Teaching

Organismal and Evolutionary Biology (Level 300/400/600): 

This course explores the remarkable diversity of life and the evolutionary theories that explain their origins. Using examples from nature and laboratory experiments, it provides a strong foundation in evolutionary mechanisms and also touches upon principles of evolutionary medicine.

Learning methods include lectures, interactive discussions, guest seminars by renowned evolutionary biologists, take-home assignments, open-book exams, and a synthesis seminar. As part of the course, students have creatively explored evolution through meme—resulting in a collection of fascinating memes, available here.

Evolutionary Genetics (Level 300/600): 

This is an advanced level course to establish a firm foundation of the theory of Evolutionary Biology. During this course, students learn the population genetic formulation of the theory of natural selection and genetic drift in the absence and presence of mutation, population bottleneck, non-random mating and population sub-structuring. The course advances by exploring the evolutionary dynamics at multiple loci, principles of quantitative genetics , the algebra of evolution and the theory of life-history evolution. The course concludes by discussing how evolutionary notions will aid our understanding of human health, and contribute to cure diseases.  Lecture sessions, class-room interactions, take-home assignments, open-book exams, and a synthesis seminar constitutes the modes of learning in this course. 

Computational/Mathematical Biology (Level 300/600): 

This is a primer course to build 'model thinking' and promote quantitative understanding in Biology. During this course, students learn several key quantitative biology techniques, including phenomenological and agent-based models, cellular automata, phase plane analysis, stability analysis, steady-state analysis, quantifying chaos and population stability, through lecture sessions, class-room participations, take-home assignments and a DIY project. Together we explore the rationale, formulation and analysis of models in various fields biology, inter alia, population dynamics, microbiology, enzyme kinetics, epidemiology, and evolution in this course.

 Python for Research in Life Sciences (Level 300): 

This is an introductory course to Python to use this versatile programming language for aiding research in Life sciences. This course does not assume any prior knowledge in programming, starts with the basic coding lessons, and builds up upon them. The course will nudge students to think intuitively in terms writing an algorithm. This skill, once mastered, is transferable to any programming language in future. In addition, after first reviewing the basics of Python 3, we shall learn how to use Python scripts to import, organize, analyze and visualize experimental data, and run own simulations to generate new in silico research data. Using a combination of a lectures, and guided hands-on sessions, students will be exposed to a variety of different Python features across various topics in Life sciences. We shall explore examples and case studies with data, inter alia, behavioural experiments, genomics, epidemiology and biostatistics. Students will also be introduced to the rapidly developing field of image processing and machine learning. Students will get a chance to hone their new Python skills by solving take-home assignments on their own.