BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//project/author//NONSGML v1.0//EN
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTEND:20210304T120000Z
UID:d69550390debc667537ab58966ec6ce9-130
DTSTAMP:19700101T120015Z
DESCRIPTION:1. The need for Inclusive STEM Education - ground realities and collaborative solutions <br> 2. Using Data to Build Better Systems and Services
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/130/1-the-need-for-inclusive-stem-education-ground-realities-and-collaborative-solutions-2-using-data-to-build-better-systems-and-services/
SUMMARY:1. India produces one of the highest number of STEM graduates in the world. India also has the highest population of visually impaired persons. However, out of the millions of visually impaired people, less than 50 students have studied STEM subjects  beyond high school due to the non-inclusive and largely inaccessible education system.  Due to this, people with visual impairments are deprived from choosing the currently flourishing careers in Science and Computing. In this talk, Vidhya will share her experiences in studying STEM subjects as a visually impaired student and role of technologies in enabling independence both in her education and work.  She will also share the various initiatives which she and the team at Vision Empower have undertaken to make STEM subjects accessible to visually impaired children over the past 3 years.
<br>
2. Today’s systems and services are large and complex, often supporting millions or even billions of users. Such systems are extremely dynamic as developers continuously commit code and introduce new features, fixes and, consequently, new bugs. Multiple problems crop up in such a dynamic environment, from misconfiguration of essential services, very slow testing and deployment procedures, and extended service disruptions when catastrophic bugs hit deployment. Nevertheless, with the advent of cloud-based services, new opportunities to use machine-learning to alleviate such problems have emerged. Large-scale services generate petabytes of code, test, and usage-related data within just a few days. This data can be potentially harnessed to provide valuable insights to engineers on how to improve service performance, security and reliability. However, cherry-picking important information from such vast amounts of systems-related data proves to be a formidable challenge. Over the last few years, we have been working on leveraging code, test logs and telemetry as data to build several tools that help develop and deploy systems faster while maintaining and even improving system reliability. My talk will first describe the challenges that arise from using machine learning on such systems-related data and metadata. Next I will do a deep-dive on the design of a few tools that we built and are being used by several of Microsoft’s services.
DTSTART:20210304T120000Z
END:VEVENT
END:VCALENDAR