Alessandro Bellina
Principal Software Engineer at NVIDIA
Champaign, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolAlessandro Bellina is a Principal Software Engineer with 11 years of experience building high-performance, distributed systems and backend infrastructure, currently leading efforts at NVIDIA from Champaign, Illinois. He has a strong track record in large-scale data processing—contributing core fixes and feature work to Apache Spark—and in GPU-accelerated data tooling through substantive changes to rapidsai/cudf. Alessandro blends systems-level engineering (YARN, ShuffleManager, Spark SQL) with pragmatic Java and native integrations, and his work often touches startup-to-enterprise deployments across finance, research, and cloud services. He holds an M.S. in Electrical Engineering and a B.S. in Computer Engineering from UIUC, and brings a research-minded approach to production problems, demonstrated by early academic and quantitative roles that shaped his focus on performant, test-covered code.
11 years of coding experience
15 years of employment as a software developer
M.S. Electrical Engineering, M.S. Electrical Engineering at University of Illinois Urbana-Champaign
English, Spanish, Italian
Stackoverflow
1,016reputation
50kreached
24answers
5questions
Github Skills (27)
spark10
dataframes10
java10
dataframe10
scala10
javas10
gpu10
yarn310
yarnpkg10
yarn-berry10
cuda10
yarn210
data-structure9
big-data9
data-structures9
Programming languages (12)
Github contributions (5)
rapidsai/cudf
Jun 2019 - Nov 2022
cuDF - GPU DataFrame Library
Role in this project:
Back-end Developer
Back-end DeveloperContributions:311 reviews, 93 commits, 62 PRs in 3 years 5 months
Contributions summary:Alessandro primarily contributed to the `java/src/main/java/ai/rapids/cudf/ColumnVector.java` file by adding methods to the `ColumnVector` class, including `isNotNull`, `isNull`, and `replaceNulls`, and providing functionality for boolean vector conversion. The user implemented the `fill` method and a new vector creation using `fromScalar`. Furthermore, the user updated the unit tests to cover the new functions.
cudadataframe-librarydata-analysiscppcudf
apache/spark
Aug 2019 - Aug 2019
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 reviews, 1 commit, 9 PRs in 1 day
Contributions summary:Alessandro contributed to the Apache Spark project by implementing and fixing core features related to the Spark SQL and YARN components. They addressed issues in task resource scheduling, enabling fractional resource requests, and enhanced the handling of custom resources within the YARN environment. Furthermore, the user improved the startup process by refactoring the initialization of the ShuffleManager to support ShuffleManagers defined in user jars, ensuring proper functionality in various deployment scenarios. The user also added a feature to display Spark master and application ID from spark-sql.
analyticspythondata-processingsqlapache
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