I’m a Ph.D. candidate at Stanford Department of Communication. My passion is to study people’s everyday behavior and its relationship to mental well-being through media usage and mobile sensing data.
Prior to my Ph.D. journey, I worked as a forensics data scientist developing fraud and bribery detection solutions. My educational background is in behavioral economics and machine learning.
“B.S. in Statistics and Machine Learning at Carnegie Mellon University
Study Area:
Statistics, Machine Learning, Behavioral Economics, Judgement and Decision Making”
“Intern at the Brookings Institution
Research Area:
Technology innovation policy, mobile economy, and health information exchange”
“Intern at PwC Advisory
Work Area:
Wrote Python and SQL-based automated ETL process for legal audit documents and financial information”
“Intern at Disney Research
Resarch Area:
Using machine learning and psychology to understand people’s behavior and experience at the park”
“Data Scientist PwC Advisory
Machine Learning:
Designed and deployed a series of ML solutions for fraud and bribery detection in the banking and electronic sector
Financial Remediation:
Performed financial remediation analyses for a major banking client
Ontology-based Fraud Investigation:
Designed fraud alerting and investigation procedures using ontology-based approaches”
“M.S. in Data Science at Stanford University
Research Area:
Stress detection from computer mouse movement
Deriving behavior features from GPS signals”
“Ph.D. student at Stanford School of Communication
Research Area:
Using machine learning to understand and predict mental wellbeing from media usage and mobile sensing data”