Problem
Reliability across heterogeneous, long-lived sensing systems
How can sensing and learning systems stay reliable when devices, homes, users, and environments keep changing — with missing labels and long-term drift?
Core research questions
RQ list TBD — structure only for now.
What this is not
Not pure building-energy product work; not pure statistical-learning theory as the identity.
Failure modes in the wild
Dropouts, cross-home shift, calibration drift, sparse supervision — placeholders.
Why doctoral work
Systems + learning novelty under real longitudinal constraints — draft later.