The American healthcare system is one of the most innovative in the world. But it’s also riddled with complex challenges, such as access to affordable medications, inefficiency and administrative burdens, and communication barriers between providers. There’s clearly a better way—and at Surescripts, we have a unique sightline into what that may be. In this series, host Melanie Marcus, Chief Marketing Officer of Surescripts, sits down with today’s most inspiring and innovative leaders in healt ...
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Innehåll tillhandahållet av Alex Lawton. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Alex Lawton eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
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Pablo Samuel Castro · Research Software Developer Google // Bedrock @ LAPIPA_Studios
MP3•Episod hem
Manage episode 320948600 series 2897879
Innehåll tillhandahållet av Alex Lawton. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Alex Lawton eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
New podcast from Data Stand-Up! Jesus talks to Pablo Samuel Castro, Research Software Developer at Google.
Pablo currently works a Research Software Developer for Google Brain in Montreal. His focus is mostly in reinforcement learning and machine learning applied to music and creativity.
In previous teams (Ads and Chrome) he focused on developing and applying large-scale machine learning solutions to difficult problems, addressing both theory and practice.
Specialties: Computer programming, Artificial Intelligence, Machine Learning, Music, Fully and Partially Observable Markov Decision Processes, Reinforcement Learning, Linear Programming.
…
continue reading
Pablo currently works a Research Software Developer for Google Brain in Montreal. His focus is mostly in reinforcement learning and machine learning applied to music and creativity.
In previous teams (Ads and Chrome) he focused on developing and applying large-scale machine learning solutions to difficult problems, addressing both theory and practice.
Specialties: Computer programming, Artificial Intelligence, Machine Learning, Music, Fully and Partially Observable Markov Decision Processes, Reinforcement Learning, Linear Programming.
21 episoder
MP3•Episod hem
Manage episode 320948600 series 2897879
Innehåll tillhandahållet av Alex Lawton. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Alex Lawton eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
New podcast from Data Stand-Up! Jesus talks to Pablo Samuel Castro, Research Software Developer at Google.
Pablo currently works a Research Software Developer for Google Brain in Montreal. His focus is mostly in reinforcement learning and machine learning applied to music and creativity.
In previous teams (Ads and Chrome) he focused on developing and applying large-scale machine learning solutions to difficult problems, addressing both theory and practice.
Specialties: Computer programming, Artificial Intelligence, Machine Learning, Music, Fully and Partially Observable Markov Decision Processes, Reinforcement Learning, Linear Programming.
…
continue reading
Pablo currently works a Research Software Developer for Google Brain in Montreal. His focus is mostly in reinforcement learning and machine learning applied to music and creativity.
In previous teams (Ads and Chrome) he focused on developing and applying large-scale machine learning solutions to difficult problems, addressing both theory and practice.
Specialties: Computer programming, Artificial Intelligence, Machine Learning, Music, Fully and Partially Observable Markov Decision Processes, Reinforcement Learning, Linear Programming.
21 episoder
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