Scholar
Veronika Thost
Google Scholar ID: TyScgJ0AAAAJ
MIT-IBM Watson AI Lab, IBM Research
Citations & Impact
Citations
1,422
H-index
18
i10-index
24
Publications
20
Co-authors
6
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Published multiple papers, including:
- - Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction (ICML)
- - Inductive Link Prediction Using Hyper-Relational Facts (IJCAI)
- - Data-Efficient Graph Grammar Learning for Molecular Generation (ICLR)
- - Combinatorial Scientific Discovery: Finding New Concept Combinations Beyond Link Prediction (ICLR)
- - CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks (NeurIPS)
- - A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving (AAAI)
- - Directed Acyclic Graph Neural Networks (ICLR)
- - Reading between the lines with graph deep learning for NLP (AAAI)
- - Attributed Description Logics: Reasoning on Knowledge Graphs (IJCAI)
Research Experience
- Worked for 1.5 years at IBM Research Zurich, focusing on AI for chemistry; was a postdoctoral researcher at TU Dresden, where she worked on query answering over knowledge graphs, existential rules, and description logics, mainly in terms of complexity theory.
Education
- Received her PhD in computer science from TU Dresden, Germany in 2017. Advisor information not provided.
Background
- She is a staff research scientist at IBM Research, working in the MIT-IBM Watson AI Lab. Her research focuses on learning over structured data, particularly on graphs in chemistry and material science. She also explores various topics in the context of large language models.
