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Education

• 2017-2020: M. S. Electrical Engineering, National University of Science and Technology, Pakistan
• 2013-2017: B. S. Electrical Engineering, Air University, Pakistan

Thesis titles:

• Master’s thesis: Formal verification of electronic voting protocols using probablistic model checking
• Bachelor’s thesis: Blind spot detection system for vehicles

Experience

• Internee Engineer at Civil Aviation Authority from June - July 2016
  • Introduction to all aviation and electronic equipment at Benazir International Airport, Islamabad.
• Internee Engineer at Pakistan Aeronautical Complex, Kamra in August 2016
  • Understanding the basic working of electronic machines used in manufacturing and overhauling air-crafts.
• Lab Technician at Ulsan National Institute of Science & Technology, South Korea from June 2021 - October 2021
  • Responsible to conduct several experiments to quantify water quality.
• Lab Technician at Ulsan National Institute of Science & Technology, South Korea from June 2022 - October 2022
  • Responsible to conduct several experiments to quantify water quality.
• Teacher at Dot & Line, Pakistan from May 2020 - March 2023
  • Introduction to all aviation and electronic equipment at Benazir International Airport, Islamabad.
• Remote Reseacher at Environmental AI, Pakistan from June 2023 - February 2024
  • Research work in the field of application of AI in water quality and wastewater treatement
• Reseach Assistant at IDE, King Abdullah University of Science and Technology, Kingdom of Saudi Arabia from March 2024 - present
  • Research in utilizing machine learning approaches to predict and interpret intricate bacterial characteristics, such as bacterial origin and antimicrobial resistance.

Skills

• Python
  • Object-oriented programming
  • Visualization (matplotlib, seaborn, plotly)
  • Array manipulation (numpy, pandas)
  • Data handling (.xlxs, .json, .csv, .h5, .nc)
• Machine Learning
  • TensorFlow (building and training neural networks for Tabular and Time series data)
  • Scikit-learn (using differnt Ensemble methods, Decision trees and Neural network models for classification and regression problems)
  • LightGBM, XGBoost, CatBoost
  • Experiment Tracking (weights&biases)
• Version Control
  • git

Publications

Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models

Iftikhar, S., Karim, A. M., Karim, A. M., Karim, M. A., Aslam, M., Rubab, F., ... & Yasir, M. (2023). Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models. Journal of Environmental Management, 328, 116969.

Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials

Sara Iftikhar, Nallain Zahra, Fazila Rubab, Raazia Abrar Sumra, Muhammad Burhan Khan, Ather Abbas, Zeeshan Haider Jaffari, Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials, Separation and Purification Technology, Volume 326, 2023

Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision

Rehan Ishtiaq, Nallain Zahra, Sara Iftikhar, Fazila Rubab, Khawar Sultan, Ather Abbas, Sze-Mun Lam, Zeeshan Haider Jaffari, Ki Young Park, Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision, Journal of Environmental Chemical Engineering, Volume 12, Issue 2, 2024, 112238, ISSN 2213-3437, https://doi.org/10.1016/j.jece.2024.112238. (https://www.sciencedirect.com/science/article/pii/S2213343724003683)