Adiba Shaira
Portrait of Adiba Shaira

Adiba Shaira

Ph.D. Candidate in Computer Science, Stony Brook University

I am a fourth-year Ph.D. candidate in Computer Science at Stony Brook University, advised by Prof. Shubham Jain in the PiCASSo Lab. I received my M.S. in Computer Science from Stony Brook University in December 2025.

My research spans health sensing, wearable computing, and digital phenotyping. My work combines physiological signal processing, multimodal time-series modeling, and large language models (LLMs).

Research interests: health sensing, wearable computing, time-series modeling, digital phenotyping, LLM agents for health data, and explainable machine learning.

News

  • Presented LiftSafe at IEEE/ACM CHASE 2026 in Pittsburgh, supported by an NSF Student Travel Award.

  • Received the Distinguished Travel Award from the Graduate Student Organization, Stony Brook University.

  • Received my M.S. in Computer Science from Stony Brook University.

  • Our paper on gene tree parsimony was published at RECOMB Comparative Genomics 2024.

  • Started my Ph.D. in Computer Science at Stony Brook University.

Research

Ongoing

2025–present

CareQA: Multi-Agent Sensemaking for Caregivers in Autism Care

CareQA is a multi-agent LLM system that answers caregivers' natural-language questions about their autistic child's sleep, activity, and physiological signals using passively sensed data from the Simons Sleep Project, including wristband physiology, EEG-based sleep staging, an under-mattress sleep sensor, and caregiver diaries. The system plans each analysis, computes the evidence from the child's own record, and explains the findings without giving medical advice.

Current work: an interactive visualization dashboard that pairs a child's sensor data with CareQA's explanations, and a caregiver study that grounds the system's questions and answers in caregivers' needs.

Published

IEEE/ACM CHASE 2026

LiftSafe: Predicting Unsafe Repetitions in Strength Training

LiftSafe predicts whether the next repetition in a strength-training set will be safe or unsafe using a single inertial measurement unit (IMU) worn on the upper arm, so that a lifter can be warned before the repetition is performed. It combines time-series forecasting with contrastive learning and a progression-aware fusion module that models fatigue across the set. In a 24-participant study of bench, overhead, and lateral presses with EMG ground truth, LiftSafe achieved 87% balanced accuracy under leave-one-subject-out validation. Paper

Ongoing

2025–present

Passive Smartphone Sleep Sensing for PTSD Monitoring

Sleep disturbance is a core symptom of PTSD. In a World Trade Center 9/11 first-responder cohort, I extract sleep metrics passively from smartphone accelerometer and screen-state data and relate them to clinical PCL scores through longitudinal statistical modeling.

Publications

  1. CHASE 2026

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

    Adiba Shaira, Hunter J. Bennett, Shubham Jain

    IEEE/ACM Conference on Connected Health: Applications, Systems, and Engineering Technologies (CHASE), 2026.

  2. RECOMB-CG 2024

    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 International Workshop on Comparative Genomics, Springer Nature, pp. 110–128, 2024.

Experience

  • May 2024 – present

    Research Assistant, PiCASSo Lab, Stony Brook University

    Wearable and mobile health sensing

  • Aug 2023 – May 2024

    Teaching Assistant, Stony Brook University

    CSE 373: Analysis of Algorithms

  • May 2022 – Jul 2023

    Lecturer, United International University, Dhaka

    Human-Computer Interaction, Analysis of Algorithms, Computer Networks

Education

Other projects

  • Predicting Sleep Efficiency and Next-Day Mood from a Single Device 2024

    Modeled next-day sleep efficiency and mood from single-device time-series data in the GLOBEM dataset. Python, scikit-learn, PyTorch, TensorFlow

  • Global Disaster Visualizer 2023

    Interactive visualization of natural disasters worldwide from 2010 to 2021 (EM-DAT). D3.js, JavaScript, React, Python

  • Healthway 2021

    Online diagnosis and consultation platform for remote care during COVID-19.

  • Corona in Bangladesh 2020

    COVID-19 information hub for Bangladesh, recognized by the Bangladesh government as a reliable news source. Node.js, MongoDB

Honors and service

  • 2026

    NSF Student Travel Award, IEEE/ACM CHASE 2026

  • 2026

    Distinguished Travel Award, Graduate Student Organization, Stony Brook University

  • 2022

    Second Runner-Up of 36 teams, Ada Lovelace Datathon (BDOC 2022)

  • 2021

    First Runner-Up of 20 teams, Ada Lovelace Datathon (BDOC 2021)

  • 2021

    Dean's List Award

  • Since 2024

    Secretary, then Vice President, Bangladesh Graduate Students Association, Stony Brook University

  • 2021–2022

    Chairperson, IEEE Computer Society Student Branch, BUET