MUZAMMIL KHAN
Researcher in the Robotics and Mechatronics (RAM) Group at the Faculty of EEMCS, University of Twente, Enschede · University of Twente
Research Interests
Computer vision and image processing; Deep/machine learning for anomaly detection; Visual SLAM for medical applications; Variational and fractional calculus; Optical flow estimation
Biography
Muzammil Khan is a researcher at the Faculty of EEMCS, University of Twente. His current research is focused on implementing simultaneous localization and mapping (SLAM) algorithms for improving laparoscopic liver resection. Prior to this, he was a Ph.D. scholar in the Department of Mathematics, Bioinformatics, and Computer Applications at the National Institute of Technology Bhopal, India. His broad research area encompasses modern computer vision and machine learning applications in the field of healthcare. He explored the topic of Optical Flow in his thesis titled “Novel Algorithms for Optical Flow Estimation and Its Application”. In his work, he developed mathematical models for accurate optical flow estimation and further utilized them with deep learning techniques to perform anomaly detection in image sequences. During his Ph.D., he wrote 12 journal articles, 15 international conferences, and 5 book chapter. He presented his research at 10 international conferences.
Education
Postdoc: Since Oct 2023 as a Computer Vision and Artificial Intelligence researcher in the Robotics and Mechatronics Group of University of Twente Ph.D.: Awarded on April 2024 and Submitted on Sep 2023 from Maulana Azad National Institute of Technology CSIR-NET exam: Qualified on Aug 2019 in Mathematical Sciences GATE (Graduate Aptitude Test in Engineering- 2019) exam: Qaulified on Mar 2019 in Mathematics Master of Science: Awarded on Sep 2018 from Bundelkhand University Jhansi Bachelor of Science: Awarded on May 2016 from Bundelkhand University Jhansi
Recent Scopus Publications
- Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction
- Ensemble learning and skip connection-based CNN framework for COVID-19 identification using CXR and CT images
- Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction
- An Ensemble Learning Model for Smoke Classification and Localization Based on Fractional Order Optical Flow
- Caputo derivative based nonlinear fractional order variational model for motion estimation in various application oriented spectrum
Links
- ORCID https://orcid.org/0000-0003-1914-6520
- Google Scholar https://scholar.google.com/citations?user=Ul4iV78AAAAJ
- Scopus https://www.scopus.com/authid/detail.uri?authorId=57422685500