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, etc spanning topics such as social media analytics, digital consumer management, persuasion, patents, and promotion, our studies are focused on providing empirical evidence and empirical generalization to develop or extend consumer behavior and market theories. 

Doctoral Students

Emaad Manzoor

Emaad Manzoor is a PhD student in Information Systems at the Heinz College, CMU. He works on machine learning and causal inference methodology for data with complex structure, such as networks and text. Substantively, he examines causal questions pertaining to persuasion and its linguistic determinants.

Update: Dr Manzoor has graduated and is a professor at U of Wisconsin-Madison

Zhaoqi Cheng

Zhaoqi Cheng is a PhD student in Business Technology at Tepper School of Business, CMU. 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.

Samuel Levy

​Sam Levy is a Marketing Ph.D. candidate in the Tepper School of Business, CMU. He uses and combines Bayesian {statistics, machine learning and econometrics} to (1) build predictive models of browsing and buying behavior using unstructured data (2) flexibly model consumer behavior and make policy recommendations for pricing and promotional strategies. 

Chengfeng Mao

Chengfeng Mao is an incoming 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.

Master's Students

Eric Zhou

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!

Nour Jedidi

Nour Jedidi is a Master of Language Technologies student in the School of Computer Science at Carnegie Mellon University. He is interested in the theory and application of machine learning, statistics, and deep learning to text data. Currently he is working on leveraging representation learning for information retrieval and business applications.

Ishani Ghoshal​

Ishani has completed her undergraduate degree in Computer Science from Delhi Technological University. She's passionately interested in the application of machine learning in business analytics and is a budding software engineer. She loves vibing to music, history, and rewatching her favourite movies.

Yuan Zou

​Yuan is a graduate student in School of Heinz - Public Policy and Data Analytics. Her research interest in NLP grew during final year where she constructed a simple neural network for stock price prediction. She holds a bachelor's degree from Nanyang Technological University in Infocomm and have worked in financial industry for 3 years. She loves playing mahjong, reading about politics, and backpacking to many corners of the world.

Qichang Zhen

Qichang is an undergraduate student in International Business School Suzhou, XJTLU. He applies Python programming to conduct simple data analysis with econometric model and solve practical problems, such as stock performance prediction, ticket-snatching.