Karthikeyan Shanmugam
Staff Research Scientist at Google DeepMind
Bengaluru, Karnataka, India
Summary
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Rockstar🎓
Top SchoolKarthikeyan Shanmugam is a Staff Research Scientist at Google DeepMind in Bengaluru with eight years of research experience at the intersection of machine learning, causal inference, and online learning, and a growing focus on diffusion and foundation models. He holds a PhD in Electrical Engineering from UT Austin and has a strong industry-research track record spanning IBM Research, Google, and Bell Labs, with publications in top ML venues like ICML, NeurIPS, and AISTATS. His work blends theoretical foundations—drawing on information and coding theory—with practical ML problems such as explainability and real-time decision-making. Known for transitioning from postdoctoral and research-staff roles into senior positions at major labs, he brings both mathematical depth and production-minded research leadership. An underappreciated thread in his profile is sustained expertise in online/streaming decision problems that complements his current work on large-scale generative models.
8 years of coding experience
4 years of employment as a software developer
Master’s Degree Electrical Engineering, Master’s Degree Electrical Engineering at University of Southern CaliforniaDoctor of Philosophy (Ph.D.) Electrical Engineering, Doctor of Philosophy (Ph.D.) Electrical Engineering at The University of Texas at AustinIndian Institute of Technology Madras
Github Skills (17)
xai10
machine-learning10
google10
ai10
machine-learning-models9
artificial-intelligence8
generalization8
theory7
deep-learning7
xgboost-classifier6
optimization6
classifier6
data-science6
testing4
xgboost2
Programming languages (2)
Github contributions (5)
karthikeyansh/AIX360
Aug 2019 - Mar 2020
Open Source library to support interpretability and explainability of data and machine learning models
Contributions:5 pushes in 6 months
data-preprocessinginterpretabilityxaidata-sciencemachine-learning
IBM/OoD
Feb 2021 - Mar 2022
Repository for theory and methods for Out-of-Distribution (OoD) generalization
Contributions:2 commits, 1 PR, 1 push in 1 year
generalizationood-generalizationoptimizationoodtheory
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