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.

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Talk : Machine-Learning for Trust & Safety at Airbnb

Other talks from track ML in Action

Tracks

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    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

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    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

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