Sunghwan Kim (김성환)

I am a first-year PhD student at UC San Diego, working at Existential Robotics Laboratory under the guidance of Prof. Nikolay Atanasov.

Previously, I was a research officer at the Agency for Defense Development (ADD), the South Korean counterpart to the U.S. DARPA. I received my B.S. in Electrical Engineering and Mathematics (double major) at KAIST.

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I am focusing on building neural representations for robot policy learning. I am interested in the following topics:

• Implicit neural representations
• Latent feature mapping
• Neural SLAM
• Mobile manipulation

We developed multi-threading C++ software that optimizes CPU and NPU resources during the inference phase of ML models, interfacing with the flight control unit of UAVs. Also, we devised a test-time diversification method for time-series images to reduce the predictive uncertainty of the models.

We implemented model compression techniques such as feature distillation and structural pruning to accelerate ML models on edge devices, including NPU, FPGA board and edge GPU. We were able to double the throughput of edge-devices by applying the techniques.

We designed real-time object detection algorithms for UAVs. We generated synthetic infrared images using a 3D engine for training data, and established an end-to-end training pipeline.

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