Data Science & Business Analytics

Researchers in Data Science and Business Analytics seek to examine and exploit the myriad promises of big data.

A concerted interest is in utilising and improving data-driven methods, statistical analysis, machine learning, and analytics techniques to extract meaningful insights and inform organisational decision-making. For example, an enduring interest is in forecasting and predicting potential relationships between variables and outcomes of concern so that organisations can better chart their future and achieve their goals.

This interest is intertwined with the fundamental big data challenges, e.g., entity resolution, missing value imputation, high-dimensional clustering, and models for transfer learning.

What We Do

Develop and test predictive models to yield actionable insights for addressing a variety of business and societal problems.

Create big data analytics tools and solutions that enable organisations to collect, process, analyse, and visualise data effectively.

Advance knowledge in managing and governing data for privacy and security.

Sub Areas

Our Research Projects

Investigation on Digital Platforms and Human-machine Co-behavior Through AI, Empirical and Experimental Methods

SUN Chenshuo

This proposal investigates the impact of digital technologies on consumer behavior across e-commerce and social media platforms. Focusing on voice AI in shopping, filter bubbles in recommender systems, and social features affecting search volume, the project aims to provide valuable insights and address key questions in these domains.

  • Machine Learning, Predictive Analytics

Understanding Online Contribution: Impact Evaluation and Crowd Wisdom Extraction

QIAO Dandan

With the massive amount of online contributions shaping individual and organizational decision-making, this project delves into utilizing and understanding this valuable resource. By combining econometrics and natural language processing, the research seeks innovative insights to leverage these vast information pools and assess their impacts on commerce and society.

  • Big Data Analytics

Academic Integrity Post-COVID-19: Multimodal Online Assessment Cheating Detection

HAHN Jungpil

Cheating cases are on the rise in academia, especially during the shift to remote learning. Using a blend of user studies and machine learning (ML) with multimodal features, this project aims to develop a robust ML model for detecting and identifying cheating patterns in various online assessment contexts.

  • Machine Learning

Quantifying the Impact of Privacy Technologies (PETs) on Firms

HAHN Jungpil

Consumers' adoption of privacy enhancing technologies creates data analytics challenges to firms that rely on data to support their business models. This project seeks to develop conceptual and methodological frameworks to quantify the impact that such technologies will have on firms' analytics capabilities and business value.

  • Privacy and Ethics of Data Management

Our Research Groups

Garbage Can Lab

HAHN Jungpil

The Garbage Can Lab ( conducts research focused on better understanding today's complex socio-technical world and developing solutions that create impact. Inspired by the Garbage Can Model of Organizational Choice (Cohen et al. 1972), our research group can best be described as an organized anarchy with problematic preferences, unclear technology and fluid participation.