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.
Spot: Team Workforce Development - August 18th 2021
For contributions to the Lab's Workforce Development & Education programs in Spring and/or Summer of 2021, and for supporting research experiences for undergraduates, teachers, and faculty collaborators.