About

I am a postdoctoral researcher at Wellcome Sanger Institute and University of Cambridge.

I received my PhD from University of Alberta, supervised by professor Nilanjan Ray from Computing Science department and professor Gilbert Bigras from cross-cancer institue (CCI) and UofA department of Pathology.

I recieved my B.Sc. and M.Sc. in computer engineering from Sharif University of Technology.

Follow me on X: @akbaRRnejad

Email: ah8 [at] ualberta [dot] ca, aa36 [at] sanger [dot] ac [dot] uk

Research

My research is about adopting machine learning for biomedical discovery. I work in the intersection of statistical machine learning, causal modelling, as well as recently developed tools (transformers, foundation models, generative modelling, etc).

Publications

Please check my google scholar

• L. Steele, A. Rose Foster, K. Roberts, C. Admane, S. Birk, P. V Mazin, A. Akbarnejad, Catherine Tudor, et al. “Hidden immune memory niches in inflammatory skin diseases”, bioRxiv.

• A. Rubbi, A. Akbarnejad, MV Sanian, A. Yazdan Parast, et al. “Shortest-Path Flow Matching with Mixture-Conditioned Bases for OOD Generalization to Unseen Conditions”, https://arxiv.org/abs/2601.11827.

• A Akbarnejad, L Steele, DJ Jafree, S Birk, MR Sallese, K Rademaker, et al. “Mapping and reprogramming human tissue microenvironments with MintFlow”. bioRxiv, 2025.06. 24.661094.

• A. Akbarnejad, N. Ray, P. J Barnes, G. Bigras, “Toward accurate deep learning-based prediction of Ki67, ER, PR, and HER2 status from H&E-stained breast cancer images,” Applied Immunohistochemistry & Molecular Morphology 33 (3), 131-141.

Link to the datasetihc4bc.githubio

• A. Akbarnejad and G. Bigras and N. Ray, “GPEX, A Framework For Interpreting Artificial Neural Networks,” Conference on Neural Information Processing Systems (NeurIPS) 2023.

Link to the github repo https://github.com/amirakbarnejad/gpex

Link to GPEX documentation https://gpex.readthedocs.io/en/latest/

• N. Guruprasad, A. Akbarnejad, PJ Barnes, G. Bigras, and N. Ray, “A Closer Look at Weak Supervision’s Limitations in WSI Recurrence Score Prediction”, 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023).

• Y. Yang and A. Akbarnejad and N. Ray and G. Bigras, “Double adversarial domain adaptation for whole-slide-imageclassification,” Medical Imaging with Deep Learning (MIDL), 2021.

• A. Akbarnejad and N. Ray and G. Bigras, “Deep Fisher Vector Coding For Whole Slide Image Classification,” in International Symposium of Biomedical Imaging (ISBI), 2021.

Code on github:https://github.com/amirakbarnejad/code_submission_isbi2021

• A. Akbarnejad and M. Soleymani Baghshah, “An Efficient Semi-supervised Multi-label Classi- fier Capable of Handling Missing Labels,” in IEEE Transactions on Knowledge and Data Engineering, 2018.

• A. Akbarnejad and M. Soleymani Baghshah, “A Probabilistic Multi-label Classifier with Miss- ing and Noisy Labels Handling Capability.,” in Pattern Recognition Letters, 2017.