Thesis Topics
Most decisions involve trade-offs. Whether made by individuals, organizations, or automated systems, decisions are hard because objectives conflict, consequences unfold over time, and the environment is uncertain. Multi-objective optimization and reinforcement learning provide the formal tools to address these problems.
The topics below are open for BSc, MSc, and PhD students. All connect to active research lines in the group. If you are interested, write to carlos.hernandez at iimas.unam.mx with a brief description of your background and which direction interests you.
Algorithmic Topics
Multi-objective reinforcement learning. Learning agents that optimize several conflicting objectives simultaneously, producing sets of policies that represent different trade-off solutions.
Interactive and preference-based optimization. Methods that incorporate human preferences into the search process, allowing decision makers to guide and refine solutions progressively.
Archivers and set representations for multi-objective algorithms. Design and analysis of strategies that maintain representative solution sets during evolutionary search, with formal convergence properties.
Optimization under uncertainty. Algorithms that find solutions with good quality and practical utility when the environment is noisy, measurements are imperfect, or parameters vary.
Automatic algorithm configuration and design. Methods that select and configure algorithmic components for optimization problems, including the use of reinforcement learning for this task.
Applied Topics
Adaptive decision systems in finance. Multi-objective frameworks for credit, portfolio, and risk decisions that balance profitability, fairness, and transparency.
Multi-objective optimization for urban mobility and sustainability. Algorithms that support participatory planning processes, integrating community knowledge and nature-based solutions.
Logistics and operations under uncertainty. Multi-objective methods for routing, scheduling, and resource allocation problems with stochastic elements.