14 October 2019
Talk 1: ML in healthcare data: practical considerations for a generalizable model by Fiona Grimson and Benjamin Bray (IQVIA)
Talk 2: Building generative models of symptomatic health data for autonomous deep space missions by Krittika D'Silva
Krittika will speak about her work at NASA FDL in which she examined how AI can be used to support medical care in space. Future NASA deep space missions will require advanced medical capabilities, including continuous monitoring of astronaut vital signs to ensure optimal crew health. She will discuss how biosensor data collected from NASA analog missions can be used to train AI models to simulate various medical conditions that might affect astronauts. She will also discuss the future of AI and space medicine.
Talk 3: Understanding text in images and videos with machine learning by Viswanath Sivakumar
Understanding text that appears on images in social media platforms is important not just for improving experiences such as the incorporation of text into screen readers for the visually impaired, but they also help keep the community safe by proactively identify inappropriate or harmful content in a way that pure object detection or NLP systems alone cannot. This talk describes the challenges behind building an industry-scale scene-text extraction system at Facebook that processes over 2 billion images each day. I'll cover the Deep Learning methods behind building models that perform detection of text in arbitrary orientations with high-accuracy, and how simple convolutional models work extremely well for recognizing text in over 50 languages. A critical aspect of the work is scaling up these models for efficient server-side inference. I'll dive into quantization methods to run neural networks with 8-bit integer weights and activations instead of 32-bit floating points, and the challenges involved in bridging the accuracy gap.
Talk 4: Applying machine learning skills to Trading by Chandini Jain
ML techniques have found a variety of applications in Trading, this session will attempt to explore some of the ways in which trading problems can be solved using ML techniques.