Teaching

Teaching philosophy

As an instructor of biology, statistics, and programming, my teaching philosophy has become grounded in the belief that students from any background can develop the scientific and quantitative skills to work critically with biological data, both in my courses and in their future studies and careers. To achieve this goal, I ensure that the learning environment in my classes is above all supportive, and that concepts are reinforced with meaningful and relevant practice. Many students enter science and programming courses with anxieties or assumptions of their abilities. A major objective of mine is to eliminate these barriers and preconceived notions by (1) demonstrating problem-solving strategies, (2) teaching students how to monitor their own learning through metacognition, and (3) creating a classroom that values curiosity and collaboration. Below, I elaborate on these pedagogical concepts and explain my strategies for implementing them in my courses.

Demonstrating problem-solving strategies with transparency. In introductory biology courses, students will often arrive with study habits shaped by prior coursework that emphasize memorization of facts and processes. While foundational knowledge is necessary, meaningful understanding of biology goes further and requires the ability to apply that knowledge to novel scenarios. To help students make this transition, I make my reasoning explicit during lectures and activities, modeling how to move from simply recalling information to using it analytically. Rather than presenting biological pathways as fixed steps to be memorized, I walk students through how to interrogate those systems under changing conditions. For example, when discussing processes such as photosynthesis or cellular respiration, I pose “what-if” scenarios that add perturbations to the system. I might ask students to predict the downstream consequences of a disruption in the Calvin cycle. This prompts them to reason through how a decrease in production of key intermediates (e.g., NADH and FADH2) would affect electron carriers and, ultimately, ATP generation during oxidative phosphorylation. As we work through these scenarios together, I verbalize the chain of logic connecting each step, emphasizing how biologists use mechanistic understanding to come to predictions. I also incorporate opportunities for students to practice this mode of thinking. I design assessments and in-class exercises that prioritize application over recall, which requires students to transfer their knowledge to new problems instead of repeating memorized steps. Through these exercises, my goal is for students to appreciate biology not as a collection of discrete facts, but as a web of systems that can be reasoned through. This transparency helps demystify problem-solving in biology and equips students with strategies they can apply to other topics and disciplines.

Teaching metacognition. As an undergraduate in my introductory biology course, the first lecture was devoted to explaining the science of learning so that students could practice metacognition and self-assessment throughout the term. This is an impactful pedagogical strategy that is applicable across disciplines, and personally changed the trajectory of my college career. Indeed, in any class, knowing how one learns is just as important as understanding the material itself. To this end, I teach students to understand what is being asked of them in every lesson through explicit learning objectives. For example, in a lesson introducing the fundamentals of phylogenetics, I frame the learning objectives as actionable and student-measurable outcomes (e.g., “Reconstruct a phylogeny that reflects uncertainties, such as polytomies, and interpret what this implies about our knowledge of a clade,” or “for a simple phylogeny of vertebrates, create monophyletic, paraphyletic, and polyphyletic groupings based on shared traits.”). After each lesson, I always encourage students to revisit these objectives so that they can monitor their learning progress. These actions not only help the students to master material in my courses, but provide them with the skills to meaningfully learn and self-assess in future courses.

Promoting curiosity and collaboration. I believe that learning is most impactful when knowledge is fluidly shared between peers. In fact, quantitative and scientific disciplines are inherently social: experimental design, workflow writing, peer review, and interpretation of results are very rarely done alone. In my courses, I model this reality by having small-group programming exercises, work sessions to collaborate on problem set questions, and peer reviews for written project assignments. Through these collaborative tasks, students learn how to verbalize their understanding to others and receive feedback from multiple perspectives. To make collaboration respectful and constructive, I establish clear expectations (e.g., guidelines for politely providing feedback, ensuring balanced participation, and respect for diverse backgrounds and skill sets). These exercises can be easily transferable to students’ future courses and professional environments.

Ultimately, my teaching philosophy combines strategies that support students in becoming confident and critical thinkers. My goal is not just to help students master biological concepts and programming skills, but to also develop the intellectual resilience necessary to feel confident in their future courses and careers.

Courses taught

• BI380: Evolution (Winter 2026)
  University of Oregon - Teaching Assistant
• BI 410: Data Visualization (Spring 2025)
  University of Oregon - Instructor of Record
• BI 221: Cells (Winter 2024, Fall 2023, Winter 2022)
  University of Oregon - Teaching Assistant
• BI 223: Ecology and Evolution (Fall 2021, Spring 2023)
  University of Oregon - Teaching Assistant
• BI 330/331: Microbiology (Spring 2022)
  University of Oregon - Teaching Assistant