Hi and welcome! I am Mathew Cherukara, I received my Ph.D in materials science and engineering from Purdue University with an emphasis on computational materials science, and a bachelors in materials engineering from the Indian Institute of Technology (IIT) Madras. I am now the Group Leader of the Computational X-ray Science group at the ​Advanced Photon Source at Argonne National Laboratory. My research involves the development of new AI-enabled nanoscale imaging methods and the use of deep learning to accelerate current x-ray analysis methods. In particular, I build AI models based on deep convolutional neural networks (CNNs) to rapidly translate X-ray imaging data to real-space structure and lattice strain information. I also perform X-ray coherent diffraction imaging (CDI) experiments and X-ray fluorescence mapping to study dynamic processes at slow and ultra-fast (sub-ns) timescales. I use inputs from our X-ray imaging techniques to build experimentally informed models that can in turn be used to make predictions at spatio-temporal scales the experiment cannot access. Underlying both the analysis of data and model development are machine learning techniques that accelerate the process of data abstraction and model development. I am the recipient of research awards from the Materials Research Society (MRS), Defense Threat Reduction Agency (DTRA) and the College of Engineering at Purdue. ​
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