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

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. 

Zhaoqi Cheng

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

​​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

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

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

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.
Master's and Undergraduate Students

Ziyuan Ding

Ziyuan is a graduate student in Statistics at Boston University. She is interested in how to
implement statistical models and machine learning in the business filed to help understand
the operation of business and make decisions, especially from the perspective of risk
management.

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.

Ian Leissner

Ian is an undergraduate student in Economics and Computer Science at Boston College. He works with Statistical Modeling and Machine Learning tools to develop insights and create predictive models for a range of data. He is currently working on writing a survey on the uses of Generative Deep Learning in business. 

Ying Li

Ying Li is a graduate student in business analytics at Boston University, Questrom School of Business. She is interested in learning how decision-making based on statistics and machine learning changes the business world. In her free time, she enjoys playing video games and organizing notes for fun.

Qichang Zheng

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.

Nyrika Bhargavaram Renuka

Nyrika is a graduate student in Computer Science at Boston University. Her passions are aligned with the development of Artificial Intelligence inventions that can aid the general public in their daily activities. She has worked on developing an autonomous system that detects and removes diseased produce. She's currently designing generative models for interpreting large-scale reviews.

Chun Zhou ​

​Chun Zhou is Msc Business Analytics student at Boston University. She holds an Applied Mathematics (Finance) bachelor from University of Toronto. She is interested in generative models and text mining in financial predictions. She has worked on anomaly detections and developed machine learning tools of portfolios for two companies. She enjoys skiing, Buddhism, and keeps a dog and a cat for fun.

Haizhou "Hydro" Li

Haizhou "Hydro" Li is a Master's student in Computer Science in Metropolitan School, Boston University. He obtained his bachelor's degree in Economics in Virginia Tech. His research interests include deep learning, applying machine learning, data analysis, Algorithm, and data mining. He has worked on automatically checking abnormal data and predictive models for company cost by using data analysis.
​

Gengjin Liu

Gengjin (Caven) Liu is an M.S. Applied Business Analytics Candidate at Boston University. He graduated from Jilin University and majored in Computer Applications and Human Resource Management. He is interested in combining machine learning with business and applying data tech to the real world.

​Zhaowei Gu

​Zhaowei Gu is pursuing a graduate degree in software development at BU MET. He got his BS degree in Technology Information Management at the University of California Santa Cruz.
He is interested in learning more about machine learning, and AI. Zhaowei is also excited to work with a team.
In his spare time, he loves to watch documentaries, Biking, play with his cat, and LeetCode"

Prakhar Varshney

Prakhar is a graduate student in Computer Science at Boston University. He is passionate about Computers, Physics and Robots. Currently, he is working on Understanding the formation of Galaxies by studying the Metallicity of Globular Clusters through convolutional neural networks. In the past he has worked on GPU abstractions for Modern Operating Systems and He loves to play and design games in his free time.

Shekhar Sharma

Shekhar is a graduate student in Computer Science at Boston University. He completed his undergraduate degree in Computer Science from SGSITS, India. His research interests lie in algorithms and data science. When not in front of a screen, you can find Shekhar indulging in street food, playing badminton or listening to music.

Affiliated Industry Leaders & Scholars

Navdeep Gill
​Lead Data Scientist, Responsible AI Lead at H2O.ai


​Navdeep is a Lead Data Scientist & Responsible AI Team Lead at H2O.ai where he leads a team of researchers and engineers working on various facets of Responsible AI. He also leads science and product efforts around explainable AI, ethical AI, model debugging, and security of machine learning. Navdeep previously focused on GPU accelerated machine learning, automated machine learning, and the core H2O-3 platform at H2O.ai.
Prior to joining H2O.ai, Navdeep worked as a Senior Data Scientist at Cisco and as a Data Scientist at FICO. Before that Navdeep was a research assistant in several neuroscience labs at the following institutions: California State University, East Bay, Smith Kettlewell Eye Research Institute, University of California, San Francisco, and University of California, Berkeley.
Navdeep graduated from California State University, East Bay with a M.S. in statistics with an emphasis on computational statistics, a B.S. in statistics, and a B.A. in psychology with a minor in mathematics.
Twitter, Github

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

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  U of Wisconsin-Madison

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"