Hector is a research scientist specializing in NeuroSymbolic methods for Artificial Intelligence. His work specializes in problems where the output includes variable assignment like multi-labelled classification, and in sequential decision-making scenarios such as symbolic planning and task-oriented dialogue. Hector’s research blends machine learning and symbolic reasoning to address two main goals:

• First, to enhance the performance of pure machine learning methods by integrating symbolic reasoning, domain knowledge, context, and user-provided preferences.
• Second, to develop distributional methods with provable guarantees, striving for higher efficiency by tackling manageable subqueries or specific problem subclasses.

In pursuing these goals, Hector is interested in the principled combination of various algorithms and models, ranging from symbolic models to the outputs of pre-trained machine learning models like LLMs. Such combinations shed light on the nature of statistical approximations and the role of symbolic reasoning in AI.

In the realm of symbolic reasoning, Hector explores constraints, SAT, planning, and causal queries. Within machine learning, his interests lie in task-oriented dialogue and structured prediction. His applied research primarily centers on NLP, though he also has experience with vision models and multi-labelled classification models.

In recent years, Hector served as a research scientist at the ServiceNow Research Group, following the acquisition of ElementAI. Upon returning to academia, Hector garnered accolades for his work on AI reasoning, introduced innovative algorithms, and honed his expertise in melding AI techniques.