KEYNOTE SPEAKERS

Dr. Lora Aroyo

Lora Aroyo is a Research Scientist at Google, NY currently working on human-labeled data quality . She is best known for her work on CrowdTruth crowdsourcing methodology. Throughout her career, Lora was a principal investigator of a large number of research projects bringing together methods and tools from human computation, linked (open) data, data science & human-computer interaction with the goal of building hybrid human-AI systems for understanding text, images, and videos with humans-in-the-loop. Her research projects focussing on personalized access to online multimedia have a major impact and established her as a recognized leader in human computation techniques for digital humanities, cultural heritage, and interactive TV. Prior to joining Google, she worked at the VU University Amsterdam as Full Professor in Computer Science and was Chief Scientist at NY-based startup Tagasauris. She is a four times holder of IBM Faculty Award for her work on CrowdTruth used in adapting the IBM Watson system to the medical domain and in capturing ambiguity in understanding misinformation. She is currently president of the User Modeling Inc, which acts as a steering committee for the UMAP conference series.

Data Excellence: Better Data for Better AI(Keynote)

Dr. David Armstrong

David Armstrong is an STFC Ernest Rutherford Fellow working on exoplanet detection, characterization and populations with the TESS mission data. He develops and use automated methods of planet detection to improve planet population statistics, both in general and with a past focus on circumbinary planets. David’s other interests include studying the habitability of known exoplanets, time-variability in planetary phase curves, and the identification and classification of eclipsing binaries and variable stars in large-scale surveys.

One of his particular interests is in applying machine learning techniques to astrophysical problems. Recently this has included the automatic selection of real candidates in transiting planet surveys, as well as creating fast detection and classification tools for eclipsing binaries and variable stars in the K2 survey. David works with the Computer Science and Statistics departments at Warwick, with the aim of applying the latest machine learning techniques to problems in the exoplanet field.

David Armstrong is currently an academic co-lead of Warwick’s Habitability GRP, a new initiative at Warwick aimed at promoting multi-disciplinary research in habitability, and he is involved with the Centre for Exoplanets and Habitability. Our current projects include analyses of galactic habitability, dynamical and biological constraints on panspermia, and the stability of DNA in non-Earth environments.

Machine Learning for Exoplanet Discovery(Keynote)