Vikesh Khanna
CTO & Co-founder at Ambient.ai
Palo Alto, California, United States
Summary
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Senior🎓
Top SchoolVikesh Khanna is a CTO and co-founder with 14 years of experience building production-grade AI, computer vision, and large-scale data systems, currently leading applied-vision efforts at Ambient.ai (YC/a16z-backed). He combines deep technical chops—from shipping infrastructure at Dropbox that handled petabytes and millions of messages per second to earlier engineering roles at Microsoft and LinkedIn—with academic rigor from an MSCS at Stanford and teaching applied ML there. Vikesh is hands-on across the stack: architecting streaming data pipelines, optimizing large-scale graph and similarity algorithms (notably contributing to SNAP), and deploying foundation vision models for real-world safety and security use cases. He has a track record of turning research-grade systems into reliable products and has published and taught tools for debugging distributed graph processing. Based in Palo Alto, he balances startup leadership with active open-source contributions, reflecting a founder who still codes and prototypes the future of intelligent devices.
14 years of coding experience
9 years of employment as a software developer
Indian Institute of Technology RoorkeeDelhi Public School, HardwarMaster of Science (MS) Computer Science, Master of Science (MS) Computer Science at Stanford University
Github Skills (7)
data-structures10
graph-algorithms10
c-language10
cprogramming-language10
network-analysis10
data-structure10
numpy7
Programming languages (3)
Github contributions (5)
snap-stanford/snap
Feb 2014 - May 2014
Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:12 commits in 3 months
Contributions summary:Vikesh's primary contribution focuses on modifying and extending the `snap-stanford/snap` repository's graph analysis functionality. They refactored the `ToGraph` function by moving it to the `snap-core/conv.cpp` file and updating the API. The user also implemented `SimJoin` and `SimJoinPerGroup` functions to support similarity-based graph analysis. Further modifications include adding conversion methods from `TVec` to Numpy arrays and improving graph functionality.
stanfordpythonminingpurposegraph
vikeshkhanna/youturn
Dec 2011 - Jul 2013
Contributions:8 commits in 1 year 7 months
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