Mana Azarm

Dr. Mana Azarm is an Assistant Professor of Analytics and Information Systems at the University of San Francisco, where she bridges the worlds of scalable data management and AI safety research. With over a decade of industry experience building scalable data infrastructure at organizations ranging from Silicon Valley unicorns like DoorDash to government agencies, she brings a unique perspective to the challenges of developing and deploying AI systems safely.

Dr. Azarm's current research focuses on AI evaluations for safety and alignment, with emphasis on open-weight AI model discovery. Her work addresses critical questions around how to systematically evaluate AI systems for potential risks while maintaining their beneficial applications.

Her interdisciplinary research combines technical expertise in data architecture from traditional relational databases to modern data lakes and AI-enabled pipelines with a human-centered perspective on technology deployment. At USF, she developed the first-ever AI Business Applications course for MBA, helping future business leaders understand both the opportunities and responsibilities that come with AI integration.

• AI evals for safety
• AI business applications
• Data modeling and data architecture
• Data warehousing and lake storage

• AI safety and alignment
• LLMs in data pipeline development and engineering
• Scaled data management systems
• Database engines and analytics systems

• University of Ottawa, PhD in Electronic Business
• University of Ottawa, MS in Systems Science
• Alzahra University, BBA & MS in Information Technology Management

• Senior Manager of Data Analytics, DoorDash
• Lecturer in Data Engineering, UC Berkeley
• Co-Founder, ModuleUp
• Part-time Professor, University of Ottawa
• Senior Analytics Strategist, City of Ottawa

• ICTC WIL Digital award (Government of Canada Information and Communication Technology Council,  Work-Integrated Learning Digital program), 2020
• uOttawa Entrepreneurship Hub startup pre-accelerator award, 2020
• Two-time recipient of the University of Ottawa’s Excellence Award, 2015, and 2017

• E. Sangari, K. Abughoush, and M. Azarm. Unveiling the dynamics of open-source ai models: Development trends, industry applications, and challenges. In Proceedings of the 58th Hawaii International Conference on System Sciences, 2025.
• J. Hendriks, M. Azarm, and P. Dumond. Structured Data Ontology for AI in Industrial Asset Condition Monitoring. Journal of Sensor and Actuator Networks 13, 2, 2024.
• M. Azarm, R. Meehan, and C. Kuziemsky. Towards a universal patient-centric health record sharing platform, Health Policy and Technology, Volume 12, Issue 4, 2023.
• M. Azarm, C. Kuziemsky, and L. Peyton. A Framework for System-level Health Data Sharing, HEALTHINF2020, 2020.
• M. Azarm, and L. Peyton. An ontology for a patient-centric healthcare interoperability framework,2018 IEEE/ACM International Workshop on Software Engineering in Healthcare Systems (SEHS),34-41, 2018, IEEE.
• M. Azarm, C. Backman, C. Kuziemsky, and L. Peyton. Breaking the Healthcare Interoperability Barrier by Empowering and Engaging Actors in the Healthcare System, Procedia Computer Science, 113, 326-333, 2017, Elsevier.