Chancellor's Fellow (AI and Datascience), School of Informatics, University of Edinburgh
Website: https://malkin1729.github.io/
Excerpt:
I work on algorithms for deep-learning-based reasoning and their applications. I am specifically interested in the following subjects.
- Machine learning for generative models, in particular, induction of compositional structure in generative models and modeling of posteriors over high-dimensional explanatory variables (including with continuous-time (diffusion) generative models). Much of my recent work is on generative flow networks, which are a path towards inference machines that build structured, uncertainty-aware explanations for observed data.
- Applications to natural language processing and reasoning in language: what large language models can do, what they cannot do, and how to overcome their limitations with improved inference procedures. I view human-like symbolic, formal, and mathematical reasoning via Bayesian neurosymbolic methods as a long-term aspiration for artificial intelligence.
- Applications to computer vision: notably, below you can find my work on AI for remote sensing (land cover mapping and change detection), which can be used for tracking land use patterns over time and monitoring the effects of climate change.