Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
portfolio
Personalized Agentic RAG System
Adaptive retrieval system with autonomous agents, iterative self-correction, and user memory over domain-specific knowledge bases (LangGraph, LangChain, FAISS).
Language-Guided Navigation Agent
A 0.5B-parameter language model that learns to follow natural-language navigation instructions and answer questions about what it finds - built with an RLHF-style recipe in miniature: LoRA SFT on BFS expert demonstrations, then REINFORCE with a KL penalty against a frozen reference.
RL Research Portfolio
Self-directed reinforcement-learning research: DQN reproductions from scratch, an end-to-end RLHF pipeline for a 0.5B language model, and a learned world model (MLP ensemble) with CEM planning that reaches 100% success on a PyBullet robot arm and stays robust where PPO fails.
publications
Class-Adaptive Focal Sharpness-Aware Minimization for Long-Tailed Visual Recognition
Published in Manuscript in preparation (target: CVPR), 2026
CA-Focal-SAM redistributes sharpness-aware minimization at the optimizer level: a frequency-conditioned GammaAdaptor assigns per-class focal parameters, improving tail-class accuracy by 6.03% on CIFAR-10-LT (IR=200) with no architectural change and zero inference overhead.
Recommended citation: Appau, Gideon Kofi Amo, and Wang, Pengkun. (2026). "Class-Adaptive Focal Sharpness-Aware Minimization for Long-Tailed Visual Recognition." Manuscript in preparation.
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Sharpness-Feedback Classwise Allocation for Long-Tailed Visual Recognition
Published in Manuscript in preparation (target: CVPR), 2026
SFCA combines a static log-scarcity prior with an online per-class SAM ascent-gap signal (robustly normalized EMA plus a bounded sigmoid mapping) to allocate per-class sharpness during training. On CIFAR-100-LT (IR=200, Logit Adjustment) it raises tail accuracy from 22.45% to 24.49% with zero inference overhead.
Recommended citation: Appau, Gideon Kofi Amo, and Wang, Pengkun. (2026). "Sharpness-Feedback Classwise Allocation for Long-Tailed Visual Recognition." Manuscript in preparation.
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talks
Homomorphic Encryption Deployment on FPGA
Published:
Presented the design and deployment of a homomorphic-encryption workload on FPGA for the USTC engineering practice module, covering the resource/latency trade-offs of encrypted computation on reconfigurable hardware.
CA-Focal-SAM for Long-Tailed Visual Recognition
Published:
Presented the CA-Focal-SAM framework and its SFCA extension to the USTC research group: how frequency-conditioned per-class SAM weights and online sharpness feedback redistribute overall vs. tail performance on long-tailed benchmarks, with the multi-seed evidence and mechanism analyses.
teaching
Peer mentoring for junior master’s students
Mentorship, USTC, Software Engineering program, 2024
Served as a peer mentor for junior master’s students in the USTC Software Engineering program (2024 - 2025): guided PyTorch debugging and SAM optimizer implementations, and explained gradient mechanics and loss-landscape visualization to labmates.