Vaibhav Gupta, M.Sc.
Profile
Vaibhav Gupta has been a research assistant at the Chair of Data Engineering since 2024. His research at HSU focuses on developing reliable and interpretable data-driven methods across healthcare and materials science. In healthcare, he works with diabetes management and cognitive datasets, focusing on data-quality benchmarking and missing-data imputation. He develops gap-size-aware imputation techniques, using polynomial methods for short gaps and deep-learning and diffusion-based approaches for larger gaps. For data-quality management, he develops comprehensive benchmarking frameworks that combine multiple evaluation dimensions to systematically assess the quality and reliability of healthcare datasets. His research also explores interpretable equation discovery and physics-informed neural networks (PINNs) to model physiological processes and incorporate domain knowledge into data-driven models. Across healthcare and materials science, he investigates equation discovery and PINN-based approaches to identify meaningful relationships within data and model the underlying behavior of physiological systems and auxetic structures. Overall, his work aims to improve data reliability while developing interpretable and domain-informed machine-learning methods for real-world applications.
Teaching
- RZ-Praktikum: NFC/ WiFi Hacking – Flipper Zero
- Large-Scale Data Management
Selected Publications
Vaibhav Gupta, Florian Grensing, Beyza Cinar, Maria Maleshkova
Image and Vision Computing Volume 164, December 2025, 105797
Vaibhav Gupta, Beyza Cinar, Maria Maleshkova
EMBC 2026, Toronto, Canada, 2026 (Accepted)
Vaibhav Gupta, Florian Grensing, Louisa van den Boom, Maria Maleshkova
Proceedings of the 2025 IEEE 25th BIBE
Letzte Änderung: 7. August 2026