Digital Health

Aidan Lee

Consulting Drop-In Hours: By appointment only

Consulting Areas: ArcGIS Desktop - Online or Pro, Bayesian Methods, Causal Inference, Cluster Analysis, Data Sources, Data Visualization, Databases and SQL, Digital Health, Excel, Experimental Design, Geospatial Data: Maps and Spatial Analysis, Git or GitHub, LaTeX, Machine Learning, Means Tests, Mixed Methods, Natural Language Processing (NLP), OCR, Python, Qualtrics, R, Regression Analysis, Research Design, Research Planning, RStudio, RStudio Cloud, SAS, Software Output Interpretation, SPSS, SQL,...

Armaan Hiranandani

Data Science & AI Fellow 2025-2026
School of Information

Armaan Hiranandani is a Master’s student in Data Science at UC Berkeley, where he also earned his B.S. in Industrial Engineering & Operations Research. Born and raised in Dubai, Armaan recently completed a software engineering internship at Netflix, working on the machine learning platform team. His interests include building scalable AI systems and applying data science to solve real-world problems.

Monica Donegan

Data Science Fellow 2022-2023
Environmental Science, Policy, and Management

Monica is a third-year Ph.D. candidate in the Environmental Science, Policy, and Management program. She uses computational tools to study the evolution and ecology of agricultural plant pathogens. Previously, she worked on a data science team at a biotech company in Boston.

Elaine (Hua) Luo

Data Science Fellow 2024-2025
School of Education

Elaine (Hua) Luo is a PhD candidate in the Graduate School of Education, School Psychology PhD program. Her research interests focus on adolescents’ identity development and well-being under the transactional influence of entities in their socio-ecological systems. In her research, Elaine has utilized not only quantitative but also qualitative and mixed methods to study her research topics of interest. Before coming to Berkeley, Elaine earned her Master’s in Human Development and Psychology from Harvard Graduate School of Education and her Bachelor of Art in Education Sciences from...

Looking Ahead: How Adolescents’ Consideration of Future Consequences Shapes Their Developmental Outcomes

March 25, 2025
Elaine Luo
by Elaine Luo. Adolescents constantly balance immediate impulses with long-term goals. Our research explored how adolescents differ in their tendency to think about immediate versus future consequences, and how these differences relate to academic performance, stress, and perceived life chances. Using Latent Profile Analysis, we identified three distinct groups: Indifferent (low consideration overall), Future-Focused (prioritizing future outcomes), and Dual-Focused (high consideration of both immediate and future outcomes). Results indicated the Dual-Focused adolescents had higher academic achievement, whereas the Future-Focused group perceived the most positive life prospects. A discussion on practical implications and future research direction for supporting balanced decision-making among adolescents is also provided.

The Case for Including Disability in Social Science Demographics

October 15, 2024
Jane (Mango) Angar
by Mango Jane Angar. As we celebrate Disability Awareness Month at the D-Lab alongside the UC Berkeley scholarly community, how can we, as social scientists, individually promote accessibility and inclusion? To advance accessibility, we should focus on addressing the barriers faced by individuals with disabilities, using our research to provide insights for effective policy recommendations. Although most of us do not focus on disability-related issues, including disability as a demographic characteristic in our data collection can greatly enhance our understanding of diverse populations and improve the comprehensiveness of our analyses. This small step can contribute to broader efforts toward inclusion and social equity.

Claudia von Vacano, Ph.D.

Availability: By appointment only

Consulting Areas: Digital Humanities, Mixed Methods, Qualitative methods, Surveys, Sampling & Interviews, MaxQDA, Career Development

Conceptual Mirrors: Reflecting on LLMs' Interpretations of Ideas

April 23, 2024
María Martín López
by María Martín López. As large language models begin to engrain themselves in our daily lives we must leverage cognitive psychology to explore the understanding that these algorithms have of our world and the people they interact with. LLMs give us new insights into how conceptual representations are formed given the limitations of data modalities they have access to. Is language enough for these models to conceptualize the world? If so, what conceptualizations do they have of us?

The More Things Change the More They Stay the Same?

December 18, 2023
Tonya Lindsey, Ph.D.
By Tonya D. Lindsey, Ph.D. Think about how often you hear someone gripe about the deterioration of society and then blame the Internet or social media. This blog suggests that the things we are exposed to virtually are not new but instead present us with more and frequent opportunities to reflect on perennial social problems and find solutions even as we better understand ourselves as individuals in a global community.

Artificial Intelligence and the Mental Health Space: Current Failures and Future Directions

October 31, 2023
María Martín López
by María Martín López. María Martín López, a PhD student in the department of psychology whose research focuses on large language models within the context of mental illness, gives an overview of current failures and possible future directions of NLP models in the mental health space. She brings up questions that must be considered by all researchers working in this space and encourages these individuals to think creatively about the use of AI beyond direct treatment.