Meng Yang杨梦
Dr. Meng Yang is the CEO of Genoria AI, a Visiting Researcher at the Shanghai Artificial Intelligence Laboratory, an Adjunct professor of Chulalongkorn University, and a Shenzhen Municipal High-Level Professional Talent. He holds a bachelor’s degree from Tianjin University, a master’s degree from the Massachusetts Institute of Technology, and a Ph.D. from the University of Copenhagen. His research focuses on the interdisciplinary intersection of computational biology and artificial intelligence, with expertise spanning synthetic biology, complex engineering systems modeling, omics computation, and laboratory automation. His work connects two ends of the scientific process: extracting insights from biological data and enabling intelligent experimental systems to generate, analyze, and iteratively feed back biological data.
杨梦博士现任涌生智能 CEO、上海人工智能实验室客座研究员、泰国朱拉隆功大学客座教授、深圳市国内高层次人才。毕业于天津大学、麻省理工学院（硕士）与哥本哈根大学（博士），专注于计算生物学与人工智能的交叉研究。具备从合成生物学、复杂工程系统建模、组学计算到实验室自动化的交叉学科背景，研究与产业实践贯穿“理解生命数据”与“让设备生产并反馈生命数据”两端。
Genoria AI focuses on AI for Bio, self-evolving laboratories, and closed-loop wet-lab/dry-lab infrastructure, with the goal of reshaping the fundamental paradigm of scientific discovery. The company integrates AI agents, experimental reasoning models, robotics, intelligent workstations, and laboratory software to transform scientific expertise, experimental workflows, instrument capabilities, and experimental data into intelligent systems that can be understood, invoked, executed, and continuously improved. This enables laboratories to evolve from automation tools operated by humans into AI-orchestrated discovery engines, creating an integrated workflow spanning experimental design, execution, data analysis, and strategy optimization.
涌生智能聚焦 AI for Bio 自进化实验室与干湿实验闭环基础设施，致力于重构科学发现的基本范式。公司将 AI Agent、实验推理模型、机器人、智能工作站与实验室工业软件深度融合，把科学家经验、实验流程、设备能力和实验数据转化为可理解、可调用、可执行、可学习的智能系统，推动实验室从“由人操作的自动化工具”升级为“由 AI 自主调度的发现引擎”，实现从实验设计、操作执行、数据分析到策略优化的全流程闭环。
Genoria AI’s vision is Scale Agentic Discovery: to move scientific research beyond traditional models centered on individual expertise and linear trial and error toward a new paradigm in which AI and physical experimentation work collaboratively to enable scientific discovery that is verifiable, reproducible, and scalable. We are not simply automating experiments; we are redefining how scientific discovery happens.
涌生智能的愿景是 Scale Agentic Discovery：推动科学研究从依赖个人经验和线性试错的传统模式，迈向由 AI 与物理实验协同驱动、可验证、可复制、可规模化的新范式。我们不只是让实验更自动化，而是重新定义科学发现如何发生。
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Research Directions研究方向
Building agentic laboratory systems that connect experimental design, automated execution, data analysis, and iterative learning. The goal is to move laboratories beyond automation toward systems that can continuously improve through real experimental feedback.
自进化实验室：构建连接实验设计、自动执行、数据分析和迭代学习的智能实验室系统，推动实验室从“自动化工具”走向能够基于真实实验反馈持续改进的系统。
Developing AI methods for protein and enzyme engineering, from self-play reinforcement learning to the computational design of thermostable and highly active enzymes. The work links sequence, structure, function, and experimental validation to accelerate biomolecular discovery.
AI 生物分子设计：利用自博弈强化学习和计算设计等方法开展蛋白质与酶工程研究，连接序列、结构、功能和实验验证，加速生物分子的发现与优化。
Using language models, contrastive learning, neural ordinary differential equations, and graph learning to interpret genomic, single-cell, chromatin, and other multimodal omics data. The aim is to uncover regulatory programs, cellular states, and disease-relevant relationships at scale.
AI 多组学研究：利用语言模型、对比学习、神经常微分方程和图学习等方法，解析基因组、单细胞、染色质及其他多模态组学数据，揭示调控程序、细胞状态和疾病相关关系。
Building secure computational frameworks for genomic analysis and biomedical data sharing using homomorphic encryption, perturbation, and blockchain. The work enables collaborative analysis and data sharing without exposing raw genomic information.
隐私计算：结合同态加密、扰动技术和区块链，构建面向基因组分析与生物医学数据共享的安全计算框架，在保护原始基因组信息的同时支持协作分析。