My research focuses on cyber-physical energy systems, by employing modeling, optimization, and control theories to design new algorithms and mechanisms for emerging technologies based on the internet-of-things (IoT), mobile sensing, and data mining, in order to facilitate the transition into an energy-efficient and system-wide resilient urban ecosystem.
To this end, I develop novel sensing paradigms for indoor environment monitoring and demand-based, occupancy-aware controls, as well as modeling and incentivization of humans to induce desirable behaviors (e.g., energy saving, demand response).
I also explore the design and operation of community-based micro-grid in support of the mega-grid, which exploits the flexibility of demands, as well as the efficiency from both thermal and electricity energy provision and shared distributed energy resources (DERs) and storages, to address the grand challenge of 20% renewable energy by 2020.
Oral presentation of paper Inverse reinforcement learning via deep Gaussian Process at UAI 2017 (slides and a blog from Daniel Seita), Sydney, Australia.June 2017
IEEE Spectrum featured an article What Does Your Smart Meter Know About You? on my recent work Virtual occupancy sensing: Using smart meters to indicate your presenceJune 2017
CO2Meter.com featured an article CO2 Sensor Occupancy Detection on my work Sensing by proxy: Occupancy detection based on indoor CO2 concentration (best paper award)June 2017
Our proposal for "Social Game for Smart Building Energy Efficiency" accepted (Total: $14,000, duration 1 year, with Ioannis Konstantakopoulos)!May 2017
Three papers "Microgrid to enable optimal distributed energy retail and end-user demand response", "A Robust Utility Learning Framework via Inverse Optimization", and "Measuring fine-grained Metro interchange time via smartphones" accepted in Applied Energy, TCST, and Transportation Research Part C.