Uploaded October 2023 | Updated September 2026, 2 weeks ago
“Equity in digital mental health interventions for marginalized populations: Lessons from the Mind-Us study.”
Giovanni Ramos
UC Chancellor's and Ford Foundation Postdoctoral Fellow, UCI Psychological Science
Abstract: In the US, racially and ethnically minoritized (REM) groups are less likely to seek or receive mental health services compared with White groups. Even when REM are able to access care, it tends to be of lesser quality, and they are more likely to leave treatment prematurely. Digital mental health interventions (DMHIs) hold promise to reduce some of these inequities by addressing the shortage of mental health professionals, mitigating logistic barriers to service utilization, decreasing costs associated with implementation, and engaging individuals in care who may not seek services otherwise. However, DMHIs are rarely designed with the unique needs of REM in mind, which may perpetuate existing inequities or, even worse, create new ones now in the DMHI space.
To address the lack of equitable DMHIs for REM, the Mindfulness for Us (Mind-Us) program—a self-guided, app-based mindfulness meditation intervention for REM who experience elevated levels of discrimination—was specifically designed to meet some of these needs. Despite using a commercially available app, the Mind-Us program used human support to improve the app's usability, promote engagement with content not explicitly designed for REM, identify the fit of the intervention with users’ needs, and increase knowledge of mindfulness principles. Following this approach, the Mind-Us program had outstanding implementation outcomes, with participants using the app consistently during the 4-week program and less than 8% leaving the trial prematurely. Furthermore, Mind-Us led to clinically significant reductions in stress, anxiety, and depression. Thus, lessons learned from the implementation of this program could help researchers and clinicians design and implement successful DMHIs for REM populations.
Bio: Dr. Giovanni Ramos received B.A.s in Psychology from Universidad Nacional Autónoma de México and Florida International University. He completed his doctoral training in clinical psychology at the University of California, Los Angeles, including a clinical internship at the Albert Einstein College of Medicine/Montefiore Medical Center. His research program focuses on addressing mental health inequities affecting racially and ethnically minoritized groups in the US. To achieve this goal, To achieve this goal, I focus on two interconnected areas: 1) improving the cultural and contextual fit of evidence-based treatments, and 2) using digital tools to make these interventions available in marginalized communities. He is currently a UC Chancellor's and Ford Foundation Postdoctoral Fellow in the Department of Psychological Science at the University of California, Irvine.
“Equity in digital mental health interventions for marginalized populations: Lessons from the Mind-Us study.”
Giovanni Ramos
UC Chancellor's and Ford Foundation Postdoctoral Fellow, UCI Psychological Science
Abstract: In the US, racially and ethnically minoritized (REM) groups are less likely to seek or receive mental health services compared with White groups. Even when REM are able to access care, it tends to be of lesser quality, and they are more likely to leave treatment prematurely. Digital mental health interventions (DMHIs) hold promise to reduce some of these inequities by addressing the shortage of mental health professionals, mitigating logistic barriers to service utilization, decreasing costs associated with implementation, and engaging individuals in care who may not seek services otherwise. However, DMHIs are rarely designed with the unique needs of REM in mind, which may perpetuate existing inequities or, even worse, create new ones now in the DMHI space.
To address the lack of equitable DMHIs for REM, the Mindfulness for Us (Mind-Us) program—a self-guided, app-based mindfulness meditation intervention for REM who experience elevated levels of discrimination—was specifically designed to meet some of these needs. Despite using a commercially available app, the Mind-Us program used human support to improve the app's usability, promote engagement with content not explicitly designed for REM, identify the fit of the intervention with users’ needs, and increase knowledge of mindfulness principles. Following this approach, the Mind-Us program had outstanding implementation outcomes, with participants using the app consistently during the 4-week program and less than 8% leaving the trial prematurely. Furthermore, Mind-Us led to clinically significant reductions in stress, anxiety, and depression. Thus, lessons learned from the implementation of this program could help researchers and clinicians design and implement successful DMHIs for REM populations.
Bio: Dr. Giovanni Ramos received B.A.s in Psychology from Universidad Nacional Autónoma de México and Florida International University. He completed his doctoral training in clinical psychology at the University of California, Los Angeles, including a clinical internship at the Albert Einstein College of Medicine/Montefiore Medical Center. His research program focuses on addressing mental health inequities affecting racially and ethnically minoritized groups in the US. To achieve this goal, To achieve this goal, I focus on two interconnected areas: 1) improving the cultural and contextual fit of evidence-based treatments, and 2) using digital tools to make these interventions available in marginalized communities. He is currently a UC Chancellor's and Ford Foundation Postdoctoral Fellow in the Department of Psychological Science at the University of California, Irvine.



![AI/ML Seminar Series: Joe Marino (2/1/2021)
UCI AI/ML Seminar Series
https://cml.ics.uci.edu/aiml/
Joe Marino
PhD Student
Computation and Neural Systems
California Institute of Technology
Connecting Variational Autoencoders Back to the Brain
Unsupervised machine learning has recently dramatically improved our ability to model and extract structure from data. One such approach is deep latent variable models, which includes variational autoencoders (VAEs) [Kingma & Welling, 2014; Rezende et al., 2014]. These models can be traced back to the Helmholtz machine [Dayan et al., 1995], which, in turn, was inspired by ideas from theoretical neuroscience [Mumford, 1992]. In the intervening years, neuroscientists have further developed these ideas into a popular theory: predictive coding [Rao & Ballard, 1999; Friston, 2005]. Yet, the machine learning community remains largely unaware of these connections. In this talk, I discuss the links between modern deep latent variable models and predictive coding, yielding several striking implications for the correspondences between machine learning and neuroscience. This motivates a more nuanced view in connecting these fields, including the search for backpropagation in the brain.
Bio:
Joe Marino is a PhD candidate in the Computation & Neural Systems program at Caltech, advised by Yisong Yue. His work focuses on improving probabilistic models and inference techniques, using neuroscience-inspired ideas, within the areas of generative modeling and reinforcement learning. AI/ML Seminar Series: Joe Marino (2/1/2021)](https://i.ytimg.com/vi/iVz6uwD7i6A/mqdefault.jpg)

![Interaction-Centric AI: Designing Useful and Usable AI Applications
Juho Kim
Associate Professor, KAIST; Chief Scientist, Ringle
Abstract:
AI-powered services and applications are introduced at a rapid pace and massive scale across various domains. Remarkable model performance, however, does not necessarily translate to an improved user experience. I argue that human-AI interaction should be considered a first-class object in designing AI-powered systems. In this talk, I will present a few novel interactive systems that use AI to support complex real-life tasks. I will discuss how we considered human-AI interaction in designing these systems, what tensions we encountered and how we addressed them, and how to design better AI-powered systems for real-world users. My ultimate proposal is that we need a fundamental shift to “interaction-centric AI”—an approach to systematically designing and engineering human-AI interaction that overcomes the limitations of the model- and data-centric views.
Bio:
Juho Kim [juhokim.com] is an Associate Professor in the School of Computing at KAIST, affiliate faculty in the Kim Jaechul Graduate School of AI at KAIST, and a director of KIXLAB (the KAIST Interaction Lab) [kixlab.org]. His research in human-computer interaction and human-AI interaction focuses on building interactive and intelligent systems that support interaction at scale, with the goal of improving the ways people learn, collaborate, discuss, make decisions, and take action online. He earned his Ph.D. from MIT in 2015, M.S. from Stanford University in 2010, and B.S. from Seoul National University in 2008. In 2015-2016, he was a Visiting Assistant Professor and a Brown Fellow at Stanford University. He is a recipient of KAIST’s Songam Distinguished Research Award, Grand Prize in Creative Teaching, and Excellence in Teaching Award, as well as 14 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP. He is currently spending his sabbatical year at Ringle Inc., a startup building an online language tutoring platform, to transfer his research on automatically analyzing and diagnosing learners’ English proficiency into a real product. Interaction-Centric AI: Designing Useful and Usable AI Applications](https://i.ytimg.com/vi/j0v1Cr74kN8/mqdefault.jpg)




