Taimur Shahzad Gill

Visiting Student Researcher

Earlham Institute, Norwich Research Park

I am a computational researcher with 1,310+ hours of research experience spanning AI, biomedical signal processing, and computational biology. Currently pursuing a Masters in Data Science for Biology at the University of East Anglia whilst serving as a Visiting Student Researcher at the Earlham Institute under Professor Irene Papatheodorou and Dr Gregory Wickham, and as an Honorary Research Assistant at the Nixon Research Group, University of Liverpool.

My research focuses on developing computational frameworks to understand complex disease mechanisms through multi-omics data integration, with expertise in machine learning, single-cell genomics, cell type deconvolution, and disease biomarker discovery. I have published 4 first-author papers including work in Epilepsy & Behavior achieving 98.4% accuracy in seizure classification.

Research Interests: Computational Biology • Single-Cell Genomics • Multi-Omics Integration • Machine Learning for Healthcare • Biomarker Discovery • Cell Type Deconvolution • Disease Modelling • Time Series Forecasting • Digital Health • Biomedical Signal Processing

Current Projects: Conducting dissertation research at Earlham Institute on predicting pathogenic cell types and biomarkers for Crohn’s Disease progression using bulk and single-cell RNA-seq integration, deconvolution algorithms, and machine learning. Leading TimeGPT development for Influenza surveillance and seasonal pattern analysis for SFTS using Complex EMD and harmonic regression at Nixon Research Group.

Research Impact: 24+ citations, H-index of 3, with contributions spanning epileptic seizure detection, time series forecasting, IoT-based systems, and computational approaches for inflammatory bowel disease.

I am passionate about translating complex biological data into actionable clinical insights through computational methods, seeking to advance precision medicine and digital health solutions that bridge the gap between research and real-world patient impact.

news

Nov 01, 2025	Joined Earlham Institute as Visiting Student Researcher working on single-cell genomics for Crohn’s Disease under Prof Irene Papatheodorou and Dr Gregory Wickham!
Sep 01, 2025	Started Masters in Data Science for Biology at University of East Anglia!
May 01, 2025	Joined Nixon Research Group at University of Liverpool as Honorary Research Assistant!
Feb 01, 2024	First-author paper published in Epilepsy & Behavior achieving 98.4% accuracy in seizure classification!

selected publications

Epilepsy Behav.
Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures

Taimur Shahzad Gill, Syed Sajjad Haider Zaidi, and Muhammad Ayaz Shirazi

Epilepsy & Behavior, 2024

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Epilepsy affects over 50 million people globally. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus. The model combines multi-head self-attention mechanism with a deep convolutional neural network to classify seven subtypes of generalized and focal epileptic seizures, achieving 0.921 weighted accuracy and 0.902 weighted F1 score.
@article{gill2024epilepsy, title = {Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures}, author = {Gill, Taimur Shahzad and Zaidi, Syed Sajjad Haider and Shirazi, Muhammad Ayaz}, journal = {Epilepsy \& Behavior}, year = {2024}, doi = {10.1016/j.yebeh.2024.109732}, journal_cover = {epilepsy-behavior-cover.jpg}, dimensions = {true}, }
ICRAI
Time Series Forecasting of KSE-100 Index Using a Hybrid ESN-LSTM Model

Taimur Shahzad Gill and Syed Ibrahim Zahid

In 2024 International Conference on Robotics and Automation in Industry (ICRAI), 2024

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This paper aims to improve the predictive accuracy of the Karachi Stock Exchange index (KSE-100) by comparing and combining advanced deep learning models. The research investigates the effectiveness of Echo State Networks (ESN), Long Short-Term Memory (LSTM) networks, and a hybrid ESN-LSTM model in capturing both short-term fluctuations and long-term trends.
@inproceedings{gill2024kse, title = {Time Series Forecasting of KSE-100 Index Using a Hybrid ESN-LSTM Model}, author = {Gill, Taimur Shahzad and Zahid, Syed Ibrahim}, booktitle = {2024 International Conference on Robotics and Automation in Industry (ICRAI)}, year = {2024}, doi = {10.1109/ICRAI62391.2024.10894346}, journal_cover = {kse-lstm-cover.jpeg}, }
Eng. Proc.
Early Detection of Mesothelioma Using Machine Learning Algorithms

Taimur Shahzad Gill, Muhammad Ayaz Shirazi, and Syed Sajjad Haider Zaidi

Engineering Proceedings, 2023

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Early detection of mesothelioma, a severe form of cancer commonly associated with asbestos exposure, is a significant challenge that greatly affects prognosis. This study addresses this issue using Machine Learning algorithms, including Gradient-Boosted Trees (GBT), Support Vector Machines (SVM), and Logistic Regression (LR), achieving 100% accuracy in detecting the disease.
@article{gill2023mesothelioma, title = {Early Detection of Mesothelioma Using Machine Learning Algorithms}, author = {Gill, Taimur Shahzad and Shirazi, Muhammad Ayaz and Zaidi, Syed Sajjad Haider}, journal = {Engineering Proceedings}, volume = {46}, number = {1}, pages = {6}, year = {2023}, doi = {10.3390/engproc2023046006}, journal_cover = {engineering-proceedings-cover.jpg}, dimensions = {true} }

ICET

IoT Based Smart Power Quality Monitoring and Electricity Theft Detection System

Taimur Shahzad Gill, Durr E Shehwar, Hira Memon, and 5 more authors

In 2021 16th International Conference on Emerging Technologies (ICET), 2021

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Electricity theft is a worldwide issue, but it is most prevalent in developing countries like Pakistan. This system comprises a network of sensors to transmit real-time power consumption data to the microprocessor, which processes the data to detect power theft. The proposed method involves transmitting power consumption readings to ThingSpeak using IoT.

@inproceedings{gill2021iot,
  title = {IoT Based Smart Power Quality Monitoring and Electricity Theft Detection System},
  author = {Gill, Taimur Shahzad and Shehwar, Durr E and Memon, Hira and Khanam, Sobia and Ahmed, Ali and Shaukat, Urooj and Mateen, Abdul and Zaidi, Syed Sajjad Haider},
  booktitle = {2021 16th International Conference on Emerging Technologies (ICET)},
  pages = {1--6},
  year = {2021},
  organization = {IEEE},
  doi = {10.1109/ICET54505.2021.9689908},
  journal_cover = {iot-power-theft-cover.jpeg},
  dimensions = {true}
}