Deep Learning · Robustness · Efficiency . Histopathology
Dhananjay Tomar
I build robust and efficient deep-learning systems for real-world data.
I am a researcher at the Institute for Cancer Genetics and Informatics (ICGI), Oslo University Hospital (OUS), and a PhD candidate at the University of Oslo (UiO). My work spans computer vision and histopathology, with a focus on robustness, efficiency, and the training dynamics of neural networks.
Deep Learning researcher Oslo, Norway
Experience
Research, teaching, and production machine learning.
Oct 2025 — Present
Scientific Developer (research role) @ ICGI
Oslo University Hospital
Researching cancer-survival prediction, including a label-denoising method and methods for making models more robust to scanner differences.
Oct 2021 — Present
PhD Candidate @ University of Oslo
Researching robust and efficient computational pathology, including NeurIPS 2024 work on nuclear-shape-guided learning; two additional manuscripts are under review.
I co-supervised two master's students and, for nearly four years, taught the course Deep Learning for Image Analysis, where I lectured, designed mandatory assignments, contributed to exams and marking, supported students, and helped run the course.
Jan 2018 — Sep 2021
Machine Learning Developer @ SAP
Designed major components of the Document Information Processing service that processed over a million financial documents per month. My work spanned the full spectrum of applied ML—from delivering a successful accruals automation POC to developing production algorithms and patenting a novel synthetic data generation method.
Selected research
Published work and earlier projects; manuscripts under review stay anonymous here.
2024
Are nuclear masks all you need for improved out-of-domain generalisation?
Uses nuclear masks during training to steer a cancer classifier toward nuclear shape and organisation, while requiring only the original image at inference; improves transfer across hospitals and robustness to corruptions and adversarial attacks.
2017
Curriculum learning and neural-network training dynamics
Tracked examples through training using time-to-learn, loss changes, and computation selected by Highway Network gates. The experiments exposed multiple notions of difficulty and showed that a batch's effect changes with the model's training state.
2017
Pruning Highway Networks for reducing memory usage
Turned transform gates into pruning signals, reducing a CIFAR-10 Highway Network from 34 MB to 5.2 MB—about 85% smaller—with a 1.72 percentage-point accuracy drop.
2017
Feature Selection Using Autoencoders
Introduced a feature-selection method that filters uninformative inputs by tracing high-variance connections backwards through an autoencoder; evaluated on five public datasets, including 59% pixel removal on MNIST with roughly a 1% accuracy loss.
Patent & service
Selected contributions beyond the main publication list.
Reviewing
NeurIPS 2025 Top Reviewer Award
Recognized as a Top Reviewer (Top 10%) for delivering high-quality, timely paper evaluations.
Official Top Reviewer ListPatent · US 20230334309 A1
Synthetic training data for document extraction
Conceived the core idea for a synthetic training-data generation method that applies macro and micro augmentations to electronic-document templates, generating new documents for training ML models.
View patentEducation
Formal training across India, Switzerland, and Norway.
2021 — Present
PhD in Informatics
University of Oslo
Robust and efficient deep learning for computational pathology.
2015 — 2017
MSc in Informatics
Università della Svizzera italiana
Master's thesis at IDSIA on curriculum learning and training dynamics.
2011 — 2015
BTech in Computer Science and Engineering
Jaypee Institute of Information Technology
Major project on neural-network-based feature selection.
Latest writing
Research stories, paper walkthroughs, and notes.
Jul 19, 2026
What happens when a cancer model can see only cell nuclei?
The story behind our NeurIPS 2024 paper on nuclear shape, hospital shifts, and robust cancer classification.
Jan 08, 2021
My favourite NeurIPS 2020 picks
Summaries of some NeurIPS 2020 papers that intrigued me.
Dec 05, 2020
4 deep learning papers that will surprise you
Some lesser known papers which show some amazing aspects of neural networks you didn't know of.
Research, collaboration, or a good paper recommendation