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Daren Lee


4800 Oak Grove Drive
M/S 321-151
Pasadena, CA 91101



Daren Lee, PhD

Research Technologist


Daren Lee is a research technologist in the Robotics Modeling and Simulation Group at the Jet Propulsion Laboratory. His research interests include high performance computing for machine vision, machine learning, and scientific visualization applications.


PhD (2001) Computer Science, University of California, Los Angeles
MS (1997) Computer Science, University of California, Los Angeles
BS (1995) EECS, University of California, Berkeley

Professional Experience

M2020 Surface Simulator (SSim) (2017-present)

  • Developed flight software (FSW) in-the-loop simulation for sequence validation for rover planner tools

AUV Under Ice Shelf SRTD (2017-present)

  • Research underwater perception with sonar sensors for creation of world model occupancy grids
  • Developed ROS-based, closed loop simulation with models of sonar sensors for world model, localization, and planner development and testing

Vertical, Integrated Passive EO Ranger (VIPER) for USV (2017-present)

Subtask Manager

  • Led software and hardware design using low SWaP co-processors for short range (< 2km) hazard avoidance that uses 4 nVIDIA Tegras for image acquisition, stereo processing, and contact tracking
  • Implemented CUDA version of stereo correlator that boosts performance 3x compared to ARM-based implementation

Mechanically Uncoupled Stereo EO (MUSE) for USV (2016-present)

Subtask Manager

  • Led software development of stereo algorithms and hardware infrastructure for using high resolution (3904x3904) cameras for long range, radar-free intelligence, surveillance, and reconnaissance (ISR)
  • Performed profiling to pinpoint bottlenecks and optimized stereo pipeline using multi-cores, multi-threads, and vectorized instructions to boost performance to 1.7Hz processing from 0.5Hz with dual 12-core Xeon

ONR Sensor Fusion for Low-Cost Perception for UGV (2016-2017)

  • Modified and tuned daytime perception pipeline of low-cost perception head to integrate IR with EO and Lidar to mitigate range data dropouts in the EO data due to textureless areas in over-and under-exposed areas
  • Developed 2D ground truth tool that quantitatively showed that with IR, fewer false positives and negatives are seen in the perception system

Ocean Worlds Mobility and Sensing (2015-2016)

  • Developed Planetary Illumination Modeling (PLUM) to study lighting conditions on ocean worlds & icy planets for implications on future robotic perception systems
  • Modeling indirect and direct lighting for a general planetary body and reflectance bodies, PLUM leverages the JPL NAIF SPICE library to provide high fidelity solar system geometry for the basis of our lighting models
  • Output data products include total, direct, and indirect irradiance plots for a given latitude and longitude on the planetary surface for a given time period and time step

NNN13R262T: Active Night-Time Stereo and Localization for UGV (2014-2017)

Task Manager

  • Led development of passive, night-time stereo algorithms using IR cameras to overcome low SNR that yields poor disparity density
  • Developed multi-resolution consistency check method to reduce false positives from mixed pixels and repeated textures; verified through extensive field trails
  • Leveraged AVX2 vectorization to boost stereo correlator performance by 35% to maintain 10Hz processing rate in ROS-based framework

Mars 2020 Sampling and Caching System Testbed (2014)

  • Evaluated RabbitMQ and Kafka message brokering frameworks on how well they can enable a real-time, scalable, and robust telemetry framework for multiple consumers across heterogeneous platforms
  • Integrated message brokering publish-subscribe service with the Sampling Lab Universal Robotic Manipulator (SLURM) server code to publish 130 channels of telemetry used during environmental testing

Research Interests

HPC for Embedded Systems
GPGPU Programming
Scientific Visualization
Physically-based rendering


  1. D. Lee, M. Pomerantz, "Message brokering evaluation for live spacecraft telemetry monitoring, recorded playback, and analysis.," AIAA SPACE 2015, p. 4468, 16 November 2016.
  2. D. Lee, Y. Cheng, & H. Nayar, "Indirect and direct planetary illumination modelling for robotic surface exploration sensing.," In AIAA SPACE 2016, p. 5445-5450, 01 October 2016.
  3. D. Lee, A. Rankin, A. Huertas, J. Nash, G. Ahuja, and L. Matthies, "LWIR passive perception system for stealthy unmanned ground vehicle night operations," Unmanned Systems Technology XVIII, vol. 9837, pp. 94–101, 15 May 2016.
  4. D. Lee, A. Rankin, A. Huertas, J. Nash, G. Ahuja,& L. Matthies, "LWIR passive perception system for stealthy unmanned ground vehicle night operations.," SPIE Defense+Security, pp. 98370D-98370D, 01 May 2016.