TEACHING & MENTORING
Teaching Experience
Teaching mathematics through problem solving, intuition, and clear mathematical reasoning. My experience includes undergraduate teaching assistance, academic tutoring, mentoring, and supporting students across mathematics, probability, statistics, and mathematical finance.
“A teacher is never a giver of truth — he is a guide, a pointer to the truth that each student must find for himself.” — Bruce Lee
EXPERIENCE
Teaching Portfolio
Selected teaching, tutoring, and mentoring roles.
Math 3338 · Probability
University of Houston
Instructor: Dr. Robert Azencott
Math 3338 · Probability
University of Houston
Instructor: Dr. Wenjiang Fu
Math 2413 · Calculus I
University of Houston
Instructor: Dr. Moses Sosa
MA5950 · Mathematical Finance
Indian Institute of Technology Madras
Instructor: Dr. Barun Sarkar
Mathematics & Statistics Tutoring
University of Houston
Center for Academic Support and Assessment (CASA)
Math Advancement Class on Sundays (MAC-S)
Indian Institute of Technology Madras
Mentored students in advanced undergraduate mathematics.
My Teaching Approach
I try to help students understand why a mathematical idea works before focusing only on formulas or procedures.
My goal is to make difficult concepts approachable by connecting formal mathematics with intuition, examples, visualization, and structured problem solving.
MATH RESOURCE LIBRARY
Math Materials
A curated collection of notes, books, references, computational tools, learning paths, and resources that I have found useful while studying mathematics and related areas.
Analysis
- Real Analysis — S. Kumaresan
- Topology of Metric Spaces — S. Kumaresan
- Metric Spaces — P. K. Jain, Khalil Ahmad
- Real Analysis — N. L. Carothers ↗
Against the common notion, books such as Rudin, Bartle–Sherbert, and Apostol can feel like admiring a masterpiece from a distance. I prefer complementing them with books that contain approachable examples and lots of problems.
Algebra
- Contemporary Abstract Algebra — J. A. Gallian
- Abstract Algebra — Frank Ayres ↗
- Abstract Algebra — Gregory T. Lee ↗
- A Course in Abstract Algebra — Khanna & Bhambri ↗
Herstein and Dummit–Foote are excellent references, but I like pairing them with problem-oriented books such as Frank Ayres. Video lectures, including Benedict Gross's algebra lectures, can also help.
Linear Algebra
- Linear Algebra Done Right — Sheldon Axler ↗
- Linear Algebra — Lipson & Lipschutz, Schaum Outlines
- Linear Algebra: A Geometric Approach — S. Kumaresan
Complex Analysis
- Complex Variables Demystified — McMahon ↗
- Visual Complex Analysis — Needham ↗
- Complex Variables — H. S. Kasana ↗
- Complex Variables — Spiegel & Lipschutz, Schaum Outlines
- Introduction to Complex Analysis — P. Duraipandian
Topology
ODE & PDE
- Differential Equations — Bronson, Schaum Outlines ↗
- Differential Equations — M. D. Raisinghania
- An Elementary Course on PDE — Amarnath
I have not referred to as many books for differential equations as for the other subjects, so this section may grow over time.
Functional Analysis
- Introductory Functional Analysis — Kreyszig ↗
- Functional Analysis — B. V. Limaye
Measure Theory
- Measure and Integration — Inder K. Rana
- Math4All notes — Notes 1 / Notes 2
I highly appreciate the work at pkalika.in ↗ for mathematics aspirants.
Platforms to know
Articles and newsletters from KDnuggets ↗ are useful for following developments in data science and AI.
Cheat sheets & articles
- Complete Data Science cheat sheet — Statistics & Mathematics
- Complete Machine Learning cheat sheet
- GATE DA materials
- Machine Learning for Beginners
- Supervised vs. Unsupervised Learning
- Seven Machine Learning Algorithms Every Data Scientist Should Know
Intro course ideas
- Python introduction using Google Colab
- Visualizing neural networks with TensorFlow Playground
Books & references
- Monte Carlo Methods in Engineering — Glasserman
- Options, Futures and Other Derivatives — John Hull
- Stochastic Differential Equations — Øksendal (not for beginners)
Articles & notebooks
Software & markets
Beginner reading & careers
My projects & Git resources
Web resources
Elementary books
Litmaps · Research Rabbit · Overleaf · LyX · ChatPDF · Paperpal · Notion · Mendeley
Google Colab ↗ · VS Code · GitHub · Codespaces
LEARNING ROADMAP
A Mathematics Learning Path
One possible progression from foundational mathematics to advanced undergraduate and graduate topics.
High School
Build fluency and intuition.
Undergraduate
Move from calculation to structure.
Graduate
Develop abstraction and research readiness.
These areas open into research directions such as:
VISUAL NOTES
Image Gallery
A small collection of mathematical notes and visual study material.
FEEDBACK
Help me improve these materials.
If you have used any of the notes or resources on this page, I would love to hear what helped, what could be clearer, or what you would like to see added.
Leave feedback ↗“Don’t pray for an easy life, pray for the strength to endure a difficult one.” — Bruce Lee