Hasindri Watawana

I am excited about the advances of Deep Learning in Computer Vision. Currently, I'm a Research Assistant at the MBZUAI, UAE working with Prof. Fahad Khan on foundation models for medical data.

I graduated from University of Moratuwa, Sri Lanka, with a first class honours in Electronic and Telecommunication Engineering (BSc. Eng. Hons). My undergraduate thesis was titled Contrastive Deep Encoding Enables Uncertainty Aware Machine Learning Assisted Histopathology advised by Dr. Dushan Wadduwage, Dr. Ranga Rodrigo, and Dr. Chamira U. S. Edussooriya. I did a research internship with Dr.Kanchana Thilakarathna at University of Sydney during my undergraduate studies.

I'm interested in applications of Computer Vision and Machine Learning in general with an emphasis on Medical Image Analysis, Interpretability in Deep Learning and Self-Supervised Learning.

UPDATE: I'm currently applying for PhD positions.

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[Mar 2024]    Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning: preprint available.
[Jul 2023]    Joined MBZUAI, UAE as a Research Assistant.
[Jul 2023]    Contrastive Deep Encoding Enables Uncertainty Aware Machine Learning Assisted Histopathology: preprint available.
[Jul 2022]   Research presentation to Information Security and Privacy group of Data61, CSIRO, Australia [slides].
[Jan 2022]   Joined University of Sydney as a Research Intern.
[Aug 2021]   Our team DigitX emerged as champions in IEEE ICAS Student Challenge.

• Description: Developed a novel language-tied histopathology image representation learning framework that explores the inherent hierarchy in histopathology image and text data.


• Outcome: Achieved state-of-the-art (SOTA) performance on two medical imaging benchmarks, OpenSRH and TCGA datasets. Our framework also provides better interpretability with the language aligned representation space.

• Description: Developed a self-supervised deep representation learning model for histopathology capable of assessing prediction uncertainty.


• Outcome: Achieved SOTA performance in patch and slide level classification on multiple cancer image datasets with only 1-10% annotations compared to benchmark. Our uncertainty-aware annotation method reaches SOTA with significantly fewer annotations compared to randomly selected annotation of data.

I borrowed this website layout from here!