Podcasts Technology Datacast Episode 40: Biological Aging, Probabilistic Programming, and Private Machine Learning with Matthew McAteer

Episode 40: Biological Aging, Probabilistic Programming, and Private Machine Learning with Matthew McAteer

Published: Aug. 22, 2020, 6 p.m.

Show Notes

  • (2:22) Matthew shared his childhood growing up interested in the field of biology.
  • (5:29) Matthew described his undergraduate experience studying Cellular and Molecular Biology at Brown University. He dropped out for a year and a half to work at MIT and test out a few company ideas in the biotech space.
  • (8:13) Matthew spent a decent amount of time in biological aging research after that, working at the Karp Lab at MIT and the Backsai Lab in Massachusetts General Hospital.
  • (13:28) Matthew recalled the story of how he switched his pursuit to a career in Machine Learning.
  • (17:14) Matthew commented on his experience as a Machine Learning Engineer freelancer on various projects in privacy and security, music analysis, and secure communications.
  • (20:36) Matthew discussed the opportunity to work with Google as a contract software developer and shared valuable lessons from contributing to the TensorFlow Probability library for probabilistic reasoning and statistical analysis.
  • (23:48) Matthew gave a quick overview of Bayesian Neural Networks (read his blog post for more details).
  • (27:18) Matthew went over his contribution to the open-source community OpenMined, whose goal is to make the world more privacy-preserving by lowering the barrier-to-entry to private AI technologies.
  • (32:29) Matthew worked on De-Moloch in late 2018, described to be "software that lets anyone easily run AI algorithms on sensitive data without it being personally identifiable" (read his blog post "Private ML Explained in 5 Levels of Complexity" for a complete description).
  • (36:17) Matthew unpacked his post "Private ML Marketplaces," - which summarizes and discusses various approaches previously proposed in this space, such as smart contracts, data encryption/transformation/approximation, and federated learning.
  • (39:45) Matthew shared his experience competing in the Pioneer Tournament.
  • (42:19) Matthew shared brief advice on how to become a Machine Learning Engineer. For the full details, read his mega-post "Lessons from becoming an ML engineer in 12 months, without a CS or Math degree."
  • (45:16) Matthew described his experience working as a Machine Learning Engineer at UnifyID, a startup that is building a revolutionary identity platform based on implicit passwordless authentication.
  • (47:52) Matthew unpacked his research paper "Model Weight Theft with Just Noise Inputs: The Curious Case of the Petulant Attacker" at UnifyID. The paper explores the scenarios under which an attacker can steal the weights of a convolutional neural network whose architecture is already known.
  • (51:55) Matthew is currently doing research with FOR.ai, a multi-disciplinary team of scientists and engineers who like researching for fun.
  • (54:14) Matthew unpacked his research at FOR.ai, namely "Optimal Brain Damage" and "BitTensor: An Intermodel Intelligence Measure."
  • (01:00:52) Matthew shared key takeaways from attending academic conferences such as ICML 2019 and NeurIPS 2019.
  • (01:03:45) Matthew unpacked his 4-part series on ML Research interview that targets aspiring ML engineers, hiring managers/senior ML engineers, and people navigating ML research that don't want to lose sight of first principles.
  • (01:07:09) Matthew unpacked his fantastic post called "Nitpicking ML Technical Debt" that breaks down relevant points of Google's famous paper on Hidden Technical Debt.
  • (01:10:49) Matthew unpacked his well-researched list that examines the under-investigated fields in 10 academic domains ranging from computer science and biology to economics and philosophy.
  • (01:14:41) Closing segment.

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