Search Results for author: Tekin Bicer

Found 5 papers, 3 papers with code

SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates

no code implementations1 Nov 2022 Baixi Sun, Xiaodong Yu, Chengming Zhang, Jiannan Tian, Sian Jin, Kamil Iskra, Tao Zhou, Tekin Bicer, Pete Beckman, Dingwen Tao

Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24. 4X speedup over PyTorch Data Loader and 3. 52X speedup over state-of-the-art data loaders.


Deep learning at the edge enables real-time streaming ptychographic imaging

no code implementations20 Sep 2022 Anakha V Babu, Tao Zhou, Saugat Kandel, Tekin Bicer, Zhengchun Liu, William Judge, Daniel J. Ching, Yi Jiang, Sinisa Veseli, Steven Henke, Ryan Chard, YuDong Yao, Ekaterina Sirazitdinova, Geetika Gupta, Martin V. Holt, Ian T. Foster, Antonino Miceli, Mathew J. Cherukara

Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells.

Joint ptycho-tomography with deep generative priors

1 code implementation20 Sep 2020 Selin Aslan, Zhengchun Liu, Viktor Nikitin, Tekin Bicer, Sven Leyffer, Doga Gursoy

In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.

Denoising Retrieval

Deep Learning Accelerated Light Source Experiments

2 code implementations9 Oct 2019 Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Ian Foster

Experimental protocols at synchrotron light sources typically process and validate data only after an experiment has completed, which can lead to undetected errors and cannot enable online steering.

TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks

3 code implementations20 Feb 2019 Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Doga Gursoy, Francesco De Carlo, Ian Foster

We present \TOMOGAN{}, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions.


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