Teaching Philosophy Statement
In the rapidly evolving discipline of Biostatistics, my teaching is anchored by a clear understanding of the goals for student learning, the essential knowledge and skills required for success, and the challenges inherent in the learning facilitation process. Standing at the exciting intersection of advanced statistical theory and its practical applications in the real world, I aim to prepare students to be not only skilled statisticians with fluency in the theory and application of biostatistical methods but also well-rounded, ethical, and innovative thinkers who are ready to meet the complex challenges of today’s data-driven world. Effective teaching is not merely about disseminating knowledge; it’s about cultivating a learning environment where critical thinking is fostered, curiosity and creativity are encouraged, blended in practicality, experientiality, and a commitment to continuous improvement. An intellectually rigorous environment that is practically relevant, ethically grounded, inclusive, and leverages the diversity of thought and background while appreciating and incorporating the rapid technological advancements in biostatistical methods in comprehensive, engaging ways. My teaching philosophy is driven by my conviction that the role of a biostatistics educator extends beyond teaching statistical methods; it involves preparing students to be thoughtful, ethical, and collaborative professionals capable of making significant contributions to the interdisciplinary field of biostatistics. I involve students in discussions about ongoing research projects, bridging the gap between theoretical knowledge and practical research applications. My courses are designed to be hands-on, encouraging students to participate actively in their learning. For instance, I integrate real-world biostatistical problems into my curriculum, allowing students to analyze challenges and apply theoretical knowledge to practical situations critically.
Teaching Experience
Supplemental Teaching Assistant, PHP 1511/2511 Regression Analysis, Brown University, Spring 2023
Under the mentorship of Professor Alice Paul:
- Developed material for the final project in PHP1511/2511 Applied Regression Analysis, including designing the final project topic, description, and evaluation criteria.
- Held office hours to provide additional individualized support and assistance to students to clarify concepts, answer questions, and help students work through the final project for the course.
- Assisted in preparing course materials for regression methods for causal inference.
- Collaborated with the primary instructor in preparing lab materials for regression methods for causal inference using R and RStudio.
Guest Lecturer: Regression Methods for Causal Inference, PHP 1511 Regression Analysis, Brown University, Spring 2023
Teaching Consultant, The Harriet W. Sheridan Center for Teaching and Learning, Brown University, Fall 2024
- The TC Program is a semester-long learning community for graduate students and postdocs where participants deepen their knowledge of evidence-based inclusive teaching practices and develop skills in workshop facilitation, online teaching, and providing feedback.