At the end of March, join us in San Francisco for an evening of lightning talks! Stitch Fix and friends from Virta Health, GitHub, and Lineage Logistics presents six short talks ranging from clothing personalization to machine learning reproducibility.
6:00 - 6:30 : people arrive
6:30 - 6:35 : Intro
6:35 - 7:20 : talks
* 6:35 - 6:50 : Hilary
* 6:50 - 7:05 : Tiferet
* 7:05 - 7:20 : Anahita
7:20 - 7:35 : mingling / break
7:35 - 8:20 : talks
* 7:35 - 7:50 : Erin
* 7:50 - 8:05 : Molly
* 8:05 - 8:20 : Chloe
8:20 - 9:15 : mingling
BIOS & ABSTRACTS
Hilary Parker (Stitch Fix)
Bio: Hilary is a data scientist at Stitch Fix focusing on what questions to ask customers in order to better understand what they’re looking for. She also hosts the Not So Standard Deviations podcast.
Talk: In Hilary’s talk she will describe how the principles of design thinking can be applied to data science problems.
Talk: Stitch Fix is a personalization company, and in order to send clients something they'll love, it first needs to stock merch that each client will love. Anna will talk about the algorithms her team uses to personalize the inventory assortment.
Anahita Hassanzadeh (Stitch Fix)
Bio: Anahita uses her optimization background to solve operations problems at Stitch Fix. Prior to Stitch Fix, she worked on digital agriculture and retail revenue management.
Talk: Anahita’s talk is about Stitch Fix’s unique styling operations problems. She’ll talk about Stitch Fix’s approach for choosing the right day to style a client’s Fix and the right stylist for the client’s particular aesthetic.
Erin Boyle (Stitch Fix)
Bio: Erin is a data scientist on the Stitch Fix AI Instruments team.
Talk: Erin’s talk will discuss modeling and interpreting the latent style preferences of Stitch Fix clients. It will also comment on the value of modular data products to benefit and align a growing company.
Molly Davies (Virta Health)
Bio: Molly Davies is a data scientist at Virta Health and a recent Stitch Fix Algorithms team alum.
Talk: Molly’s talk will share an approach to variable importance that works well with a wide range of machine learning algorithms and makes it easy to compare binary and continuous variables to each other.
Tiferet Gazit (GitHub)
Bio: Tiferet develops machine learning algorithms at GitHub, and is passionate about the positive impact that AI can have on society. She currently engineers deep learning solutions to problems in NLP and semantic understanding of source code, and previously worked in medical computer vision.
Talk: How can we democratize access to software development? This talk will discuss how machine learning can bridge the gap between natural language and code, empowering everyone to reap the benefits of computing.
Chloe Mawer (Lineage Logistics)
Bio: Chloe Mawer is a Principal Data Scientist at Lineage Logistics, the largest temperature-controlled warehouse owner and operator in the world and responsible for storing a third of the US food supply. She is also an adjunct lecturer at Northwestern University, where she teaches future data scientists what they need to know to take a machine-learning-based solution from proof of concept to production.
Talk: In this talk, Chloe will cover the ingredients necessary for developing reproducible machine learning models, which will enable effective and speedy deployment, allow for team members to build on and leverage your work, and make life easier for your future self.