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Can Karakus is an Applied Scientist in AWS, working on distributed machine learning problems. He received his PhD in Electrical Engineering from UCLA, in 2018, focusing on information theory and distributed optimization. He has previously worked as a research intern at EPFL, Qualcomm Research, and Technicolor Resarch. He is a recipient of UCLA Graduate Division Fellowship and Qualcomm Roberto Padovani Award. His broad interests include machine learning, optimization, distributed computing, information theory, and wireless networks.
Accelerators, FPGA, and GPUs
Machine Learning, Deep Learning and Artificial Intelligence
Scalable Computing
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