Bio: Alexandra is an AI Product Manager at Citrine Informatics, and she loves creating simple and easy to use interfaces for complicated ML products. Previously, she was the Platform Tech Lead at SigOpt, where she and her team worked on productizing Bayesian optimization as a service. Her undergraduate degree is in Computer Science, from Carnegie Mellon University. She is based out of San Francisco and is the co-organizer of the Bay Area chapter of Women in Machine Learning and Data Science.

Talk: API Design Stories

Abstract: Hear stories about successes and failures designing and building an API for Bayesian optimization as a service. Walk away from the talk with lessons and techniques you can incorporate into your own work.

Bio: Jie Chen is Managing Director in the Advanced Technologies for Modeling (AToM) Group of Corporate Model Risk at Wells Fargo. She is leading the Statistics and Machine Learning team, focusing on development of cutting-edge models, algorithms, and a computing platform to advance the Bank’s practice in the areas of credit, operational, and market risk management. She has over ten year experience on machine learning, artificial intelligence and advanced statistics in the banking industry. Jie holds a Ph.D. in Statistics from the Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology.

Talk: Some insights into interpretability of machine learning algorithms and applications to risk management

Abstract: The “black box” nature of machine learning (ML) models have limited their widespread adoption in banking and finance. We provide a framework and a suite of algorithms and associated visualization tools that help to resolve the opaqueness of ML algorithms. These are based on our research at Wells Fargo as well as recent results in the literature. The class of techniques include global diagnostics, local models, and structured neural networks for interpretability.

Bio: Anastasia is a Software Engineer at the Perception Team at Level 5 - the Self-Driving Division at Lyft. Anastasia completed her Postdoc at Stanford University, where she worked on machine learning applications for 3D shape analysis, synthesis and completion. Anastasia received a PhD in Computer Science from the Technion - the Israeli Institute of Technology. In her PhD, Anastasia worked on variational methods for image segmentation, and non-rigid three-dimensional shape correspondence.

Talk: Perception for Autonomous Cars

Abstract: In this talk, I will review the challenges involved in building the perception stack for an autonomous vehicle, and how state-of-the-art neural network models can be utilized in the perception pipeline to allow self-driving cars understand their surroundings on the road.

Bio: Anna Schneider is a Data Science Manager on the Merch Algorithms team at Stitch Fix. Her team uses ML, OR, HCI, experimentation, and more to recommend how Stitch Fix sources half its inventory. Past lives include co-founding a clean energy data startup, earning a PhD in biophysics from UC Berkeley, and co-organizing the Unconscious Bias Project. 

Talk: Just ask: Designing intent-driven algos

Abstract: Classic recommender systems are great for answering the question “what does a user want in general?”. However, they only get you partway to an answer to “what does a user want right now?”. To close the gap, it helps to capture and act on explicit user intent. I’ll share examples of this paradigm at Stitch Fix, and the resulting changes to our algorithms and architectures.