Presentation: Machine-Learning for Trust & Safety at Airbnb

Track: ML in Action

Location: Cyril Magnin III

Duration: 4:20pm - 5:10pm

Day of week: Tuesday

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Abstract

In this talk, I will review some of the Trust & Safety challenges faced by Airbnb and other peer-to-peer marketplaces. Getting a deep understanding of the user’s identity is the foundation of trust for such marketplaces, where transactions are born online, but transition to offline and often intimate interactions. We shall cover the three crucial stages of establishing trustworthiness of a user:
(1) “verification” of the user’s identity;(2) “screening” the past of the user;(3) “predicting” the future risk in the behavior of this user.
We shall focus on the machine-learning challenges in each of these stages, and some of the solutions that have proven successful at Airbnb and Trooly.

Speaker: Anish Das Sarma

Engineer Manager @Airbnb

Anish Das Sarma is an Engineering Manager at Airbnb. Prior to joining Airbnb, Anish was the Founder and Chief Technology Officer of Trooly , a company that was acquired by Airbnb. At Trooly, Anish built the founding team, set the vision and strategy of Trooly, raised $10M in funding, and grew and managed the technology team of 20 machine-learning engineers and data scientists. Prior to starting Trooly, Anish worked at Google and Yahoo's research labs respectively. Prior to joining Yahoo research, Anish did his Ph.D. in Computer Science at Stanford University, advised by Prof. Jennifer Widom. Anish received a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology (IIT) Bombay in 2004, an M.S. in Computer Science from Stanford University in 2006. Anish is a recipient of the Microsoft Graduate Fellowship, a Stanford University School of Engineering fellowship, and the IIT-Bombay Dr. Shankar Dayal Sharma Gold Medal. Anish has written over 40 technical papers, filed over 10 patents, and has served as associate editor of Sigmod Record, on the thesis committee of a Stanford PhD student, and on numerous program committees. Two SIGMOD and one VLDB paper co-authored by Anish were selected among the best papers of the conference, with invitations to journals. While at Stanford, Anish had co-founded Shout Velocity, a social tweet ranking system that was named a top-50 fbFund Finalist for most promising upcoming start-up ideas.

Find Anish Das Sarma at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.