teaching

TEACHING PHILOSOPHY

My teaching philosophy starts from a simple premise; teaching is not separate from research. It’s one of the most important ways we invite people into the scientific enterprise. Whether I’m teaching chronobiology, neurogenetics, or quantitative methods, I try to structure courses around the kinds of questions working scientists actually ask. I want students to practice forming alternative hypotheses, checking assumptions, and deciding what counts as convincing evidence. When students experience that process firsthand, rather than only hearing about results, they begin to see science as something they can do, not just something they can memorize.

Because much of modern biology and neuroscience is now data-rich and model-driven, I place a strong emphasis on quantitative thinking. At the same time, I’m very intentional about how I teach it. Many students arrive excited about biology but uneasy about math or coding. I’ve found that careful scaffolding, starting with intuition, building confidence through small wins, and connecting each tool to a meaningful biological problem can transform that anxiety into competence and even enjoyment. I often teach computation in R through real datasets and workflows, so students leave with skills they can immediately apply in labs, field contexts, or independent projects.

I also work hard to build classrooms that are collaborative, inclusive, and intellectually safe. I don’t see rigor and kindness as opposites; I see them as partners. Students learn best when expectations are clear, feedback is constructive, and asking a “basic” question is treated as a normal part of thinking seriously. I use discussion-based teaching (often Socratic, almost always grounded in the history of ideas) to help students articulate their reasoning and learn how to disagree productively.

Finally, I see adaptability as part of good teaching. Programs evolve, cohorts vary, and science changes quickly. That includes AI. I prefer to teach students to use emerging tools ethically and critically. Testing outputs, identifying bias, and reflecting on uncertainty are important skills. This way they become thoughtful scientists, not just efficient ones. My goal is for students to leave my courses more capable, more independent, and genuinely curious about the living systems they study.</i></b>

TEACHING and MENTORSHIP EXPERIENCE

Sleep and Circadian Rhythms April, 2026
Guest Lecture Psychological and Brain Sciences, IU Bloomington, IN, USA.

Drosophila Sleep States April, 2026
NeuroCURE Class, University of New Mexico, NM, USA.

Time-series Analysis and Entrainment March, 2025
EMBO Chronobiology School, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India.

Statistical Hypothesis Testing for Biologists October, 2024
Graduate Center, CUNY, New York, USA.

Statistical Hypothesis Testing and R Programming for Biologists Spring, 2024
Advanced Science Research Center, CUNY, New York, USA.

Scientific Rigor and Data Management April, 2024
Graduate Center, CUNY, New York, USA.

Responsible Conduct of Research March, 2024
Graduate Center, CUNY, New York, USA.

Statistical Hypothesis Testing (Graduate level) Sep 2022
Jawaharlal Nehru Centre for Advanced Scientific Research, India.

Basic Chronobiology (Graduate level) 2016 – 2020
Jawaharlal Nehru Centre for Advanced Scientific Research, India.

Advanced Chronobiology (Graduate level) 2017
Jawaharlal Nehru Centre for Advanced Scientific Research, India.

Experimental Design and Statistical Hypothesis Testing for Biologists (Graduate level) 2016 – 2020
Jawaharlal Nehru Centre for Advanced Scientific Research, India.

Analysing Biological Rhythms (Graduate level) 2019
CCS University, India.

Undergraduate and High School Student Research 2015 – Present
Jawaharlal Nehru Centre for Advanced Scientific Research, India. Advanced Science Research Center, CUNY, New York, USA.
Gill Institute for Neuroscience - Indiana University, Bloomington, IN, USA.