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FilippoCMC/README.md

Filippo Corponi, MD, PhD

Consultant psychiatrist and applied machine-learning researcher. I build and test AI that turns wearable and real-world health data into earlier, more precise mental-health care, with a focus on bipolar disorder and depression.

NIHR BRC-funded Clinical Research Fellow at Imperial College London · Honorary Consultant Psychiatrist, West London NHS Trust

filippocmc.github.io · LinkedIn · Google Scholar · ORCID · f.corponi@imperial.ac.uk

Current projects

  • RISE UP: open-source wearable AI platform for early risk detection of depression, anxiety and psychosis. Principal Investigator and team lead; funded by Wellcome through the Mental Health Data Prize.
  • HOUDINI-BD: remote monitoring of bipolar disorder with a wearable, a smartphone app and AI voice-assistant calls. Principal Investigator; funded by the NIHR Imperial Biomedical Research Centre.
  • Mood disorders in All of Us: rest-activity patterns and cognition between mood episodes, from linked health records and Fitbit data. Two first-author medRxiv preprints (2026): rest-activity, cognition.
  • TIMEBASE: open clinical wearable dataset and benchmark; PhysioNet release in preparation.

Code for papers

Repository Paper
wear-your-scales Automated mood disorder symptoms monitoring from multivariate time-series sensory data. Translational Psychiatry, 2024
e4selflearning Wearable data help detect acute mood disorder episodes via self-supervised learning. JMIR mHealth and uHealth, 2024
bayesian-hrv A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder. npj Mental Health Research, 2024
JMIR2023 Exploring digital biomarkers of illness activity in mood episodes. JMIR mHealth and uHealth, 2023
TS4H2022 Inferring mood disorder symptoms from multivariate time-series sensory data. NeurIPS 2022 Workshop on Learning from Time Series for Health
aou-wearable-cognition-mood Domain-specific cognitive impairment in inter-episode mood disorders and its relationship with rest-activity phenotypes. medRxiv, 2026

Tools: Python · PyTorch · PyMC · NumPy · SciPy · pandas · scikit-learn · SQL/BigQuery · Google Cloud · Weights & Biases

Popular repositories Loading

  1. FilippoCMC.github.io FilippoCMC.github.io Public

    Personal website of Filippo Corponi, MD, PhD: consultant psychiatrist and applied machine-learning researcher working on AI, wearables and mental health

    HTML

  2. wear-your-scales wear-your-scales Public

    Forked from april-tools/wear-your-scales

    Code for paper "Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number".

    Jupyter Notebook

  3. e4selflearning e4selflearning Public

    Forked from april-tools/e4selflearning

    Code for paper "Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Pros…

    Python

  4. JMIR2023 JMIR2023 Public

    Forked from INTREPIBD/JMIR2023

    Code for JMIR mHealth and uHealth paper "Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study."

    Python

  5. TS4H2022 TS4H2022 Public

    Forked from INTREPIBD/TS4H2022

    Code for NeurIPS2022 TS4H workshop paper "Inferring mood disorder symptoms from multivariate time-series sensory data"

    Python

  6. bayesian-hrv bayesian-hrv Public

    Forked from april-tools/bayesian-hrv

    Code for paper "A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder".

    Python