Geometry Dash Agent (vision-based)
A gameplay agent trained from pixels with a reproducible evaluation harness. The goal is strong generalization across levels through curriculum design and robust training pipelines.
Kelvin Peng
Math (Combinatorics & Optimization + Statistics) @ University of Waterloo. Currently focused on reinforcement learning agents, efficient fine-tuning, and on-device ML.
I like problems that sit between theory and engineering: training stability, evaluation discipline, and “product-level” UX for technical tools. Right now I’m building a Geometry Dash agent (vision-based) and learning Isaac Lab for robotics simulation.
A gameplay agent trained from pixels with a reproducible evaluation harness. The goal is strong generalization across levels through curriculum design and robust training pipelines.
Memory-efficient fine-tuning with quantization and careful eval splits for reasoning improvements on consumer GPUs.
Large-scale instruction fine-tuning with distributed training and checkpointing; emphasis on stable training + ablations.
FastVLM + OCR pipeline; SwiftUI front‑end with CoreML for low-latency, private on-device translation.