Seeing the finalized cohort of Siegel PiTech PhD Impact Fellows has been the most rewarding aspect of my work as PiTech Initiative Director. Much thoughtful curation goes into matching students with organizations, both from the PiTech side, as well as on the part of our wonderful partners, to align students’ interests and skills and organizations’ specific technical explorations. I’m very excited to announce the Fellows and their hosts for Summer 2024!

As we finalize the 2024 summer cohort of Siegel PiTech PhD Impact Fellows, we’d like to look back and reflect on the Fellowship since its pilot in 2021 and share some key facts and stats on how we’ve evolved.

Highlighting the shortcomings exposed by Hurricane Ida in 2021, James underscores the need for accurate, localized weather information. His solution, developed during his tenure as a PiTech PhD Impact Fellow with New York City Emergency Management (NYCEM), is an innovative online dashboard. This tool, created using R and Shiny software, integrates data from the National Weather Service into an interactive map, offering real-time and predictive weather insights for each neighborhood in New York. This advancement not only streamlines the data consolidation process but also enhances decision-making capabilities for city officials and emergency responders. James' work represents a significant step towards improving urban response to hazardous weather, though he notes the necessity for further enhancements to the system.

The organization YAI, who has been dedicated to supporting the intellectual and developmental disabilities (I/DD) community in different ways for decades, seeks to reshape the dental experience for the I/DD community, particularly for those with autism. With the increasing accessibility of AR/VR technology, and its unique capability to enable different interactions within physical spaces, we're confronted with an intriguing challenge: Can mixed reality be the antidote to SPD challenges in current dental settings? Can we envision a new future of dental visits for those with I/DD through the use of AR/VR?

Block Party, a New York City-centered volunteer-supported organization, is working to built a tool to automatically summarize the text of community board meetings using speech-to-text efforts of YouTube, Artificial Intelligence (AI), and Natural Language Processing (NLP) and to automatically disseminate the resulting summary in the free Block Party newsletter mailing list for each community board. During my tenure as a PiTech Impact Fellow, I was able to leverage my research and expertise in NLP in order to support the meeting summarization process. I worked closely with Sarah, brainstorming ways we might envision a new summarization process utilizing LLMs.

The Mayor’s Office of People with Disabilities (MOPD) charioted the effort of creating an accessible chatbot for the NYC’s government website. Chatbots and large language models (LLMs) have emerged as promising technologies to sift through enormous amounts of text, as well as infer information to respond to questions. I sought out to explore these possibilities with the MOPD as a Siegel Family Endowment PiTech PhD Impact Fellow in summer 2023 which later required partnering with the Office of Technology & Innovation (OTI), and other city agencies.

In an effort to reclaim provider capacity, increase patient safety, and reduce alert fatigue, a team of health informatics experts was established at NYC H+H in 2020. This working group routinely assesses new and past BPAs, and modifies or removes any that are found to be unhelpful.

This study delved into the factors that influence consumer decisions around personal data management, inquired about the specific companies and data types that trigger high levels of concern, and sought to discern consumers' expectations for privacy tools.

As a Siegel PiTech PhD Impact Fellow, I collaborated closely with the City of Ithaca and partnered with BlocPower, a climate technology company, over the summer to develop a web application. The web application was designed to aid stakeholders, not only within Ithaca but across various municipalities in New York state, in estimating the quarterly workforce increase required to achieve the goal of complete building electrification by 2030.

Employing a Bayesian latent variable model to navigate the complexities of library usage, the New York Public Library (NYPL) system was scrutinized to uncover disparities in usage patterns across its branches, paving the way for operational strategies aimed at balancing these disparities, ensuring that the benefits of the library system are fairly distributed among all New Yorkers.

Work with Good Call NYC to analyze New York City’s arrest data. Early legal representation has a large impact on the future of arrested individuals but not everybody has access to it. Although US citizens have the legal right to a lawyer, public defenders are not assigned a case until several hours before the arraignment trial, which can be up to 72 hours after arrest. In effect, early legal representation is only accessible to individuals with the money to hire private lawyers and the means to contact them from the police station.

Application of natural language processing techniques to analyze data consisting of 4,000 independent text entries, each representing a New Yorker’s idea (see the pipeline here). My primary analysis used unsupervised learning to highlight key themes and visualize their relationship, using BERTopic, which implements topic modeling to identify 39 topics and provides hierarchical analysis to help merge these topics into 16 key themes.

This year, we extend a warm welcome to 12 exceptional PhD students specializing in computer and information science, who have committed their summers to applying their technological prowess towards addressing pressing public needs at their respective host organizations. We are honored to have them join us!

Biodiversity monitoring is an important task in conservation biology, however, it is resource intensive, requiring time, money, expertise, and a large on–the–ground presence that is not always possible due to conflict, politics, etc. Potentially traditional biodiversity monitoring techniques can be supplemented with an inexpensive tool that utilizes remote sensing data and deep learning models to remotely monitor biodiversity.