Sagar Samtani

Areas of Expertise

Artificial intelligence for cybersecurity, cyber threat intelligence, deep learning, smart vulnerability assessment, Internet of Things, Dark Web analytics, cybersecurity, mobile health analytics

Academic Degrees

• PhD, University of Arizona, 2018
• MSMIS, University of Arizona, 2014
• BSBA, University of Arizona, 2013

Professional Experience

• Indiana University, 2020-present
• University of South Florida, 2018 – 2020
• National Science Foundation (NSF) Scholarship-for-Service (SFS) Fellowship. 2014 – 2017
• Guest Associate Editor, ACM Transactions on Management Information Systems (2019 – 2020)
• Guest Editor, IEEE Transactions on Dependable and Secure Computing (2020 - Present)

Awards, Honors & Certificates

• National Science Foundation Grant: “SaTC EAGER: AI for Cybersecurity (AI4Cyber) Research Education at Scale,” $300K (IU Share: 100K), 2020 – 2022.
• National Science Foundation Grant: “CICI: SSC: Proactive Cyber Threat Intelligence and Comprehensive Network Monitoring for Scientific Cyberinfrastructure: The AZSecure Framework,” $1M (IU Share: $120K), 2019 – 2022.
• National Science Foundation Grant: “CRII: SaTC: Identifying Emerging Threats in the Online Hacker Community for Proactive Cyber Threat Intelligence: A Diachronic Graph Convolutional Autoencoder Framework,” $175K, 2019 – 2021.
• ACM SIGMIS Doctoral Dissertation Award. 2019.
• NSF CISE Research Initiation Initiative (CRII) Award. 2019.
• Runner-up, Best Paper Award. IEEE Intelligence and Security Informatics (ISI). 2017, 2018.
• Runner-up, INFORMS Information Systems Society (ISS) Nunamaker-Chen Dissertation Award. 2018.
• Paul S. and Shirley Goodman Award in International Computer Technology. 2017.
• National Science Foundation (NSF) Scholarship-for-Service (SFS) Fellowship. 2014 – 2017.
• James F. LaSalle Award for Teaching Excellence. University of Arizona. 2016.

Selected Publications

• Samtani, S., Zhu, H., and Chen, H. (2020). Proactively Identifying Emerging Hacker Threats on the Dark Web: A Diachronic Graph Embedding Framework (D-GEF). ACM Transactions on Privacy and Security, 23(4), 1–33.
• Zhu, H., Samtani, S., Brown, R., and Chen, H. A (2020). Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal Patterns. Management Information Systems Quarterly, in press.
• Liang, Y., Samtani, S., Guo, B., and Yu, Z. (2020). Behavioral Biometrics for Continuous Authentication in the Internet of Things Era: An Artificial Intelligence Perspective. IEEE Internet of Things Journal, 7(9), 9128-9143.
• Zhu, H., Samtani, S., Chen, H., and Nunamaker, J. F. (2020). Human Identification for Activities of Daily Living: A Deep Transfer Learning Approach. Journal of Management Information Systems, 37(2), 457-483.
• Pour, M., Mangino, A., Friday, K., Rathbun, M., Bou-Harb, E., Iqbal, F., Samtani, S., Crichigno, J., and Ghani, N. (2020). Into Data-driven Curation, Learning and Analysis for Inferring Evolving IoT Botnets in the Wild. Computers and Security, 91(101707).
• Samtani, S., Yu, S., and Zhu, H. (2019). Fear Appeals and Information Security Behaviors: An Empirical Study on Mechanical Turk. AIS Transactions on Replication Research, 5(5), 1-22.
• Samtani, S., Yu, S., Zhu, H., Patton, M., Matherly, J., and Chen, H. (2018). Identifying SCADA Systems and their Vulnerabilities on the Internet of Things: A Text Mining Approach. IEEE Intelligent Systems, 33(3), 63-73.
• Samtani, S., Chinn, R., Chen, H., and Nunamaker, J. F. (2017). Exploring Emerging Hacker Assets and Key Hackers for Proactive Cyber Threat Intelligence. Journal of Management Information Systems, 34(3), 1023-1053.

Edited on September 18, 2020