Haji Gul in Hamburg for DAAD AINeT Fellowship
Glad to share Haji Gul from my lab, working under my and Dr. Ghani’s supervision, has been selected as a...
MuCoS: Drug Target Discovery via Knowledge Graphs
Knowledge Graphs (KGs) have emerged as powerful tools for drug discovery, but existing methods often fail to capture the multi-contextual nature of biomedical relationships. We introduce MuCoS (Multi Context Aware Sampling), a novel approach that efficiently discovers drug targets by sampling diverse contextual neighborhoods in biomedical KGs. Our method significantly improves target identification accuracy while reducing computational overhead.
Causal Learning & Body Schema in Robotics
This project focuses on how robots can abstract cause-effect relations from experience, enabling intuitive actions with unseen objects. By developing body schema models and causal learning algorithms, robots gain the ability to understand their physical capabilities and predict interaction outcomes, leading to more natural and effective manipulation skills.
Applied AI and AI-Enabled Robotics for Intelligent Manufacturing
Event: 2025 CHINA (GUANGXI) – ASEAN Vocational Education and Development Conference on “Artificial Intelligence + Advanced Manufacturing” Talk: Applied-AI &...
MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion
Traditional knowledge graph completion methods often ignore the rich contextual information available in multi-relational graphs. We propose MuCo-KGC, a multi-context-aware approach that leverages diverse contextual signals to improve link prediction performance. Our method achieves state-of-the-art results on several benchmarks while maintaining computational efficiency.
Knowledge Graph Completion & AI-Recommender Systems
Developed innovative models for predicting missing graph links in knowledge graphs, with applications spanning drug discovery, recommendation systems, and human resources. This research leverages advanced machine learning techniques to solve complex graph completion problems and has been recognized at top-tier conferences including ACL, NeurIPS, and ICML.
Open Vacancies - Master's and PhD Positions Available
Vacancies are available for local and international candidates interested in pursuing a Master’s or PhD degree in our lab. Our...
KG-EDAS: Evaluating Knowledge Graph Completion Models
Current evaluation practices for knowledge graph completion models lack standardization and often fail to capture model capabilities comprehensively. We introduce KG-EDAS (Knowledge Graph Evaluation through Data-Aware Scoring), a meta-metric framework that provides more robust and interpretable evaluation of KGC models. Our framework addresses key limitations in existing evaluation protocols.
Language & Visual Exploration in Infants
This project developed computational models explaining how babies learn language through visual exploration patterns. By studying the intersection of vision and language development, we created AI systems that mirror infant learning processes and match behavioral patterns observed in developmental psychology studies.
