Research Interests

My research has two major areas: (i) developing applied math/statistical methodology and (ii) taking a data-driven approach to solving business and policy issues usually in the areas of Information Systems, Operations, and Finance.

Many of my research projects use text documents to predict economic variables, especially in the context of online reviews. The goal is show that online reviews (like those from Yelp or TripAdvisor) can be useful for understanding economic and business events, such as firm survival, hygiene and food poisoning, service quality, and so on. Another major area of research is the modeling of networks to identify communities, influential agents, and to characterize evolutions of the network structure over time. This has major implication in the study of systemic risk (e.g., see this working paper and below for an NSF supported project).

I am currently advising one PhD student (Yuan Cheng) and have served on several PhD thesis committees in the fields of Applied Mathematics, Statistics, Computer Science, and Management.


Below are recent publications. You can download my full CV here (Updated: July 2017).

NSF BIGDATA: Collaborative Research: IA: F: Too Interconnected to Fail? Network Analytics on Complex Economic Data Streams for Monitoring Financial Stability

The recent financial crisis has accentuated the need for effective monitoring, oversight and regulation of financial markets and institutions. Complex market structures involving intricate interconnected relationships among financial institutions can help propagate and amplify shocks and hence also foster systemic risk. This project develops an integrative framework, based on accounting principles, that leverages a wide array of diverse quantitative financial datastreams, complemented by metadata and market announcements for the purpose of identifying and predicting market participants that could endanger the overall financial system.

The proposed research builds upon modern statistics and computer science works, as well as recent financial and economic ideas aimed at assessing threats to financial stability and uncovering the complexity of financial systems in different market conditions. It will result in both new methods for complex Big Data and empirical results that can advance the state-of-the-art in financial research, as well as tools that support and enhance financial policymaking and decision-making. Key tasks of the project include: (1) Develop a rigorous accounting framework to integrate multiple financial and econometric data streams from many platforms and technologies. (2) Develop and customize a range of new network models and analysis tools for use with multiple financial data streams. An important idea will be to extend network and econometric tools in order to compare the structural evolution of different types of networks in response to external events and policy changes.