Jan 20 • 1HR 3M

Episode 81: Research, Engineering, and Product in Machine Learning with Aarti Bagul

 
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Datacast follows the narrative journey of data practitioners and researchers to unpack the career lessons they learned along the way. James Le hosts the show.
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Timestamps

  • (02:00) Aarti shared her upbringing growing up in India and going to New York for undergraduate.

  • (04:47) Aarti recalled her academic experience getting dual degrees in Computer Science and Computer Engineering at New York University.

  • (07:17) Aarti shared details about her involvement with the ACM chapter and the Women in Computing club at NYU.

  • (10:46) Aarti shared valuable lessons from her research internships.

  • (14:16) Aarti discussed her decision to pursue an MS degree in Computer Science at Stanford University.

  • (20:27) Aarti reflected on her learnings being the Head Teaching Assistant for CS 230, one of Stanford’s most popular Deep Learning courses.

  • (23:59) Aarti shared her thoughts on ML applications in both clinical and administrative healthcare settings.

  • (26:47) Aarti unpacked the motivation and empirical work behind CheXNet, an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.

  • (29:39) Aarti went over the implications of MURA, a large dataset of musculoskeletal radiographs containing over 40,000 images from close to 15,000 studies, for ML applications in radiology.

  • (32:50) Aarti went over her experience working briefly as an ML engineer at Andrew Ng’s startup Landing AI and applying ML to visual inspection tasks in manufacturing.

  • (36:56) Aarti talked about her participation in external entrepreneurial initiatives such as Threshold Venture Fellowship and Greylock X Fellowship.

  • (43:41) Aarti reminisced her time in a hybrid ML engineer/product manager/VC associate role at AI Fund, which works intensively with entrepreneurs during their startups’ most critical and risky phase from 0 to 1.

  • (48:43) Aarti shared advice that AI fund companies tended to receive regarding product-market fit and go-to-market fit strategy.

  • (54:04) Aarti walked through her decision to onboard Snorkel AI, the startup behind the popular Snorkel open-source project capable of quickly generating training data with weak supervision.

  • (56:36) Aarti reflected on the difference between being an ML researcher and an ML engineer.

  • (01:00:18) Closing segment.

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About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.

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