My research focuses on developing AI-supported decision-making systems that solve complex hierarchical optimization problems in real-world applications. Using game-theoretic models and advanced machine learning, I create algorithms that help organizations make better strategic decisions in competitive and resource-constrained environments.
Research Domains
Artificial Intelligence
Creating intelligent systems for strategic decision-making, autonomous agents, and complex problem-solving in competitive multi-agent environments. Developing multi-agent reinforcement learning approaches where coordination and optimization are critical for effective collaboration and competition.
Machine Learning
Advancing deep learning, automated machine learning, multi-task learning, and meta-learning approaches that enable efficient optimization and intelligent decision-making in complex, data-scarce environments. Exploring hyperparameter optimization for deep neural networks and complex models.
Optimization
Designing efficient algorithms for hierarchical, Bayesian, multi-objective, and hyperparameter optimization that handle complex black-box problems where traditional methods fail. Developing methods for optimizing deep learning models and complex decision systems.
Application Areas
Strategic Decision-Making
Diplomacy, negotiations, competitive strategy, and multi-party decision systems
Defense & Security
Military strategy, resource allocation, tactical optimization, and crisis management
Crisis Management
Emergency response optimization, resource allocation during crises, and strategic decision-making under uncertainty
Criminal Network Disruption
Network analysis for identifying key nodes, disrupting criminal organizations, and optimizing intervention strategies
Supply Chain & Logistics
Supply chain optimization, logistics planning, and resource management
Network Analysis
Network optimization, influential node identification, and graph-based decision-making
Environmental Economics
Sustainable resource allocation and environmental policy optimization
Smart Manufacturing
Production optimization, process control, and industrial decision systems
Current Research Interests
AI4Diplomacy
Developing AI systems for diplomatic negotiations and international relations. Creating intelligent algorithms that support strategic decision-making in multi-party diplomatic scenarios, using game-theoretic models and multi-agent reinforcement learning to optimize negotiation strategies and outcomes.
Crisis Management & Network Disruption
Applying optimization and network analysis techniques to crisis management and criminal network disruption. Developing algorithms for resource allocation during emergencies, identifying critical nodes in criminal networks, and optimizing intervention strategies to maximize impact and effectiveness.
Operational Research Problems
Addressing complex operational research challenges using AI and optimization techniques. Developing solutions for resource allocation, scheduling, routing, and strategic planning problems across diverse domains including logistics, supply chain, and organizational decision-making.
AutoML
Advancing automated machine learning systems that optimize the entire ML pipeline from data preprocessing to model selection and hyperparameter tuning. Developing efficient methods for automated feature engineering, algorithm selection, and hyperparameter optimization to enable effective machine learning with minimal human intervention.