معرفی
Elena Umili is an Assistant Professor (RTD-A) at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome, specializing in Neurosymbolic AI research. Her work bridges deep learning with symbolic reasoning systems.
She received her PhD in Engineering in Computer Science from Sapienza University of Rome in 2023 under the supervision of Prof. Giuseppe De Giacomo and Prof. Roberto Capobianco. Her doctoral thesis focused on 'Discovering Logical Knowledge in Non-Symbolic Domains'.
Her research interests center on Neurosymbolic AI integration, particularly:
- Combining deep machine learning with symbolic reasoning
- Temporal logic specifications in neural systems
- Automata learning through neural relaxations
- Non-Markovian reinforcement learning tasks
- Visual grounding of logical specifications
Analysis of her recent publications reveals a strong focus on neural-symbolic integration where she develops frameworks like DeepDFA and Neural Reward Machines. Her work consistently addresses the challenge of incorporating logical constraints into deep learning systems, with applications spanning robotics, sequence generation, and visual reasoning. The research demonstrates increasing sophistication in handling temporal logic specifications within neural architectures.
She is an active member of research groups focused on Artificial Intelligence and Knowledge Representation, as well as Artificial Intelligence and Robotics at Sapienza University.
While no formal advisees or major awards are currently documented in her public profile, her recent publications indicate significant contributions to the neurosymbolic AI field through top-tier conferences including ECAI, KR, and specialized workshops. Her work shows strong potential for future impact in bridging the gap between neural and symbolic AI paradigms.
