• UChicago Computer Science 25300/35300 and Applied Math 27700: Mathematical Foundations of Machine Learning, Fall 2020
• Fall 2020: MATH 37794 / CAAM 37794 / CMSC 35490 / STAT 37794: Learned Neural Emulators of Physics Simulations, Fall 2020
• STAT 37710 / CMSC 35400: Machine Learning, Spring 2020
• UChicago Computer Science 25300/35300 and Applied Math 27700: Mathematical Foundations of Machine Learning, Fall 2019
• UChicago STAT 31140: Computational Imaging — Theory and Methods
• UChicago Computer Science 25300/35300 Mathematical Foundations of Machine Learning, Winter 2019
• UW-Madison ECE 830 Estimation and Decision Theory, Spring 2017
• UW-Madison ECE 203 Signals, Information, and Computation, Fall 2016
• UW-Madison ECE 830 Estimation and Decision Theory, Spring 2016
• UW-Madison ECE 533 Image Processing (semi-flipped), Fall 2015
• UW-Madison ECE 901 Online Learning, Spring 2015
• UW-Madison ECE 203 Signals, Information, and Computation, Fall 2014
• UW-Madison ECE 830 Estimation and Decision Theory, Spring 2014
  • Lecture 1: Elements of Statistical Signal Processing
  • Lecture 2: Review of Linear Algebra
  • Lecture 3: Review of Probability and Statistics
  • Lecture 4: Sufficient Statistics
  • Lecture 5: Introduction to Detection Theory
  • Lecture 6: Neyman-Pearson Detectors
  • Lecture 7: Hypothesis Testing and KL Divergence
  • Lecture 8: Receiver Operating Characteristic (ROC) Curve
  • Lecture 9: Sequential Testing
  • Lecture 10: Composite Hypothesis Testing
  • Lecture 11: The Generalized Likelihood Ratio
  • Lecture 12: Multiple Hypotheses
  • Lecture 13: Parameter Estimation
  • Lecture 14: Maximum Likelihood Estimation
  • Lecture 15: Minimum Variance Unbiased Estimation
  • Lecture 16: Cramer Rao Lower Bounds
  • Lecture 17: Best Linear Unbiased Estimators
  • Lecture 18: Bayesian Estimation
  • Lecture 19: Conjugate Priors
  • Lecture 20: Wiener Filters and Deconvolution
  • Lecture 21: Wavelet Denoising
  • Lecture 22: Signal Subspaces and Sparsity
  • Lecture 23: Sparsity and Error Decay Rates
  • Lecture 24: Dynamic Filtering
  • Lecture 25: Kalman Filters
  • Lecture 26: Stochastic Gradient Descent
• Duke Signals and Systems, ECE 280 (formerly 54)
• Duke Digital Image and Multidimensional Signal Processing, ECE 489 (formerly 189)
• Duke Advanced Digital Signal Processing — Estimation Theory, ECE 582 (formerly 282)
• Institute for Advanced Studies 2011 Program for Women and Mathematics Lecturer. Slides here.