Weitong Qian钱韦潼

An open-source agent system that autonomously writes, profiles, and optimizes CUDA kernels — pushing AI further down its own software stack.

LLM-guided Monte Carlo Tree Search for the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem. We use the LLM as a structural prior over the pipeline space, making Bayesian optimization dramatically more sample-efficient.

A memory-centric agent system for the full scientific research lifecycle — from literature ingestion and idea generation to experiment execution, manuscript writing, and reviewer rebuttal. Structured persistent memory separates reusable knowledge from project-level artifacts; a feedback-driven self-improvement loop converts experiment outcomes and review signals into versioned updates to skills and memory, so the system compounds across projects.

An AI research-onboarding tool, built on the OpenClaw skill platform. Given a topic, it constructs a self-contained, growing knowledge base — field overview, foundational and frontier papers, peer reviews, knowledge graph — that a newcomer can converse with directly.

Vision-based, RL-driven non-prehensile grasping; RL controllers for stable and adaptive locomotion on quadruped and biped robots over varied terrain. Advised by Prof. He Wang.

A system in which embodied AI agents autonomously plan and conduct scientific experiments — research automation extended into the physical world.