About

I'm a Computer Science PhD student in the PiCASSo Lab advised by Prof. Shubham Jain. My research focuses on passive health sensing and intelligent inference from wearable and mobile data, with an emphasis on integrating large language models into time-series analysis pipelines. I develop systems that extract clinically meaningful signals from continuously collected sensor data to model human health states — spanning physical performance, behavioral patterns, and mental well-being.

  • Research areas: Health Sensing, Wearable Computing, Time-Series ML, LLM-Integrated Pipelines, Digital Phenotyping

    • Research Projects

    Wearable-based Detection of Muscle Failure Onset

    Designed and validated wearable novel metrics to analyze performance in strength training complementing traditional methods.

    Under submission at a multidisciplinary journal

    Predicting Behavioral Dysregulation in ASD Children through Passively Sensed Data

    Developing a passive sensing pipeline to predict behavioral dysregulation episodes in children with Autism Spectrum Disorder using multimodal wearable and mobile data.

    Multimodal Digital Phenotyping for PTSD Patients

    Building multimodal digital phenotyping pipelines leveraging passively sensed behavioral signals to model PTSD symptom trajectories and support clinical decision-making.

    Publications

    LiftSafe: Predicting Unsafe Repetitions in Strength Training using a Single Wearable Sensor

    Adiba Shaira (Stony Brook University), Hunter J. Bennett (Old Dominion University), Shubham Jain (Stony Brook University)

    IEEE/ACM CHASE 2026

    Gene Tree Parsimony in the Presence of Gene Duplication, Loss, and Incomplete Lineage Sorting

    Prottoy Saha, Md. Shamiul Islam, Tasnim Rahman, Adiba Shaira, Kazi Noshin, Rezwana Reaz, Md. Shamsuzzoha Bayzid

    RECOMB Comparative Genomics Workshop, 2024

    View publication (ACM DOI)

    Technical Projects

    Using a Single Device to Predict Sleep Efficiency and Next-Day's Mood

    This project builds a single-device, real-world sleep analytics and mood prediction system using only smartphone-based behavioral signals. We detect drops in sleep efficiency before they happen and study their link to next-day mood — enabling proactive health awareness and mental-wellbeing support. This is likely the first work to predict sudden drops in sleep efficiency (sleep anomalies) using only smartphone multimodal data.

    View Project | 2024

    Global Disaster Visualizer

    This project presents an interactive visual exploration of global natural disasters from 2010–2021, based on the International Disaster Database (EM-DAT). It highlights spatio-temporal patterns, affected regions, disaster frequency, casualties, and seasonal trends across countries and disaster types.

    View Project | 2023

    Corona in Bangladesh

    A comprehensive data visualization and tracking platform for COVID-19 cases in Bangladesh, providing real-time insights and analytics during the pandemic.

    Visit Website

    Healthway — Online Diagnostic and Consultation Center

    This project was developed during the COVID-19 pandemic to enable safe, contact-free medical diagnostics and consultation. The platform allows patients and healthcare providers to collaborate remotely, reducing the risk of virus transmission while ensuring timely medical care.

    View Project | 2021

    Experience

    Service & Leadership

    Awards