Microsoft Research

Episode 110 | March 11, 2020 If you want an inside look at how a research idea goes from project to prototype to product, you should hang out with Gavin Jancke for a while.

Track : Fairness, Transparency and Privacy in Digital systems Astha Kapoor, Aapti Institute ; Emre Kiciman, Microsoft Research ; Yair Zick, National University of Singapore Track Moderator : Amit

Panel Discussion: Cyber Security – National Policy and Preparedness G Narendra Nath, joint Secretary, NCSC Subrata Rakshit, CAIR DRDO Track Moderator : Madhavan Mukund, Chennai Mathematical

Privacy & Security in Healthcare Bill Thies (Microsoft Research) in conversation with Dr.

Talk: Security & Privacy around Foundational Identity for Sovereign Nations – Anadi Mishra, Architect MOSIP & Sasikumar Ganesan Security Architect, MOSIP/India Stack See more at

Track on Verification – Sanjiva Prasad, IIT Delhi ; S P Suresh, CMI ; Aseem Rastogi, Microsoft Research.

Episode 01 | March 6, 2020 Dr. Eric Horvitz is a Technical Fellow and director of the Microsoft Research Labs.

Episode 109 |

For decades, various companies, people, and institutes have worked on Brain-Computer Interface (BCI) systems aimed at healthy users, including pilots, drivers, soldiers, and the public at large.

We will be giving an overview of the recent efforts towards universal translation at Google Research.

Fireside Chat with Maarten de Rijke See more at microsoft.com/en-us/research/video/fireside-chat-with-maarten-de-rijke

Episode 108 | February 26, 2020 Dr.

In this internal Microsoft video, produced in 2001 and released publicly in 2020, research scientist Eric Horvitz provides glimpses of a set of research systems developed within Microsoft’s research

Episode 107 | February 19, 2020 Dr.

One of the ways Microsoft trains autonomous systems is participating in research focused on solving real-world challenges, like aiding first responders in hazardous scenarios.

Episode 106 |

Episode 105 | February 5, 2020 There’s an old adage that says if you fail to plan, you plan to fail. But when it comes to AI, Dr.

Cross-validation is probably the most widely used method for risk estimation in machine learning and statistics.

Data Science Summer School 2019 - Replicating "An Empirical Analysis of Racial Differences in Police Use of Force" This project replicates and extends a recent paper on racial bias in police use of

Episode 104 | January 29, 2020 Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research.

Episode 103 | January 22, 2020 Dr. Sriram Rajamani is a Distinguished Scientist and the Managing Director of the Microsoft Research lab in Bangalore.

From our recent work, it appears that we in accessibility/inclusion have been underestimating the challenge of cognitive disabilities — and digital literacy.

Sequence-level knowledge distillation (KD) -- learning a student model with targets decoded from a pre-trained teacher model -- has been widely used in sequence generation applications (e.g.

Performing well on standardized exams has been a longstanding challenge for AI. Even in 2016, the best AI system achieved less than 60% on an 8th Grade science exam challenge.

The Private AI Bootcamp offered by Microsoft Research (MSR) focused on tutorials of building privacy-preserving machine learning services and applications with homomorphic encryption (HE).

The Private AI Bootcamp offered by Microsoft Research (MSR) focused on tutorials of building privacy-preserving machine learning services and applications with homomorphic encryption (HE).

Speakers: Martha Norberg Hovd, David Froelicher, Loannis Demertzis, Kristin Lauter, Kim Laine, Yongsoo Song, Wei Dai The Private AI Bootcamp offered by Microsoft Research (MSR) focused on tutorials

How might we convey the information that is traditionally returned by a search engine in the form of a complex search engine result page (SERP) in a meaningful and natural conversation?

Michael Kearns gives a MSR AI Distinguished Lecture Series talk on his general audience AI book The Ethical Algorithm.

AutoML for neural networks aims to take out the manual guess work of finding the right architecture for a given task.