Inspired by Giorgia Lupi and Stefanie Posavec's Dear Data project, collect data surrounding daily routine and understand something so personal from a new perspective by sketching it on a small postcard. After gaining initial insights and feedback, translate the visualization to a printed poster to explore a new aspect of the data.

Over the course of two weeks, I tracked my social interactions - as someone who would usually categorize myself as an introvert but often feels bursts of characteristics of extroversion, I wanted to see if intentionally collecting data on many factors that make up my social time could help me discover patterns in what leads me to gravitate more towards feeling drained or recharged by spending time with people.

I tracked 30 data points, with 10 columns each.

Following the project that inspired this work, I interpreted my data by creating a 3" x 5" hand-drawn postcard, attempting to visually encoding all of the many factors about each social interaction that I gathered data for. My approach prioritized abstraction - since the data was so personal, I leaned in to visually representing each attribute according to my own emotional interpretation (e. g. encoding certain colors for relationships based on how close I felt to the person, geometric vs. organic shapes for how I felt an activity was active vs. passive).

In translating my small postcard to a large-scale (18" x 24") poster, I focused on why I had initially collected this data and let the "feelings after" column dictate the poster layout. 

I gathered references surrounding formal elements like color, composition, and type to inspire how I might translate this poster and explore different visual approaches. 

I created visual encodings for the rest of the columns that I deemed necessary for understanding trends, and laid each data point out using area to answer my original question.

After an initial round of printing and feedback, I re-examined which of my visual encodings were successful and which were in conflict. Below is the final poster that translates this personal data into a layered print visualization, capturing the emotion behind the initial question: am I an introvert or an extrovert?

A poster and hand-drawn postcard to visualize personal data around social time collected over the course of two weeks.

Build a database through scraping sports media to explore and analyze what language and narratives follow different WNBA players and teams over the past two seasons as the league has grown exponentially in popularity and media coverage (2024-2025). Explore game and season average statistics to discover if the loudest stories are justified with statistical evidence or if the data tells a different story.

Uncover and uplift largely untold stories, less covered players, and teams that fly under the radar.

I scraped articles from 8 news sources’ WNBA pages (see below), and after initial analysis of the headlines and summaries I had gathered, I decided to focus on the theme of physicality and explore how I could contrast additional quantitative data with the media language I had collected.

I then scraped Fox Sports’ Misc Stats page for technical and flagrant foul aggregates throughout the season, and scraped wnba.com to get the game schedule for each season. I used this to scrape each game’s play-by-play data, gathering video source URLs for each play that involved a foul and collapsing all of this data into a Pandas dataframe with lookups by player, team, and game.

I worked on categorizing articles by teams and players by building entity lookups using players’ last names, weighing some players above others depending on which teams were first detected and filtering out non-WNBA overlap names (e. g. people like Serena Williams who show up on WNBA-related articles because they are investors but shouldn't match to articles about Courtney Williams or Elizabeth Williams).  

I decided to normalize personal fouls, technical fouls, flagrant fouls, and fouls drawn over minutes played to get an idea of how much players were fouling or getting fouled related to how much they're actually on the court as opposed to the sometimes inflated numbers we see reported out of this context. The final step was merging the foul data with the articles data, which I linked using player/game/team ids.

I built a website to display both the qualitative and quantitative data I collected using React, Next.js, and some beginning sentiment/language analysis Javascript libraries like Wink and Rita. It functions as an exploratory interface for the database, organized in pyramid-like structure with the highest number of headlines at the bottom so that less-featured player/game stories are uplifted by being displayed on the top of the page.

The main goal of this visualization was to contrast the qualitative data like headlines and videos with the normalized foul data to give the viewer a holistic understanding of the topic of physicality in the WNBA.

This project is very much still a work in progress - now that the data has been gathered, cleaned, and organized, some next steps for phase two will include:

Increased language analysis and categorization 

Pulling out language sentiment themes manually then using Claude & Gemini to iteratively categorize remaining articles and identify more themes

A database collected to explore how media attention follows professional female athletes and teams and whether game-play statistics uphold these narratives.

Python, Pandas, Beautiful Soup, Playwright, React, Next.js

Using Netflix’s What We Watched the First Half of 2025 dataset of movie titles, runtime, and views, analyze and examine what attributes lead to higher viewership (e. g. what makes people click on a movie while scrolling through searching for what to watch?).

I investigated the Netflix data through some simple aggregation to uncover trends and found that 72% of movie titles have 5 words or fewer. I found this interesting as it shows that more value can be placed on each individual word when considering user behavior like clicks, which ultimately leads to increased viewership. I then filtered the dataset to the top 50 highest occurring words and created overarching and overlapping word categories manually based on personal word association.

I also found trends in runtime, with most movies being around 80-140 minutes but some outliers. In considering the act of scrolling through Netflix searching for something to watch, I decided to investigate this link between words and runtime to see if these factors impact views by catching users' attention to click play on a movie.

I created a heatmap that buckets titles based on word/word category and runtime and fills color opacity based on average number of views. The first iteration showed a tooltip with an overview of the data that makes up a cell on hover and a panel to the right of the heatmap that gets populated with individual title/view details upon user interaction.

After investigating these general trends, I used my existing dataset to fetch qualitative data from The Movie Database API with the goal of uncovering how higher viewership relates to more verbal and visual descriptive factors. I integrated movie posters and descriptions into my existing heatmap, and used Wink.js library to quantify the sentiment of movie titles and descriptions to add to the details available upon interaction. I also redesigned my interface to be more user-friendly and use a more neutral color scale after red/green felt more intuitive to display sentiment analysis. Using hover and click, users can explore details about individual movies, words, word categories, and views. 

An interactive interface using Netflix data to analyze what qualitative and quantitative movie attributes correspond to increased viewership.

As Northeastern University’s community experiences an influx of students new to campus and a transition back to hybrid/in-person classes after more than a year of remote learning, there is a need for more space for meditation and mindfulness on campus.

The university provides many study and collaboration spaces on campus for students, as well as community gathering spaces, but there are not enough places on campus to easily be alone and focus on balancing the busy lifestyle of being a student.

I worked with a group of 3 other students to create an interactive light installation based on color theory that gives community members an immersive space for meditation and mindfulness on campus by using animations to display visual representations of emotions, with the aim of giving students a convenient place of restoration and release. I owned the development portion along with one of my other team members, collaborating to learn new technology to create our final exhibition. By introducing color therapy in the form of animated lights, students could exist in a restorative space whose appearance is powered by their manifestations of emotions. The central location on campus allowed students to visit the room when on the way to class, the library, or home, and the installation was constructed so that students could begin using the space during finals week.

We began with determining how to approach this problem through user research, surveying students about their current relaxation habits and what their ideal experience looks like both on and off campus. 

We surveyed about 15 Northeastern students about their current relaxation habits. Many students answered that what made them feel calm were having a clear mind, organized thoughts, guided meditation, and healthy habits. This led us to think about how we could approach mental wellness through the lens of providing an interactive sensory experience.

About 40% of the students surveyed have practiced meditation at least once. We thought that it could be valuable to introduce those who hadn’t yet experienced guided meditation to it based on the majority of answers to ‘What makes you feel calm’ being centered around peaceful activities and mental clarity.

We then conducted a competitive analysis of existing products and experiences focused on immersive mindfulness and meditation. We found inspiration from similar immersive experiences that led us to research color theory and the effect that colors can have on emotions. We combined our findings to create the table below linking emotional themes to the colors that can be used to evoke them. 

Our competitive analysis also guided us to think about how we might create an experience that created the feeling of movement within a static space and immersed the viewer in experiencing a color, which led us to research large-scale light installations from artists like Olafur Eliasson and Micha De Hann. The interactivity and feeling of expansiveness that works like Snoosphere were able to achieve inspired us to think of our final product as an experience for all of the senses, considering all of the ways that users may choose to interact with a space beyond the happy path.

After settling on our final deliverable, we began to think about where and how to construct this installation. We created a question exercise within our group to provoke conversations about how to push our idea during the planning and design process.

We began considering where we could host this installation somewhere on campus to serve our previously identified users, and partnered with Northeastern's Center for Spirituality to use a spare room 12' by 12' room meant for fostering community to host the installation for a few months.

Once we knew the size and space available to us, we drew a rough outline of what we thought the space could look like, envisioning using LED light strips with material placed over them to diffuse the light, and blacking out any windows to make sure that the only light source within the space was coming from the installation.

We identified that we would be using our color research to help viewers manifest the emotions that they wanted to feel, and worked on mockups of different light animations, using Adobe After Effects to generate simple color animations and user testing among students to see how different types of movements evoked emotions and pairing these results with our color theory research to produce final animations to serve as wireframes for the light implementation. See here for an example of our "healing" animation mockup and below for a preview of our "joy" animation.

In considering the main ways that users could interact with our installation to allow their manifestations of emotions to inform its appearance, we thought about what they would bring into the space. Since we could assume that most students would have their phones on them, we knew that we needed our exhibition to either be triggered by pressing buttons within the space or be accessible on a mobile device. We chose the route of having our installation triggered from user input on a mobile device when we realized that we would also need to account for multiple users interacting at one time, and could build a queue management system by implementing a SQL database with a simple webpage. 

We decided to power the animation using a Raspberry Pi with connected LED light strips. Since my engineering partner and I had no previous experience with creating installations of this sort, it was important to select a tech stack that had widespread documentation and support available as we were learning how to leverage this to fulfill our goal. We created the diagram below to map how each element of our tech stack interacts with the system.

After having a finalized design and technical plan, we were able to create wireframes, set up the database and server, and begin to experiment with powering the lights using the Raspberry Pi. 

For this last piece, we set up a grid to plan out how each light on an LED strip could correspond to a pixel from our animation plan. We began by testing out a smaller scale with one light strip and testing out the wirings with our Raspberry Pi before translating it to the larger space. We found that the colors we had settled on appeared differently when programmed as RGB values into an LED light than on a webpage, and had to adjust our color grading in order to translate this to the light strips. We created Python scripts for each animation that looped through and set each pixel, changing the pixels with timed intervals to create the effect of movement. 

Once the animation scripts were finalized, we spent our time constructing the installation. We put light-blocking paper over the windows in the space and used adhesive to stick the LED light strips to the wall, overflowing them and threading them into the ceiling to produce a more organic effect. We constructed shelves to hold overflow technical equipment like the Raspberry Pi, batteries, and extension cords, and put this out of the way so as not to subtract from the viewing experience. As seen below in the photo taken during the construction process, we used cotton stuffing placed over the light strips to diffuse the light and have it appear softer, with the intention that this could double as another sensory option for those navigating the space.

We created the website using React with styled components and deployed it on Netlify, and set up the server using Spring Boot to manage creating, updating, reading, and deleting records from our Heroku database, implementing the animation queue logic using Java. 

We included a confirmation page on the website to indicate to the user that their animation was in the process of being displayed, and included a preview from our animation mockups to begin to draw the user into the space and have them feel the effects of the corresponding colors. We also included a page about our color theory research and sources on the website in case users were interested in learning more about what powered the decisions behind this project.

To integrate all of the pieces together, we connected our Python scripts to a server hosted by our Raspberry Pi, and set up our webpage to hit an endpoint exposed by our Spring Boot server when the user filled out a form. The code that powers the web UI can be viewed here, with the server code located here. 

We created a series of posters to publicize our exhibition during Mindful March and put them up around the campus to inform students of the exhibit before its opening date. Our final website welcomed the viewer into the exhibit, provided background on the installation, and guided them to fill out a form to specify what they were currently feeling and what they wished to feel instead.

Our input form included a way for users to mention the negative emotions that they may be feeling and select from a dropdown of what they wished to manifest. This allowed them to release any negative emotions and be open to immersing themselves in the installation's colored lights after clicking "Visualize" and seeing it reflected on the wall in front of them.

Some videos of the final LED light animations on the installation can be found here.

An interactive LED light installation to promote community mindfulness and meditation.

Python, Raspberry Pi, Adobe After Effects, MySQL, Java, React

Create a visualization tool to make the vastness of the Smithsonian Institution’s Revolutionary-era data more digestible, pulling out narratives and highlighting stories hidden within the Open Access archives.

In investigating groupings of data within the Smithsonian search API and various Revolutionary era-specific Hugging Face datasets provided by the institution, I found stamps dating back to 1765 that prompted historical research on the significance of the Stamp Act in American history. After discovering its magnitude marking stamps as a key symbol of resistance and tension leading up to the American Revolution, I became interested in visualizing the National Postal Museum's vast collection of stamps within an online interface. Stamps in modern American society are an interesting example of something used for both practical purpose and personal collection - I wanted to explore how their introduction as such a point of tension in American history may have influenced their journey to this place over the hundreds of years since then.

I collected “stamps” and “covers” object type data and metadata from Smithsonian API, and used their titles to pull out themes/theme buckets and categorize stamps by decade. My initial visualization used a timeline and bar graph to explore the frequency count of stamps that fall into these themes throughout decades. I gathered historical research to supplement what the theme frequency counts were revealing about each decade and contextualize these trends within early American history.

In exploring where stamp images can fit into the website and how they might be displayed effectively, I used machine learning models like SigLip and OWLv2 to process the collection and standardize the images displayed. This enabled sorting images by similarity and cropping to the artifact, standardizing the visual information displayed and pulling out sub-clusters within each already grouped theme. I also decided to extract more metadata to see trends in colors and materials over time.

With this final iteration, I integrated my timeline with scrolling to allow users to easily travel through time while exploring the collection. I then rewrote my historical context to expose more word trends over time, and created filter buttons using words and colors to enable discovery and allow users to drill down to make sense of smaller historical trends that might get lost in the overarching views. I also created an individual stamp view for users to zoom in on a single artifact and view its data isolated from the whole.

An archival visualization that traces the Smithsonian’s digital collection of postage stamps from their introduction in revolutionary America to where they begin to live more permanently in modern day postal system and culture.

Explore climate-focused data from the New York City Open Data portal. Translate the data to an installation that reflects the impermanence of what the city’s residents treasure about their surrounding environment and makes the numbers more concrete - something community members can experience sensorially.

I was intrigued by the “Favorite Trees” dataset within the NYC open data portal - this dataset's integration with a mobile app has allowed NYC residents to provide input on their favorite trees in the city over the past 10 years. I merged this with the 2015 NYC Tree Census data to link more attributes. I aggregated the final dataset by species type, averaging fields like "Risk Rating" and "Diameter" to get means for how large each species is or at-risk depending on environmental factors. I sorted this by times favorited and then looked at the data for the top 10 most frequently favorited trees in New York City.

To bring this dataset to life and visualize it using qualitative data, I found images from NYC parks websites of the top 10 favorited trees. I processed these by removing the backgrounds and created a photo collage, layering images whose relative size is based on “Diameter” data. I exported this collage as two images: one with full opacity and one with each element's opacity set relative to the tree's “Risk” data.

To understand how these images could translate to something that could be experienced more viscerally, I sketched some potential installation setups, considering where viewers would be in the space, where the placement of the different technical elements could be, and how these factors would affect viewers' experiences.

For the final installation digital setup, I imported the two exported images into MadMapper and created scenes to flicker back and forth between the full and low opacity images.

I mapped the projected video onto the corner of room with white walls, spilling into the ceiling to create a “tree canopy” made up of light and imagery that makes climate data more concrete. New York City residents were able to walk through and experience at scale how their favorite aspects of their environment are at risk and watch them actively fade away.

A video of the final installation is available here.

A projected light installation to materialize public climate and community environmental interaction data.

Python, Pandas, MadMapper, Adobe Photoshop, Short Throw Projector

Recruiters' jobs can be made easier through various features integrated into the tools they already use to post jobs and identify candidates. As a Software Engineer at LinkedIn working on Enterprise Jobs features, we havd the opportunity to leverage AI to help recruiters using LinkedIn Talent Solutions to perform their tasks more seamlessly and efficiently. My teams focused on assisting our users in posting jobs, managing applicants, and ensuring that jobs attract qualified candidates. 

I led the front-end development of a few key projects, from collaborating with cross-functional partners on planning and feasibility to writing technical plans, executing across tight timelines, and implementing metrics to determine success based on user interaction tracking. Most of these projects were built using Ember, which the LinkedIn Recruiter product uses for all of its front-end development, with the use of some SQL queries to create dashboards for success metrics.

AI-Assisted Job Posting and Applicant Management

The first impactful project I worked on involved adapting another team's system that produced AI-based recommendations exposed to the user within a chat-based widget. The goal of this project was to reduce the time that it takes for users to post jobs manually on LinkedIn by providing them with recommendations for saved drafts, previous job postings, or updates to fields based on what they had already inputted. I worked with a backend engineer to extend the existing system to provide recommendations for job posting fields, consulting with the owning team for guidance and collaboration in contributing to their system. 

One main difference was the input - for the existing system, recommendations were produced when the user interacted with the widget by requesting help using a chat input. For our implementation, we considered how this could be disruptive to the user's experience of filling out a long form, and decided to have recommendations appear on the widget based on the input of filling out form fields instead. This choice involved some technical changes including adding a way to hide the chat within the existing system, hiding some chat messages, and using a service to trigger the request for recommendations rather than handling it within the component.

Our different approach also raised accessibility concerns - if a visually impaired user interacting with the form wasn't notified of new content on the page, they could miss the new feature entirely until after they had filled out the form. On the other hand, we didn't want to interrupt the flow for those navigating the job posting form with a screen reader by shifting the focus to the widget every time there was a recommendation. I considered a few solutions and ended up moving forward with leveraging the aria-live attribute with the "polite" value to notify screen readers of new content on the screen without moving their focus away from their place on the form. The job posting form and widget with recommendations are can be seen side-by-side in the image below.

This project was part of a key launch of new LinkedIn Recruiter features, and I worked recently to extend the widget to be used to manage applicants, providing recommendations for recruiters in filtering large lists of candidates based on their hiring criteria. More about the feature can be found here.

In the fall of 2024, I worked on the delivery of LinkedIn's first AI Agent, which helped place the company at the forefront of innovation in the hiring space when it comes to AI integrations. I collaborated with one other front-end engineer and three back-end engineers to deliver the applicant management pieces of this agentic experience. The goal of these features was to reduce the more repetitive and mundane tasks that recruiters spend a lot of time on in identifying qualified candidates. The hope was that this assistance would free up time that could be used for getting to know applicants on a more personal level, and therefore making more informed hiring decisions. I created the technical plan for this project and executed it in time to be highlighted at LinkedIn's Talent Connect. 

The features I developed included a place to customize thresholds for the extent to which candidates would need to meet qualifications in order to be considered a good fit, in-product notifications when top applicants were evaluated by the agent, and evaluation summaries shown on applicant profiles to show how well they fit the specified criteria. 

The size of this technical undertaking was quite large as it involved understanding the entire Hiring Assistant system, which was part of a bigger launch that so many teams were working on at the same time and involved a lot of cross-team collaboration to unblock each other and ensure we all worked efficiently. This project also involved touching parts of the project that had been static for years, and we were constantly unearthing cleanup tasks or areas of potential improvement that came with reinvesting into previously very stable features, and figuring out how to prioritize these in relation to our already tight timeline. The press release for this overall project can be found here.

After these projects were launched, I worked on creating internal dashboards built using SQL queries that pulled data from user interactions, with the goal of analyzing how recruiters were navigating our features both with and without AI-assisted features. With this, we were able to determine which specific features users were integrating into their existing product use and how much time was saved using AI-assisted features, which helped inform future product iterations and decisions.

During my time at LinkedIn, I have identified ways to improve our technical process and weave in issues that I am passionate about including accessibility and design/technical collaboration. Within my first few months after joining, I noticed that there was not a large focus on accessibility within the projects that we were creating, and began searching for ways to encourage this within my team. I worked with my manager to allot time to be a member of an internal company-wide program meant to educate about web accessibility practices and increase overall company accessibility compliance, and I ended up convincing others within my larger area of the product to complete the program as well. 

After the completion of this program, I wanted to bring this back to my team. My main goals were to educate and persuade my team members to engage with accessibility - I identified that the main barrier to making my team's features usable to all regardless of background or disability was mindset and investment in doing so. 

I conducted research leveraging the external connections I had made during the program to find statistics related to business earnings and engineering hours, drawing from my observations of what powered leadership decisions within my subset of the company. I wanted to emphasize how creating products for all brings in more money in order to appeal to those with decision-making power, and how not doing so means that eventually, we will need to make features accessible when issues arise in the future through customer escalations and yearly accessibility audits, effectively creating tech debt that takes more time to fix in the long run. 

I presented my findings along with some tangible plans for how we could encourage a "shifting left" mindset to catch accessibility bugs before they reach development, focusing on how to weave this into design handoffs and code reviews, and achieved investment from my team's management in adding this to our design and development process. I have served as a champion for accessibility within the various teams that I have been a member of since, bringing up conversations about accessibility during project planning stages and ensuring that accessibility standards are considered in my daily code reviews, as well as fixing bugs when identified during internal and external product reviews and audits.

A collection of AI integration and UI optimization features to reduce the time it takes for LinkedIn Recruiter users to find qualified candidates.

This project was inspired by Suzanne Lacy's site-specific installations that use interactive exhibitions to engage people on a subject, with the aim of engaging with my community and disrupting a daily process. After a few years of navigating the art and design buildings on campus whose appearance did not reflect the creativity happening in the classrooms inside of them, I wanted to apply Lacy's methodology to my college community to provoke conversations about the importance of public art. 

I created various murals using painter's tape in some of the busiest passageways on campus during different times of the day with high foot traffic. Masking tape as a material situated the installation as semi-permanent due to its possibility of being easily removed. With these choices of medium and location, I hoped to call attention to the amount of blank spaces on campus that could be used for similar types of installations and push the boundaries of how as students we can contribute to our environment and community. I switched colors every time someone interacted with me to track how many students questioned the installation as it was happening and draw conclusions about students' daily engagement with their school environment.

The first murals that I completed are still up in the main stairwell of the business school building on Northeastern's main quadrangle almost 6 years later - with the only change being that the school took down the website link that contains the description of the project that I put up a few weeks after the installation occurred. 

This responsive website was created using HTML/CSS and hosted on AWS and was meant to explain the intentions behind the project. By posting the website link at the sites of the murals, I hoped to draw more engagement from students on the subject matter of the installation and build on the curiosity of people passing by the murals. A screen recording of the final website with images of the murals can be found here.

A semi-permanent installation to provoke conversation about public art within my community.

Rae's Roots is a tea company based on Chinese medicine that aims to be a resource for people of color before, during, and after pregnancy. Coming into the project, they had packaging and a logo that reflected their mission in a way that the owner of Rae's Roots was happy with to be sold on e-commerce websites like Amazon, but this needed to be translated to a web brand and Shopify website to set up the company to achieve the potential that it has to be influential in the space, and I partnered with my client to create those deliverables as an independent contractor.

I began by asking the client to fill out a few brand exercises that helped me get an idea of what she wanted out of the final deliverables. 

I then translated this to the next phase of my creative process when working with freelance clients - creating moodboards. I drew adjectives and themes from the brand exercises to create 4 different moodboards that visualize the different directions that the client specified verbally as part of the previous stage so that she could select a direction or two to move forward with and guide the visual brand creation.

After collecting feedback from the client, I took this into the next stage of the project by using it as inspiration for creating a design strategy. We went through a few rounds of critique to settle on some visual rules to apply to the pages of the website. This design strategy highlights the balance of sophistication and playfulness that makes up Rae’s Roots. Contrasting spring colors with warmer, earth-toned shades brings the brand to life while keeping it grounded and reserved. Photos are treated as cutouts to put more emphasis on the subject of the picture rather than the background noise. Below are some pages from the final design strategy titled "Playful, earthy, sophisticated" with a preview of all of the elements working together on a UX concept.

While working on the design strategy, I created some low-fidelity wireframes to lay out how the content would be arranged on the website. After the design direction was finalized, I worked on applying the design strategy to the wireframes to produce a final prototype.

The most important feature to my client was to be able to update all content independently, so I decided to use the theme-building experience that came with Shopify Online Store 2.0 to create the website. I built the theme using LiquidJS and created variables for all of the copy, images, colors, and products, leveraging the data types exposed by Shopify to have everything hosted in one place for my client.

Below is a preview of the product page of the final website, and a walkthrough of some of the key pages of the website within the Shopify theme editor can be found here.

I also created a web brand book to hand off to my client so that any web design work can be consistent with what we created as part of this project, and to standardize visual identity so that future packaging can follow these brand guidelines.

A brand and Shopify website for a tea company dedicated to helping people through pregnancy.

Adobe Photoshop, Adobe Illustrator, Figma, LiquidJS

From 2021-2022, I was the Technical Director for Scout, Northeastern's student-led design studio, an organization with 100+ members. I worked with the Design Director, Sam Marchesi, to oversee 10 teams of students through their creation of deliverables like brand identity, packaging, marketing and e-commerce websites, identifying opportunities along the way to improve our community and process to enable Scout Studio to continue to push the standard and produce a high caliber of work.

We interviewed potential clients at the beginning of each semester, understanding their design and development needs to select projects that would help students learn and produce deliverables with the potential to push the client to the next level. After selecting clients, we drew up project proposals and interviewed students whose skills fit the agreed-upon deliverables, building teams of project leads, designers, and developers structured around fostering mentorship and learning opportunities. Sam and I conducted orientations to onboard members at the beginning of each semester, adding to documentation guides for key steps in the traditional design and development process, and led workshops throughout the year to encourage community and knowledge transfer among students. 

While teams worked with their clients, I supported the developers and designers, providing feedback each week on the feasibility of website designs and user stories and reviewing pull requests to enforce organization-wide code standards. I helped students prioritize features based on the tight timeline of a semester and translate user stories and sitemaps into technical feature analysis, guiding them on how to use this to select a tech stack.

Projects completed during my time as Technical Director include Sto, Ornami Skincare (below), and Cashflo.

While assisting students in selecting a tech stack, I realized I was providing a lot of guidance based on my personal experience with certain technical decisions throughout my previous semesters as a developer in Scout and what I had learned from going through this process with previous tech directors. I identified this as an opportunity for knowledge transfer and building out Scout's technical documentation, and created a template in Notion to gather information about previous projects' technical decisions, digging through the archive and connecting with alumni to fill out the guide for the past 5+ years of projects.

Drawing from our previous experiences as members of Scout and what we had learned from our co-op experiences, Sam and I identified that there was a lack of standardization and focus on accessibility within the organization-wide design and development process. We established an interdisciplinary team to focus on research surrounding web accessibility practices and how this could sustainably fit into our process at Scout with the goal of creating a final deliverable that improved our organization's understanding of and commitment to creating accessible products. This team produced a comprehensive Scout-specific accessibility guide that included checklists, workshop outlines, and pull request templates to encourage investment in and commitment to accessibility throughout all design and development work.

An overview of the design and development projects and initiatives led as technical director of a studio.