Dr Nikesh BajajLecturer in Data ScienceEmail: email@example.comRoom Number: G. O. Jones Building, Room 410ProfileTeachingResearchPublicationsProfileNikesh Bajaj is a Lecturer in Data Science at Queen Mary University of London, for a Hainan Program. Nikesh worked as a Research Associate at Imperial College London, in National Heart & Lung Institute (NHLI) from 2021 to 2023, and currently associate with Imperial as an Honorary Research Associate. His research work at Imperial is involved Electrocardiography Imaging (ECGI) inverse problem and quantifying the organisation of electrical activities of the heart. During 2019 to 2021, Nikesh worked as a Research Fellow at University of East London, on a Innovate UK funded project - Automation and Transparency across Financial and Legal Services, in collaboration with Intelligent Voice Ltd., and Strenuus Ltd. The work was focused on Deception Detection in Conversations using Linguistic Markers, which produced a Patent along with a few publications. Nikesh completed his PhD at Queen Mary University of London, UK and University of Genova, Italy, in a joint program. His PhD work was focused on Predictive Analysis of Auditory Attention from Physiological Signals – PhyAAt. Nikesh also has a 5.5 years of teaching experience in a university in India, which included courses and labs on Signal Processing. Nikesh is also a mentor and a consultant (alpha testers) at Deeplearning.ai for courses & specializations offered at Coursera, such as NLP, GANs, Tensorflow, MLOps. Nikesh has a few python libraries, such as spkit, phyaat, pylfsr. Links: Homepage: http://nikeshbajaj.in/PhD Work : PhyAAt Project PagePython Library spkit: https://spkit.github.io TeachingQHP4701: Introduction to Data Science ProgrammingResearchResearch Interests:Nikesh’s research is mainly focused on signal processing and machine learning. He has been working on following projects: · PhyAAt: Physiology of Auditory Attention · EEG Artifact Removal Algorithm - ATAR · Deception Detection in Conversations · Game, Emotion and EEG · ECGI, Inverse Problems, Computational ModelsPublicationsFull list of publications can be found on Google Scholar Papers Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks. Biomedical Signal Processing and Control55 (2020): 101624. Deception detection in conversations using the proximity of linguistic markers. Knowledge-Based Systems(2023): 110422. Analysis of factors affecting the auditory attention of non-native speakers in e-learning environments. Electronic Journal of e-Learning3 (2021): pp159-169. Fraud detection in telephone conversations for financial services using linguistic features. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), NeurIPS, AI for Social Good Workshop. Comparative Evaluation of the EEG Performance Metrics and Player Ratings on the Virtual Reality Games. 2021 IEEE Conference on Games (CoG). IEEE, 2021. Indian sign language recognition. 2012 1st international conference on emerging technology trends in electronics, communication & networking. IEEE, 2012. Patent: System and method for understanding and explaining spoken interactions using speech acoustic and linguistic markers. U.S. Patent Application No. 17/308,222. Preprint Phyaat: Physiology of auditory attention to speech dataset. arXiv preprint arXiv:2005.11577(2020). Deep representation of EEG data from Spatio-Spectral Feature Images. arXiv preprint arXiv:2206.09807(2022).