Hsinchun Chen

About

Hsinchun Chen is a Regents' Professor of Management Information Systems at the University of Arizona and holds the Thomas R. Brown Chair in Management and Technology. He earned his BS from National Chiao-Tong University in Taiwan, an MBA from SUNY Buffalo, and MS and Ph.D. degrees from New York University. Dr. Chen is a Fellow of ACM, IEEE, AAAS, and AIS, and has received numerous awards including the IEEE Computer Society Technical Achievement Award, the INFORMS Design Science Award, and the IEEE Big Data Security Pioneer Award. He has graduated 36 Ph.D. students, with several receiving prestigious awards, and has served as lead Program Director at NSF for the Smart and Connected Health Program. As an author and editor, he has contributed to over 20 books and published more than 320 SCI journal articles and 220 conference papers covering artificial intelligence, digital libraries, data/text/web mining, business intelligence, security informatics, and health informatics. Dr. Chen is the Director of the Artificial Intelligence Lab at the University of Arizona, which has received over $60 million in research funding from various agencies. He has served as editor-in-chief and senior editor for major ACM, IEEE, and MIS journals, and has been a conference chair for key events in digital library, information systems, security, and health informatics. An accomplished entrepreneur, his COPLINK/i2 system for security analytics was commercialized and later acquired by IBM, becoming a…

Research topics

• Computer Science
• Sociology
• Data science
• Computer Security
• Social Science
• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• Engineering management
• Management science

Selected publications

• Connecting Systems, Data, and People: A Multidisciplinary Research Roadmap for Chronic Disease Management

  MIS Quarterly · 2020 · 160 citations

  Introduction to Special Issue: The Role of Information Systems and Analytics in Chronic Disease Prevention and Management

  PublisherDOI

• Performance Modeling of Hyperledger Sawtooth Blockchain

  2019-07-01 · 94 citations

  articleSenior author

  With the rapid development of blockchain platforms, it is important that different implementations are tested and analyzed for comparative purposes. One such implementation is Hyperledger Sawtooth, a new member of the Hyperledger family. Sawtooth blockchain is a permissioned implementation developed in part by Intel. While research has been done on Hyperledger Fabric, research on Sawtooth is not well documented. Using the Hyperledger Caliper benchmarking tool, we aim to test the performance of t…

  PublisherDOI

• Trailblazing the Artificial Intelligence for Cybersecurity Discipline

  ACM Transactions on Management Information Systems · 2020 · 82 citations

  Senior authorCorresponding

  Cybersecurity has rapidly emerged as a grand societal challenge of the 21st century. Innovative solutions to proactively tackle emerging cybersecurity challenges are essential to ensuring a safe and secure society. Artificial Intelligence (AI) has rapidly emerged as a viable approach for sifting through terabytes of heterogeneous cybersecurity data to execute fundamental cybersecurity tasks, such as asset prioritization, control allocation, vulnerability management, and threat detection, with un…

  PublisherOA PDFDOI

• A Deep Learning Architecture for Psychometric Natural Language Processing

  ACM transactions on office information systems · 2020 · 74 citations

  Senior authorCorresponding

  Psychometric measures reflecting people’s knowledge, ability, attitudes, and personality traits are critical for many real-world applications, such as e-commerce, health care, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this arti…

  PublisherOA PDFDOI

• Fall Detection with Wearable Sensors: A Hierarchical Attention-based Convolutional Neural Network Approach

  Journal of Management Information Systems · 2021-10-02 · 37 citations

  article

  Falls are among the most life-threatening events that challenge senior citizens’ independent living. Wearable sensor technologies have emerged as a viable solution for fall detection. However, existing fall detection models either focus on manual feature engineering or lack explainability. To advance the state-of-the-art of wearable sensor-based health management, we follow the computational design science paradigm and develop a deep learning model to detect falls based on wearable sensor data.…

  PublisherDOI

Recent grants

• EAGER: Long-term View on Nanotechnology R&D as Reflected in Scientific Papers, Patents, and NSF Awards

  NSF · $280k · 2010–2014

• EAGER: SaTC-EDU: Artificial Intelligence and Cybersecurity Research and Education at Scale

  NSF · $298k · 2020–2024

• Cybersecurity Big Data and Analytics Sharing Platform

  NSF · $180k · 2017–2022

Frequent coauthors

• Daniel Zeng

  Chinese Academy of Sciences

  77 shared

• Michael Chau

  42 shared

• Zan Huang

  Zhongnan Hospital of Wuhan University

  34 shared

• Yilu Zhou

  Fordham University

  23 shared

• Christopher C. Yang

  23 shared

• Jialun Qin

  University of Massachusetts Lowell

  22 shared

• Bruce R. Schatz

  University of Illinois Urbana-Champaign

  22 shared

• Catherine Larson

  Cleveland Clinic

  21 shared

Labs

• Artificial Intelligence Lab at The University of ArizonaPI

Awards & honors

• NCTU Distinguished Alumnus Award (2005)
• IEEE Computer Society Technical Achievement Award (2006)
• INFORMS Design Science Award (2008 and 2023)
• AIS Impact Award (2020)
• IEEE Big Data Security Pioneer Award

Similar researchers at University of Arizona

• Jay Quadeh-83
• Weigang Wangh-72
• Natasha Warnerh-30