Jeremy Klotz

jklotz [at] cs.columbia.edu

I'm a Ph.D. student at Columbia University. I am advised by Shree Nayar, and my work is generously funded by the NDSEG fellowship. Before my Ph.D., I worked with Aswin Sankaranarayanan at CMU.

My research is in computational imaging. Specifically, I work on visual sensing methods that capture the least information necessary to solve a task. My work demonstrates that a minimalist approach to visual sensing brings a variety of benefits over conventional cameras, such as preserving privacy and enabling self-powered cameras. Recently, my research has taken a foray into imaging and vision for solar energy. Before coming to Columbia, I worked on structured light systems for 3D scanning and predicting fruit freshness.

Resume / Google Scholar / LinkedIn

Research

An Eternal Irradiance Camera

Jeremy Klotz and Shree K. Nayar

SIGGRAPH Asia, 2026

[paper][project page][video][supplemental]

We introduce FluxCam, a self-powered and untethered irradiance camera, and we demonstrate a few of its applications.

Minimalist Visual Inertial Odometry

Francesco Pasti, Jeremy Klotz, Nicola Bellotto, and Shree K. Nayar

IEEE Robotics and Automation Letters (RA-L), 2026

[paper][project page][video][code]

We propose a minimalist sensing approach for robot odometry using 4 pixels and an IMU.

Forecasting Solar Energy Using a Single Image

Jeremy Klotz and Shree K. Nayar

Solar Energy, 2026

[paper][project page]

We introduce a method to forecast a solar panel's irradiance (and hence its harvested energy) at any time in the future using a single image taken at the panel's location.

Minimal Sensing for Orienting a Solar Panel

Jeremy Klotz and Shree K. Nayar

Solar Energy, 2025

[paper][project page][video][code]

We introduce a minimal sensing method to iteratively orient a solar panel toward the optimal orientation in any lighting environment, regardless of the complexity of the illumination.

UrbanSky: A Dataset of Outdoor HDR Lighting Environments

Jeremy Klotz and Shree K. Nayar

[dataset page]

UrbanSky consists of 1,067 outdoor HDR lighting environments (spherical images) in New York City.

Minimalist Vision with Freeform Pixels

Jeremy Klotz and Shree K. Nayar

ECCV, 2024 (Best Paper Award)

[paper][project page][video][code][supplemental]

A minimalist camera captures the smallest number of measurements needed to solve a task. Rather than using square pixels, a minimalist camera uses freeform pixels whose shape are automatically learned for the task at-hand.