Ajey obtained his MS degree in Electrical Engineering from the Indian Institute of Technology, New Delhi. During his study, he worked on developing self-supervised methods for tissue segmentation in H&E stained tissue resections of the human duodenum. Thereafter, he moved to the Netherlands for his PhD. His work as a PhD student entails two main things. He develops deep learning algorithms to assess disease recurrence in women with early stage breast cancer to understand if any patient may be spared from adjuvant chemotherapy. Along with this, he uses deep learning for clinical histopathology to improve pathological classification of ovarian epithelial stromal tumors and sex-cord stromal tumors including other rare subtypes of ovarian cancers. Since it’s a challenge to avail richly annotated datasets of rare cancer subtypes, He focuses on developing representation learning methods that use limited or sparsely annotated data. This is achieved by leveraging the inherent features existing in the data before using them to perform tasks like tissue segmentation or tumor classification. Additionally, he uses weak labels as supervisory signals to ultimately arrive at the desired model performance. As a part of his PhD, he plans to develop these methods to be useful in clinical settings under the expert supervision of Dr. Jonas Teuwen who leads the AI for Oncology group, Dr. Hugo Horlings who heads the computational pathology group at the NKI and Dr. Clarisa Sanchez who is a full professor for AI in health at University of Amsterdam.