Business Insights through Text
The BIT LAB
We explore, examine, and extract consumer behavior or market insights through abundantly available, yet severely untapped text data. Via a variety of methodologies including causal inference, generative models, deep learning, neural NLP, bayesian statistics, interpretable machine learning, spanning topics such as social media analytics, digital consumer management, persuasion, platform design, innovation, human-ai collaboration, our studies are focused on providing empirical evidence and empirical generalization to develop or extend consumer behavior and market theories. In particular, we study friction and solutions in applying cutting-edge technologies to business arising from human-technology interface.
Dr. Mohamed Zaki Balboula is a postdoctoral researcher in the Information Systems Department at Boston University, Questrom School of Business. He also held an Assistant Professor of Accounting position at Delta University for Science & Technology- Egypt. He employs predictive models to predict corporate going concern/bankruptcy to support the auditor's judgment via rough sets theory, neural networks, and particle swarm algorithm using structured financial data. He is currently exploring ways to apply machine learning, NLP, to address various research questions in corporate finance using textual data. His research interests include corporate disclosures, environmental, social, and governance (ESG), corporate social responsibility (CSR), corporate risk, and financial performance.
Zhaoqi Cheng is an Information Systems PhD student at Boston University, Questrom School of Business. He combines machine learning with econometrics models to explore large-scale data with text-heavy attributes, such as patent files, online forums and user reviews. He is currently working on generative models related to the representation and characterization of innovations.
Chen Jing is a PhD student in Quantitative Marketing at Questrom School of Business, Boston University. His research interests include applying machine learning, econometrics, and statistical models to study brand communication, corporate social responsibility, and brand activism.
Chengfeng Mao is a PhD student in Marketing at the MIT Sloan School of Management. He obtained his master's degree in Computer Science at Carnegie Mellon University. His research interests include applying machine learning to draw business and economic insights from unstructured data, such as text, image, and network graph.
Eric will be a first year quant marketing doctoral student at Wash U. He holds a Bachelor’s degree in Finance and Marketing from Washington University In St. Louis and MBA from Tepper. He worked for one year at Nielsen BASES as a research analyst specializing in product innovation.
In his free time, he enjoys exercising and dancing and plans on choreographing his own piece some day!
Sungjoon Park is an incoming PhD student in Computing & Data Sciences at Boston University Fall 2023. He is equipped with an interdisciplinary academic background composed of economics and data science. He pursues studies in extracting business insights via applying machine learning and natural language processing. This research interest stems from his professional experience, through which he worked as an economist at LG Economic Research Institute conducting research on capturing patterns and trends implied in economic phenomena.
Hazel Hyeseung Kang
Hazel Hyeseung Kang is a PhD student in Information Systems at Boston University, Questrom School of Business.
She holds a bachelor's degree in Economics at Yonsei University and master's degree in Economics at Yonsei University.
Her research interests include applying econometrics and machine learning to derive business insights from the voice of customers on online platforms.
Zhuoyan Ma is a PhD student in Marketing at Boston University, Questrom School of Business. She holds a bachelor’s degree in Economics and Mathematics at Boston University and a master’s degree in Data Science at Columbia University. Her research interests include leveraging machine learning and econometrics models to learn business insights from unstructured data and to study the creator economy.
Yi Liu is a PhD student in Computing & Data Sciences at Boston University in Fall 2023. She holds a master’s degree in Data Analytics from University of Southern California. Her research interests include extracting customer psychological or behavioral patterns via applying large language models & NLP models, and deriving explainable business insights through deploying machine learning algorithms