穿梭于不同文化，她们一路追寻心之所向
在世界的广阔画卷中，不同地域的文化如同细腻的画笔，于无声处勾勒出不同的思维方式与独特风格。在微软亚洲研究院，许多女性研究员于各自的成长旅程中亲身领略了多元文化的碰撞与交融。她们怀揣着对广阔天地的热忱向往，穿梭于不同文化之间，汲取了丰富的见识与新知，也在这一过程中，逐渐找到了自己的心之所向。 如今，她们活跃于微软亚洲研究院不同地区的实验室，沉浸于多元文化所营造的自由、包容的研究氛围之中，收获着成长，...
The AI Revolution in Medicine, Revisited: An Introduction
| Peter Lee
Host Peter Lee, Microsoft Research president, discusses the motivation behind the new series and the GPT-4 encounter that helped him view the tech not only as a potential tool for improving healthcare but a chance to reexamine what it means…
Advancing biomedical discovery: Overcoming data challenges in precision medicine
| Mandi Hall
Our recent study in Nature Scientific Reports identified key challenges in the biomedical data lifecycle and offered 7 actionable recommendations.
人工智能如何助力精准医疗？
编者按：如今，生成式人工智能为人类社会带来了前所未有的能力，它就像一种“自然语言”可以加速生物医学发现，从而大幅提升医疗质量，同时也为降低医疗成本提供了可能。 在 Microsoft Research Forum 第五期中，来自微软研究院及行业的全球顶尖科学家们共同探讨了人工智能在医疗健康领域的变革潜力，内容涵盖医学影像、家庭健康监测、电子病历和药物发现等话题。 Hoifung Poon：大家好，...
In the news | BBC
Tech Life: The doctor will see you now
Find out how holoportation 3D telemedicine technology is helping patients in Ghana in this BBC TechLife podcast.
In the news | Fierce Healthcare
HIMSS25: Microsoft launches voice-activated AI assistant for clinicians
Microsoft has rolled out a new AI assistant for healthcare professionals that it bills as an all-in-one technology that combines voice dictation, ambient listening and generative AI. The new tool, Dragon Copilot, combines the natural language speech recognition capabilities of Dragon…
In the news | The Sequence
Making Small Models Great Achieve GPT-o1 Levels in Math Reasoning with Microsoft rStar-Math
rStar-Math is a novel approach that significantly boosts the mathematical reasoning capabilities of small language models (SLMs). This innovative system enables SLMs to achieve performance levels comparable to, and even exceeding, OpenAI’s o1, despite a significantly smaller model size. This…
WANet与WALoss：突破量子化学中的哈密顿量计算瓶颈
编者按：量子化学中的哈密顿量计算一直是制约药物筛选与新材料设计效率的瓶颈。近期，微软研究院科学智能中心、耶鲁大学及加州大学戴维斯分校联合研究提出的全新损失函数 WALoss 和深度学习框架 WANet，为这一难题提供了创新的解决方案。通过优化计算过程，WANet 显著提高了哈密顿量预测的精度与效率，尤其在大分子系统中展示了强大的扩展能力。搭配 WALoss，WANet 则能够准确预测分子能量，并有...