Dr. Lamberti
Assistant Professor | George Mason University
Welcome! I am an Assistant Professor in the Department of Computational and Data Sciences at George Mason University, where I serve as Course Lead for CDS 130: Computing for Scientists. My work bridges education, data science, and artificial intelligence, with a particular focus on making computational methods accessible and meaningful for students and practitioners alike.
My research encompasses explainable AI, image analysis, machine learning applications in medicine and biology, and the integration of generative AI in higher education. I am passionate about developing interpretable models that not only perform well but can be understood and trusted by their users.
Before joining the faculty at George Mason, I worked as a Data Scientist at Google, where I led research on Kubernetes cost optimization, and completed postdoctoral research at the University of Virginia focusing on computational analysis of genetic imaging data. I hold a Ph.D. in Computational Sciences and Informatics with a specialization in Data Science from George Mason University.
Current Work & Leadership in Data Science Education
Teaching & Mentorship in Computational Sciences
Leading CDS 130 across multiple modalities, mentoring 5-8 teaching assistants per semester, and guiding the course’s transition to Canvas for over 15 sections.
AI in Education Research
Conducting pilot studies on generative AI integration in classrooms and serving on the College of Science AI Policy Taskforce.
Student Research Supervision
Supervising undergraduate and graduate students on projects ranging from generative AI implementation to student engagement analytics.
Research Interests: Machine Learning & AI
Publications & Academic Contributions
Since 2017, I have been the founder of LDC, providing data science education and training to organizations including NASA, the American Statistical Association, and the National Opinion Research Center. I am also the creator of RGalleon.com, a resource for non-programmers to learn R and statistics, and the author of “An Introduction to R for Non-Programmers.”
My work has been published in journals such as Computer Methods and Programs in Biomedicine Update, and I was the lead author to Google’s white paper on “State of Kubernetes Cost Optimization.” My research has been presented at conferences including the IEEE Radar Conference, Joint Statistical Meetings (JSM), and the Symposium on Data Science and Statistics.