Machine learning holds significant promise for fields like proteomics, therapeutics, and more—but blockers like access to datasets and issues of health privacy make progress complicated. Alexander Johansen is a Ph.D. student in computer science studying the intersection between computer science, bioinformatics and digital health.

Within his lab at Stanford University, Alexander has applied Natural Language Processing to proteins, explored the history of wearables data privacy, and more. In this episode, SigOpt’s Head of Engineering Michael McCourt speaks with Alexander about his pioneering work and how SigOpt has played a role in advancing progress.

• 1:29 - Intro 2:09 - What Alexander's lab works on
• 5:40 - Who makes up Alexander's team
• 8:39 - Should only the FDA be involved?
• 12:31 - NLP for proteomics 
• 17:44 - How Alexander uses SigOpt to power his research
• 21:24 - How Alexander collaborates with other areas of study
• 24:28 - Picking the right health data to prioritize 

Follow Alexander on Twitter: https://twitter.com/AlexRoseJo 

Learn more about the Stanford Center for Personalized Health: https://stanford-health.github.io/ 

Learn more about SigOpt at sigopt.com and follow us on Twitter at twitter.com/sigopt 

Subscribe to our YouTube channel to watch Experiment Exchange interviews: https://www.youtube.com/channel/sigopt