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商学院“李达讲坛”【第43期】:Data-driven massive business data analyticsin Modern Data science

主     题:Data-driven massive business data analyticsin Modern Data science

          现代数据科学中数据驱动的大商业数据分析 

主 讲 人:韩晓旭 教授

  间:2019620日(星期四) 上午 9:00-11:00

主讲内容:

        The rise of high-frequency trading brings challenges both in finance and business analytics for its ultra-fast trading speed and huge data size. In this work, we propose a novel trading marker prediction approach in HFT by employing manifold learning: locally linear embedding (LLE) and state-of-the-art clustering techniques: Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The proposed method demonstrates its advantage over its peers in trading marker prediction. Furthermore, we present effective deep learning models in price discovery and profitable high-frequency trading and compare its performance with those of peers. To the best of our knowledge, it is the first work in Fintech to address market marker prediction and HFT trading modeling problems.

主讲人介绍:

        韩晓旭,美国福特汉姆大学计算机与信息科学系教授、大数据分析实验室主任。2004年毕业于爱荷华大学并获得博士学位。目前的研究领域包括:数据科学、大数据分析、生物信息、金融科技和网络安全,在数据科学领域出版了近80篇论文。韩教授是福特汉姆大学网络安全硕士项目的发起者、主任和副主席,从2005年起指导了60名博士、硕士和本科生,获得美国自然科学基金、美国国家卫生研究院和企业的多个资助项目。

        Henry Han is Professor of computer science in the Department of computer and information science at Fordham University. He is the director of the laboratory of big data and analytics. He earned his Ph.D. from the University of Iowa in 2004. His current research interests include data science, big data, bioinformatics/health informatics, fintech, and cybersecurity. He published nearly 80 papers in leading journals and conferences in data science fields. He was the founding director of Fordham University’s master program in cybersecurity besides department associate chair. He has supervised about a total of 60 Ph.D., master and undergraduate students since 2005. His research has been supported by NSF, NIH, and research contracts from the industry.


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