CV

Download CV (PDF)

Research profile

Interdisciplinary researcher with experience in microbial genomics, antimicrobial resistance, infectious-disease epidemiology, chemical genomics, microbiological laboratory work, and machine learning. My research at KAUST focuses on linking bacterial genomic variation to clinically and biologically important phenotypes using population-genomic, bioinformatic, and interpretable machine-learning approaches.

Education

• M.S. Electrical Engineering (Digital Signal and Systems Processing), National University of Sciences and Technology, Pakistan, 2017–2020
  • Thesis: Formal Verification of E-Voting Protocols Using Probabilistic Model Checking
• B.E. Electrical Engineering (Electronics), Air University, Pakistan, 2013–2017
  • Thesis: Blind Spot Detection System for Vehicles

Research experience

Research Assistant, King Abdullah University of Science and Technology (KAUST) March 2024–present

• Analyze bacterial whole-genome sequencing data and AMR profiles using bioinformatics and machine-learning workflows.
• Develop predictive and interpretable models relating genomic variation to bacterial phenotypes and clinical outcomes.
• Contribute to genomic epidemiology and population-level analyses of pathogens including Klebsiella pneumoniae and Staphylococcus aureus.
• Conduct antimicrobial susceptibility testing and genomic DNA extraction.
• Integrate genomic, clinical, phenotypic, and environmental data for infectious-disease research.
• Support laboratory safety, risk assessment, procurement, and research continuity.

Remote Researcher, Environmental AI, Pakistan June 2023–February 2024

• Applied machine learning, explainable AI, data analysis, and scientific software development to environmental and biomedical research problems.

Lab Technician, Ulsan National Institute of Science and Technology, South Korea June–October 2021 and June–October 2022

• Conducted laboratory experiments to quantify water-quality parameters.

Teacher, Dot & Line, Pakistan May 2020–March 2023

Selected projects

• Saudi Pathogen Atlas — genomic surveillance of clinical and environmental antimicrobial-resistant bacterial isolates in Saudi Arabia.
• GenoPredict — interpretable, genome-based prediction of clinically relevant outcomes.
• AI-Assisted Antibiotic Prescription — comparison of physician decisions and LLM outputs using structured clinical vignettes.

Technical and laboratory skills

• Computational: Python, NumPy, pandas, xarray, Matplotlib, TensorFlow, scikit-learn, XGBoost, LightGBM, CatBoost, Git, and Weights & Biases.
• Modeling: neural networks, time-series modeling, genotype-phenotype prediction, model deployment, and large multimodal datasets.
• Explainable AI: SHAP, partial dependence, integrated gradients, and attention-based methods.
• Bioscience: microbial genomics, bioinformatics, AMR profiling, genomic pattern interpretation, antimicrobial susceptibility testing, and genomic DNA extraction.
• Laboratory operations: safety, risk assessment, procurement, and research continuity.

Open-source software

• AutoTab — owner; machine-learning pipeline optimization for tabular and time-series data.
• easy_mpl — owner; publication-ready scientific visualization utilities.
• SeqMetrics — contributor; unified regression and classification metrics for Python.
• AquaFetch — contributor; acquisition and harmonization of water-resource datasets.
• AI4Water — contributor; data-driven environmental modeling framework.

Awards and languages

• Final Year Project Research Grant, National ICT R&D, Pakistan, 2016.
• English: IELTS 7.0, 2026.

Publications

High-dimensional characterization of genome-environment fitness landscapes in Klebsiella pneumoniae

Zhou, G., Williams, G., Millner, M. T., AlHirayban, R., Alosaimi, W., Fallatah, O., Hart, A. J., Malaikah, M., Iftikhar, S., et al., & Moradigaravand, D. (2026). High-dimensional characterization of genome-environment fitness landscapes in Klebsiella pneumoniae. medRxiv.

Genomic signatures and prediction of clinical severity in Klebsiella pneumoniae infections in a multicenter cohort

Malaikah, M., Alyami, R. Y., Huang, J., Fallatah, O., Milner, M., Zhou, G., Hirayban, R., Iftikhar, S., et al., & Moradigaravand, D. (2026). Genomic signatures and prediction of clinical severity in Klebsiella pneumoniae infections in a multicenter cohort. medRxiv.

Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment

Iftikhar, S., Ishtiaq, R., Zahra, N., Rubab, F., Lam, S.-M., Abbas, A., & Jaffari, Z. H. (2025). Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment. Chemosphere, 370, 144031.

Methicillin-resistant Staphylococcus aureus in Saudi Arabia: genomic evidence of recent clonal expansion and plasmid-driven resistance dissemination

Alhejaili, A. Y., Zhou, G., Halawa, H., Huang, J., Fallatah, O., Hirayban, R., Iftikhar, S., et al., Moradigaravand, D., & Al Salem, W. (2025). Methicillin-resistant Staphylococcus aureus in Saudi Arabia: genomic evidence of recent clonal expansion and plasmid-driven resistance dissemination. Frontiers in Microbiology, 16, 1602985.

AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization

Iftikhar, S., Abbas, A., & Beck, H. (2025). AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization. EGU General Assembly Conference Abstracts, EGU25-19958.

AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization

Abbas, A., Iftikhar, S., & Beck, H. E. (2025). AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization. Journal of Open Source Software, 10(112), 8051.

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)

SeqMetrics: a unified library for performance metrics calculation in Python

Rubab, F., Iftikhar, S., & Abbas, A. (2024). SeqMetrics: a unified library for performance metrics calculation in Python. Journal of Open Source Software, 9(99), 6450.

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

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.