A candidate for the COH Biostats Teaching position is a data scientist and educator with a Ph.D. in Computational Sciences and Informatics (Data Science specialization) from a US university. Their research focuses on image analysis, computer vision, and machine learning, particularly in biological and medical applications, including shape analysis and explainable AI. The candidate's doctoral work, supported by a Presidential Scholar Fellowship, resulted in a patent application for a pill shape classification system. Their postdoctoral work at a US unversity involved analyzing 3D microscopy images of genetic data. They are also a dedicated educator as an assistant professor at a US university, where they co-lead the "Computing for Scientists" course. The candidate's leadership and instructorship directly impacts hundreds of students each semester, who are based across the globe. Furthermore, they mentor teaching assistants to assist with various aspects of the course such as grading, holding office hours, while providing professional mentorship to the teaching assistants. They founded a consulting organization, providing data science training to various clients, including NASA, and developed educational resources like the Chat-R iOS app and RGalleon.com. Their industry experience at Google as a Data Scientist involved developing modeling algorithms for Google Cloud and leading the authorship of a white paper on Kubernetes cost optimization.
 

Talk Title: Thoroughness, Clarity, and Community: Pillars of Effective Instruction

Abstract: This talk will delve into the core principles that guide the candidate's approach to education: thoroughness, clarity, and community. They will illustrate how these principles translate into practical teaching strategies through concrete examples from their diverse teaching experiences, spanning from the traditional university classroom setting to professional development workshops. They will share specific assignments, in-class activities, and mentorship approaches that demonstrate their commitment to deep understanding, accessible explanations, and inclusive learning environments. These examples will highlight how they adapt their teaching style to different audiences and learning modalities, including online, in-person, and hybrid formats. They will also discuss their philosophy of mentorship and how they empower students to develop both technical proficiency and the collaborative skills essential for success.