Project
Quickr: Cost-Effective Data Analytics at Scale
Quickr explores cost-effective data analytics at scale, breaking new ground in research on offering approximate answers for complex ad-hoc queries.
Algorithms
Group
Deep Learning Group
The Deep Learning group advances the state-of-the-art in deep learning to achieve general intelligence. We develop algorithms, models, and systems in deep supervised and unsupervised learning, deep reinforcement learning, and neural-symbolic reasoning, and then pursue breakthroughs in computer vision,…
Video
Faculty Summit 2016: Hot Topics
Generating Natural Questions About an Image CryptoNets: Machine Learning Inference on Encrypted Data Everest: Deploying Verified-Secure Implementations in the HTTPS Ecosystem Molecular Programming
Podcast
Neural architecture search, imitation learning and the optimized pipeline with Dr. Debadeepta Dey
Dr. Debadeepta Dey is a Principal Researcher in the Adaptive Systems and Interaction group at MSR and he’s currently exploring several lines of research that may help bridge the gap between perception and planning for…
Publication
Statistical Adaptive Stochastic Gradient Methods
Project
EdgeML
EdgeML is an open source machine learning library for enabling privacy-preserving, energy-efficient, off-the-grid intelligence in low-resource computing devices.
Project
DiskANN: Vector Search at Web Scale
We design algorithms to address the challenges of scaling ANNS for web and enterprise search and recommendation systems. Our goal is to build systems that serve trillions of points in a streaming setting cost effectively.
Group
Machine Learning and Optimization
The CORE group conducts research in the intersection of algorithms, operations research and computer systems. Our focus is on designing large-scale optimization solutions for cloud systems and applications.