编程 / 框架Programming
- Python, C++, Mathematica, SQL
- PyTorch, TensorFlow
RealRain · AI4Science · 计算机视觉/多模态 RealRain · AI4Science · Computer Vision & Multimodal
非雨(ThisRainIsNotARealRain)创始人/CEO。我们在RealRain上以“工业4.0”的理念构建动漫内容生产的数字孪生系统与多Agent 协作产线,让高质量内容生产成为稳定、可规模化的协作流程。我的技术背景来自 AI4Science、计算机视觉与多模态、Physics ML 与可复现工程化训练流程。 Founder/CEO of ThisRainIsNotARealRain. With RealRain, we’re building an Industry 4.0–inspired digital-twin and multi-agent production system for animation—making high-quality creation a stable, scalable workflow. My background spans AI4Science, computer vision & multimodal learning, physics ML, and reproducible engineering pipelines.
位置Location
北京海淀Haidian, Beijing
方向Focus
AIGC · AI4Science
工作Role
创始人/CEOFounder/CEO
我现在在创业,担任非雨(ThisRainIsNotARealRain)创始人/CEO。我们面向动漫内容生产的工业化升级,在RealRain上构建“可控、可编辑、可协作、可规模化”的 AIGC 多Agent 生产系统:将传统产线映射为可模拟/回放/调度的数字孪生产线,并用多模态资产管理与一致性控制沉淀长期内容资产。我的工程与研究经验来自计算机视觉/视觉语言与多模态、AI4Science 与 Physics ML,擅长把研究原型推进为可复现、可交付的系统。 I’m currently building ThisRainIsNotARealRain as Founder/CEO. With RealRain, we’re upgrading animation production via a controllable, editable, collaborative, and scalable AIGC multi-agent system: a digital-twin pipeline with simulation/replay/scheduling, plus multimodal asset management and consistency control for long-term content assets. My background spans computer vision/vision-language and multimodal learning, AI4Science, and physics ML—bridging research prototypes into reproducible, shippable systems.
Machine Learning and Big Data in the Physical Sciences(2023–2024,物理系) MRes in Machine Learning and Big Data in the Physical Sciences (2023–2024, Department of Physics)
Physics(2020–2023,一等荣誉) BSc in Physics (2020–2023, First Class Honours)
将扩散模型与Transformer结合,用于 Reynolds-Averaged Navier–Stokes 空气动力学仿真(翼型流动)。 Diffusion Transformers for Reynolds-Averaged Navier–Stokes simulations of airfoil flows.
模拟多源引力波时序信号,训练 WGAN 以从信号中重建参数;使用 HPC 进行训练与可视化分析。 Simulated multi-source gravitational-wave time series and trained WGANs to reconstruct parameters; trained and analyzed on HPC.
面向大规模地理遥感任务(道路提取、轨迹建模、卫星影像理解)构建 VL/多模态训练与评估流程,包含 LoRA 与全参微调实验。 Built vision–language / multimodal pipelines for large-scale GIS and remote sensing tasks (road extraction, trajectory modeling, satellite understanding), including LoRA and full fine-tuning experiments.
在宇宙学推断中结合嵌套采样与机器学习(masked aggressive flow),用于后验分布生成与参数估计;并参与维护开源 Python 包。 ML-enhanced Bayesian inference for cosmology: nested sampling + masked aggressive flow for posterior generation and parameter inference; contributed to an open-source Python package.
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