Ilya Chugunov

I'm a PhD student in the computer science program at Princeton University. I work in the Princeton Computational Imaging Lab, advised by Professor Felix Heide, and have been gratefully awarded an NSF graduate research fellowship. I completed my bachelor's in electrical engineering and computer science at UC Berkeley.

Contact: [Last-Name]@princeton.edu

CV  /  Google Scholar  /  Github

I'm interested in the end-to-end optimization of imaging pipelines, beginning at modeling signal collection and continuing through to feature extraction and high-level scene understanding tasks. From MRIs to Time-of-Flight cameras, I love getting hands-on with real devices and making the most of inevitably noisy data.

Jupyter Notebook labs can offer a similar experience to in-person lab sections while being self-contained, with relevant resources embedded in their cells. They interactively demonstrate real-life applications of signal processing while reducing overhead for course staff.

Leveraging sparsity in the Z-spectrum domain, multi-scale low rank reconstruction of cardiac chemical exchange saturation transfer (CEST) MRI can allow for 4-fold acceleration of scans while providing accurate Lorentzian line-fit analysis.

Point cloud data integrated from two structured light sensors for gesture recognition implicitly via a 3D spatial transform network can lead to improved results as compared to iterative closest point (ICP) registered point clouds.

Property inspection routes can be formulated into graph optimization problem, decomposed into weekly/yearly constraints for reduction to team orienteering problem (TOP).

I love teaching and am always looking for new ways to convey information and foster learning; be that in lectures, office hours, or even just work meetings. Feel free to steal our signal processing lab exercises for your classes or personal education (and contribute to them if you can, some of those figures can definitely be improved c:).

Spring 2020

Student Instructor — EE120 Signals and Systems — UC Berkeley

Fall 2019

Student Instructor — EE120 Signals and Systems — UC Berkeley

Course Reader — EE290T 3D Computer Vision — UC Berkeley

Graduate course covering projection models, epipolar geometry, structure from motion, object detection, and other computer vision essentials for 3D scene understanding.

Received EECS Outstanding Instructor Award (Top 10%)

Spring 2019

Student Instructor — EE120 Signals and Systems — UC Berkeley

Mezzanine-level course meant to introduce students to thinking in the language of signals and transforms. Covers continuous and discrete Fourier analysis, Laplace and Z-transforms, as well as a range of topics in sampling and reconstruction

Fall 2018

Spring 2018

Fall 2017

Lab Assistant — EE16B Designing Information Devices II — UC Berkeley

Introductory course for circuit analysis and design. Topics include signal filtering, k-means clustering, closed/open-loop control systems, and transistor logic.