As heat becomes an increasingly urgent risk, the New York City Office of Technology and Innovation’s Smart Cities and IoT team is exploring whether a citywide heat sensor network could be valuable to city services and operations. As a Siegel PiTech PhD Impact Fellow placed with the team, I set out to 1) map the current landscape of urban heat monitoring and 2) assess NYC agencies’ needs for enhanced heat monitoring solutions.

By the time I joined the team, more than 19,000 of those photos had piled up. Each one is a picture of exactly what a specific pantry handed out on a specific day. Not one of them had ever been read by anything.

As a Siegel PiTech PhD Impact Fellow, I spent the summer building the computer vision system that reads them. I was the only machine learning engineer on it, working directly with Lemontree's Chief of Staff.

Through the PiTech Fellowship, I worked with the New York City Council Data Team to explore this challenge by developing an AI agent for Council staff that uses open-source models, connects to internal government tools, and incorporates guardrails designed for responsible use in legislative and administrative workflows.

In my Siegel PiTech PhD Impact Fellowship, we explore how algorithmic and data-analytic techniques can be integrated into Food Bank for NYC’s workflow to augment expert decision-making.

This summer, I worked with Switchbox as a Siegel PiTech PhD Impact Fellow to understand how TOU rates affect EV charging. My work aimed to answer the following questions:

1. How does a TOU rate change the amount and timing of residential electricity demand compared with a flat rate, and how does this effect vary as EV adoption increases?

2. How much can EV owners save on their electricity bills by choosing a TOU rate instead of a flat rate?

As a Rubinstein PiTech PhD Impact fellow with OTI, I conducted research about how principles of algorithmic fairness can be operationalized. In particular, I studied and synthesized AI policy that has been deployed by other jurisdictions (including cities, states, and countries) and academic literature that defines best practices for algorithmic  transparency, accountability, and fairness.  

How can public health organizations evaluate the strengths and limitations of AI-generated health and wellness information? As a Siegel PiTech PhD Impact Fellow with the New York City Department of Health and Mental Hygiene, I worked with public health professionals to compare four commercially available large language models: Gemini, Claude, ChatGPT, and Grok. 

As a Rubinstein PiTech PhD Impact fellow with OTI, I conducted research about how principles of algorithmic fairness can be operationalized. In particular, I studied and synthesized AI policy that has been deployed by other jurisdictions (including cities, states, and countries) and academic literature that defines best practices for algorithmic  transparency, accountability, and fairness.  

As a Siegel PiTech PhD Impact Fellow working with the New York City Police Department, I explored two key questions: Can historical 311 call data be used to explain forecasted service demand? And can it help identify geographic areas with chronically high call volume?

Over the summer, I worked on two challenges from the Hall. The first is the limited gallery space for a growing number of inductees. The current exhibition shows only inductee names on long rolls of paper, which offers very limited information about their lives and achievements. The second looks forward to future Hall experiences. As the Hall prepares to open more spaces in the mill, it is also asking what a future visit could feel like: how might emerging technologies like AI and interactive exhibits reshape the museum experience? And what is the ethical and responsible way of applying these technologies to archival data?

This summer, as a Siegel PiTech PhD Impact Fellow working with YAI, I explored whether AI-assisted 3D design tools could help bridge this gap. I focused on AdamCAD, a tool that allows users to create and modify CAD models through natural language.

New York City Emergency Management (NYCEM) is the primary city agency responsible for coordinating citywide planning and response to major emergencies that impact New Yorkers. These include unexpected incidents, such as major transit strikes or building collapses, as well as more common seasonal ones, like coastal storms and severe winter weather events. While NYCEM uses numerous data-driven tools to prepare for and respond to disasters, the agency is still exploring whether and how predictive tools may support their work.

As a PiTech Rubinstein PhD Innovation Fellow, I worked on a project titled Modeling Heat Pump Adoption, System Costs, and Bill Alignment Under Alternative Electricity Rates in New York.

As a PiTech Rubinstein PhD Innovation Fellow, I’ve been working  over the past few months on a project centered on AI usage in behavioral intervention work: “How can we support Behavioral Intervention Specialists (BISs) in making sense of diverse, complex behavioral data when evaluating support plans for individuals with Intellectual/Developmental Disability (I/DD)?”

As a Siegel PiTech PhD Impact Fellow with NYCPS, I designed an informational intervention to target students from middle schools that have not historically sent many students to the highest performing high schools, helping them discover high-performing, nearby high schools where they have a strong chance of admission.

How do New Yorkers feel about the services that city agencies provide in response to their 311 requests? To help answer this question, as a Siegel PiTech PhD Impact Fellow, I collaborated with the New York City Office of Technology and Innovation’s Office of Data Analytics to analyze data from the 311 Resolution Satisfaction Survey. We developed a unified framework that allows city agencies to measure and understand changes in customer satisfaction over time. This effort marks an important step toward improving how government agencies listen to and serve their residents.

As a Frederick and Susan Rubinstein PiTech PhD Impact Fellow this summer with Switchbox, I explored whether policy interventions in the form of varying electricity rates can push EHP adoption from current levels to targets needed for decarbonization. For this, I developed a building-level consumer behavior modeling framework that goes beyond traditional economic assumptions of utility-maximizing behaviour to capture how real families adapt to rate signals through experience and learning.

Navigating NYC housing laws can be very challenging for tenants, especially when facing potential eviction and housing insecurity. Oftentimes, the question is not “am I protected by Good Cause Eviction”, but “I have not heard of Good Cause Eviction. What does it have to do with me?”.  That’s why, this summer, I worked with Justfix as a Siegel PiTech PhD Impact Fellow, to find out if they could utilize LLMs to build chatbot-like tools to help the tenants know their rights and what actions to take to assert those rights.

As a Siegel PiTech PhD Impact Fellow this summer, I worked with the Research and Collaboration team at the New York City Office of Technology and Innovation (OTI) to develop a practical framework for assessing machine translation. Even though a wealth of academic research exists on the subject of machine translation quality evaluation (MTQE),  it does not necessarily translate well (pun intended) into practical settings such as the City’s use cases.

I worked alongside the DOT to build bike demand estimates that leverage large-scale cycling GPS trace data. Specifically, we used data from CitiBike–New York City’s bike rideshare system.