Hello, I’m Nino!
I’m currently a third-year Ph.D. student in Computer Science at the University of Washington, where I am fortunate to be advised by Katharina Reinecke and René Just, and to collaborate closely with Spencer Wood. I truly admire each of them, both as researchers and as people, and I’m grateful for the opportunity to learn from them.
My research broadly explores the intersection of technology and society, with a particular focus on understanding, measuring, and evaluating AI-related opportunities and challenges. I have collaborated across multiple departments throughout UW, working with the ASILI Lab, the eScience Institute, the Outdoor Recreation & Data Lab, and have recently started working with the U.S. Forest Service.
Selected Projects
Preventing Homelessness: Evidence-Based Methods to Screen Adults and Families at Risk of Homelessness in Los Angeles
The overall homeless population in Los Angeles County continues to grow as inflows into homelessness outpace exits to housing. The key to preventing homelessness is to ensure scarce prevention resources are going to people who will become homeless without those resources. In this study, we evaluate the surveys used to screen adults and families who self-identify as being at risk of homelessness. Specifically, we evaluate screening surveys called Prevention Targeting Tools (PTTs) currently used by homelessness prevention service providers in the City and County of Los Angeles.
Detecting and Harmonizing Scanner Differences in the ABCD Study-Annual Release 1.0
In order to obtain the sample sizes needed for robustly reproducible effects, it is often necessary to acquire data at multiple sites using different MRI scanners. This poses a challenge for investigators to account for the variance due to scanner, as balanced sampling is often not an option. Similarly, longitudinal studies must deal with known and unknown changes to scanner hardware and software over time. In this study, we explore scanner-related differences in the dataset recently released by the Adolescent Brain Cognitive Development (ABCD) project, a multi-site, longitudinal study of children age 9-10. We demonstrate that scanner manufacturer, model, as well as the individual scanner itself, are detectable in the resting and task-based fMRI results of the ABCD dataset.
Parsimony and Machine Learning in Neuroimaging
The disparity between an individual’s brain age and their chronological age can be an indicator for various neurological disorders. In our preregistered study, we used anatomical MRI data from the NIMH/NHLBI Data Sharing Project (NNDSP) dataset to compare accuracy in prediction of age for a complex machine learning model with a large number of features to a simple machine learning model with only four features: white matter fraction, grey matter fraction, CSF fraction and intracranial volume, chosen a priori. With samples from a large lifespan sample (N=441, age 5-77) as our training and test data we found that the predictive ability of the complex model was similar to the predictive ability of the simple model on out of sample data.
RDoFlow: Automatically Assessing Under-Specified Statistical Analyses in HCI
When applying null-hypothesis significance testing (NHST) in research, it is important to know what the alternative hypothesis states and how it is actualized. However, in practice, scientific texts often under-report hypotheses. To support understanding of the content and missing pieces of hypothesis reporting, we contribute RDoFlow, a workflow for automatically assessing statistical analyses through their hypotheses. Using an adapted protocols from psychology for reviewing Questionable Research Practices in hypothesis formalization that we evaluated with our hand-coded annotations of HCI and psychology hypotheses and texts, we contribute RDoFlow, an LLM workflow that applies the protocol automatically.
Regulating AI: Where U.S. State Policy and HCI (Mis)align
Artificial intelligence (AI) technologies are increasingly adopted into everyday life, with most investment and development concentrated in the U.S. In response to rapid AI integration and scant federal guidelines, U.S. states have formed AI committees charged with studying AI-related societal trade-offs. We analyzed the 18 existing state-level AI committee reports to understand how policymakers discuss AI-related benefits and risks. We then compared the risks surfaced by policymakers to an established taxonomy of AI risks aggregated from literature and examined how policymakers’ concerns align---or misalign---from those of HCI scholars.
Wildfire and Forest Management: Opportunities for HCI Research
Wildfire and forest management increasingly relies on geospatial technologies, i.e., data and tools contributing to the geographic mapping and analysis of the Earth, to inform measures for the control of wildfires. Nevertheless, challenges arising from domain experts adopting these complex, non-intuitive technologies are not well understood. We interviewed 12 participants in wildfire and forest management, revealing that (1) knowledge and data are fragmented across stakeholders, ranging from governmental agencies to small landowners. This fragmentation causes participants to (2) struggle in sharing knowledge and expertise. Participants (3) voice concerns about model bias since decisions informed by geospatial technologies can have far-reaching impacts. Yet, they (4) face barriers engaging people most impacted by these decisions.
About Me
I believe that the places where I’ve lived have distinctly shaped every part of who I am and am becoming. The map represents the journey of all the towns, cities and countries that have taken root within me.
I was originally born in Georgia, but also lived in Austria before moving to the U.S. I have a deep love for languages and culture — if I could have any superpower, it would be speaking all the languages of the world. It also instilled in me a tantalizing wanderlust and a yearn to constantly travel.
When I am not doing research, you can find me going on long walks or reading quietly with a hot mug of chamomile tea (even in the summer).
“To be whole is to be part; true voyage is return.”