workshop](http://www.wimlworkshop.org/), the 9th Annual WiML was again co-located with [NeurIPS](http://nips.cc).  As the Finance and Sponsorship chair, I lead our efforts to secure corporate sponsorship and our sponsor relations.
 
-+++
+:::
 
----
-:column: col-4
-
-
-National Society of Black Engineers
-
-^^^
+:::{grid-item-card} National Society of Black Engineers
 
 
 I've been an active member of [NSBE](http://nsbe.org) since fall 2006 serving in numerous leadership positions at both the chapter and national levels.
@@ -72,19 +60,14 @@ As Vice Chair to the Empowering African American Women in STEM Task Force in 201
 
 In 2013-14 I served as the Collegiate Initiative Chair for [NSBE Boston](http://www.nsbeboston.org/), in this capacity I served as a liaison for area collegiate chapters to the local professional chapter.
 
-+++
+:::
 
----
+:::{grid-item-card} Student Research Engangement Committee
 
-:column: col-4
 
-Student Research Engangement Committee
-
-^^^
 
 As founding chair through 2015, I lead a team of students to work with the Engineering Dean's office to increase student engagement in research, specifically undergraduate involvement and graduate ownership, within the college by providing resources and hosting educational workshops.  Some of our most successful initiatives include a workshop for students applying to Research Experience for Undergraduates programs, a college wide undergraduate lab fair, and an experimental design workshop for engineering graduate students.
 
-+++
-
+:::
 
-````
+::::

speaking.md

@@ -4,21 +4,9 @@
 In addition to standard research talks, I also speak on leadership topics though my service activities.  
 
 
-````{panels}
-:card: mb-4
-:body:
-:column: pl-0
-:container:
+::::{grid} 3
+:::{grid-item-card} Data Science for Social Impact
 
----
-:column: col-6
-
-
-
-
-Data Science for Social Impact
-
-^^^
 
 Presented at NSBE
 
@@ -42,11 +30,9 @@ Sources cited in the workshop:
   - [Toward A Rigorous Science of Interpretability](http://drsmb.co/interpret)
   - [Transparency](http://drsmb.co/transparent)
   - [Fairness](http://drsmb.co/fair)
+:::
 
----
-REU Application Workshop
-
-^^^
+:::{grid-item-card} REU Application Workshop
 
 
 **Context**
@@ -58,12 +44,10 @@ A research experience for undergraduates can be a great way to spend a summer. 
 **Presented**
 Northeastern University, Student Research Engagement Committee (targeted to 1st year students) Jan 2013, November 2013, November 2014
 
+:::
 
----
+:::{grid-item-card} Intro to LaTeX
 
-Intro to LaTeX
-
-^^^
 
 
 This is a hands-on workshop I have delivered on multiple occasions.  In this workshop, I provide an introduction and motivation for why $latex LaTeX$ is an important tool to know for engineering students and especially those interested in research.  Then we work through the components of a $latex LaTeX$ project using a sample physics lab report as an example to expose a number of features.  This is a hands-on workshop, so participants must have computers with internet access in order to reach the tutorial materials and practice along the way.  All tutorial activities take place within the browser, leveraging a cloud-based {% raw %}$\LaTeX$ {% endraw %}compiler and editing environment, so an ongoing internet connection is required for the duration of the workshop.   This workshop is best for 90 minute blocks, but can be shortened or lengthened.  It is easiest for people with some programming experience, any language so as to be familiar with core programming concepts (editor, compiler, debugging, etc) but does not require any $latex LaTeX$ experience or specific language experience.
@@ -72,13 +56,14 @@ This is a hands-on workshop I have delivered on multiple occasions.  In this wo
 
 Have you ever struggled to put together a lab report, fighting the equation editor in your word processor every step of the way?  Are you on the way to your first technical publication, but having trouble getting the template for Word to cooperate?  Are you planning to go to graduate school and looking for a pain-free way to manage all of your writing?  If yes to any of these, $latex LaTeX$ is the tool for you. $latex LaTeX$ is a programming language it can do everything a standard language can, but it's designed for producing beautiful documents.  When you compile, the result is a well formatted, consistent pdf, with all the features you could want.  In this tutorial, we'll start from zero, and get you up to speed with $latex LaTeX$ basics and you'll leave with a number of resources to keep working on your own and become a master of well formatted documents.  If you're not a programmer, don't worry, $latex LaTeX$ is simple to learn and can be used for just basics or to create documents as complex as you want.
 
----
+:::
+
+:::{grid-item-card} Empowering AA Women in STEM with Twitter Chats
 
-Empowering AA Women in STEM with Twitter Chats
 
-^^^
 
 This session will highlight how to use Twitter as a platform to explore prime opportunities with a unique reflective format based on experiences of the NSBE Empowering African American Women in STEM Task Force. According to the Pew Internet Research Project, 40% of the African-American female population who use social networking sites, use on Twitter. As a result, to build awareness for the initiative and generate momentum, the task force hosted several Twitter chats. While a Twitter chat is largely an unstructured conversation, the open social nature produces a better response than a survey. Participants will leave with a guide to facilitate their own Twitter chats and a better perspective on how to use digital communities in order to facilitate a discussion in real-time. By encouraging engagement, we hope to demonstrate the livelihood of the community of Minority women in STEM.
 
+:::
 
-````
+::::

teaching.md

@@ -4,73 +4,90 @@
 In my teaching, I aim to engage learners in a conversation about the material. I take care to practice the strategies for creating an inclusive computer science learning environment I learned in the [Carpentries instructor Training](http://carpentries.github.io/instructor-training/).   
 
 
-````{panels}
-:card: mb-4
-:body:
-:column: pl-0
-:container:
+::::{grid} 3
 
----
-:column: col-4
+:::{grid-item-card} Computer Systems and Programming Tools
 
-Machine Learning for Science and Society
+This is a new course that I designed to fill gaps identifies by
+students and 
 
-^^^
++++
+[course site, with syllabi by semester - current](https://compsys-progtools.github.io/)  
 
-[course site](https://ml4scisoc.github.io/about.html)  
+[course site - first iterations](https://introcompsys.github.io/)  
 
----
-Programming for Data Science Fall
-^^^
-[Fall 2021 course site](https://rhodyprog4ds.github.io/BrownFall21/)  
+:::
 
-In teaching CSC/DSP310 the second time, I made minor revisions to reinforce understanding for students through:
+:::{grid-item-card} Machine Learning for Science and Society
 
-- more code inspection  
-- more authentic tasks in assignments
 
-[Fall 2020 course site](https://rhodyprog4ds.github.io/BrownFall20/)
++++
+[course site, with syllabi by semester](https://ml4scisoc.github.io/about.html)  
+
+:::
 
-In teaching CSC/DSP310 the first time, I made major revisions to a more learner-centric format:
+:::{grid-item-card} Programming for Data Science 
 
+My key revisions to this course have been to:
+- more code inspection  
+- more authentic tasks in assignments
 - participatory live coding instruction  
 - mastery based  grading  
 - more, smaller assignments
 
----
+I also [wrote a workshop paper](https://proceedings.mlr.press/v170/brown22a.html) on the design of the course
 
- Packaging and Publishing in Python
+<!-- Syllabi: 
+- [Fall 2023](https://rhodyprog4ds.github.io/BrownFall23/) 
+- [Spring 2023](https://rhodyprog4ds.github.io/BrownSpring23) 
+- [Fall 2022](https://rhodyprog4ds.github.io/BrownFall22/) 
+- [Fall 2021](https://rhodyprog4ds.github.io/BrownFall21/) 
+- [Fall 2020](https://rhodyprog4ds.github.io/BrownFall20/) -->
++++
 
-^^^
+[course site, with syllabi by semester](https://rhodyprog4ds.github.io/)
+:::
 
-a [lesson](https://carpentries-incubator.github.io/python-packaging-publishing/) on how to organize, package, and document a python project in the Carpentries Incubator
 
----
+:::{grid-item-card} Data & Society
 
-Data & Society
-^^^
-Brown University MS in DS
+a required course in the Brown University MS in DS
 - [lecture notes(partial)](https://github.com/brownsarahm/data_society_lesson_plans)
 
 
----
+:::
+
+::::
+
+## Short Courses
+
+::::{grid}
+
+
+:::{grid-item-card} Packaging and Publishing in Python
+
+
+a [lesson](https://carpentries-incubator.github.io/python-packaging-publishing/) on how to organize, package, and document a python project in the Carpentries Incubator
+
+:::
+
+:::{grid-item-card} Software Carpentry Workshop at UCSF
 
-Software Carpentry Workshop at UCSF
-^^^
 
 I taught the python sections of the two day Software Carpentry workshop hosted by the [UCSF Library March 10-11, 2018](https://gboushey.github.io/2017-03-10-UCSF-Python/).  
 
 I used the [SWC Python Gapminder](http://swcarpentry.github.io/python-novice-gapminder/) curriculum with [minor modifications to emphasize extra features of jupyter notebooks](http://sarahmbrown.org/python-novice-gapminder/).  I used github to manage the student downloads (by .zip files) and the [final post workshop content](https://github.com/brownsarahm/python-novice-gapminder-files/tree/ucsfpostworkshop) and [documented the process](https://github.com/brownsarahm/python-novice-gapminder-files/blob/master/instructor_resources/create_workshop_branch.ipynb) I used to for setup with this jupyter notebook.  
 
----
-Bayesian Nonparametric Guest Lectures
-^^^
+:::
+
+:::{grid-item-card} Bayesian Nonparametric Guest Lectures
+
 I guest lectured in my PhD adviser Jennifer Dy's graduate machine learning course. I taught two lectures, the first introducing bayesian nonparamerics with the Dirichlet Process/ Chinese Restaurant Process and the second extending to the Indian Buffet Process.
 
 Materials:
  - [Part 1 slides](files/bnp_lecture1_2015.pdf)
  - [Part 1 slides](files/bnp_lecture2_2015.pdf)
 
+:::
 
-
-````
+::::