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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. 
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Postdoctoral Researcher

Mohamed Zaki Balboula

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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.

Doctoral Students

Zhaoqi Cheng

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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

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​​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

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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.

Eric Zhou

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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!

Hazel Hyeseung Kang

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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.

Zhengrong (Jenn) Gu

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Zhengrong (Jenn) Gu is a PhD student in Marketing at Boston University Questrom School of Business. Prior to joining BU, she received her M.S. in Data Science from Georgetown University. Her research interests include deploying machine learning and econometrics to improve firms' business strategies and help consumers in decision-making.



Master's and Undergraduate Students

Shahaf Dan

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Shahaf Dan is a Computer Science and Artificial Intelligence at Boston University. With experience in Machine Learning and Data Science, Shahaf utilized Python and its various ML libraries to construct ML models and Neural Networks for research purposes. Shahaf's interests lie within Deep Learning, and generative AI. He hopes to generate ML and AI models for the sake of operationalizing creativity generated by various AI tools in the forms of both images and text.

​​Yusen Wu

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​Yusen Wu is a senior undergraduate pursuing a dual degree in Statistics and Business Administration at Boston University. His research interest lies at the intersection of business and machine learning. He is currently working on large-scale pretrained language models to draw insights from unstructured text. ​​

Shivam Juneja

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Shivam Juneja is currently pursuing a Master of Science in Business Analytics from University of Massachusetts at Boston majoring in Big Data. He is interested in Generative AI and Machine Learning with an aim to solve economic problems for businesses.

Jingran Xu

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Jingran Xu is an M.S. Applied Business Analytics candidate at Boston University. She got her BS degree in E-commerce. Her research interests include applying data-driven and technology-enabled methods to solve marketing and operation problems.

​Zhijie Yan ​

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​​Zhijie Yan is a senior undergrad in Computer Science & Electronic Engineering at the University of Liverpool. He is interested in machine learning and data analysis, especially in applying artificial intelligence techniques to solve real-world problems.
Currently he is researching generative AI for business

Manyuan Lu

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Manyuan Lu is a recent graduate from Boston University with a dual degree in Computer Science (BA/MS). Her research interests include artificial intelligence and machine learning. She is excited to be joining the BIT Lab as a Research Assistant. She is eager to apply her knowledge and skills to make a meaningful impact in various industries such as healthcare, society, and transportation. ​

​Yan Fang

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Yan Fang is an undergraduate student in Applied Mathematics in Xi’an Jiaotong-Liverpool University. He utilizes Python to conduct machine learning and data analysis. He is interested in deep learning insight for business, especially for entertainment industry.

Animikh Aich

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Animikh is pursuing his M.S. in Artificial Intelligence at Boston University. He previously led a team of vision engineers to build real-time video analytics solutions in the industry. His research interests include autonomous systems, self-supervised learning, and generative AI. 


Affiliated Industry Leaders & Scholars

Christina Van Houten 
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Technology Executive & Board of Many Tech Companies
Founder of Women@Work &Unbiased|AI

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Christina is a veteran of the enterprise technology industry, having spent two decades with some of the world’s largest firms, including Oracle, IBM and Infor Global Solutions as well as Netezza and ProfitLogic, the entrepreneurial companies that were acquired by them.
Most recently, Christina was the Chief Strategy Officer for Mimecast (NASDAQ: MIME), a global leader in cyber-security, where she led Product Management, Market Strategy, Corporate Development, and M&A and also served as an employee Board Officer. Currently, Christina continues to be an Advisor to the CEO at Mimecast while also serving on the Board of Directors for TechTarget (NASDAQ: TTGT). She is also involved an Advisory Board member for several emerging technology firms, including Theatro, Ludis Analytics and Teikametrics.
Prior to evolving into the technology sector, Christina founded a women’s athletic apparel brand and spent her younger years focused on public interest work with The John D. & Catherine T. MacArthur Foundation, DC City Government, U.S. Treasury Department, and several political campaigns. In 2017, she launched a resource platform dedicated to the economic advancement and self‐reliance of women and girls around the world called Women@Work, which includes several books, a mentor matching program, and other related initiatives.
Christina earned a BA in Government and Theology from Georgetown University and attended the University of Chicago Booth School of Business where she received an MBA in Business Strategy. Originally from Oklahoma, Christina now resides in Boston with her husband and two teenage sons. 


Graduated

PhD
Emaad Manzoor: Assistant Professor at  Cornell University (previously U of Wisconsin-Madison)
Vitaly Meursault (Thesis Committee) [Federal Reserve]
Domonkos Ferenc Vamossy (Thesis Committee) [Amazon]
Daehwan Ahn (Co-author), The Wharton School PostDoc [Faculty at University of Georgia]
Federico Siano (Thesis External Reader) [Faculty at UTDallas]
Qinglai He (Co-author, General Advising) [Faculty at University of Wisconsin-Madison]
Dongwon Lee, (Reader, Thesis Committee, Co-author) [Faculty at HKUST]
Shunyuan Zhang (Co-author, general advising) [Faculty at HBS]
June Shi (Co-author, general advising) [Faculty at HKUST]

Master's Student
Alka Isac, Independent Study (Natural Language Processing using Deep Learning) (Fall 2016)
Adit Bharat Sanghvi, Independent Study (Machine Learning and Recommender Systems) (Spring 2017)
Sahil Gupte, Independent Study (Neural Networks and Word Embeddings) (Mini 4 2017)
Maksim Khaitovich, Independent Study (Deep Learning in Business) (Mini 4 2017, Fall 2017, 2018)
Jiati Le, Internship (Vision Algorithm in Real Estate) (Summer 2017)
Sangmin Cho, Internship (Vision Algorithm in Fashion) (Summer 2017, Fall 2017)
Akshay Thorat, Independent Study (Application of Generative Adversarial Networks) (Spring 2018)
Aniket Jain, Independent Study (Application of Generative Adversarial Networks) (Spring 2018)
Rohan Sangave, Independent Study (Interpretable Deep Learning Based Text Mining) (Summer, Fall 2018)
Harsh Johari, Independent Study (Project Management Insight Mining) (Summer, Fall 2018)
Adarsh Rajkumar Saboo, Independent Study (Project Management Insight Mining) (Summer, Fall 2018)
Yichen Chen (Tsinghua University), Independent Study (Heterogeneous Treatment Effect) (Summer 2018)
Philipp Schneider, Independent Research (Quantitative Persuasion) (Spring 2019)
Stella Xinci Weng, Independent Study (Deep Learning in Business) (Spring 2019, Fall 2019)
Yan Gao (Deep Learning for Patents) (Spring 2020)
Yuan Zhou (Deep Learning for Patents) 

"Causal Inference without Data Mining is Myopic and Data Mining without Causal Inference is Blind"

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