Kavitha Srinivas
Research Staff Member at IBM Research
Village of Tarrytown, New York, United States
Summary
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SeniorKavitha Srinivas is a research staff member at IBM Research and co-founder/CTO of Rivet Labs, combining 12 years of applied AI experience with a 25-year background in semantic technologies, knowledge graphs, reasoning, and planning. She builds recommendation engines for knowledge workers and has hands-on experience with TensorFlow, Keras, and program synthesis tools like Rosette and Racket. Her research blends deep semantic expertise with practical system design, driving AI research that scales to real-world data workflows. An active contributor to program-analysis tooling, she improved graph-coloring and partial-coloring algorithms in the well-regarded WALA codebase, showing strength in core algorithms and static analysis. Based in Tarrytown, NY, she moves between research and product roles, translating theoretical advances into usable engineering solutions. Colleagues value her ability to bridge symbolic reasoning and modern machine learning in production-grade systems.
12 years of coding experience
Stackoverflow
21reputation
973reached
0answers
1question
Github Skills (11)
javas10
graph-algorithms10
static-analysis10
java10
program-analysis9
data-structure9
data-structures9
testing8
google-cloud-spanner6
google-cloud-dataflow6
callgraph6
Programming languages (11)
Github contributions (5)
wala/WALA
Nov 2013 - Dec 2013
T.J. Watson Libraries for Analysis, with frontends for Java, Android, and JavaScript, and may common static program analyses
Role in this project:
Backend Developer
Backend DeveloperContributions:5 commits in 7 days
Contributions summary:Kavitha primarily focused on implementing and improving the `WelshPowell` algorithm within the context of the WALA project. Their work included refactoring and modifying the algorithm's implementation in Java, specifically targeting partial coloring functionality and refining the comparator used for node ordering. They also contributed to associated test cases and configuration files to improve the correctness and quality of the algorithm. These changes suggest a focus on core program analysis techniques and graph-based algorithms.
frontendsanalysesslicingcallgraphabstract-interpretation
Quetzal-RDF/synthesis
Apr 2017 - Oct 2018
Contributions:181 pushes in 1 year 5 months
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