Manu Sharma is a software engineer with 10 years of experience building scalable, fault-tolerant distributed backend systems and production-grade RESTful microservices, currently an SDE II at Amazon. He blends strong object-oriented design, data structures and algorithms, and design patterns with hands-on expertise in C++, Java, Python, TypeScript, Spring Boot, AWS, and NoSQL technologies like Cassandra. Manu has applied his skills across domains from healthcare data consistency at Gilead to real-time visual recognition and deploying deep learning solutions on cloud and edge platforms. He contributed to the well-regarded DIPY medical imaging library as a QA/test automation engineer, improving test quality and reliability for advanced imaging algorithms. Comfortable operating at the intersection of data, systems, and ML, he focuses on production performance and real-world impact. Based in Newark, California, he combines academic training from UT Dallas and IIIT Vadodara with a pragmatic, quality-first engineering approach.
10 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at The University of Texas at Dallas
Bachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at Indian Institute of Information Technology Vadodara
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:35 commits, 25 PRs, 39 comments in 1 month
Contributions summary:Manu's commits primarily focus on fixing PEP8 style issues within the test files of the `dipy` repository. They also made updates to test functions within the `tracking` and `direction` modules, indicating a focus on maintaining and improving the testing infrastructure. The changes suggest a role in ensuring code quality and functionality through comprehensive testing of various aspects of the library, including distances, metrics, and peak direction algorithms.
Contributions:3 pushes, 2 branches in 7 years 3 months
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