Mon, 26 July 2021
Today on the show we have Elizabeth Barnes, Associate Professor in the department of Atmospheric Science at Colorado State University, who joins us to talk about her work Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. Find more from the Barnes Research Group on their site. Weather is notoriously difficult to predict. Complex systems are demanding of computational power. Further, the chaotic nature of, well, nature, makes accurate forecasting especially difficult the longer into the future one wants to look. Yet all is not lost! In this interview, we explore the use of machine learning to help identify certain conditions under which the weather system has entered an unusually predictable position in it’s normally chaotic state space.
Direct download: opportunities-for-skillful-weather-prediction.mp3
Category:general -- posted at: 6:00am PDT |
Mon, 19 July 2021
Today on the show we have Andrea Fronzetti Colladon (@iandreafc), currently working at the University of Perugia and inventor of the Semantic Brand Score, joins us to talk about his work studying human communication and social interaction. We discuss the paper Look inside. Predicting Stock Prices by Analyzing an Enterprise Intranet Social Network and Using Word Co-Occurrence Networks. |
Mon, 12 July 2021
Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. Works Mentioned: |
Mon, 5 July 2021
Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future. |
Mon, 28 June 2021
Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work “Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.” |
Mon, 21 June 2021
Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images. Follow Lio on Twitter @shamir_lior
Direct download: automatic-identification-of-outlier-galaxy-images.mp3
Category:general -- posted at: 12:11pm PDT |
Wed, 16 June 2021
Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work “Do We Really Need Deep Learning Models for Time Series Forecasting?”
Direct download: do-we-need-deep-learning-in-time-series.mp3
Category:general -- posted at: 9:10am PDT |
Thu, 10 June 2021
Sam Ackerman, Research Data Scientist at IBM Research Labs in Haifa, Israel, joins us today to talk about his work Detection of Data Drift and Outliers Affecting Machine Learning Model Performance Over Time. Check out Sam's IBM statistics/ML blog at: http://www.research.ibm. |
Mon, 31 May 2021
Julien Herzen, PhD graduate from EPFL in Switzerland, comes on today to talk about his work with Unit 8 and the development of the Python Library: Darts. |
Mon, 24 May 2021
Welcome to Timeseries! Today’s episode is an interview with Rob Hyndman, Professor of Statistics at Monash University in Australia, and author of Forecasting: Principles and Practices. |