I am a postdoctoral researcher at the MIT-IBM Watson AI Lab. I received my PhD from the University of Edinburgh, under Prof. Charles Sutton, and my MSc in Computer Science from the University of Oxford. I have also completed research internships at Amazon and Meta.
Research Highlights
Modular Continual Learning - showed that neurosymbolic methods can attain different types of knowledge transfer ( ) and can scale to large data streams ( ).Continual Pretraining - showed that continual pretraining of foundational models can be framed as a multi-armed bandit problem. The resulting method achieves SOTA results on reducing forgetting ().
Research
Focus: Enhancing learning efficiency of neural networks to reduce their reliance on large data sets.
Approach: Introducing inductive biases into models:
- by biasing a model's weights based on similar tasks
- by augmenting a model's architecture using neurosymbolic methods.
Current Research Directions
- Continual Learning
- Reasoning for LLMs
email: <first name>valkov@gmail.com