Arvind Pillai

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I am a 4th year PhD student at Dartmouth College, advised by Prof. Andrew Campbell. Previously, I was a research intern at Nokia Bell Labs (Cambridge, UK) (Summer 2024), and a AI & Data Graduate Scientist at AstraZeneca (2019-2021).

My interdisciplinary research enables the examination of mental health and behavior of individuals through mobile sensing and robust machine learning. In particular, I am interested in these two areas:

  1. AI for Mental Health: I develop models from different modalities to evaluate the presence of mental illness symptoms: (a) rare life events for sensor data [CHIL’23], (b) suicidal ideation detection from speech [UbiComp’24], and (c) depression detection from front-facing smartphone images [CHI’24].

  2. Robust ML: I like analyzing how healthcare ML models perform when tested against out-of-distribution data. In general, these models do not perform well, and proposing algorithms to address this issue is very interesting. To this end, I have investigated the generalization of speech-based suicidal ideation detection [UbiComp’24].

news

Oct 29, 2024 :parrot: PaPaGei, a foundation model for PPG data is now available! Paper, Models, and Code
Oct 08, 2024 :microphone: Presented “Investigating Generalizability of Speech-Based Suicidal Ideation Detection Using Mobile Phones” @ Ubicomp 2024
Jun 01, 2024 :bell: I will be interning at Nokia Bell Labs in Cambridge, UK for Summer 2024
May 14, 2024 :microphone: Presented our work MoodCapture at CHI 2024 in Honolulu

Selected Publications

  1. CHI
    MoodCapture: Depression Detection Using In-the-Wild Smartphone Images
    Subigya Nepal*, Arvind Pillai*, Weichen Wang, and 8 more authors
    In Proceedings of the CHI Conference on Human Factors in Computing Systems , 2024
  2. UbiComp
    Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones
    Arvind Pillai, Subigya Kumar Nepal, Weichen Wang, and 8 more authors
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2024
  3. CHIL
    Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
    Arvind Pillai, Subigya Nepal, and Andrew Campbell
    In Conference on Health, Inference, and Learning , 2023