Dr. Kezia Saini

About

I am, Dr. Kezia Saini. I am working as an Assistant Professor of Mathematics at Madanapalle Institute of Technology and Science (MITS), Andhra Pradesh, India, specializing in Boolean functions, cryptography, and mathematical foundations of information security.

I received my Ph.D. in Mathematics from the LNM Institute of Information Technology, Jaipur, after completing my M.Sc. and B.Sc. (Hons.) in Mathematics from the University of Delhi. My research focuses on the construction and analysis of Boolean bent functions, higher-order nonlinearity, and their applications in cryptography and secure communications. Recently I started working with the applications of AI & ML in Engineering, Reliability Analysis of civil engineering structures, and probabilistic deep learning. Alongside my academic work, I am actively engaged in teaching and mentoring, combining rigorous theoretical insights with practical applications in cryptography and machine learning.

Employment

• Madanapalle Institute of Technology and Science Assistant Professor
  2024 - 2026

Education

• LNM Institute of Information Technology Ph.D.
  2018 - 2025

Projects & Funding

Projects & funding information is unavailable.

Publications (13)

• Predictive modeling of regional economic benefits from solar energy adoption: a multi-model regression and spatial analysis approach
  Mitigation and Adaptation Strategies for Global Change 2026 DOI: 10.1007/s11027-026-10302-8
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• AI-driven Poverty Risk Mapping and Causal Assessment of Piped Water Supply in Rural Region: A Policy Simulation Framework Aligned with SDG 1
  Water Resources Management 2026 DOI: 10.1007/s11269-026-04588-1
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• Eco-Modified asphalt binders: enhancing pavement longevity with Delonix seed extract and optimized crumb rubber granulometry
  Journal of Material Cycles and Waste Management 2026 DOI: 10.1007/s10163-025-02455-6
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• Multimodal AI framework for forecasting tree cover loss and carbon emissions in india: integrated time-series modeling, spatial volatility mapping, and explainable causal analysis
  Mitigation and Adaptation Strategies for Global Change 2026 DOI: 10.1007/s11027-025-10279-w
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• Reliability-Based Pavement Design in Hot Climates: A Probabilistic Framework Using Environmental Damage Index
  International Journal of Pavement Research and Technology 2025 DOI: 10.1007/s42947-025-00691-5
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• A Scalable Machine Learning Framework for Hydrological Water Quality Monitoring Using Physicochemical and Microbial Parameters
  Water 2025 DOI: 10.3390/w17142158
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• AI-Driven Optimization of Nano-modified Bitumen: CO2 reduction Efficiency Through Machine Learning and Optimization Framework
  International Journal of Pavement Research and Technology 2025 DOI: 10.1007/s42947-025-00593-6
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• Addressing uncertainty in pavement performance prediction: a quasi-Monte Carlo simulation-based reliability approach
  Road Materials and Pavement Design 2025 DOI: 10.1080/14680629.2025.2527202
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• A hybrid machine learning framework for predicting moisture-induced pavement failure: integrating sensitivity analysis and data augmentation
  Road Materials and Pavement Design 2025 DOI: 10.1080/14680629.2025.2520554
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• On the higher-order nonlinearity of a new class of biquadratic Maiorana–McFarland type bent functions
  Applicable Algebra in Engineering, Communication and Computing 2025 DOI: 10.1007/s00200-023-00611-9
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