Statement of Teaching Philosophy

Preparing the next generation of public health practitioners and scholars requires helping students develop epidemiologic reasoning alongside quantitative proficiency. I approach teaching as a scholarly practice demanding intentional, evidence-informed course design and continual refinement. For disciplines like epidemiology, this involves designing curricula around the kinds of analytic decisions practitioners actually make, prioritizing scientific reasoning rather than a singular or isolated focus on specific methods. My core mission as an educator is to help students develop epidemiologic reasoning and analytic decision-making so they can apply quantitative methods thoughtfully and responsibly to real-world public health challenges. Throughout my teaching, this commitment shapes how I approach course philosophy, structure learning activities, and foster rigorous disciplinary expectations for all students.

My emphasis on teaching students to think like epidemiologists reflects my view that critical thinking and scientific reasoning form the foundation of effective epidemiology instruction. Therefore, in my graduate epidemiology and analytics courses, lessons begin with students articulating clear study questions and key assumptions before any statistical models are introduced. Methods are presented as components of a single inferential system, linking question formulation, study design, analysis, and interpretation, rather than disconnected statistical techniques. When statistical methods are taught without integration, students may learn how to fit models without fully understanding why a particular approach is appropriate or how to interpret results in context. To address this, I explicitly communicate the rationale for each method I teach and highlight how analytic choices connect back to the inferential system. By emphasizing this integration from the outset, students come to understand epidemiology as a coherent mode of scientific reasoning rather than a sequence of disconnected procedures.

I operationalize this philosophy by designing course structures that repeatedly engage with the full inferential process, from question formulation through interpretation. For example, in my Multilevel Statistical Methods course, the curriculum is organized around a single substantive problem and dataset that is revisited throughout the term. As complexity increases, students reformulate questions, revisit assumptions about the data-generating process, and adapt analytic strategies as new challenges emerge. Multilevel models are introduced as tools for addressing these evolving inferential needs rather than as isolated techniques. This course architecture helps students articulate when and why particular models are appropriate, reinforcing quantitative skill alongside scientific judgment under uncertainty.

I design course content and daily teaching practices using evidence-based teaching principles to develop students’ epidemiologic reasoning and quantitative proficiency. Classes include lecture and practical lab components that provide repeated opportunities to move through the full inferential process. Lectures are organized around applied public health problems that motivate discussion on study questions and analytic approaches. When presenting statistical models, I begin with visual representations and plain-language descriptions before moving to formal mathematical notation and code. Throughout, I model expert reasoning explicitly, drawing on concrete examples from my research, to show how methodological choices shape substantive conclusions. Students are regularly asked to recall prior concepts, articulate assumptions and potential sources of bias, and predict results before they are shown, creating frequent low-stakes opportunities to refine understanding. Lab sessions extend this approach through progressive practice, beginning with my demonstration of reproducible workflows, followed by guided interactive examples, and ultimately with independent or small-group applications. Interactive programming notebooks and simulations allow students to experiment with data and code while observing in real time how analytic decisions shape results and interpretation. Lab exercises draw on clean and complex real-world datasets, allowing students to focus initially on core concepts before engaging with the nuances of authentic epidemiologic work. Assignments, including problem sets, article critiques, replication studies, and independent research projects, require students to justify analytic decisions, interpret results in context, and communicate findings to varied audiences. Across activities, scaffolding is gradually withdrawn, but feedback is consistently provided, emphasizing reasoning and interpretation alongside technical execution.

Students in public health programs enter with diverse educational pathways, cultural backgrounds, and levels of quantitative preparation. This was evident during my tenure as a graduate instructor at Northeastern University, where I observed substantial variation in students’ prior preparation for courses such as Intermediate Analytics, alongside cohorts with a high proportion of multilingual international students. To support diverse classrooms and varying levels of preparation without lowering standards, I adapted courses using principles from Transparency in Learning and Teaching and Universal Design for Learning. Learning objectives were held constant while structured supports were embedded throughout the course, including reviews of essential concepts, recorded lectures for asynchronous reference, and supplemental study materials such as epidemiology dictionaries and instructor notes. Expectations and evaluation criteria were clarified in advance with detailed rubrics and annotated examples that aligned to learning objectives and distinguished technical execution from epidemiologic reasoning. Assignments often included optional challenge extensions, while larger projects were organized around milestone submissions that provided early, diagnostic feedback on inferential reasoning and preparing professional scientific products. These design choices reduced unnecessary barriers while preserving rigor, keeping advanced students engaged, and improving learning conditions for all learners.

I approach teaching as a core component of my professional identity as an epidemiologist, grounded in deliberate, evidence-informed design and responsive to reflection and feedback. Student evaluations consistently highlight the clarity of my explanations, my emphasis on conceptual understanding, and a supportive classroom environment that facilitates engagement with challenging material. When evaluations identified areas for improvement, particularly around the timeliness of feedback during periods of high workload, I adjusted assignment sequencing, grading timelines, and course management practices. These changes resulted in more timely and actionable feedback for students and a more sustainable teaching workflow.

My goal as an educator is to prepare students to think like epidemiologists and apply quantitative methods thoughtfully in service of population health. To me, teaching is an ongoing professional responsibility requiring intentional, evidence-based design, introspection, and responsiveness to student learning and feedback. I look forward to contributing to a collaborative academic community committed to rigor, equity, and the careful cultivation of future public health scholars and practitioners.