Dr Pavithra Mohanadas

Dr Pavithra Mohanadas

  • Lecturer
Department of Data Science and Artificial Intelligence
  • School of Computing and Artificial Intelligence
Faculty of Engineering and Technology
SDGs Focus

Biography

Dr Pavithra Mohanadas is a PhD-qualified astrophysicist with strong expertise in data-intensive research, specializing in the analysis of large-scale astronomical datasets from multi-wavelength observations. Her academic background bridges astrophysics, statistical analysis, and computational modelling, with hands-on experience in data processing, pipeline development, and extracting insights from complex, high-dimensional datasets. She has applied advanced quantitative and programming skills to real-world scientific problems, giving her a strong foundation in data science methodologies and artificial intelligence concepts. Passionate about teaching, she aims to translate her research experience into engaging and practical learning in Data Science and AI, equipping students with both theoretical understanding and industry-relevant analytical skills.

Academic & Professional Qualifications

  • Doctor of Philosophy - Astrophysics, The National University of Malaysia, Malaysia (2024)
  • Bachelor of Science - Physics, Universiti Putra Malaysia, Malaysia (2020)

Research Interests

  • Intersection of data science, artificial intelligence, and scientific data analysis
  • Application of machine learning and statistical methods for large-scale and high-dimensional data 3
  • Deep learning, pattern recognition, and anomaly detection in complex datasets
  • Development of data-driven models for predictive analytics and scientific computing

Teaching Areas

  • Programming Python, Java for data science and AI
  • Mathematical foundations for AI, including statistics, linear algebra, and discrete mathematics

Courses Taught

  • Programming Principles
  • Community Service for Planetary Health

Notable Publications

  1. NGC 4117: A New Compton-thick AGN Revealed by Broadband X-ray Spectral Analysis – Applied advanced spectral fitting and data modelling.
  2. Broadband X-ray Spectral Analysis of the Changing-look AGN Candidate NGC 4102 – Applied advanced spectral fitting and integrated multi-wavelength datasets for cross-validation.
  3. The Compton-thick AGN Population and the NH Distribution of Low-mass AGN in our Cosmic Backyard

Achievements & Accolades

PHD Project: Characteristics of Faint Active Galactic Nucleus in the SWIFT-BAT 105th Month Catalog and Their Relation to the Characteristics of Occupied Galaxy