Teaching

A list of my own teaching experience:

Courses and workshops

• [2024] Lecturer and co-developer for UG course at UW Bothell: Sensation, Perception, and Beyond
• [2021] Teaching Assistant (TA) for BIOL0029: Computational Biology, UCL (link)
• [2020, 2021] Course instructor and content creator for Data Science and Machine Learning in Python, UCL (link)
• [2020, 2021] TA for NEUR0019: Neuroinformatics course (methods in quantitative neurophysiology), UCL (link)
• [2018, 2019] TA at LIDO-PhD SysMIC course: Systems Training in Maths, Informatics, Statistics and Computational Biology, UCL (link)
• [2016] TA at Computational Approaches to Memory and Plasticity (CAMP), Bangalore (link)
• [2016] TA for MB208: Theoretical and Computational Neuroscience, IISc (link)

Mentoring

• [2023-] Mentoring high-school, undergraduate and graduate students
• [2018-21] Mentoring of undergraduate thesis and PhD students
• [2019-2020] Weekly mentoring at ReachOut, UK (working with grade 5/6 students)

Training

• [2024] STEP-WISE Mentored Teaching Program at UW

Teaching Quantitative Methods for Biology Majors

Quantitative skills, including not just fundamental statistics but mathematical modeling and analysis of large-scale data, is becoming indispensible for discovery in biological systems and is useful for formalizing intuition. I’m keen on integrating coding and math in training for biology and neuroscience majors; this requires curriculum reform and thinking deeply about how to teach these skills in a manner appropriate for the work that it will be used for, but not compromising on creating a sense of curiosity (and not just practicality) that is essential for future self-motivated learning.

Some ideas have oft been suggested for such a quantitative training:

1. Discipline-based programming, i.e. designing examples and problem sets that the audience finds valuable and brings expertise in.
2. Address negative emotional priors and initial disparities but emphasize growth mindset
3. Flexibility with software for class assignments
4. Collaborative programming - teach problem solving in real time and how to “think through” coding and normalize not knowing
5. Teach data wrangling and visualization
6. Focus on tradeoffs to encourage critical thinking, not cookie-cutter analyses
7. Problem-based and active learning approaches, encouraging “how to learn” as much as “what to learn”
8. Start with fundamentals in your/their field

I will soon put up a list of resources on inclusive and student-centred pedagogy that I have found to be useful.