59 citations · 60 across the 9 of their papers we have counts for
13 papers
Scaling Therapy Supervision: Assessing Psychotherapy Fidelity Using Large Language Models
Arvind Pillai, Alexis Roach, Jason Holden +11
Psychotherapy fidelity assessment measures how faithfully a therapist adheres to a treatment protocol, a critical determinant of positive patient outcomes. However, fidelity assess…
MontageFlow: Physically Valid High Fidelity EEG Generation
Sudarshan Regmi, Nabaraj Subedi, Arvind Pillai +6
Generative modeling offers a promising solution to the scarcity of labeled clinical electroencephalography (EEG) data under strict privacy constraints. Although recent generative m…
Lapras: Latent Reasoning for Time Series Language Models
Yuliang Chen, Yu Yvonne Wu, Patrick Langer +11
Time Series Language Models (TSLMs) offer a promising path toward time series understanding by reasoning over temporal signals and producing natural language answers and explanatio…
TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models
Sudarshan Regmi, Arvind Pillai, Yu Yvonne Wu +7
Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for question-answering tasks. Howev…
BALMS: Benchmarking Agentic LLMs for Longitudinal Mental Health Sensing
Yu Yvonne Wu, Arvind Pillai, Yuliang Chen +7
Mental health assessment relies on episodic self-report scales, which convert subjective states such as stress into numerical scores but provide only sparse snapshots of wellbeing.…
ReliaGate: Reliability Routing for Low-Stakes Wearable Stress Prediction
Jaden Moon, Yu Wu, Arvind Pillai +1
We study when a wearable stress system should surface a prediction rather than change it. In low-stakes reflection and summary settings, aggregate accuracy is insufficient because…
Learning Transferable Sensor Models via Language-Informed Pretraining
Yuliang Chen, Arvind Pillai, Yu Yvonne Wu +6
Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured from diverse healthcare sensors. However…
MotionTeller: Multi-modal Integration of Wearable Time-Series with LLMs for Health and Behavioral Understanding
Aiwei Zhang, Arvind Pillai, Andrew Campbell +1
As wearable sensing becomes increasingly pervasive, a key challenge remains: how can we generate natural language summaries from raw physiological signals such as actigraphy - minu…
LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models
Wenxuan Xu, Arvind Pillai, Subigya Nepal +6
Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements into natural language remains challen…
Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting
Arvind Pillai, Dimitris Spathis, Subigya Nepal +6
Large language models (LLMs) show promise for health applications when combined with behavioral sensing data. Traditional approaches convert sensor data into text prompts, but this…
MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
Subigya Nepal, Arvind Pillai, William Campbell +14
Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pionee…
Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App
Subigya Nepal, Arvind Pillai, William Campbell +11
MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a…
Social Isolation and Serious Mental Illness: The Role of Context-Aware Mobile Interventions
Subigya Nepal, Arvind Pillai, Emma M. Parrish +4
Social isolation is a common problem faced by individuals with serious mental illness (SMI), and current intervention approaches have limited effectiveness. This paper presents a b…