Advancing probabilistic seismic hazard analysis through short-term time-dependent forecasts and deterministic simulations 

Panel data forecasting and application to epidemic disease 

USC Computer Science Technical Reports, no. 942 (2014) 

Smart buildings: employing modern technology to create an integrated, data-driven, intelligent, self-optimizing, human-centered, building automation system 

How sequels seduce: consumers' affective expectations for entertainment experiences 

"Watts per person" paradigm to design net zero energy buildings: examining technology interventions and integrating occupant feedback to reduce plug loads in a commercial building 

Predicting mission power requirement for mobile robots 

Environmental effects from a large-scale adoption of electric vehicle technology in the City of Los Angeles 

Expectation dynamics and stock returns 

Probabilistic data-driven predictive models for energy applications 

Electric vehicle integration into the distribution grid: impact, control and forecast 

Essays on the econometrics of program evaluation 

High-frequency Kelly criterion and fat tails: gambling with an edge 

Rupture synchronicity in complex fault systems 

Optimal distributed algorithms for scheduling and load balancing in wireless networks 

Building occupancy modeling and occupancy-loads relationships for building heating/cooling energy efficiency 

Seismicity distribution near strike-slip faults in California 

Prediction and forecasting in international relations: Some theoretical issues 

User-centric smart sensing for non-intrusive electricity consumption disaggregation in buildings 

Data-driven methods for increasing real-time observability in smart distribution grids 

Prediction And Forecasting In International Relations: An Exploratory Analysis 

Planning for capital goods firms: Frames of reference and forecasts 

Integrated model for highway -based travel time forecasting with application to truck transportation 

Using demand-side management for decarbonization: developing methods to quantify the impact of altering electricity consumption patterns 

Deep learning for subsurface characterization and forecasting