Ling Jin has multidisciplinary training in air quality engineering, statistics, and resources economics. She develops and applies diagnostic and sensitivity analysis tools in photochemical transport modeling systems to identify effective pollution control strategies. She also strives to bring state-of-the-art data science (statistical, machine learning, and econometric techniques) to the domains of climate/atmospheric science, electricity market, and transportation. She has led projects on air quality modeling, spatial pattern and time series mining, social sequence analysis, with work published by AGU, ACS, Atmospheric Environment, AAAI, IEEE, and ACM. She is currently a Principal Scientific Engineering Associate and holds a PhD in Energy and Resources, a MA in Statistics, both from UC Berkeley, and a BS in Physical Geography from Peking University.
Principal Scientific Engineering Associate
Go for the Silver? Evidence from field studies quantifying the difference in evaluation results between “gold standard” randomized controlled trial methods versus quasi-experimental methods
Time Will Tell: Using Smart Meter Time Series Data to Derive Household Features and Explain Heter ogeneity in Pricing Programs
Evaluating clouds, aerosols, and their interactions in three global climate models using satellite simulators and observations
Role of meteorological processes in ozone responses to emission controls in California's San Joaquin Valley
Meteorology-induced Variations in the Spatial Behavior of Summer Ozone Pollution in Central California