Jeremy Klotz

jklotz [at] cs.columbia.edu

I'm a fourth-year 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

Minimalist Visual Inertial Odometry

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

Preprint, 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 in New York City. At each location, we capture the illumination using a 360° camera, we measure the global horizontal irradiance, and we record the capture date, time of day, location, and current weather conditions.

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.