Frank Xing

Frank Xing is an information scientist interested in natural language processing and predictive analytics, with a special focus on financial applications. Broadly speaking, he studies the role of human knowledge in information systems: how knowledge can be represented and engineered to support decision-making, and what consequences would knowledge-driven algorithms and systems cause to our society.

Frank spent his boyhood in Wuhan and Beijing. He earned his bachelor's degrees in Information Systems and Economics from Peking University, and a PhD in Computer Science and Engineering from Nanyang Technological University with the support of Temasek Research Scholarship. After a short industrial experience with Continental, he was awarded the NTU Presidential Postdoctoral Fellowship for his systematic research on intelligent asset allocation models. In July 2021, Frank joined the NUS faculty as a Visiting Assistant Professor in the Department of Information Systems and Analytics.

Today, Frank works closely with the Asian Institute of Digital Finance (AIDF), NUS Fintech Society, and NUS AI Lab (νSAIL). He also serves as guest editors for journals like IEEE Transactions on Artificial Intelligence, and area chairs for conferences, e.g., COLING. His research has been featured by news media, e.g., Dow Jones.

Education

• Doctor of Philosophy (PhD) Nanyang Technological University (2016 — 2019)
• Bachelor of Arts - BA Peking University (2011 — 2015)

Companies

• Assistant Professor National University of Singapore (2021)
• Presidential Research Fellow Nanyang Technological University (2019 — 2021)
• Machine Learning Specialist Continental (2018 — 2019)

Publications

• Du, K.; Xing, F.Z.; Cambria, E. (2022). Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis. Under submission.
• Chen, X.; Xing, F.Z. (2022). Financial Risk Tolerance Profiling from Text. Under submission. [data] [code]
• Young, T.; Xing, F.Z.; Pandelea, V.; Ni, J.; Cambria, E. (2022). Fusing task-oriented and open-domain dialogues in conversational agents. AAAI. [data]
• Xing, F.; Hoang, D.-H.; Vo, D.-V. (2021). High-frequency news sentiment and its application to forex market prediction. HICSS. [media]
• Xing, F. Z.; Malandri, L; Zhang, Y.; Cambria, E. (2020). Financial Sentiment Analysis: No Silver Bullet and An Investigation into the Common Mistakes. COLING. [data]
• Cambria, E.; Li, Y.; Xing, F., Poria, S.; Kwok, K. (2020). SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. CIKM.
• Xing, F. Z.; Cambria, E.; Welsch, R. E. (2019). Growing Semantic Vines for Robust Asset Allocation. Knowledge-Based Systems 165 pp 297-305. [code]
• Bai, H.; Xing, F. Z.; Cambria, E.; Huang, W.-B. (2019). Business taxonomy construction using concept-level hierarchical clustering. FinNLP. [data]
• Xing, F. Z.; Pallucchini, F.; Cambria, E. (2019). Cognitive-Inspired Domain Adaptation of Sentiment Lexicons. Information Processing & Management 56(3) pp 554-564. [code]
• Xing, F. Z.; Cambria, E.; Zhang, Y. (2019). Sentiment-Aware Volatility Forecasting. Knowledge-Based Systems 176 pp 68-76.
• Xing, F. Z.; Cambria, E.; Welsch, R. E. (2018). Intelligent Bayesian Asset Allocation via Market Sentiment Views. IEEE Computational Intelligence Magazine 13(4) pp 25-34. [code]
• Xing, F. Z.; Cambria, E.; Malandri, L.; Vercellis, C. (2018). Discovering Bayesian Market Views for Intelligent Asset Allocation. ECML-PKDD. [data] [code]
• Malandri, L; Xing, F.; Orsenigo, C.; Vercellis, C.; Cambria, E. (2018). Public Mood-Driven Asset Allocation: the Importance of Financial Sentiment in Portfolio Management. Cognitive Computation 10(6) pp 1167–1176.
• Xing, F. Z.; Cambria, E.; Welsch, R. E. (2018). Natural Language based Financial Forecasting: A Survey. Artificial Intelligence Review 50(1) pp 49–73.