LeetCode-Go
LeetCode Online Judge is a website containing many algorithm questions. Most of them are real interview questions of Google, Facebook, LinkedIn, Apple, etc. and it always help to sharp our algorithm Skills. Level up your coding skills and quickly land a job. This is the best place to expand your knowledge and get prepared for your next interview. This repo shows my solutions in Go with the code style strictly follows the Google Golang Style Guide. Please feel free to reference and STAR to support this repo, thank you!
支持 Progressive Web Apps 和 Dark Mode 的题解电子书《LeetCode Cookbook》 Online Reading
离线版本的电子书《LeetCode Cookbook》PDF Download here
通过 iOS / Android 浏览器安装 PWA 版《LeetCode Cookbook》至设备桌面随时学习
I am a research-oriented machine learning systems engineer working on foundation model infrastructure, alignment, and evaluation. My work focuses on building efficient, reliable systems for large language models while studying the algorithms and data choices that make these models more useful, controllable, and cost-effective in real applications.
At TikTok, my recent work centers on Model-as-a-Service platforms and high-performance LLM inference. I develop serving infrastructure with vLLM and SGLang across model runtime integration, scheduling and continuous batching, KV-cache and memory management, distributed execution, observability, and reliability. This systems work is closely connected to my research on distributed disaggregated inference, preference optimization, instruction-tuning data selection, multimodal evaluation, and retrieval-augmented biomedical summarization.
My broader research spans reinforcement learning for robotics, healthcare sequence modeling, privacy-preserving machine learning, and motion planning. I am especially interested in model-system co-design: how model architecture, inference algorithms, data curation, hardware utilization, scheduling, and distributed runtimes interact. My goal is to advance frontier AI systems that are faster to experiment with, more rigorous to evaluate, and dependable enough to serve at scale.