Ashish Katnoria
CTO at ThinkVAL
Singapore
Summary
👤
Senior🎓
Top SchoolAsh Katnoria is a seasoned CTO based in Singapore with nine years focused on shipping production multi-agent systems, LLM orchestration, and distributed workflows that help organisations operationalise AI. He combines deep hands-on engineering roots from over two decades across financial services and enterprise software—building low-latency trading middleware and automations—to strategic product leadership at ThinkVAL, aligning technology with customer outcomes. Ash contributes to open-source (notably improving documentation in the privacy-preserving PySyft project), reflecting a practical emphasis on maintainability and collaborative engineering. Trained in mathematics and reinforced with AI and deep reinforcement learning nanodegrees, he bridges quantitative rigor with modern ML deployment patterns to turn complex research into reliable production systems.
9 years of coding experience
17 years of employment as a software developer
Bachelor of Science - BS Mathematics and Computer Applications, Bachelor of Science - BS Mathematics and Computer Applications at Goa UniversityMachine Learning Specialization, Machine Learning Specialization at CourseraDeep Reinforcement Learning Nanodegree, Deep Reinforcement Learning Nanodegree at Udacity
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
pytorch10
python10
documentation10
federated-learning8
cryptography7
deep-learning6
deeplearning-ai6
Programming languages (5)
Github contributions (5)
OpenMined/PySyft
Dec 2018 - Dec 2018
Perform data science on data that remains in someone else's server
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 commits, 3 PRs, 5 comments in 3 days
Contributions summary:Ashish primarily contributed to improving the code's documentation by adding Google-style docstrings. They modified existing docstrings to follow this style and added documentation for specific methods, such as `initialize_tensor`. The user's work focused on the `syft/frameworks/torch` directory, specifically updating docstrings across various tensor classes. This suggests a focus on improving code readability and maintainability within the PySyft framework.
pytorchcryptographyacquiringpythonscience
katnoria/cifar10-native-vs-colab
Oct 2018 - Jun 2019
Comparison of CNN training time between local GPU, Colab GPU and Colab TPU
Contributions:10 commits, 1 PR, 7 pushes in 8 months
deep-learninggpucomparisontputraining
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