More & more companies are using data science & machine learning for their products & business processes. In this event by WiMLDS & Yelp, you will hear from data science leaders from Yelp & other top tech companies sharing their machine learning best practices. The event will start with a talk from Yelp on adversarial machine learning, followed by a panel discussion with invited speakers.

SCHEDULE

6:00 Doors open
6:30 Welcome from WiMLDS, Yelp
6:35 Talk by Divya Sambasivan from Yelp
7:00 Panel discussion
8:00 Networking
9:00 Doors close

ABSTRACTS & BIOS

TECH TALK: Best Practices in Adversarial Machine Learning

Adversarial Machine Learning is building ML models against adversaries who are expected repeatedly to analyze the models & devise attacks.
Along with standard challenges in machine learning, adversarial machine learning poses other interesting challenges such as modeling in a very imbalanced data space & having the ability to quickly detect & counter-act against attack vectors & hence model drift.

This talk will specifically discuss modeling in an imbalanced data space (sampling techniques), importance of having robust features, optimization metrics, monitoring feature & model drift & quick retraining. It will also cover the infrastructure that is required to support quick response to attacks.

SPEAKER BIO:

Chole Liu - Head of Data Science @ Lumos Labs
Chloe leads the data science team at Lumos Labs. The goal of data science at Lumos Labs is to help creating better user experience as well as more efficient brain training system. Prior to Lumos Labs, Chloe worked at Doximity, & Hotwire, where she helped grow the impact of the data science & analytics team, with the focus on marketing, pricing, & product engagement.

Divya Sambasivan - Software Engineer @ Yelp
Divya is a software engineer & a member of the Spam Detection team at Yelp. She has an undergrad & Masters degree in Computer science with a specialization in machine learning. She has spent the last three years working in the space of adversarial machine learning as part of ensuring the highest quality of content at Yelp! Outside of work she loves to run, travel & try out new restaurants in the city!

Inaz Alaei Novin - Software Engineer- Machine Learning @ Yelp
Inaz is originally from Iran. She did her M.A.Sc at The University of Toronto in Canada, & worked there for a year at Modiface. Currently she works as a Machine Learning engineer at Yelp.

Jayodita Sanghvi - Director of Data Science @ Grand Rounds
Jayodita is Director of Data Science at Grand Rounds. Grand Rounds focuses on navigating patients to high quality, appropriate clinical care. Data Science at Grand Rounds uses messy, challenging healthcare data to try to understand provider quality & patient needs. Prior, Jayodita was a doctoral & post-doctoral researcher building stochastic simulations of living cells, at Stanford & Berkeley respectively. Jayodita loves learning from & mentoring the Women in Tech community!

Jenny Lin - Data Scientist @ Yelp
Jenny is a Data Scientist at Yelp, working on machine learning, statistics & experimentation. In her former life, she was an Assistant Professor of Economics at Oregon State University specializing in international trade theory & international finance. Jenny completed a Ph.D. in Economics at the University of Michigan, where she built mathematical models of trade composition, validated by econometric models of actual trade data.

Sarah Loos - Senior Software Engineer @ Google
Sarah Loos is a senior software engineer at Google AI, working on deep network guided proof search. She completed a Ph.D. in the Computer Science Department at Carnegie Mellon University, creating differential Refinement Logic for comparing & formally verifying hybrid systems. During her time at CMU, Sarah was a Department of Energy Computational Science Graduate Fellow & a National Science Foundation Graduate Research Fellow.