Available to collaborate on projects within the realms of data, technology, design, and the arts. I'd love to hear from you!

Drawing across disciplines to inspire and shape enjoyable, accessible stories and experiences.

Resume 7.20.pdf

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

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. I owned the development portion along with one of my other team members, learning new tools to bring our idea to life. 

We turned a small, central location on campus into an immersive space for meditation and mindfulness. Visitors to the space were able to shape the room's appearance around their own emotions, using color as a form of therapy, and we constructed the installation to open during finals week when there would be the most need for this type of space.

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, and introduce students to mindfulness and meditation along the way.

We gathered references, examining existing products and large-scale installations focused on immersive mindfulness and meditation that ultimately led us to focus our concept on using light and color to evoke emotion.

From there we researched how color affects emotion, and built a reference table connecting specific colors and shades to the feelings they tend to evoke, which became the backbone of our animation design.

We partnered with Northeastern's Center for Spirituality to gain access to a 12x12 room that already existed within a space dedicated to mindfulness. We planned to line it with diffused LED strips, blacking out the windows so the light itself became the only source in the space. 

Using Adobe After Effects, we mocked up color animations and tested them with students to see which movements read as which emotions, refining them against our color research until we landed on final designs.

For interaction, we decided the exhibit should be triggered from users' phones rather than in-room buttons, since we needed to support multiple people using the space at once. This required the use of a queue system, which we implemented using a SQL database to control the logic behind a simple webpage. We powered the lights with a Raspberry Pi connected to LED strips, choosing this stack for its strong documentation and community support while learning how to use the tool for our use case. I planned the technical architecture using a diagram so that we could understand how all the different pieces of our system worked together to display an animation on the light strips.

We mapped each LED to a "pixel" in our animation plan and started at a smaller scale, testing wiring and timing on a single strip before installing within full room. Color values shifted once translated from screen to RGB values on physical lights, so we spent some time recalibrating these to match our original research. We created Python scripts for each animation to loop through and set each pixel's color, changing with timed intervals to create the effect of movement. 

To construct the installation, we put light-blocking paper over the windows in the space and installed 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 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.

Our final website, available through a QR code upon entering the space, introduced visitors to the project, explained the color research behind it, and asked them to name what they were feeling and what they wanted to feel instead. After clicking "Visualize," they would watch their answer become reflected in light on the wall in front of them.

We publicized the installation with posters around campus in the weeks leading up to the installation opening. Some videos of the installation can be found here.

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

A visual identity system, marketing website, member-facing mobile app, and admin website for a private golf course's rebrand.

An interactive narrative that visualizes how an artist's identity is shaped by public data extracted from institutional archives and self-authored statements.

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

Inspired by Giorgia Lupi and Stefanie Posavec's Dear Data project, collect data surrounding daily routine and understand personal experience 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 30 social interactions, with 10 columns each. 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.

Following the project that inspired this work, I interpreted my data by creating a 3" x 5" hand-drawn postcard, attempting to visually encode the many factors about each social interaction's data. Since the data was so personal, I leaned in to visually representing each attribute according to more abstract emotional interpretation - encoding colors and shapes based on the energy behind relationships and activities.

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. The final poster translates this personal data into a layered print visualization, capturing the emotion behind the initial question: am I an introvert or an extrovert?

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

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. 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 translated these verbal descriptions to the curation of visual examples. Drawing adjectives and themes from the brand exercises, I created 4 different moodboards that visualized the different directions identified as part of the previous stage so that my client could select a direction or two to move forward with and guide the visual identity 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. The final design strategy was playful, earthy, and sophisticated.

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.

I translated the final prototype wireframes to the website using Shopify's content management system and custom theme builder. The final website visually explained the ingredients of each tea at a micro level alongside the core idea of the company and how its values and history connect to each individual product choice.

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.

Adobe Photoshop, Adobe Illustrator, Figma, Liquid.js

A data sculpture using layers of acrylic to map locations in the United States according to their light pollution levels.

Using the Global Nighttime Radiance data from 2025, this project maps locations in the United States to their light pollution levels. Each layer of acrylic corresponds to a quartile on the polluted scale for this region, the least polluted layer visible at the top. Points are grouped using DBSCAN clustering into organic shapes that are cut away from each layer to reveal the light underneath. The areas with the highest levels of light pollution are visible from how much light shines through.

I experimented with different forms of clustering to group data points into areas that could be translated into the acrylic surfaces, using Python to process the data and initially bucket it into 4 groups, or layers, by pollution level. I ultimately decided to move forward with hierarchical clustering, which identified clusters by starting with the highest pollution levels and creating groupings of points within that layer, and iteratively moving to the next highest pollution level layer and gathering nearby points to each previously identified cluster.

The final cluster shapes were exported to an SVG, one for each layer, their edges smoothed, then prepared for laser cut using Adobe Illustrator.

The initial concepts focused on how to use physical materials to show where development has physically gotten in between the natural environment, with light shining from below to both highlight and project an image onto the surrounding space. Paper prototyping resulted in flipping the initial layering method - the areas with higher light pollution levels should be letting more light through rather than the initial idea of the inverse.

4 layers of uniformly engraved 12" x 18" acrylic, each with a range of pollution levels ranging from lowest levels at the top of the stack to highest at the bottom, showing depth of pollution and concentration across areas and lit from below with a lightbox.

The ideal version of this sculpture is a large-scale installation in a dark room, with scale allowing for subtle engraving pattern differences on each layer that result in that different amounts of light being able to pass through each layer. Visitors should be able to walk around the map and see the layers and locations highlighted from a hidden light source underneath. Mockup was created using Google Gemini.

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

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

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

A large-scale video installation and interactive website that pairs imagery of the urban environment with materialized climate and community environmental interaction data.

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.

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

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

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 were beginning to have 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 later extended this project to apply 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 in the press release 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 identified ways to improve our technical process and weave in issues that I am passionate about: accessibility and cross-disciplinary 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 served as a champion for accessibility within the three different teams that I was a member of at LinkedIn after this experience, 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.

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

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

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 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.

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.

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.

Magdalena

Navracruz