Jaesung Park

jaesung.cs@gmail.com | Github | CV

Senior Software Engineer (Ph.D.) specializing in GPU systems, real-time rendering, and performance-critical C++ infrastructure.
Architect and optimize GPU-accelerated systems for 3D rendering and ML workloads using Vulkan and CUDA.
Former IOI Gold Medalist and ICPC World Finalist.

Work Experience

Presto Labs – Quant Researcher Sep 2023 – Present
- Designed and maintained low-latency C++ infrastructure for research and production deployment.
- Improved simulation throughput by up to 10x compared to legacy systems.
NAVER LABS – Research Software Engineer Feb 2022 – Sep 2023
- Implemented high-performance rendering pipelines for massive point clouds in OpenGL applications.
- Designed Vulkan-based real-time Neural Radiance Fields (NeRF) rendering engine with GPU shader pipeline.
- Implemented WebGL-based 3D visualization for real estate virtual tours (contributed to KR patent).
Cupix – Research Software Engineer Jul 2020 – Feb 2022
- Developed compression algorithms for large-scale unstructured point clouds.
- Implemented real-time WebGL rendering systems for interactive 3D visualization in browser environments.
- Contributed to indoor 360° panorama reconstruction and photogrammetry systems.
Moloco – Software Engineer Intern May – Aug 2017
- Performed data analysis.
- Contributed to data infrastructure engineering.

Programming Skills

Languages: C++17/20, Python, JavaScript, TypeScript
GPU / Graphics: CUDA, Vulkan, OpenGL, WebGPU, WebGL
Systems: Performance optimization, low-latency systems, parallel computing
ML / Vision: Neural Radiance Fields, Gaussian Splatting

Education

Ph.D. in Compute Science Sep 2015 – May 2020
University of North Carolina at Chapel Hill USA
- Advisor: Prof. Dinesh Manocha
- Research: Robot motion planning, collision detection, ML-based human motion prediction.
B.S. in Computer Science, Minor in Mathematics Mar 2011 – Feb 2015
Seoul National University South Korea
- GPA: 4.06/4.30 (cumulative), 4.22/4.30 (major), Summa Cum Laude

Research Experience

Optimization-Based Robot Motion Planning 2015 – 2020
- Jae Sung Park, Chonhyon Park, Dinesh Manocha.
  I-Planner: Intention-Aware Motion Planning Using Learning-Based Human Motion Prediction.
  The International Journal of Robotics Research (IJRR) 38 (1), 23-39, 2019.
- Jae Sung Park, Chonhyon Park, Dinesh Manocha.
  Intention-Aware Motion Planning Using Learning Based Human Motion Prediction.
  Robotics: Science and Systems (RSS), 2017.
- Chonhyon Park, Jae Sung Park, Steve Tonneau, Nicolas Mansard, Franck Multon, Julien Pettre, Dinesh Manocha.
  Dynamically Balanced and Plausible Trajectory Planning for Human-Like Characters.
  Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games. ACM, 2016.
Learning-Based Human Intention Prediction 2016 – 2020
- Jae Sung Park, Dinesh Manocha.
  HMPO: Human Motion Prediction in Occluded Environments for Safe Motion Planning.
  Robotics: Science and Systems (RSS), 2020.
- Jae Sung Park, Biao Jia, Mohit Bansal, Dinesh Manocha.
  Generating Realtime Motion Plans from Attribute-Based Natural Language Instructions Using Dynamic Constraint Mapping.
  IEEE International Conference on Robotics and Automation (ICRA), 2019.
- Jae Sung Park, Chonhyon Park, Dinesh Manocha.
  Human Motion Prediction from Noisy Point Cloud Data for Human-Robot Interaction.
  IEEE RO-MAN workshop on Communicating Intentions in Human-Robot Interaction, 2016.
Probabilistic Collision Detection Under Uncertainty 2016 – 2020
- Jae Sung Park, Dinesh Manocha.
  Efficient Probabilistic Collision Detection for Non-Gaussian Noise Distributions.
  IEEE Robotics and Automation Letters 5.2 (2020): 1024-1031.
- Chonhyon Park, Jae Sung Park, Dinesh Manocha.
  Fast and Bounded Probabilistic Collision Detection for High-DOF Trajectory Planning in Dynamic Environments.
  IEEE Transactions on Automation Science and Engineering (TASE) 15 (3), 980-991, 2018.
- Jae Sung Park, Chonhyon Park, Dinesh Manocha.
  Efficient Probabilistic Collision Detection for Non-Convex Shapes.
  IEEE International Conference on Robotics and Automation (ICRA), 1944-1951, 2017.
- Jae Sung Park, Chonhyon Park, Dinesh Manocha.
  Fast and Bounded Probabilistic Collision Detection for High-DOF Robots in Dynamic Environments.
  Workshop on Algorithmic Foundations of Robotics (WAFR), 2016.
Sweep-Based Surface Modeling 2013 – 2014
- Jaesung Park, Minsub Shim, Seon-Young Park, Yunku Kang, Myung-Soo Kim.
  Realistic deformation of 3D human blood vessels.
  Computer Animation and Virtual Worlds 24.3-4 (2013): 317-325.
- Seon-Young Park, Jaesung Park, Minsub Shim, Yunku Kang, Myung-Soo Kim.
  Sweep-based Compression and Deformation of 3D Blood Vessel Models.
  HCI 2013 (2013): 11-14.

Awards

ACM-ICPC World Finals: 36th place (2012), 51st place (2015)
ACM-ICPC Daejeon Regional: 1st place (2011, 2014), 3rd place (2012), 5th place (2013)
International Olympiad in Informatics (IOI): Gold Medal (2009), Silver Medal (2008)
Asia Pacific Informatics Olympiad (APIO): 1st place (2009)
Korea Computer Graphics Society Thesis Competition: Excellence Prize (2014)
Korean Mathematical Competition for University Students, Major Division: Bronze Prize (2013, 2014)
Undergraduate Research Program (URP): First Prize (2012)

Scholarship/Assistantship

Doctoral Merit Assistantship, University of North Carolina at Chapel Hill, 2015
National Science and Engineering Undergraduate Scholarship, South Korea, 2011-2014

Personal Projects

vulkan_radix_sort: High-performance Vulkan-based GPU radix sort implementation.
- Achieved performance competitive with CUDA CUB library.
vkgs: Vulkan-based Gaussian Splatting viewer optimized for real-time performance.
- Achieved 2× speedup over the original viewer through GPU pipeline restructuring and memory optimization.
- Cited by Meta’s vkraygs research project and referenced by NVIDIA’s Vulkan demo repository vk_gaussian_splatting.
splatstream: Vulkan-based Gaussian Splatting viewer with Python bindings for research.