Overview

This graduate course explores how machine learning can be used to infer physical states, properties, and dynamics from real-world sensor measurements. Rather than treating sensor data as arbitrary inputs, we study how measurements arise through physical phenomena, propagation and interaction, and sensor transduction, and how this physical structure can inform learning and inference. Topics include signal and measurement models, inverse problems, spatial and computational sensing, physics-informed learning, state estimation and dynamics, multimodal perception, and robust generalization, with applications spanning acoustics, RF/radar, vision, inertial, and underwater sensing. Learning is through lectures, research-paper discussions, hands-on assignments, and a team research project.

  • Prerequisite: Graduate standing (MS/PhD) or permission of instructor.
  • Modality: In‑person; Tu–Th 12:30–1:45pm, Iribe 2107.
  • Readings: Research papers & tutorials (see Detailed Syllabus).

Grading Breakdown

Assessment components and weights.
ComponentWeightNotes
Term Project (x1)50%One semester-long project completed in groups of two students. Evaluation will be based on the project report (30%) and the end-semester project presentation (20%).
Paper presentation (x1)20%One in-class presentation of a scientific article.
Programming Assignment (x1)15%One programming assignment in a group of two students.
MCQ Quiz (x2)10%Two in-class multiple choice questions quizzes.
Attendance5%Attendance and participation in class discussions.

Topics

  1. Physical sensing & measurement models · Propagation, interaction, transduction, noise & uncertainty
  2. Signals as physical measurements · Sampling, spectra, phase, convolution, correlation & time-frequency analysis
  3. Inverse problems for physical inference · Identifiability, conditioning, regularization, sparsity & physical priors
  4. Learning for physical inference · Learned priors, algorithm unrolling, physics-informed learning & system identification
  5. Spatial sensing · Time-of-flight, phase, Doppler, sensor arrays, beamforming & direction finding
  6. Computational imaging & sparse sensing · Tomography, synthetic aperture, compressive sensing & learned reconstruction
  7. Representations for physical signals · Waveforms, spectra, spectrograms, CSI, range-Doppler, point clouds & neural representations
  8. Dynamics & state estimation · State-space models, Kalman/particle filtering, sensor fusion & learned dynamics
  9. Multimodal physical perception · Cross-modal representation, alignment, fusion & self-supervised learning
  10. Generalization in physical sensing · Domain shift, simulation, domain randomization, sim-to-real & physical priors
  11. Acoustic sensing & spatial audio · Source localization, separation, room acoustics & learned acoustic fields
  12. RF, radar & wireless perception · Localization, tracking, imaging & environmental sensing
  13. Neural fields & physical scene representations · Implicit representations, NeRF/SIREN & volumetric sensing
  14. Emerging physical perception · Inertial, underwater, multimodal & language-assisted sensing
  15. Research projects · Presentations & demonstrations

Class Schedule

Topics and study materials will be added before each class session; check back for updates. Holiday rows (no class) are highlighted.

Lecture dates, topics, and downloadable slides.
DateTopicStudy Materials
Sep 1Course OverviewClass slides
Sep 3From the Physical World to Sensor DataClass slides
Sep 8Signals as Physical MeasurementsClass slides
Sep 10Continued
The "Worm"-up programming assignment has been released on ELMS. Collect your pet bug from the TA.
Sep 15Inverse Problems in PracticeClass slides
Sep 17
Sep 22
Sep 24
Sep 29
Oct 1
Oct 6
Oct 8
Oct 13Fall break — no class
Oct 15
Oct 20
Oct 22
Oct 27
Oct 29
Nov 3
Nov 5
Nov 10
Nov 12
Nov 17
Nov 19
Nov 24
Nov 26Thanksgiving — no class
Dec 1
Dec 3
Dec 8
Dec 10