Catalyst is an interdisciplinary machine learning and systems research group exploring problems to automate learning systems. Our research spanning multiple layers of the machine learning and system stack. Our group is a collaboration between researchers from the Machine Learning Department, Computer Science Department and Electrical & Computer Engineering Department at the Carnegie Mellon University.



End-to-end compilation of ML applications with dynamic and irregular control flow and data structure accesses

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Apache TVM Stack

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

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Hyperband and ASHA

Principled early-stopping approaches for hyperparameter optimization

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Automatically Discovering Fast Parallelization Strategies for DNN Training

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The Tensor Algebra SuperOptimizer for Deep Learning

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A Scalable Tree Boosting System

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The rapid advance in ML models and ML-specific hardware makes it increasingly challenging to build efficient and scalable learning systems that can take full advantage of the performance capability of modern hardware and runtime environments. Today's ML systems heavily rely on human effort to optimize the training and deployment of ML models on specific target platforms. Unlike conventional application domains, learning systems need to address a continuously growing complexity and diversity in machine learning models, hardware backends, and runtime time environments. Our response to this unique challenge in ML systems is Catalyst (CMU automated learning systems group), a joint research group across the area of machine learning, systems, programming languages, and computer architecture. Our mission is to build ML algorithms and learning systems that automate cross stack optimizations by leveraging mathematical and statistical properties of ML computations and by co-designing systems, hardware, and ML algorithms.



Tianqi Chen
Assistant Professor
Zhihao Jia
Assistant Professor
Ameet Talwalkar
Assistant Professor
Eric Xing


Sheng Xu
M.S, 2020. Now at AWS.
Poojan Palwai
B.S, class of 2023