Market maps groups companies providing similar services together for industry analysis. Its optimization addresses limitations of manual creation and user customization. Through NLP techniques including keyword extraction and topic modeling, we have achieved improved segmentation, better naming, enhanced brand-image and credibility. The project's results include automation of a cumbersome manual task and faster refresh, resulting in reduced latency and improved customer experience. By enabling customers to find companies in newer segments as a starting point for research, our project makes a significant difference in the lives of investors, entrepreneurs and stakeholders, empowering them with accurate, timely insights for better decision-making.

Microsoft Azure Lab Services (ALS) provides on-demand or scheduled access to pre-configured VM’s to execute classes, trainings, and workshops. Last year, ALS released a new update consisting of improved and enhanced functionalities. Research insights suggest that customers take 1-month for migration, facing problems like frequent manual configurations, inconsistent documentation & hassle of communicating with customer service teams. The goal of this project is to design and develop a seamless migration experience, that enables customers to seamlessly migrate to the latest version. This migration experience reduced operational overhead for customers, empowering them to focus on tasks that effectively matter the most.

Trying to find information can be tough, especially when a website has lots of product offerings. Microsoft Learn users are facing difficulty because of the myriad of information, confusing UI, and lack of filter options. We conducted in-depth user research with surveys, interviews, and analysis and rendered 5 evidence-based solutions to improve the search experience on Microsoft Learn. Further, We developed an end-to-end roadmap to implement these recommendations and visualized them with a high-fidelity prototype. We expect these changes to provide improved content quality and relevance along with ease of use, saving significant time for the users.

Understanding collation is an important part of studying material texts. However, current tools for representing this information, such as collation formulae, can provide obstacles to accessibility. The Collation Visualization project (VisColl) helps to remedy these issues by providing a means of visualizing a codex’s collation via a digital model. In this project, I created models for 39 codices held by Penn Libraries using VCEditor, the software implementation of VisColl. I then helped prepare the models for upload to ScholarlyCommons, University of Pennsylvania’s open-access institutional repository, and created documentation to assist future VisColl contributors.

With the expanding growth of music streaming platforms and subscriptions, listeners around the world use music as a way of expression and personal enjoyment. However, there are flaws within the music recommendation algorithms which recycle oversaturated content, lack transparency, and have innate popularity bias. Our app, Moments, aims to combat restrictive algorithms by giving users autonomy in the music discovery process. Through profiles and a map interface, people can discover and share music in an organic way by exploring others' music tastes around them. But that's just our Moment, what's yours?

The goal of this project was to help a preschool-8th grade independent school to evolve their Innovation Vision through research about stakeholders goals, review of existing resources, collaboration with other school learning lab spaces, and a foundation in the school’s own mission. By synthesizing input from each of the stakeholder groups with my MLIS experiences, I was able to write an Innovation Vision for the school, along with a specific list of observations and suggestions for development of their new Innovation Center. This Vision will serve as a guide for the evolution of the Innovation Curriculum in the coming years.

More than ⅓ of small businesses lack social media accounts, despite it being one of the most popular resources for discovering new restaurants. Rather than needing to spend time learning complex interfaces, Munch is a user-friendly web app that enables small restaurants to upload content and improve visibility. Munch allows restaurants in Seattle’s University District to post updates, events, and promotions to a shared community Instagram page. Our Instagram page engages current and potential customers by providing a stream of content from multiple restaurants, fostering stronger customer relationships, and broadening audience reach.

As children around the world are spending less time outdoors compared to previous generations, their screen time has increased. This can negatively impact their nature-related science learning, as well as cause a decline in their behavioral and emotional development. NatureCollections is a mobile app that seeks to harness the power of emerging technologies to engage elementary school children in an exploration of the natural world. Leveraging kids’ love for collecting things, the app encourages kids to go outside and have fun while developing a stronger connection with their natural surroundings.

E-commerce has been around for a while, however, shoppers are faced with decision paralysis with the abundance of options available online. Our backend technology, E-Telligence API, automatically searches the web to identify other websites where a specific product is available, enabling shoppers to compare prices and make an informed decision quickly. Our sponsor, NetPrism, plans to add this technology in their browser extension.

NetPrism develops software for understanding e-commerce trends. Our capstone project addresses the common problem of uncertainty in determining the best time to purchase a product. To solve this issue, we developed a Machine Learning model that leverages thousands of historical price data points to accurately predict the price of a product in the next 30 days. Our Price Prediction model provides graphical price predictions that enable individuals to make informed purchasing decisions, leading to more effective allocation of resources and less overspending. Our project demonstrates the utility of data analytics in bettering everyday finances.