In this project, I worked with fellow researchers at MIT’s Social Cognitive Neuroscience Lab to show that social emotional processing in the brain is similar in adults with and without ASD, despite the social emotional behavioral impairment in ASD. This is contrary to previous findings in the literature that have made claims about neural differences in social emotional processing in ASD.

To achieve this, I collected behavioral and neural data from 60+ research participants with and without ASD using various methods including: in-person and computer-based psychometric/cognitive testing, survey design, in-person interviews, and fMRI. I analyzed data using statistical methods using MATLAB, and co-authored an academic paper.

I worked on various projects to characterize people's attitudes to polarizing information at MIT’s Social Cognitive Neuroscience Lab. In one project, I found that personal narratives were more likely to shift people’s perspectives on social and political topics, compared to fact-based arguments, in a study with 40+ participants. In another project, I examined the attitudes of Palestinians and Israelis (n = 20) to extremist political viewpoints, and found that similarity in behavioral attitudes towards extremist information was correlated with brain responses of others within the same group, and anti-correlated with the brain responses of members of the opposing group.

For these projects, I designed the experiments, created surveys, developed text and video materials, collected data using fMRI and Qualtrics, and analyzed data using statistical methods.

I led the largest study to date of diverse languages in the brain (41+ languages) at the Fedorenko Language Lab (formerly at Harvard/MGH) and showed that language processing is highly similar in the brain, across various features, in 80 adult participants. As part of the study, I developed a set of audio stimuli based on recordings of Alice in Wonderland in several languages, which I created with the help of speakers of diverse languages. The audio material is freely available for download.

To achieve this, I designed surveys, developed auditory materials, collected data using fMRI and online surveys, analyzed data using statistical methods, and co-authored an academic publication (currently in preparation).

In this project, I worked fellow researchers at the Fedorenko Language Lab (formerly at Harvard/MGH) to show that social brain regions, in addition to language regions, are involved in language processing in conversations involving co-listeners. I showed that when participating in a multi-person conversation, our brains engage language regions in addition to social regions, in order for us to understand what is being said, as well as to track the co-listeners' comprehension of the same conversation.

I used surveys combined with EEG to collect behavioral and neural data from over 30 participants, analyzed data using statistical methods, and co-authored an academic paper.

In this project, I worked fellow researchers at the Fedorenko Language Lab (formerly at Harvard/MGH) to conduct a meta-analysis of language processing in the brains of adults with ASD. Unlike neurotypical adults who show a left brain language lateralization for language processing, the meta-analysis showed that adults with ASD show a reduced language lateralization, suggesting this as a novel phenotype of ASD.

To achieve this, I created a database of existing data from adult ASD participants and aggregated behavioral, psychometric, and fMRI data into a standardized format, and I established a process for data aggregation and pre-processing. The resulting database was used by collaborating researchers to conduct the statistical analyses. The academic paper is available here.

In this project, I examined the effects of the technological knowledge gap on the development and regulation of new technologies. In effect, the pace at which technology is developed is faster than the public's understanding of that technology. One reason for that is that technological knowledge is largely concentrated amongst a few select entities: academics who conduct basic research, researchers and developers who apply said research, and industry professionals who build products that implement technological knowledge. The concentration of technological knowledge results in an underinformed public that is unable to make an informed choice to the extent to which it wants to engage with technological products, and regulatory bodies that are unable to track the developments and (mal)practices of technologists, creating a severe technological power imbalance.

This project was completed as part of the Emerging Technology Policy Network at the Harvard Kennedy School's Technology and Public Purpose Project

Through the MIT Internet Policy Research Initiative, I participated in a workshop with a diverse group of professionals to develop a framework for global AI policy. Along with my cohort, I assessed the priorities, opportunities, and trade-offs of various AI-based technologies, and considered perspectives from the history of technology and ethics to inform policy considerations.

As an undergraduate and graduate student at MIT’s School of Architecture and Planning, I was interested in characterizing the role of urban environments in shaping people’s perceptions of one another, especially during periods of political and cultural transition. As a case study, I examined the urban and cultural reintegration efforts in Berlin 22 years after the fall of the Berlin Wall, by qualitatively evaluating the redevelopment of key public sites in the city and their effect on re-shaping the identity of a “new” Berlin and that of its citizens. This project evolved into my master’s thesis, available here.

To carry out this project, I relied on archival research in addition to conducting interviews and surveys, and mapping out urban change over time in ArcGIS and Adobe Creative Suite (Illustrator, Photoshop, InDesign).