Db Tsai Email & Phone Number

San Francisco, Ca, Us

Wilmington, Delaware, Us

Wilmington, Delaware, Us

– My contributions, https://github.com/apache/spark/commits/master?author=dbtsai– Designed and implemented L-BFGS, Multinomial / Binomial LogisticRegression in Spark, and several other features.

Cupertino, California, Us

I lead three teams at Apple: Spark, Flink, and Data Security, growing them from 2 to 25 members. It's an honor to lead these award-winning teams, recognized with the ACM SIGMOD Award in 2022 and 2023, demonstrating our impactful work and leadership in the big data industry.1. Our Spark team at Apple is exceptionally strong, with several Spark PMC members and committers. Many members co-received the 2022 ACM SIGMOD System Award for their contributions to Apache Spark. We leverage our open-source influence to align developments with Apple's needs. The team has open-sourced Apache DataFusion Comet, a Spark native accelerator that accelerates data insights, improving business outcomes and significantly reducing compute expenses, saving millions each month for Apple.2. The Flink team at Apple includes several Flink PMC members and committers, with two co-receiving the 2023 ACM SIGMOD System Award for their contributions to Apache Flink. They developed a new Java-based Flink operator tailored to Apple's unique use cases and open-sourced it under Apache Flink. Recently, Google retired their Flink K8s operator and now recommends ours. It's incredible to see our project's adoption beyond Apple.3. At Apple, safeguarding customer data is paramount. Our security team developed Parquet Modular Encryption and Apache Iceberg Table Encryption, providing scalable columnar data encryption compatible with query engines like Spark, Trino, Flink, and Hive. This ensures the protection of vast amounts of sensitive data, spanning multiple petabytes. We've open-sourced this technology, and its widespread adoption as an industry standard has been particularly gratifying, enabling broader accessibility and utilization.

Cupertino, California, Us

Cupertino, California, Us

Responsible for creating Apple Data Platform strategies, innovating Spark functionalities for Apple's needs, and significantly contributing to Apache Spark's development. As an Apache Spark PMC member, advocated for Apple's requirements in the broader community.

Los Gatos, Ca, Us

– Lead and architect the personalized recommendation pipelines and machine learning infrastructure using Apache Spark.– Architect and implement Distributed Time Travel Machine for Feature Generation using Apache Spark, which enables our researchers to quickly try ideas with new features on historical data such that running offline experiments and transitioning to online A/B tests is seamless. This framework reduces the time to bring an offline experiments to online A/B tests from months to weeks, and significantly removes the offline/online discrepancy because of sharing the feature generation logics between offline/online. U.S. Patent filed February 2016. Patent Pending.– Implement categorical feature learner in Netflix’s in-house GBDT (Gradient Boosting Decision Tree implementation as part of the global algorithm effort to incorporate the country and language categorical signals for global launch.– Implement Weighted Logistic Regression in open source Apache Spark ML which is used in Netflix’s personalized page algorithms for constructing the rows in the homepage.– Work closely with Apache Spark community to merge our changes, and implement new features for our needs.

San Francisco, Ca, Us

– Developed scalable Multinomial Logistic Regression and Linear Regression with elastic-net regularization which linearly combines the L1 and L2 penalties in Apache Spark. Implemented OWLQN for L1/L2 regularized optimization.– Developed scalable algorithms such as Decision Tree, Variable Selection based on Information Gain, exact one-pass Linear Regression with L2 penalty, and PCA in Hadoop MapReduce.– Migrated build infrastructure from ANT to SBT for better third party library dependency management using the Maven central repository, better integration with Jenkins for continuous integration, better developement/debuging experience for developers, and easier release build.

Doctor Of Philosophy (Ph.D.) Program, Applied Physics

Master'S Degree, Electrical Engineering

Master'S Degree, Physics

Bachelor'S Degree, Physics