AIhub monthly digest: February 2025 – kernel representation learning, fairness in machine learning, and bad practice in the publication world

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we explore kernel representation learning for time series, learn about fairness in machine learning, and tackle bad practice in the publication world. During 2024, we spoke to thirteen of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research and PhD life. Following the success of that series, we're back in 2025 to talk to this year's cohort. We began the series with two great interviews, hearing from Kunpeng Xu, a final-year PhD student at Université de Sherbrooke, and Kayla Boggess, who is studying for her PhD at the University of Virginia.