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Hao (Howard) Zhong is currently a tenure-track assistant professor in the Information and Operations Management department of ESCP-Europe (Paris campus). He received his doctoral degree in Management (conc. on Data Mining) from Rutgers, the State University of New Jersey in USA. He is also holding a master's degree of Computer Science from York University in Canada and a bachelor's degree of Electronic Information Science and Technology from the University of Science and Technoloy of China (USTC).

His research interests are in data mining and business analytics, more specifically including venture capital data mining, quantitative corporate analysis, data-driven analytics and solutions for FinTech problems. He has several publications in top venues of data mining and operations research, including refereed journals (Annals of Operations Research, IEEE Transactions on Mobile Computing, IEEE Transactions on Knowledge and Data Engineering) and conference proceedings (ACM SIGKDD, IEEE Interntional Conference on Data Mining, SIAM International Conference on Data Mining, etc). He has teaching interests in machine learning, data mining, business statistical analysis, Python programming language, etc.

He enjoyed a three-year entrepreneurial experience before joining ESCP, as a founding member and Big Data Scientist of a new FinTech company in China, which leverages the power of Big Data and AI to provide financial risk management products and solutions for top financial clients. He also served as a senior financial modeling advisor in several FinTech companies in China.

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14 publications

Conference Proceedings

2023

ZHONG, H., C. LIU

Firm Profiling and Competition Assessment: A Heterogeneous Occupation Network–based Method

Hawaii International Conference on System Sciences (HICSS)

Conference Proceedings

2022

ZHONG, H., C. LIU

Firm Profiling and Competition Assessment via Heterogeneous Occupation Network

International Conference on Information Systems (ICIS)

Conference Proceedings

2022

ZHONG, H., C. LIU

Career Path Clustering via Sequential Job Embedding and Mixture Markov Models

International Conference on Information Systems (ICIS)

Conference Proceedings

2022

ZHANG, D., H. ZHONG, J. YANG

Acqui-hiring or Acqui-quitting: Data-Driven Post-M&A Turnover Prediction via a Dual-fit Model

Conference on Information Systems and Technology (CIST)

ESCP Impact Papers

2022

ZHONG, H

AI as a new geopolitical battleground: What are we competing for?

ESCP Impact Papers, 2022-08-EN

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Conference Proceedings

2021

ZHONG, H., S. ZHANG, Z. YUAN, H. XIONG

Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

Academic Articles

2020

XU, T., H. ZHU, H. XIONG, H. , H. ZHONG, E. CHEN

Exploring the Social Learning of Taxi Drivers in Latent Vehicle-to-Vehicle Networks

IEEE TRANSACTIONS ON MOBILE COMPUTING, 19 (8), 1804 - 1817

Academic Articles

2019

XU, T., H. ZHU, H. , H. ZHONG, G. LIU, H. XIONG, E. CHEN

Exploiting the Dynamic Mutual Influence for Predicting Social Event Participation

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 31 (6), 1122 - 1135

Academic Articles

2018

ZHONG, H., H., C. LIU, J. ZHONG, H. XIONG

Which startup to invest in: a personalized portfolio strategy

ANNALS OF OPERATIONS RESEARCH, 263, 339-360

Conference Proceedings

2016

ZHONG, H., H., C. LIU, X. LU, H. XIONG

To be or Not to be Friends Exploiting Social Ties for Venture Investments

IEEE International Conference on Data Mining (ICDM)