Abstract: 

We live in the era when everything is personalized. Fitness and running plans, nutrition and supplements recommendations, restaurant and hotel reviews are targeted specifically to our patterns and preferences. People want to have flawless, fast responses and recommendations, and yet they also want a human-like presence behind those recommendations.

One way to get closer to the goal of automated personalization is to build personalized scores based on everything that we know about a given customer (food preferences, lifestyle, data from their wearable, blood work). From there we can build a score reflecting where they are right now. Next, we suggest changes that they can make so that this score goes up.

The tricky part is deciding how we can combine different sources of data and prioritizing them. We can order them by being actionable versus just informative, we can also order them by how much they can change over time. And we can use a machine learning approach to do all this in a way that is intelligent and seamless.

Bio: 

Svetlana Vinogradova is a Lead Data Scientist at Inside Tracker, leading the Data Science team to integrate blood biomarkers and DNA data with physiological data from activity trackers to improve lifestyle recommendations and discover new patterns and optimal zones in sleep, heart rate, and blood biomarkers.

Prior to Inside Tracker, Svetlana was a research fellow at Dana Farber Cancer Institute and Harvard Medical School where she worked as a bioinformatician developing statistical methods to study epigenetic mechanisms affecting gene expression.

Svetlana has a PhD in Bioinformatics and Mathematical Biology from Lomonosov Moscow State University. During her PhD, she worked on developing algorithms to study RNA secondary structures and also enjoyed teaching and developing bioinformatics, algorithms, and R programming courses.