- Email:
- ankit@iu.edu
- Department:
- Operations and Decision Technologies (ODT)
- Campus:
- IU, IU Bloomington
- Major:
- Information Systems
Dr. Ankit Shah is an Assistant Professor of Operations and Decision Technologies at the Kelley School of Business at Indiana University (IU). Before coming to IU in 2024, Dr. Shah was a faculty member at the University of South Florida (USF), where he also directed its Artificial Intelligence Research Laboratory for Secure and Efficient Systems and was a member of its Institute for Artificial Intelligence + X (AI+X). Previously, he was a researcher in the field of Reinforcement Learning at the Lawrence Livermore National Laboratory in California and the Center for Secure Information Systems in Virginia. With over nine years of industry experience in the Information Technology and Security sector, he has held diverse roles, bringing both technical expertise and executive experience.
Dr. Shah’s research focuses on enhancing organizational cybersecurity by advancing threat detection and mitigation technologies, optimizing human resource management strategies, and refining operational processes to improve overall security resilience. He develops advanced business technologies by integrating machine learning (ML), deep learning (DL), and artificial intelligence (AI) to strengthen cybersecurity systems (AI/ML in Cybersecurity). His work also investigates the resilience and robustness of AI-enabled defense systems, employing deep reinforcement learning and generative ML/DL methodologies for rigorous red team evaluations (Security of AI-enabled Systems). His mission is to advance the field of cybersecurity by generating critical knowledge in these key areas, essential for protecting organizations against both known and emerging threats. His research sponsors are the Department of Defense (DoD), the Department of Homeland Security (DHS), and industry partners. Dr. Shah's work has been published in top security journals, including IEEE Transactions on Information Forensics and Security, IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Privacy and Security, ACM Transactions on Intelligent Systems and Technology, and IEEE Transactions on Parallel and Distributed Systems, among others. His Erdős number is 3 and Dijkstra number is 4. He is a senior member of IEEE and INFORMS, lifetime member of AAAI, and a member of AIS and ACM, among others.
Dr. Shah has been elected to serve as a board member for the INFORMS Military and Security Society (MAS) for the upcoming 2025-2028 term. He has also served in various capacities for both conferences and journals. He is currently serving as an Organizing Committee Member for the Workshop on Artificial Intelligence for Cyber Security (AICS) at the AAAI 2025 conference. He recently served as an Organizing Committee Member for the 2024 INFORMS Conference on Security, coordinating the Data, Artificial Intelligence and Cyber track and for the Workshop on AICS at the AAAI 2024 conference. He also served as a Program Committee Member for the International Joint Conference on Artificial Intelligence (IJCAI-24). He was also the Workshop Chair for the IEEE Conference on Dependable and Secure Computing (IEEE DSC) 2023 and Session Chair for the Cybersecurity Analytics session for the Military and Security Society at the 2023 INFORMS Annual Meeting. Dr. Shah served as the Cybersecurity Cluster Chair at the INFORMS Annual Meeting in 2020 and 2022. He also co-chaired the AI for Cybersecurity session at the 2022 INFORMS Annual Meeting, led the Data Analytics and Simulation session at the 2021 INFORMS Annual Meeting, and chaired the Simulation and Reinforcement Learning session at the 2021 INFORMS Annual Meeting. His expertise has led to invitations for numerous technical talks and seminars from government organizations, industry partners, and technical associations.
Dr. Shah’s research interests encompass the development of resilient network architectures, the design of optimal security policies, and the optimization of vulnerability management to enhance organizational cybersecurity. His research methodology focuses on security-aware machine learning, deep reinforcement learning, computer vision, and combinatorial optimization.