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063 Kedar Hippalgaonkar: AI, Materials Science, and the Future of Discovery

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Manage episode 485326340 series 3467754
Content provided by A*StartCentral. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by A*StartCentral or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This is One North Stories, an A*START CENTRAL Podcast

Turn all the knobs at once and use AI to figure it out. This was the inspiration for Kedar Hippalgaonkar to bring AI into the scientific discovery process. Kedar is a Materials Scientist at the Institute of Materials Research and Engineering (IMRE), A*STAR with cross appointment at the Nanyang Technological University (NTU). Trained in thermoelectrics, he has been at the forefront of using AI for materials discovery in Singapore.

Kedar discusses the intersection of AI and material science, explaining how high-performance computing, machine learning, and high throughput experimentation can accelerate the discovery and development of new materials, in theory at least. And now his team is working to put it into practice.

In parallel to all that AI can do, possibly even autonomize itself and the discovery process, Kedar emphasises the need to continually invest in training the next generation of scientists in the fundamentals, absent of AI. There will always be a critical need for domain experts to rebut or validate AI's findings.

Learn more about Kedar, his lab and the amazing work they are doing:

The HIP Lab (Website)

Kedar Hippalgaonkar (LinkedIn)

Kedar Hippalgaonkar (NTU)

Thank you for listening. If you like what you’ve heard, please share with a friend. After that, please subscribe and review us wherever you get your podcasts.

Check out A*START CENTRAL (Website, LinkedIn)

If you have future episode ideas, segments ideas or want to partner with us on this exciting journey, please get in touch: [email protected]

  continue reading

63 episodes

Artwork
iconShare
 
Manage episode 485326340 series 3467754
Content provided by A*StartCentral. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by A*StartCentral or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This is One North Stories, an A*START CENTRAL Podcast

Turn all the knobs at once and use AI to figure it out. This was the inspiration for Kedar Hippalgaonkar to bring AI into the scientific discovery process. Kedar is a Materials Scientist at the Institute of Materials Research and Engineering (IMRE), A*STAR with cross appointment at the Nanyang Technological University (NTU). Trained in thermoelectrics, he has been at the forefront of using AI for materials discovery in Singapore.

Kedar discusses the intersection of AI and material science, explaining how high-performance computing, machine learning, and high throughput experimentation can accelerate the discovery and development of new materials, in theory at least. And now his team is working to put it into practice.

In parallel to all that AI can do, possibly even autonomize itself and the discovery process, Kedar emphasises the need to continually invest in training the next generation of scientists in the fundamentals, absent of AI. There will always be a critical need for domain experts to rebut or validate AI's findings.

Learn more about Kedar, his lab and the amazing work they are doing:

The HIP Lab (Website)

Kedar Hippalgaonkar (LinkedIn)

Kedar Hippalgaonkar (NTU)

Thank you for listening. If you like what you’ve heard, please share with a friend. After that, please subscribe and review us wherever you get your podcasts.

Check out A*START CENTRAL (Website, LinkedIn)

If you have future episode ideas, segments ideas or want to partner with us on this exciting journey, please get in touch: [email protected]

  continue reading

63 episodes

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