Parvez Mohammed
Papers
1
Total Citations
5
H-Index
1
About
Parvez Mohammed is a researcher at the intersection of artificial intelligence and mental health diagnostics, with a growing focus on leveraging computational methods to improve clinical assessment. His most cited work, a 2024 pilot study on AI-supported diagnosis of depression using clinical interviews, has already garnered 5 citations, signaling early impact in this emerging field. This research explores how natural language processing and machine learning can analyze patient-clinician dialogue to detect depressive symptoms, potentially offering a scalable, objective screening tool. Beyond mental health, Mohammed’s broader interests extend to robotics and autonomous navigation, where he has investigated semantic mapping and path planning—moving beyond simple 2D costmaps to incorporate obstacle semantics for safer, more efficient robot movement. His work bridges the gap between cognitive AI and physical robotics, aiming to create systems that understand both human emotional states and physical environments. As a rising voice in AI-driven healthcare, Mohammed’s contributions are paving the way for more empathetic, context-aware technologies that could transform both clinical practice and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1