1 Introduction

My teaching focuses on making rigorous theoretical material accessible and useful for both undergraduate and graduate students. I balance precise, board-centered explanations with carefully selected slides and examples, and I emphasize problem-solving, algorithmic thinking, and connections to current research. This statement summarizes my teaching experience, philosophy, course design work, and future plans.

2 Teaching Experience

I have taught forty-five complete iterations of fourteen different courses across undergraduate and graduate programs. Major courses I have taught (selected):

• Design and Analysis of Algorithms (Undergraduate; 10 iterations)

• Advanced Algorithms and Complexity (Graduate; 10 iterations)

• Cryptography (Undergraduate; 10 iterations)

• Theory of Computation (Undergraduate; tutorials and full courses)

• Advanced Compilation Techniques, Operating Systems, Discrete Mathematical Structures, and several programming and algorithms labs.

I teach both large lectures and small tutorials, and I have experience with in-person and online instruction, including creating and using recorded lecture videos for online teaching.

3 Teaching Philosophy

My core pedagogical goals are clarity, accessibility, and active engagement:

1. Start from first principles. I do not assume prior mastery; I explain definitions and the intuition behind them before formal proofs.

2. Board-first exposition. Students tell me they learn best when concepts are worked out on the board step-by-step. I use slides selectively for overviews, diagrams, or material that benefits from prepared visualizations.

3. Active problem-solving. I integrate short in-class problems, tutorial sheets, and take-home exercises that reinforce lecture concepts and develop technical maturity.

4. Frequent feedback. I solicit and act on student feedback. In online teaching, I respond promptly to chat and forum questions to maintain interaction.

5. Research-informed teaching. Where appropriate, I present recent research results and open problems to graduate classes to motivate advanced coursework and student projects.

4 Course Design and Innovation

I have designed and taught a new undergraduate course titled Computational Complexity (IIT Jodhpur, 2014). The course syllabus included:

• Turing machines and uncomputability;

• Time and space complexity classes (P, NP, PSPACE, etc.);

• Polynomial hierarchy and nonuniform classes (P/poly);

• Randomized complexity (BPP, RP, ZPP);

• Interactive proofs and PCP; and

• Applications to cryptography and quantum complexity.

I structure new courses with clear learning objectives, a sequence of scaffolded problem sets, and project options that let motivated students explore research directions.

5 Assessment and Student Projects

My assessment strategy combines homework, quizzes, projects, and exams to evaluate both understanding and creative application:

• Homeworks: Regular problem sets that mix routine exercises with one or two open-ended questions.

• Quizzes: Short in-course quizzes to encourage continuous learning.

• Projects: Semester-long projects for advanced students (theoretical experiments or implementations), often resulting in undergraduate research reports or small publications.

• Office hours and mentoring: I hold regular office hours and actively mentor students for thesis and research internships.

6 Lecture Media and Resources

During the COVID-19 period I produced a set of lecture videos for Advanced Algorithms and Complexity and related topics. These videos cover core topics such as P vs NP intuition, reductions and NP-completeness, randomized algorithms, interactive proofs, and approximation techniques. (Links and detailed listings of these lecture videos are available on request.)

7 Future Teaching Plan

In the future, I will be interested in teaching the following courses:

• Fine-Grained Complexity and Algorithms

• Classical and Quantum Number Theoretic Algorithms

8 Conclusion

Teaching is central to my work as a scholar and mentor. I aim to create an inclusive classroom where students with different backgrounds gain confidence and technical skill, and where motivated students can transition smoothly into research.