معرفی
Nikesh Bajaj is a Lecturer in Data Science and Director of Education at Queen Mary University of London (QMUL), affiliated with the School of Physical and Chemical Sciences. He holds a PhD from QMUL and the University of Genova, focusing on auditory attention prediction from physiological signals. Previously, he worked as a Research Associate at Imperial College London, exploring cardiac electrophysiology via ECG imaging, and as a Research Fellow at the University of East London on deception detection in financial conversations (resulting in a U.S. patent).
His research spans signal processing, machine learning, and biomedical applications, including projects like PhyAAt (auditory attention modeling), ATAR (EEG artifact removal), and computational models for cardiac arrhythmia. He also collaborates with industry on gamification, VR/AR, and forensic interaction analysis.
- Key Research Areas: ECGI inverse problems, EEG artifact correction, deception detection, and data-driven healthcare.
- Teaching: Leads courses on data science programming, exploratory data analysis, and machine learning principles at QMUL.
- Software: Developed Python libraries spkit, phyaat, and pylfsr for signal processing and ML.
Publications: Over 20 peer-reviewed papers across biomedical signal processing, machine learning applications, and game design. Recent work emphasizes AI-driven cardiac diagnostics and mixed-reality user experience metrics.
