News and Awards

Microsoft Research congratulates Yasuyuki Matsushita on being named a 2025 IEEE Fellow for his outstanding contributions to photometric 3D modeling and computational photography.

SPARROW, developed by Microsoft’s AI for Good Lab, is a solar-powered, AI-driven system for monitoring biodiversity in remote areas.

Over the past few decades, advances in information technologies have allowed firms working to optimize their supply chains to move from decision-making on the basis of intuition and experience to more automated and data-driven methods,…

Chemical synthesis is essential in developing new molecules for medical applications, materials science, and fine chemicals. This process, which involves planning chemical reactions to create desired target molecules, has traditionally relied on human expertise. Recent…

In the guts of a new type of computer, a bunch of tiny LEDs emit a green glow. Those lights have a job to do. They’re performing calculations. Right now, this math is telling the…

Microsoft launched a new artificial intelligence model today that achieves remarkable mathematical reasoning capabilities while using far fewer computational resources than its larger competitors. The 14-billion-parameter Phi-4 frequently outperforms much larger models like Google’s Gemini…

Microsoft has revealed the newest addition to its Phi family of generative AI models. Called Phi-4, the model improves in several areas over its predecessors, Microsoft claims, particularly in solving math problems. That’s partly the…

One of the key questions driving Ece Kamar’s research as managing director of Microsoft’s AI Frontiers Lab is how to coordinate networks of these agents—AI systems that can perform autonomous tasks beyond the scope of…

According to Ashley Llorens, corporate vice president and managing director at Microsoft Research, AI models will soon be able to handle far more complex tasks, such as triaging customer requests or tracking employee expenses. Additionally,…

Today we will share how the Applied Sciences team used a multi-interdisciplinary approach to achieve a breakthrough in power efficiency, inference speed and memory efficiency for a state-of-the-art small language model (SLM), Phi Silica.