Dr. Ayaz's current academic interests include;

1. Failure Mode and Effects Analysis (Risk identification and prioritization of potential failure modes, Criticality assessment and severity–occurrence–detection analysis, Reliability improvement in manufacturing and service systems, Integration of FMEA with quality management systems and Six Sigma, Applications in healthcare, engineering design, and supply chains)

2. Data Mining (Classification, clustering, and association rule mining, Predictive modeling from large industrial datasets, Text and sentiment analysis for social media and customer feedback, Time-series data mining for forecasting applications, Integration of data mining with decision support systems)

3. Recommender Systems (Content-based and collaborative filtering approaches, Hybrid recommendation frameworks, Context-aware and personalized recommendation models, Applications in e-commerce, e-learning, and healthcare systems)

4. Statistical Quality Control (Control charts for variables and attributes, Statistical sampling plans for acceptance quality control, Six Sigma methodology and process capability analysis, Design of experiments (DOE) and response surface methodology, Quality assurance in production and service sectors)

5. Scheduling (Flow shop, job shop, and open shop scheduling models, Hybrid production–distribution scheduling problems, Integration of scheduling with vehicle routing problems, Energy-efficient and sustainable scheduling approaches, Real-time dynamic scheduling under uncertainty)

6. Multi-Criteria Decision Making (Classical and modern MCDM techniques (AHP, TOPSIS, VIKOR, ABAC, etc.), Fuzzy and neutrosophic extensions for decision making under uncertainty, Weighting and ranking methods for criteria evaluation, Applications in supplier selection, project evaluation, and innovation management, Hybrid frameworks combining MCDM with optimization and machine learning)

7. Machine Learning (Supervised, unsupervised, and reinforcement learning approaches, Predictive modeling and classification in industrial datasets, Industrial applications in predictive maintenance, demand forecasting, and anomaly detection)

8. Metaheuristic Optimization (Evolutionary algorithms (Genetic Algorithms, Differential Evolution, Evolutionary Strategies), Swarm intelligence approaches (Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, Grey Wolf Optimizer, etc.), Hybrid metaheuristic–mathematical programming frameworks, Applications in scheduling, logistics, energy systems, and complex engineering problems)