I'm a second-year Ph.D. student at Stanford University. I've previously completed my B.S. and M.S. degrees in Electrical Engineering at Brigham Young University and am interested in problems in imaging, optimization, computer vision, and remote sensing. I'm currently working in Stanford's Computational Imaging Lab on time-of-flight sensors, imaging around corners, and next-generation lidar systems.

Education

2016-Present
Stanford University
Ph.D Electrical Engineering
2009-2016
Brigham Young University
B.S. Electrical Engineering Summa Cum Laude
M.S. Electrical Engineering

Research

2016-Present
Stanford University, Ph.D. Student
Advisor: Gordon Wetzstein
Area: Computational Imaging, single photon detectors, virtual reality
Project: Reconstruction of transient images, non-line of sight imaging, virtual reality rendering
2014-2016
Brigham Young University, M.S. Student
Advisor: David Long
Area: Radar Image Processing, Resolution Enhancement, Geoscience
Project: Arctic ice classification, soil moisture estimation from C-band/Ku-band scatterometers and radiometers
2013-2014
Brigham Young University, B.S. Student
Advisor: Aaron Hawkins
Area: Semiconductor Devices, Cleanroom Fabrication, Circuit Design
Project: Fabrication of a solid-state single ion detection unit

Publications

M. O’Toole, F. Heide, D. B. Lindell, K. Zang, S. Diamond, G. Wetzstein, “Reconstructing Transient Images from Single-Photon Sensors”, IEEE Int. Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
D. B. Lindell and D. G. Long, "Multiyear Arctic Sea Ice Classification Using OSCAT and QuikSCAT," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 167-175, Jan. 2016. doi: 10.1109/TGRS.2015.2452215
D. B. Lindell and D. G. Long, "Multiyear Arctic Ice Classification Using ASCAT and SSMIS," in Remote Sensing, vol. 8, no. 4, pp. 294, Mar. 2016. doi: 10.3390/rs8040294
D. B. Lindell and D. G. Long, "High-Resolution Soil Moisture Retrieval With ASCAT," in IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 7, pp. 972-976, July 2016. doi: 10.1109/LGRS.2016.2557321

Selected Projects

Deep Networks for Robust Depth Estimation with Single-Photon Sensors
For LIDAR systems operating at extreme long range or low power, it is possible that only a few photons are detected from the laser pulse return. Using deep networks, we show that accurate depth maps can be recovered despite the challenging circumstances.
Virtual Reality Motion Parallax with the Facebook Surround-360
Current virtual reality displays for viewing captured 360-degree stereo videos typically provide the wearer with a view from a single vantagepoint. We demonstrate that images from a commercial 360-degree camera rig can be processed to enable head-motion parallax while viewing with a head-mounted display. Such a viewing experience more closely mimics how we experience the real-world and can help to alleviate virtual-reality sickness and viewing discomfort.

Experience

March 2016-August-2016
Mankato, MN (remote)
Software For Hire
Position: Computer Vision Specialist
Project: Developed fast, multithreaded vision algorithm for a pharmaceutical tablet counter using open source software, including Boost, OpenCV, and Point Cloud Library.
June 2016-July-2016
Tucson, AZ
Rincon Research Corporation
Position: Electrical Engineering Intern
Project: Developed a cloud-based digital video recording system to stream and record live video. Integrated live broadcast television demodulation capability using GNU Radio and Rincon Research Corporation signal processing hardware.

Last updated on 31 Jul 2017