Affiliated Projects

Early Childhood Mobile Intervention Project

The Early Childhood Mobile Interventions Project follows families with young children in everyday life using smartphones and wearable devices. We are developing mobile applications for ongoing monitoring of multimodal data streams and using machine learning to automatically and passively detect and intervene upon family processes in real time and real life. Our goal is to build and systematically evaluate a just-in-time adaptive intervention (JITAI) to increase access to health care and improve family well-being and mental health wellness.

Bias in Artificial Intelligence Workgroup

The application of technology and machine learning to psychological treatments has the potential to improve access to and quality of mental health care. At the same time, unrepresentative samples and biased development methodologies could perpetuate or amplify them. Our interdisciplinary team of engineers, data scientists, and psychologists is developing and implementing “fair-aware AI” methodologies for assessing and mitigating the impact of bias in machine learning algorithms relating to mental health wellness.

Co-creation Workgroup

Our Co-Creation workgroup focuses on building mental health wellness apps via a "co-creation" model of software development. Co-creation involves including the user of the therapy as an active participant in the model development process. We also consider how our own biases as researchers impact the therapies we develop. We aim to use co-creation to collaboratively build software technologies that improve the reach and impact of mental health wellness care for all people.

AI Modeling Workgroup

Our AI Modeling Workgroup is focused on developing machine learning models for passively and automatically detecting psychological and behavioral states relevant to child and family mental health wellness in daily life using smartphones and wearable sensor technology. In this workgroup, we write code to process and synthesize large amounts of data harnessed from these devices, build machine learning algorithms, and evaluate their accuracy and clinical utility. We plan to incorporate these algorithms into an adaptive and personalized mobile mental health wellness intervention system and evaluate its efficacy via a micro-randomized clinical trial.

Couple Mobile Sensing Project

The Couple Mobile Sensing Project examines daily functioning of young adult romantic couples via smartphones and wearable devices. We seek to how understand how small-scale everyday relationship processes (e.g., feelings of annoyance or closeness between dating partners) relate to multimodal assessments of functioning in day-to-day life (e.g., vocal pitch and tone, word usage, electrodermal activity, heart rate, physical activity). Moreover, we seek to understand how everyday, microlevel processes in young adult couples relate to macrolevel variables, such as family-of-origin and dating aggression. We are also developing machine learning algorithms designed to detect and even predict stages of conflict escalation in couples in real life.

Colliga

The TIES Lab uses an app known as Colliga, which was created by Dr. Adela Timmons and her team, to gather data from our participants. Colliga is a platform for mental health wellness monitoring and intervention recommended by top psychologists, therapists, and mental health wellness experts. Colliga enables users to easily and inexpensively build, access, and share digital therapies through smartphones and wearable devices. A few of its main goals are to:

• Collect data in daily life to capture accurate, valid, context-specific information;
• Model dynamic processes as they naturally unfold
• Build, test, and disseminate therapies that are accessible and inexpensive
• Provide on-demand support for people in their everyday lives
• Uncover unique insights and push the field forward via machine learning and smart technologies

Research Approaches

Research at the TIES Lab integrates psychological science, technological innovation, and advanced quantitative methods to understand human behavior and develop interventions that improve mental health and relationships.

Interdisciplinary Team Science:

Our research brings together expertise in clinical and developmental psychology, data science, engineering, and software development. Through interdisciplinary partnerships, we address complex scientific questions that require perspectives and methods spanning multiple fields.

Participatory Design:

We incorporate the perspectives and experiences of intended users into the development and refinement of digital interventions. This approach helps ensure that the technologies we create are responsive to the needs, preferences, and everyday contexts of the people they are designed to serve.

Computational Modeling and Artifical Intelligence

We use intensive longitudinal data, mobile sensing, and advanced statistical and machine learning methods to investigate how psychological and interpersonal processes unfold in everyday life. Our work explores how these methods can inform more personalized, timely, and adaptive interventions.

Responsible Research and Technology Development:

We recognize the importance of evaluating the validity, generalizability, and potential limitations of emerging technologies. Our approach emphasizes rigorous scientific evaluation, thoughtful design, and attention to the populations and contexts in which technologies are deployed.

Team Science and Cross-Disciplinary Collaboration:

Incorporating technology into conducting psychological research on families in daily life is a complex methodological undertaking. As a result, the TIES lab is a cross-disciplinary team composed of psychologists, data scientists, software developers, engineers, and business personnel. We rely on the expertise of individuals with a diverse range of perspectives and skill sets to conceptualize and carry out our research. Some of our interdisciplinary partnerships include:

i. Colliga Apps Corportation ii. Sidebench iii. The Center for Children and Families iv. The USC Families Studies Project v. The Human Bio-Behavioral Signals Lab (HUBBS) vi. The Signal Analysis and Interpretation Laboratory (SAIL) vii. Mental Health Interventions and Technology Lab (MINT)

Selected Publications and Patents

Selected Publications

Walters, S. N., Duong, J. B., Carta, K. E., Jumonville, G. A., Carrasco, A. S., Romero, D. N., Ahle, M. W., Chaspari, T., Narayanan, S. S., Comer, J. S., & Timmons, A. C. (2026). Contextual influences on parenting and adversity: An examination of multisystemic effects. Journal of Child and Family Studies.10.1007/s10826-026-03345-7

de Barbaro, K., Madden-Rusnak, A., & Timmons, A. C. (2026). Thinking critically about algorithms for automated detection of behavior: 11 guidelines for developmental researchers. Developmental Science

Carta, K. E., Jumonville, G. A., Duong, J. B., Walters, S. N., Carrasco, A. S., Romero, D. N., Khurd, I. P., Chaspari, T., Han, S. C., Kim, Y., Narayanan, S., Margolin, G., & †Timmons, A. C. (2026). Effects of emotional spillover and exposure to interparental psychological aggression on momentary physiological linkage during naturalistic couple conflicts. Biological Psychology

Duong, J. B., Simo Fiallo, N., Walters, S. N., Carta, K. E., Jumonville, G. A., Carrasco, A. S., Romero, D. N., Khurd, I. P., Ahle, M. W., Comer, J. S., Frazier, S. L., Chaspari, T., Narayanan, S., & †Timmons, A. C. (2025). Caregiver perceptions of healthcare barriers across traditional and digital contexts: A mixed-methods analysis. npj Digital Medicine

†Timmons, A. C., Tutul, A. A., Avramidis, K., Duong, J. B., Carta, K. E., Walters, S. N., Jumonville, G. A., Carrasco, A. S., Freitag, G. F., Romero, D. N., Ahle, M. W., Comer, J. S., Narayanan, S., Khurd, I. P., & Chaspari, T. (2025). [Developing personalized algorithms for sensing mental health symptoms in daily life. (https://pmc.ncbi.nlm.nih.gov/articles/PMC12329041/) npj Mental Health Research

†Timmons, A. C., Duong, J. B., Walters, S. N., Carta, K. E., Jumonville, G. A., Carrasco, A. S., Romero, D. N., Ahle, M. W., Comer, J. S., Khurd, I. P., & Chaspari, T. (2025). Bridging fair-aware artificial intelligence and co-creation for equitable mental health. Nature Reviews Psychology

Jusko, M. L., Smith, J. N., Campez, M., Timmons, A. C., Morrow, A. S., Lozano, C., Fosco, W. D., Little, K., Villodas, M., & Raiker, J. S. (2025). [An application of time series analysis to single case designs in the context of an intensive behavioral intervention for ADHD.] (https://pubmed.ncbi.nlm.nih.gov/40380799/) Journal of Attention Disorders

Patents

Timmons, A. C., Chaspari, T., Narayanan, S., & Margolin, G. (filed March 4, 2019). US Serial No. 16/291,225: A technology-facilitated support system for monitoring and understanding interpersonal relationships.

Timmons, A. C., Chaspari, T., Ahle, M., Narayanan, S., & Margolin, G. (filed March 4, 2019). US Serial No. 16/291,399: An expert-driven, technology-facilitated intervention system for improving interpersonal relationships.

Funding and Awards

Grants and Fellowships

NIMH Commercialization Readiness Pilot Program (2025-2028) Sponsor: National Institute of Mental Health PIs: Timmons, A. C., Ahle, M. W., & Comer, J. S. (Multi-PI) Title: A Centralized Clinical Research Tool for Accelerating the Development, Evaluation, and Dissemination of Expert-Developed, Evidence-Based, and AI-Enhanced Digital Mental Health Therapies

NIMH Small Business Innovation Research Grant (2020-2025) Sponsor: National Institute of Mental Health PIs: Timmons, A. C., Ahle, M. W., & Comer, J. S. (Multi-PI) Title: The Development and Systematic Evaluation of a Just-in-Time Adaptive Intervention for Improving Child Mental Health

NSF Innovation Corps Program Grant (2018-2020) Sponsor: National Science Foundation PI: Timmons, A. C. Title: A Technology-Facilitated Intervention System for Improving Child Mental Health

NSF Graduate Research Fellowship Program Fellowship (2012-2015) Sponsor: National Science Foundation PI: Timmons, A. C. Title: The Psychobiological Covariation of Electrodermal Activity between Dating Pairs in Early Adulthood and its Links with Parent-Child Relationships

Awards

NIMH Supplement Award (2022-2025) Sponsor: National Institute of Mental Health PIs: Timmons, A. C., Ahle, M. W., & Comer, J. S. (Multi-PI) Title: Supplement to the Development and Systematic Evaluation of a Just-in-Time Adaptive Intervention for Improving Child Mental Health

NIMH Supplement Award (Administrative; 2023-2025) Sponsor: National Institute of Mental Health PIs: Timmons, A. C., Ahle, M. W. & Comer, J. S. (Multi-PI) Title: Supplement to the Development and Systematic Evaluation of a Just-in-Time Adaptive Intervention for Improving Child Mental Health

NIMH F31 Individual Predoctoral Fellowship National Research Service Award (2026-2028) Sponsor: National Institute of Mental Health PI: Walters, S. N. Role: Sponsor and Primary Mentor Title: The Shape of Family Conflict: Differentiating Adaptive and Maladaptive Parent-Child Conflict in Daily Life

NICHD F31 Individual Predoctoral Fellowship National Research Service Award (2021-2023) Sponsor: National Institute of Child Health and Human Development PI: Cristello, J. Role: Consultant Title: Identifying Drug and Alcohol Displays on Social Media Using a Machine Learning Approach and Mechanisms that Impact Adolescent Substance Use

NICHD F31 Individual Predoctoral Fellowship National Research Service Award (2019-2021) Sponsor: National Institute of Child Health and Human Development PIs: Acosta, J. Role: Secondary Mentor Title: Examining the Influence of Parenting Practices on Youth’s Sleep during a Sensitive Developmental Period