Dr. Intisar Rizwan i Haque

Adjunct Professor
Intisar Rizwan i Haque has diverse experiences and knowledge as a Data Scientist/Biomedical/Electrical (Telecommunication) Engineer and researcher with 10+ years of experience in Electrical and Biomedical Engineering, Data Science, Lean Start-up Entrepreneurship, teaching, staff management, and coordination positions in various educational and commercial organizations.

In his recent projects, he has developed machine learning models to help assess the structural integrity of critical energy assets to minimize risk from failure.

He has extensively worked on researching and developing data science and artificial intelligence applications to create new solutions based on cloud platforms, machine learning, and deep learning to support the effective workflow of healthcare/clinical teams and core business processes.

Previously, he received the U.S. National Science Foundation's Innovation Corps (I-Corps™) award for participating in an immersive, entrepreneurial training program. 
 
PhD Biomedical Engineering, University of North Dakota, USA, 2021
MS Electrical (Telecommunication) Engineering, National University of Sciences and Technology, Pakistan, 2012
BE Electrical (Telecommunication) Engineering, National University of Sciences and Technology (NUST), 2009
 
  • Rizwan-I-Haque, I., Lacy, R., & Bellemare, S. (2024). Non-Destructive Evaluation of Pipe Seam Toughness via Frictional Sliding. 15th International Pipeline Conference and Exhibition (IPCE 2024).
  • Rizwan-I-Haque, I., Feigel, B., Willey, B., Patel, P., & Bellemare, S. (2023). Combining Nondestructive Techniques to Obtain Full Vintage Pipeline Asset Fracture Toughness at Both the Seam and Pipe Body. 35th International Pipeline Pigging and Integrity Management Conference (PPIM 2023), 611–626. https://doi.org/10.52202/068696-0040
  • Safari Loaliyan, S., Rizwan-I-Haque, I., Salamat, Y., Lacy, R., Mahmoudabadbozchelou, M., Bellemare, S., Powers, B., & Eagar, T. (2022). Overcoming Material and Test Variability Challenges in In-Situ Material Verification. Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining. https://doi.org/10.1115/IPC2022-87361
  • Bearse, P., Mohammad, A. F., Rizwan-I-Haque, I., Kuypers, S., & Fournier, R. (2021). Using Natural Language Processing to Optimize Engagement of Those with Behavioral Health Conditions that Worsen Chronic Medical Disease (pp. 601–610). https://doi.org/10.1007/978-3-030-71051-4_47
  • Mohammad, A. F., Bearse, P., & Rizwan-I-Haque, I. (2020). Healthcare Big Data Normalization Graph Theory Implementation. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 819–824. https://doi.org/10.1109/CSCI51800.2020.00153
  • Bearse, P., Manejwala, O., Mohammad, A. F., & Rizwan-I-Haque, I. (2020). An Initial Feasibility Study to Identify Loneliness Among Mental Health Patients from Clinical Notes. 2020 3rd International Conference on Information and Computer Technologies (ICICT), 68–77. https://doi.org/10.1109/ICICT50521.2020.00019
  • Rizwan-I-Haque, I., & Neubert, J. (2020). Deep learning approaches to biomedical image segmentation. Informatics in Medicine Unlocked, 100297. https://doi.org/10.1016/j.imu.2020.100297

Data Science, Artificial Intelligence, Machine Learning

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