Private AI Bootcamp: Microsoft researchers share knowledge on cryptography, security, and privacy with PhD students
The fields of cryptography and machine learning (ML) are evolving at a rapid pace and are each complex in their own ways. The Cryptography and Privacy Research Group at Microsoft Research has led the way…
Exploring Massively Multilingual, Massive Neural Machine Translation
We will be giving an overview of the recent efforts towards universal translation at Google Research. From training a single translation model for 100+ languages to scaling neural networks beyond 80 billion parameters with 1000…
Data Management, Exploration and Mining (DMX)
The Data Management, Mining and Exploration (DMX) Group at Microsoft Research was established in 1999 and was managed by Surajit Chaudhuri. In 2020, DMX and the Database Group merged to form the current Data Systems…
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
Technology for Mental Health and Well-Being Interventions
In the mental health context, this means that we strive to reduce the gap between people who suffer from mental illness, whether it is diagnosed, long-term, or situational, and the tools and interventions that can…
AI Security Engineering—Modeling/Detecting/Mitigating New Vulnerabilities
AI and machine learning present a litany of unmitigated security threats. Research is pivoting from contrived vulns to weaponized exploitation. As a security engineer, how do I protect and defend my services against these threats?…
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…
Project Causica: Decision Optimization with Causal ML
Project Causica aims to develop machine learning solutions for efficient decision making that demonstrate human expert-level performance across all domains.