(00:00) Intro: Negative connotations in AI
(00:21) Synthetic data fills gaps
(00:35) Guest introduction
(01:23) Importance of data quality
(02:14) Data-centric machine learning focus
(03:02) Bias mitigation strategies
(03:41) Role of human in AI loop
(04:34) Synthetic data in AI
(05:29) Pre-trained models and data quality
(06:02) Experiments with data quality
(06:39) Leading AI and research projects
(07:24) Explainability in AI models
(08:57) Privacy concerns in AI analysis
(10:34) Open source model benchmarking
(11:33) Motivation for open source contributions
(12:28) Long-term open source involvement
(13:50) Mentoring in open source projects
(15:19) Starting with open source
(16:35) Contributing beyond code
(17:50) Building community through collaboration
(18:48) Power of open source accessibility
(19:52) Open source challenges
(20:38) Success factors for open source projects
(22:58) Career-defining moments
(24:49) First encounter with open source
(26:28) Introduction to AI through NLP
(28:02) Pivoting from PhD to industry
(29:02) Career lessons and continuous learning
(30:13) Advice for women in tech