Curriculum Vitae

EDUCATION

Doctor of Philosophy – AI for Medical Diagnosis and Care
University of Leeds 2022 — Present

• Thesis Title: A virtual patient to support medical training
• EPSRC PhD Studentship
• Expected completion: Oct 2026

Master of Computer Applications
University of Delhi 2019 — 2022

• Thesis Title: XAI-based Biomarker Discovery for Breast Cancer Stratification using Multi-Omics Data
• \(1^{st}\) Class Honours (79.1%)

B.Sc. (Hons) Computer Science
University of Delhi 2016 — 2019

• Indraprastha College for Women
• \(1^{st}\) Class Honours (84.5%)

SELECT EXPERIENCE

Teaching Assistant

School of Computer Science, University of Leeds Oct 2023 – Present

• COMP5122M Data Science
  Module Lead: Dr Duygu Sarikaya
• COMP2121 Data Mining
  Module Lead: Prof Eric Atwell
• COMP5840M Data Mining and Text Analytics
  Module Lead: Prof Eric Atwell
• COMP2611 Artificial Intelligence
  Module Lead: Dr Brandon Bennett

Researcher

Data Study Group, Alan Turing Institute Dec 2023 – Dec 2023

A short research project in collaboration with the UK Centre for Ecology & Hydrology to apply machine learning for biodiversity monitoring. REPORT

• Developed methods for analysing how variations in weather (temperature, rainfall, sunshine and wind) affect species abundance for moth populations.
• Produced interactive data visualisations showing spatial distributions of different species to promote stakeholder engagement and interest in biodiversity monitoring.

Fellow

Data Science for Social Good Foundation and University of Warwick Jun 2022 – Aug 2022

A joint project with UNICEF and Save the Children. The aim was to predict multi-dimensional child poverty in Sub-Saharan Africa to optimise resource allocation.

• A Python package to produce high-resolution poverty estimates using data from Google Earth Engine, Facebook’s ads connectivity graph and Relative Wealth Index, and open telecommunications tower networks. Demographic and Health Surveys (DHS) to ground predictions of the machine learning model(s).
• Led the development of an autoencoder-based feature extractor for GEE rasters and produced uncertainity estimates for the model(s) using conformal prediction.
• Results presented at the UN World Data Forum 2023 and published as a chapter in the Handbook on Child Poverty and Inequality.

TECHNICAL SKILLS

• Languages: Python, R, C++, SQL, HTML/CSS, JavaScript, LaTeX.
• Frameworks & Tools: Git, Slurm, Tensorflow, PyTorch, Keras, Transformers, Scikit-Learn, Streamlit, Quarto.