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 Research Scientist 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.
Energy/Environmental Policy Research Scientist/Engineer
Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States
Describing the users: Understanding adoption of and interest in shared, electrified, and automated transportation in the San Francisco Bay Area
Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization