Teaching & Talks

Research-Informed Teaching

My teaching is directly shaped by active research in ML-powered infrastructure systems. Students engage with live methods — risk modeling, intelligent asset management, geospatial analytics — through real datasets and case studies drawn from ongoing work. This connection between lab and classroom means students are not learning yesterday’s tools; they are engaging with approaches at the frontier of their field. Graduate mentorship extends this further: students participate in research planning, manuscript development, and scholarly dissemination as co-contributors, not passive observers.

AI-Integrated Engineering Education

Engineering education must evolve at the pace of the tools reshaping the field. Across courses in infrastructure risk management and machine learning applications, I embed data analytics, predictive modeling, and intelligent systems into the curriculum alongside traditional engineering methods. Students gain hands-on experience with the computational approaches — simulation, ML pipelines, optimization — that define modern infrastructure practice. The goal is to produce engineers who are as fluent in algorithmic thinking as they are in structural analysis.

Experiential & Inquiry-Driven Learning

Technical proficiency without problem-solving instinct produces capable but reactive engineers. My courses are built around authentic challenges: team-based projects, real infrastructure datasets, and capstone experiences developed in partnership with municipal agencies and industry collaborators. I use flipped classroom formats and structured discussion to shift students from passive recipients to active investigators — asking not just how to solve a problem, but why it matters, who it affects, and what the limits of the solution are. This mindset — critical, curious, and adaptive — is what separates engineers who can execute from those who can lead.

Ethics & Societal Responsibility

As ML methods become embedded in high-stakes infrastructure decisions, the ethical dimensions of those systems become engineering problems, not afterthoughts. I treat algorithmic bias, model transparency, and sustainability as core curriculum — not optional modules. Students are asked to interrogate their models: who benefits from this prediction, who is harmed by an error, and what is obscured by the data. The engineers I aim to develop are technically rigorous and ethically grounded — capable of deploying intelligent systems responsibly and of communicating their limitations honestly to non-technical stakeholders.

Equity & Inclusion

My own path has made equity a deeply personal commitment, not an institutional requirement. I apply accessible content design, differentiated instruction, and culturally responsive mentorship across every course I teach. Graduate advising is individualized: I meet students where they are analytically, help them develop their own research voice, and create genuine pathways to publication and conference participation regardless of prior background. A classroom where every student can contribute is not just more inclusive — it is intellectually richer for everyone in it.