Researcher, Designer
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Academic Research

 I am currently a PhD Candidate in Information Science (HCI) as part of the  Social Technologies Lab  at  Cornell Tech , the New York City technology-focused branch of Cornell University. I am also the Specialization Project Coordinator for the Master's Degree Programs in  Connective Media  and  Health Tech . These are a few of the research projects I've worked on and presented at CHI and CSCW:

I am currently a PhD Candidate in Information Science (HCI) as part of the Social Technologies Lab at Cornell Tech, the New York City technology-focused branch of Cornell University. I am also the Specialization Project Coordinator for the Master's Degree Programs in Connective Media and Health Tech. These are a few of the research projects I've worked on and presented at CHI and CSCW:

TAMIES (Technology Acceptance Model for Indirect Exchange Systems)

TAMIES (Technology Acceptance Model for Indirect Exchange Systems)

Peer-to-peer indirect exchange services, such as Peerby and NeighborGoods, have not been as widely adopted as direct exchange systems, such as Uber and Airbnb. Building upon the results of interviews with 37 residents of New York City, a survey with 195 respondents, previous technology acceptance models, critical mass theory, and prior research on peer economies, we propose a technology acceptance model for indirect exchange systems that includes generalized trust and ease of coordination.

  A recent mobile dating application, happn, adds a temporal dimension to location-based dating, showing users the number of times and recent overlap that they crossed path with each other. We conducted qualitative interviews with 15 happn users and discuss the findings in the context of Uncertainty Reduction Theory. The warranting power of the device driven location data was accepted as valuable and generated little concern about misrepresentation. Our findings suggest the potential for utilizing location data outside of the domain of online dating.

A recent mobile dating application, happn, adds a temporal dimension to location-based dating, showing users the number of times and recent overlap that they crossed path with each other. We conducted qualitative interviews with 15 happn users and discuss the findings in the context of Uncertainty Reduction Theory. The warranting power of the device driven location data was accepted as valuable and generated little concern about misrepresentation. Our findings suggest the potential for utilizing location data outside of the domain of online dating.