Catalyst Lab
뉴스 & 기능
뉴스에서 | New benchmarks and models for low-resource languages, medical and brain foundation models, AI in space, and innovation kits
“Cognitive Load Estimation Using Brain Foundation Models and Interpretability for BCIs” paper featured in MSR Research Focus Channel
The paper “Cognitive Load Estimation Using Brain Foundation Models and Interpretability for BCIs”, accepted for publication during ICASSP 2026, with authors Deeksha M Shama, Dimitra Emmanouilidou, Ivan Tashev, is featured in the MSR Research Focus Channel.
Holoportation™ technology, enabling real-time 3D telecommunications, has evolved from the lab to real-world use. After a decade of refinement and real-world deployment, it’s been released via open source license to encourage wider use and development.
뉴스에서 | Our Digital Life Episode 7: Audio Signal Processing in the Era of AI
Audio Signal Processing in the Era of AI with Dr. Ivan Tashev
In this episode of the IEEE Signal Processing Society podcast, Felicia Lim, a staff software engineer at Google, where she works on audio signal processing and machine learning, interviews Dr. Ivan Tashev, Partner Software Architect at Microsoft Research (MSR) –…
Crescent library brings privacy to digital identity systems
| Christian Paquin 그리고 Greg Zaverucha
Crescent helps make digital IDs private by preventing tracking across uses while letting users only disclose what’s necessary from their credentials.
Project Ire autonomously identifies malware at scale
| Brian Caswell, Dustin Fraze, Sarah Smith, Rodrigo Racanicci, Tim Middleton-Sally, Shelby Hayes, Stanley He, Katy Smith, Bhakta Pradhan, 그리고 Mike Walker
Designed to classify software without context, Project Ire replicates the gold standard in malware analysis through reverse engineering. It streamlines a complex, expert-driven process, making large-scale malware detection faster & more consistent.
Claimify: Extracting high-quality claims from language model outputs
| Dasha Metropolitansky
Claimify, created by Microsoft Research, is a novel LLM-based claim-extraction method that outperforms prior solutions to produce more accurate, comprehensive, and substantiated claims from LLM outputs.