Teaching
Instructional Leadership
Data Science Labs
Since Fall 2023, I have coordinated the Data Science Labs sequence at Purdue University, a collection of hands-on laboratory courses designed to connect mathematics with modern applications in data science, engineering, and scientific computing. Through programming, sensor-based data collection, signal processing, image analysis, and computational modeling, students learn how abstract mathematical concepts are applied to real-world problems. The labs emphasize active learning and interdisciplinary problem solving while giving students practical experience working with hardware, coding, and real experimental data. The curriculum is fully open-source and supported through publicly available online textbooks developed and maintained at Purdue University.
Course Development for Signals & Systems for Mathematicians (MA 34900)
I developed MA 34900, “Signals & Systems for Mathematicians,” for the Applied Mathematics and Mathematical Data Science majors. The course integrates continuous and discrete Fourier analysis with applications in filtering, denoising, and image processing, while emphasizing the mathematical consequences of sampling and approximation. The course includes programming-based homework assignments and collaborative in-class activities designed to strengthen both theoretical understanding and computational fluency.
Large Lecture Mathematics Instruction and Coordination
In large lecture calculus courses, I have implemented a variety of evidence-based instructional practices including classroom response systems for formative assessment, collaborative recitation activities, and standardized quiz pools designed to improve consistency across recitation sections. I have also trained and mentored teaching assistants in active-learning instructional practices for coordinated courses.
Open Educational Resources
Courses taught:
- MA 34900 - Spring 26: syllabus, calendar, remaining content on Brightspace
- MA 16100 - Spring 26: syllabus, calendar, course page, remaining content on Brightspace.
- MA 27101 - Fall 25: syllabus, remaining content on Brightspace
- MA 266 - Spring 25: content on Brightspace
- MA 349 - Spring 25
- MA 266 - Fall 24: content on Brightspace
- MA 161 - Fall 24: content on Brightspace
- MA 349 - Spring 24
- MA 303 - Spring 24
- MA 261 - Fall 23: (course coordinator)
- MA 162 - Spring 23: (course coordinator)
- MA 161 - Fall 22: content on Brightspace (course coordinator)
- MA 166 - Spring 22 (course coordinator)
- MA 303 - Summer 21
- MA 266 - Spring 21
- MA 303 - Fall 20
- MA 266 - Spring 20: course content on Blackboard
Teaching Resources
As coordinator for large lecture mathematics courses and the Data Science Labs sequence, I have developed instructional resources to support graduate and undergraduate teaching assistants, collaborative recitation activities, and coordinated course administration.
- TA Quick Reference Sheet for Large Lecture Calculus (PDF)
- Collaborative Recitation Activity Guide (PDF)
I created a standardized form developed for coordinated large lecture calculus courses to streamline collection and documentation of evening exam conflicts and accommodations requests. (Email me if you would like an editable template)
Training and Development
In Spring 2026, I was a member of the Mathematics Community of Practice.
In 2023, I was selected as a Teaching for Tomorrow Junior Fellow at Purdue.
I completed the Kaufman Teaching Certificate Program at the Teaching + Learning Lab at MIT in 2018, where I studied evidence-based teaching techniques.
I completed IMPACT X Access at Purdue University in 2020, where I learned how to adapt my teaching to an online format.
Teaching Recognition
Honoree, Purdue Guru Vandana (Teacher Appreciation), Spring 2026, Purdue Hindu YUVA (Student Organization)