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
| Component | Weight | Notes |
|---|---|---|
| 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. |
| Attendance | 5% | Attendance and participation in class discussions. |
Topics
- Physical sensing & measurement models · Propagation, interaction, transduction, noise & uncertainty
- Signals as physical measurements · Sampling, spectra, phase, convolution, correlation & time-frequency analysis
- Inverse problems for physical inference · Identifiability, conditioning, regularization, sparsity & physical priors
- Learning for physical inference · Learned priors, algorithm unrolling, physics-informed learning & system identification
- Spatial sensing · Time-of-flight, phase, Doppler, sensor arrays, beamforming & direction finding
- Computational imaging & sparse sensing · Tomography, synthetic aperture, compressive sensing & learned reconstruction
- Representations for physical signals · Waveforms, spectra, spectrograms, CSI, range-Doppler, point clouds & neural representations
- Dynamics & state estimation · State-space models, Kalman/particle filtering, sensor fusion & learned dynamics
- Multimodal physical perception · Cross-modal representation, alignment, fusion & self-supervised learning
- Generalization in physical sensing · Domain shift, simulation, domain randomization, sim-to-real & physical priors
- Acoustic sensing & spatial audio · Source localization, separation, room acoustics & learned acoustic fields
- RF, radar & wireless perception · Localization, tracking, imaging & environmental sensing
- Neural fields & physical scene representations · Implicit representations, NeRF/SIREN & volumetric sensing
- Emerging physical perception · Inertial, underwater, multimodal & language-assisted sensing
- 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.
| Date | Topic | Study Materials |
|---|---|---|
| Sep 1 | Course Overview | 0_overview.pptx |
| Sep 3 | From the Physical World to Sensor Data | 1_PhysicalWorld_Sensor_Data.pptx |
| Sep 8 | Signals as Physical Measurements | 2_signals_as_measurements.pptx |
| Sep 10 | Signals as Physical Measurements (Continued) The "Worm"-up programming assignment has been released on ELMS. Collect your pet bug from the TA. | |
| Sep 15 | Inverse Problems in Practice | 3_Inverse_Problems_In_Practice.pptx |
| Sep 17 | Inverse Problems in Practice (Continued) | |
| Sep 22 | Physics-Guided Learning | 4_Physics_Guided_Learning.pptx |
| Sep 24 | Physics-Guided Learning (Continued) | |
| Sep 29 | ||
| Oct 1 | ||
| Oct 6 | ||
| Oct 8 | ||
| Oct 13 | Fall break — no class | |
| Oct 15 | ||
| Oct 20 | ||
| Oct 22 | ||
| Oct 27 | No class — Exam preparation | |
| Oct 29 | In-class exam | |
| Nov 3 | ||
| Nov 5 | ||
| Nov 10 | ||
| Nov 12 | Guest lecture | |
| Nov 17 | ||
| Nov 19 | ||
| Nov 24 | ||
| Nov 26 | Thanksgiving — no class | |
| Dec 1 | ||
| Dec 3 | ||
| Dec 8 | ||
| Dec 10 |