PiTech Impact Studio is Cornell Tech's practicum course dedicated to building technology in service of the public good. Working alongside nonprofits, city agencies, and social ventures, multidisciplinary teams of graduate students spend the Spring semester building technical products to serve their partners’ missions.

This year, co-instructors Matt Klein (Robin Hood Foundation) and Ariel Kennan (Beeck Center for Social Impact & Innovation) guided student teams through the full arc of the work: from scoping and stakeholder mapping, to prototyping, testing, and final handoff. Projects addressed family policing advocacy, workforce development, college course access, affordable housing policy, constituent services, homeless services navigation, family shelter operations, and food bank delivery logistics. All of the projects aimed to expand opportunity and address poverty in New York City and involved working closely with partner organizations to translate complex operational and policy challenges into tools that the organizations could incorporate into their workflow. 

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.

As a Siegel Family Endowment PiTech PhD Impact Fellow, I worked with the Block Party team to improve the quality, clarity, and usability of their meeting summaries.

As a Rubinstein PiTech PhD Impact Fellow, I worked with NYCEM’s Office of Strategic Operations to improve preparedness and response efforts using machine learning.

As a Siegel PiTech PhD Impact Fellow working with NYPL this summer, I used an Agent-Based Model (ABM) simulation as a data-centric approach to modeling community use patterns of a library.

As a Siegel PiTech PhD Impact Fellow, I explored how AI could streamline NYC's Noise Camera Enforcement Program, which manually reviews thousands of noise violation recordings each month.

As a PiTech PhD Impact Fellow this summer, I worked on two data-driven projects with the New York City Housing Authority (NYCHA), both aimed at improving the safety and resilience of their large portfolio of buildings. NYCHA manages hundreds of developments across the city, many of which face aging infrastructure and growing maintenance challenges

This summer, as a Siegel PiTech PhD Impact Fellow working with the Mayor's Office of Environmental Justice, my project focused on three critical aspects of this accessibility challenge:

1. Understanding user needs through collaboration with environmental justice experts and community stakeholders

2. Consolidating data sources from technical platforms into a unified digital tool with a user-friendly interface

3. Designing intuitive interactions that make complex environmental data actionable for residents

How can we build data systems that help young people thrive in STEM? New York City’s educational landscape is full of afterschool programs, classrooms, and community hubs, but NYC FIRST’s growing network of STEM Centers occupies a unique position: it is a “third space” – not quite school, not quite home, but a vibrant, in-between environment where students, mentors, and community members collaborate, take risks, and discover their STEM identities (Bhabha, 1994; Beal, 2024).

This summer, as a Frederick and Susan Rubinstein PiTech PhD Impact Fellow, I worked with the Center for Family Support (CFS) on a “Smart Home Integration Proposal” for residential care settings. CFS is a non-profit dedicated to promoting independence and community integration for over 1,050 people with intellectual and developmental disabilities (IDD) across New York and New Jersey through services like group homes, day programs, and self-directed support.

This summer, I worked as a PiTech PhD Impact Fellow with the Clinic to End Tech Abuse (CETA) to examine how new and emerging technologies could be weaponized for interpersonal violence.