Ashish Sinha

Machine Learning Researcher @ Noah's Ark Lab

ashish_sinha [AT] sfu [DOT] ca"

Bio

I recently graduated with an MSc in Computer Science from Simon Fraser University (SFU), where I was working with Prof. Ghassan Hamarneh on 3D representation learning, reconstruction, and generation of humans and vascular structures. Upon graduation, I joined Huawei Noah's Ark Lab as a Researcher in the Autonomous Driving Team under the supervision of Tongtong Cao to work on perception and simulation. Prior to this, I graduated during COVID with a Bachelors in Materials Science from Indian Institute of Technology (IIT) Roorkee where I was advised by Prof. K.S Suresh for my thesis.
My research interests lie at the intersection of 3D vision and graphics, especially single view reconstruction and neural rendering. I am also passionate about Materials informatics and discovery.
Briefly, I worked with Prof. Jonghyun Choi on domain adaptation of point cloud data. For my Bachelors' thesis, I worked on Localizing and Classfiying surface defects in surface and Scanning Electron Microscope (SEM) images of metals. During the summers of my junior year, I interned at Preferred Networks where I worked on 3D GAN-based reconstruction. I am also fortunate to have been supervised by Prof. Jose Dolz on biomedical image segmentation.

The best way to reach me is via this email.

Publications

Most recent publications on Google Scholar.
* indicates equal contribution.

Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields

Ashish Sinha and Ghassan Hamarneh

MICCAI. 2024

DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images

Ashish Sinha*, Jeremy Kawahara*, Arezou Pakzad*, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, Ghassan Hamarneh

Medical Image Analysis. 2024

MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds

Ashish Sinha, Jonghyun Choi

CVPR. Workshop on Learning with Limited Data. 2023

Deep Learning based Dimple Segmentation for Quantitative Fractography.

Ashish Sinha, K.S. Suresh

ICPR. Industrial Machine Learning Workshop. 2020. Spotlight

Multi-scale self-guided attention for medical image segmentation.

Ashish Sinha, Jose Dolz

JBHI: Journal of Biomedical and Health Informatics. 2020.

GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention.

Ashish Sinha, Yuichiro Hirano, Yohei Sugawara

NeurIPS'19, Workshop on Medical Imaging Meets NeurIPS. 2019.

Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields

Ashish Sinha and Ghassan Hamarneh

MICCAI. 2024

DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images

Ashish Sinha*, Jeremy Kawahara*, Arezou Pakzad*, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, Ghassan Hamarneh

Medical Image Analysis. 2024

MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds

Ashish Sinha, Jonghyun Choi

CVPR. Workshop on Learning with Limited Data. 2023

Deep Learning based Dimple Segmentation for Quantitative Fractography.

Ashish Sinha, K.S. Suresh

ICPR. Industrial Machine Learning Workshop. 2020. Spotlight

Ntire 2020 challenge on image demoireing: Methods and results.

Yuan et. al., Ashish Sinha

CVPR'20: NTIRE Challenge on Image Demoireing. 2020. (Rank 13)

Multi-scale self-guided attention for medical image segmentation.

Ashish Sinha, Jose Dolz

JBHI: Journal of Biomedical and Health Informatics. 2020.

GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention.

Ashish Sinha, Yuichiro Hirano, Yohei Sugawara

NeurIPS'19, Workshop on Medical Imaging Meets NeurIPS. 2019.

Vitæ

Full Resume here | Single Page Resume here.

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