Julian Chan, PhD
Julian Chan drives the firm’s adoption of AI, cloud, and data science technologies to deliver cutting-edge data and economic analysis in litigation.
Julian Chan is an expert in econometrics, statistics, sampling, artificial intelligence (AI), machine learning, and big data analytics. Julian has significant experience conducting quantitative and qualitative analyses in support of expert economic testimony on matters related to cryptocurrency fair lending and fairness AI, intellectual property, and consumer finance. He has submitted an expert report assessing the potential for disparate impact based on race for a discrimination matter in the rental housing market.
As the firm’s Lead Data Scientist, Julian cofounded the Data Science Committee. He provides strategic direction and implementation strategies regarding technology such as cloud computing, big data, and AI technology in the firm and across matters.
Prior to joining Bates White, Julian was a lecturer in the Questrom School of Business at Boston University and a visiting graduate fellow at the Federal Reserve Bank of Boston. He is an experienced researcher with interests in applying econometric, statistics, AI, machine learning, and big data methods to analyze economic phenomena, including questions related to the labor market, social networks and media, the real estate market, and cryptocurrencies. He applies AI and machine learning to study the real estate market and to detect changes in government policy and has received substantial research grants for his work in AI and machine learning. His work has been featured in the Wall Street Journal, Bloomberg, and the BBC, among others.
Education
PhD, Economics, Boston University
MA, Economics, University of Hong Kong
BSS, Economics, Chinese University of Hong Kong
Practice areas
Expert SpotlightJulian is the firm’s Lead Data Scientist. His experience and expertise include both developing practical strategies and effective economic and financial solutions and working with big data. In this Q&A, he talks about data science, big data, and why we approach data-heavy matters with a different mindset.
