Ashish Sinha

CV/ML Researcher

Toronto, Canada

I am currently an ML Resident at AMII in the Advanced Technology team.

My research interests lie at the intersection of computer vision and machine learning, especially it’s application in solving meaningul problems in healthcare and life sciences. I am interested in building intelligent and interpretable systems that assist humans in their daily lives.

If you are interested in discussing these research topics, please feel free to reach out to me.

I hold an MSc (thesis) in Computer Science from Simon Fraser University, where I worked with Prof. Ghassan Hamarneh and a Bachelor’s in Materials Science from Indian Institute of Technology Roorkee (IITR). In the past, I’ve worked as – an ML Researcher at Huawei’s Noah’s Ark Lab, a Risk Analyst at Wells Fargo, a Research Engineer at Preferred Networks in the Bio Team, and a Research Intern with Prof. Jonghyun Choi and Prof. Jose Dolz.

Apart from research, I enjoy reading, watching movies/anime, running, eating/cooking, travelling/photography, and playing ultimate. I do everything in moderation, except for watching movies and reading.

news

Sep 2, 2025	Starting as Machine Learning Resident at Amii
Aug 1, 2025	UnPose is accepted to CoRL ‘25
Jul 3, 2024	Started as ML Researcher at Huawei Noah’s Ark Lab, Toronto

selected publications

2025

CoRL

UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation

Zhaodong Jiang, Ashish Sinha, Tongtong Cao, Yuan Ren, Bingbing Liu, and Binbin Xu

In Conference on Robot Learning (CoRL), 2025

Bib PDF Website

@inproceedings{jiang2025unpose,
  title = {UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation},
  author = {Jiang, Zhaodong and Sinha, Ashish and Cao, Tongtong and Ren, Yuan and Liu, Bingbing and Xu, Binbin},
  booktitle = {Conference on Robot Learning (CoRL)},
  year = {2025},
}

2024

MedIA

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, and Ghassan Hamarneh

Medical Image Analysis, 2024

Bib PDF Code Website

@article{sinha2024dermsynth3d,
  title = {DermSynth3D: Synthesis of in-the-wild annotated dermatology images},
  author = {Sinha, Ashish and Kawahara, Jeremy and Pakzad, Arezou and Abhishek, Kumar and Ruthven, Matthieu and Ghorbel, Enjie and Kacem, Anis and Aouada, Djamila and Hamarneh, Ghassan},
  journal = {Medical Image Analysis},
  volume = {95},
  pages = {103145},
  year = {2024},
  publisher = {Elsevier},
}

MICCAI

TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields

Ashish Sinha and Ghassan Hamarneh

In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024

Awarded Bib PDF Code

Best Paper runner-up @ SFU CS Industry Day

@inproceedings{sinha2024trind,
  title = {TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields},
  author = {Sinha, Ashish and Hamarneh, Ghassan},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  volume = {15012},
  year = {2024},
}

2020

JBHI

Multi-scale self-guided attention for medical image segmentation

Ashish Sinha and Jose Dolz

IEEE journal of biomedical and health informatics, 2020

Bib PDF Code

@article{sinha2020multi,
  title = {Multi-scale self-guided attention for medical image segmentation},
  author = {Sinha, Ashish and Dolz, Jose},
  journal = {IEEE journal of biomedical and health informatics},
  year = {2020},
  publisher = {IEEE},
}