Teaching Philosophy

I believe in teaching through storytelling and building mathematical intuition before diving into formalism. My approach emphasizes:

• Connecting abstract concepts to real-world applications
• Developing computational thinking alongside theoretical understanding
• Encouraging hands-on experimentation with code and data
• Building fluency through worked examples and progressive complexity

Current Courses

Medical College of Wisconsin (2025–Present)

Developing new graduate courses in:

• Computational Imaging for Biomedical Applications
• Bayesian Machine Learning
• Advanced Topics in Medical Image Analysis

Previous Teaching

University of Tennessee, Knoxville (2023–2025)

Graduate-level courses in electrical engineering and computer science focusing on signal processing and machine learning.

Wells Fargo (Corporate Education, 2019–2021)

Learning to Teach Machines (2020)

• Corporate training program on machine learning model development
• Designed for quantitative analysts and data scientists
• Topics: ML fundamentals, model validation, explainability, production deployment

Python Programming & ML Modeling (2019)

• Intensive course for Decision Science & AI team
• Hands-on training in Python, scikit-learn, and production ML workflows

Purdue University (2012–2014)

MA 153: Algebra (Spring 2013)

• Sole instructor for undergraduate algebra course
• Developed curriculum and assessments
• Taught 40+ students per semester

ECE 637: Digital Image Processing (Fall 2013)

• Graduate teaching assistant
• Led recitation sections and developed problem sets

MA 165: Calculus (Fall 2012)

• Teaching assistant for undergraduate calculus
• Graded assignments and conducted help sessions

University of Windsor, Canada (2010–2012)

ELEC 8550: Computer Arithmetic (2011)

• Teaching assistant for graduate-level course
• Assisted with lab sessions on hardware implementation

Various Electrical Engineering Laboratories (2011)

• Supervised undergraduate laboratory sessions
• Topics: circuits, signals and systems, digital logic

Guest Lectures and Invited Talks

Over 15 invited talks at leading institutions including:

• Stanford University
• MIT
• Caltech
• Harvard-MIT Health Sciences & Technology
• Carnegie Mellon University
• Indian Institute of Science
• Los Alamos National Laboratory
• Oak Ridge National Laboratory

Recent Topics

• “Are ground-up language models relevant in the age of ChatGPT?” (Toronto ML Summit, 2023)
• “Plug and Play: A General Approach to AI & Sensor Model Fusion” (SIAM Prize Talk, 2020)
• “Advanced Bayesian Prior Models for Computational Imaging” (Los Alamos, 2018)
• “Distributed Optimization Methods for Scientific Imaging” (Oak Ridge, 2017)

Educational Materials

Books in Progress

• Bayesian Estimation for Computational Imaging (SPIE Press, expected 2027)
• Mathematical Fluency (Personal textbook project on mathematical foundations)

Open Educational Resources

Developing open-source tutorials and code repositories for:

• Plug-and-Play Priors implementation
• Consensus Equilibrium algorithms
• Differential geometry for explainable AI