Khalid Osman

Khalid Osman is an Assistant Professor of Civil and Environmental Engineering at Stanford University and a Center Fellow at the Woods Institute for the Environment. He joined the university in 2022, focusing on integrating equity into infrastructure systems through mixed-methods research. His work bridges socio-technical analysis with engineering solutions to address environmental justice challenges in water systems.

Education:

• Bachelor of Science in Civil Engineering, University of Portland (2016)
• Masters of Science in Civil Engineering, University of Texas at Austin (2018)
• Doctor of Philosophy in Civil Engineering, University of Texas at Austin (2022)

Research Interests:

Osman’s research emphasizes equitable infrastructure design, particularly in water systems, through frameworks that incorporate community perspectives and participatory approaches. Key themes include:

• Operationalizing equity in decentralized sanitation systems
• Water affordability and stakeholder engagement
• Socio-technical systems analysis for resilience
• Climate adaptation and flood mitigation strategies

Recent Trends in Publications:

His recent work highlights community-driven solutions for water infrastructure challenges, leveraging machine learning for decision-making, and analyzing disparities in water access. Notable topics include green stormwater infrastructure adoption, flood resilience in vulnerable regions, and the role of social media in crisis communication.

Awards:

• Bill and Melinda Gates Millennium Scholars Graduate Fellowship
• Ford Foundation Predoctoral Fellowship

Advising & Lab Activities:

As head of the Osman Lab, he mentors students in advancing equitable infrastructure solutions. Current advisees include PhD candidates Shuojia and Clara. The lab focuses on socio-technical frameworks to improve underserved communities’ access to resilient infrastructure.

Labs/Teams:

The Osman Lab collaborates with interdisciplinary teams to develop actionable policies and technologies. Recent projects involve:

• Community-based participatory research (CBPR)
• Graph neural networks for infrastructure analysis
• Large language models for translatable water reports