Teaching Statement

Teaching Philosophy

My teaching philosophy is anchored in three core principles that guide how students engage, discover and grow through the learning process: 

1. Cultivating aCollaborative Learning Environment:

   Students thrive when they feel included and respected. In my classes, students bring a range of academic and professional experiences. Recognizing that students enter with different levels of experience, I use scaffolding techniques—such as pre-lecture quizzes, starter code templates, and post-assignment sample solutions—to support all learners. These tools give students entry points into complex material and create multiple pathways for succes. For instance, RMarkdown templates with embedded instructions and code chunks help students focus on their analytical thinking rather than formatting and foster reproducible workflows. By sharing a sample solution after grading, I offer a growth-oriented model of success that students can use for self-reflection and skill-building. This practice also promotes consistency and fairness in evaluation, especially for students unfamiliar with statistical conventions or coding best practices. Students also direct their own learning by working on datasets aligned with their interests—from ecological surveys to climate projections—promoting ownership and deeper engagement.

2. Bridging Theory with Applications and Professional Development

     Students are more motivated and successful when they see how statistical techniques inform real-world decision-making. My teaching encourages them to connect classroom tools to environmental problems that matter to them. In one module, for instance, students use real hydrological and land-use data from my conservation work in India to investigate how satellite information can inform wetland protection. Assignments ask students not just to apply methods but to contend with data uncertainty, collaborate on reproducible workflows, and communicate findings clearly to stakeholders. These experiences prepare students to think critically and act confidently in both academic and applied settings. In mentoring graduate students and Data+ teams, I emphasize critical thinking, reproducible workflows, and interdisciplinary collaboration, mirroring the way environmental challenges are tackled in research and industry settings.    

3. Building Intuition Behind Complex Statistical Models

    Many practitioners and even researchers apply statistical models without fully understanding their assumptions or theoretical underpinnings. I help them move beyond plug-and-play modeling by emphasizing intuition. For example, when teaching the Central Limit Theorem, I facilitate a live sampling exercise using student heights in the classroom. By collecting and analyzing real data, students gain a tangible understanding of how sample means approximate a normal distribution, reinforcing key statistical concepts in an interactive and engaging manner.

Looking ahead, I aim to develop and teach courses integrating environmental data science and spatial modeling in ecological applications. I am particularly excited about designing field-based and computational courses that combine GIS, remote sensing, and conservation decision-making. My peer observations highlight strengths in using real-world examples and visual scaffolding, and they also point to opportunities to deepen student-to-student engagement. To build on this, I am integrating group-based labs and peer learning exercises that allow students to build on each other's reasoning. I also plan to expand my use of diagnostic tools, such as background quizes, to adapt instruction to student needs from the outset and also develop student-driven grading methods. Beyond the classroom, I will continue fostering a learning environment where students are encouraged to ask critical questions, experiment with new methodologies, and apply their skills to pressing environmental issues.

Ultimately, my teaching philosophy is rooted in intellectual curiosity, and real-world relevance. By fostering environments where students build skills, ask critical questions, and learn from one another, I hope to support the next generation of environmental scientists and policy leaders.

Teaching Experience

1. Teaching Assistant, Applied Statistical Modeling for Environmental Management, Duke University, 2023

2. Teaching Assistant, Remote Sensing for Environmental Analysis, Duke University, 2022

3. Teaching Assistant, Landscape Analysis and Management, Duke University, 2022

4. Teaching Assistant, Examining the Ethics of Engaging in Environmental Professions, Duke University, 2021

Mentorship and Research Projects

1. Team Leader, Data+ 2023, Duke University – Led a student team developing a prototype for assessing forest wildlife distance using camera trap data.

2. Graduate Student Mentor, Wetland Biodiversity in India: A Scoping Review, Duke University, 2022.

3. Activity Leader, Femmes+ Capstone 2023 – Led an interactive STEM activity for 4th-6th grade students in Durham, fostering early engagement in science and technology.