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b'With over 20 years of experience working in math, applied linguistics and engineering in both Higher Education and industry, applying his skills to everything from cloud & middleware infrastructures to data science, Natural Language Processing to Machine Learning/AI for knowledge networks, graph systems, interactive visualization platforms, and behavioral modeling, our guest this week, Microsoft\\u2019s Krishna Madhavan is easily one of the best examples we\\u2019ve probably had so far in Season 3 of what we mean by a \\u2018Learning Scientist.\\u2019 You\\u2019ll recall that in Season 3, our \\u2018The Learning Scientists\\u2019 mini-season on LITNW, we\\u2019re meeting the innovators drawing on Data/Social Science/Computer Science and Neuroscience-based practices to move the L&D profession forward and mining the new insights and tools we need to help us build a better model for Workplace Learning, especially as we start to move to a post-COVID \\u2018New Normal.\\u2019 A winner of multiple academic rewards and a former tenure faculty member at a leading mid-Western US University, Krishna is now a co-founder and Director of the new Worldwide Learning Innovation Lab at Microsoft, an innovation center set up in Redmond to cross boundaries and experiment - again, things we love to hear on this podcast! Please note that we recorded our chat with Professor Madhavan before the Lockdown, but in our convo we still heard a lot of great things, starting with how a math and stats guy ended up with a PhD from an English department to: what his 1.5-year old research entity is all about, and why being able to sit across so many product groups at Microsoft helps it achieve that; the kind of higher-order problems he\\u2019s interested in now, and in his past, all the way back to his start in India; why speaking six languages isn\\u2019t seen as that big a deal where he comes from; the differences (good and bad) between The Academy and The Corporation; the central role of ethics in what he and his team are looking at; why accessing Microsoft\\u2019s incredible data treasures is actually (and reassuringly) made as hard as possible; how, in practical terms, you lead for innovation and set up an experiment-minded culture; the benefits of a truly multi-disciplinary approach and what that means for the Learning Science project; and much more.'