• Nicole Pearcy (2015-2024)
• Ivan Derbenev (2020-2021)
• James Gilbert (2015-2019)
• Michelle Baker (2016-2018)

• Eve Gately, Pharmacy (2024-)
• Joshua Morrison, Veterinary Medicine and Science (2023-)
• Samuel Windle, Veterinary Medicine and Science (2023-)
• Daniel Yanes, Pharmacy (2023-)
• Hamzah Abdi, Computer Science (2022-)
• Oran Maguire, Pharmacy (2019-)
• Saqer Alarifi, Pharmacy (2020-2024). Development of Self-Regulating non-Viral Vector to Restore MeCP2 Activity to Treat Rett Syndrome.
• Alexandre Maciel Guerra, Computer Science (2017-2022). Subspace-Based Dynamic Selection.
• Nathan Jones, Life Sciences (2015-2020). The exploration of the relationship between recombination and replication in Haloferax Volcanii.
• Edward Acheampong, Mathematics (2015-2020). Mathematical Modelling of Wastewater Treatment Processes for the removal of Emerging Pollutants.
• Sophie Vaud, Life Sciences (2015-2019). Development of high-throughput genome editing tools towards ethylene production in Cupriavidus species.
• Victoria Ciampani, Engineering (2015-2019). Development and characterisation of optogenetic tools for non-invasive control of cellular functions in epithelia.
• Suresh Bonthala, Computer Science (2014-2018). Translating nucleic acid binding protein function from model species to minor crops using transfer learning.
• Chao Chen, Computer Science (2014-2018). A novel framework for the implementation and evaluation of type-1 and interval type-2 ANFIS.
• Elisa Tonello, Mathematics (2014-2018). Graph properties of biological interaction networks.
• Jamie Gilbert, Computer Science (2012-2015). A Probabilistic model forthe evaluation of module extraction algorithms in complex biological networks.
• Joshua Pilkington, Engineering (2012-2015). Developing novel, adaptive bioprocessing approaches for natural product processing utilising Artemisia annua as a case study.

• Vanisha Patel, Computer Science (2015-2022). Investigating ensemble methods for essential gene predictions in bacteria.

• Raghunath Marimouthou (2023). Parkinson’s Disease Progression Prediction.
• Genyang Chang (2023). Analysing Primary School and Secondary School Data with A Cluster Analysis: Using OPTICS Algorithm to Understand the Relationship Between Expenditure, Staffing, and Student Achievement.
• Alexander Adams (2023). Determining Feature Importance in Non-Coding DNA with Machine Learning.
• Pui Kit Li (2023). Predicting gene essentiality from experimental data using machine learning approaches.
• Dominic Falla (2022). Constraint Based Modelling of Cross Feeding Interactions within a Synthetic Microbial Community.
• Hongxin Huang (2022). Travellers segmentation and travel spend prediction from UK travel data using machine learning approaches.
• Xiaohan Chen (2022). Price Momentum Identification and Prediction of GBPUSD Using Unsupervised Machine Learning Algorithms.
• Zhangyao Wang (2022). UK epidemic analysis to help international students choose universities.
• Haosheng Shen (2021). Predicting Keypresses using an Audio Side-Channel Attack and Machine Learning.
• Runzhong Xu (2021). A Comparison Study to Identify Storm Petrel and Manx Shearwater Based on Bird Calls.
• Samuel Ellis (2021). Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning.
• Richard Parke (2020). Use of Convolutional Neural Networks for Classification of Individual Manx Shearwaters.
• Abdullah Kucuk (2020). Comparison of two deep learning methods to classify bird calls: the convolutional neural network and the autoencoder based k-nearest neighbour.
• Daniel Allott (2019). Visualizing Phylogenetic Trees in Virtual Reality using Hyperbolic Space.
• Ross Putman (2017). Dynastat Systems Medical Records.
• Thomas Price (2017). Dynastat Systems Medical Records.
• Ben Easton (2016). Helping medication users discover and solve nutritional deficiencies.

• Daniel Weston (2023). Building and Using an Underwater Drone to Track Fish.
• Yebei Wang (2023). Performance Comparison of Supervised Machine Learning Approaches for Gene-Essentiality Prediction.
• Adam Masters (2022). Predicting Key Presses over Video Calls using an Audio Side-Channel Attack and Machine Learning.
• Cheng Han Pang (2022). Selecting Cryptocurrency Trading Pairs.
• Hashem Elbiali (2022). Applying the LSTM model on Bitcoins Historical Prices.
• Yedi Hu (2022). Detecting suicide and self-harm in social media using machine learning, naturallanguage processing and computer vision techniques.
• Chunfeng Xia (2021). Comparative Analysis of Sentiment Analysis Models for Cryptocurrency Sentiment Analysis.
• Ezequiel Vigo Fernandez (2021). Uni Groceries.
• Tara Rose Dilley (2021). Machine Learning for Automated Monitoring of a Hydroponic System.
• Zhiwei Peng (2021). Informing School Decision Making.
• Zain Ali (2020). A feet prediction and correction system for a 3D character in Unity.
• James Cordon (2020). Software Solution to Enable Distributed Collaborative Video Editing.
• Toby Sheldon (2020). User Data and How It Can Be Used.
• Humraj Chadha (2020). An Anxiety and Depression focused Application for Mental Health Students.
• Lee Taylor (2019). Using Machine Learning Techniques to Detect, Predict, and Analyse the Progression of Food Rot.
• William Hickling (2019). A Predictive Modelling Software for Baseball Teams Performances.
• Thomas Hemery (2019). 3D Virtual Reality Visualisation of Phylogenetic Trees.
• Aaron Mason (2019). Measuring process complexity in the diagnosis of gastrointestinal infections.
• Prashil Pattni (2018). Visualising synthetic chemical reaction variables.
• Muhammad Rosli (2018). Music Emotion Recognition.
• King Cheam (2018). Optical Character Recognition for Handwritten Characters using Artificial Neural Networks.
• Patricia Buchner Santos (2017). EssentialView - A circular interactive visualisation tool of essential genes.
• Philip Lewis (2016). Quadcopter Steady-cam.

• Robert Clarke (2021)
• Jinjang Zhang (2015)
• Laurence Herbert (2013)

• Zane Hartley (2024). Synthetic Data Driven Deep Learning for Plant Phenotyping, Ph.D. Computer Science, University of Nottingham (internal examiner).
• Johann Benerradi (2024). A benchmarking framework for imporiving machine learning with fNIRS neuroimaging data, Ph.D. Computer Science, University of Nottingham (internal examiner).
• Cailean Carter (2023). Energy Management of Uropathogenic Escherichia coli During Trimethoprim Challenge for the Rapid Antimicrobial Susceptibility Testing of Urinary Tract Infections, Ph.D., Quadram Institute & Norwich Medical School, University of East Anglia (external examiner).
• Peter Von Holy (2023). Application of Markov Stability for graph-based clustering on protein-protein interaction networks, M.Res. Bioinformatics, University of Nottingham (internal examiner).
• Ezenwoko Benson (2021). Ultra high-resolution segmentation of biological images using reduced-memory convolutional neural networks with inter-tile communication, Ph.D. Computer Science, University of Nottingham (internal examiner).
• Utkarsh Agrawal (2019). Developing and Improving Methods for Robust Ensemble Classification: An Aggregation Operator and Clustering-Classification Approach, Ph.D. Computer Science, University of Nottingham (internal examiner).

www:    https://www.cs.nott.ac.uk/~pszjpt
email:  jamie.twycross AT nottingham.ac.uk
office: B48 School of Computer Science
        Jubilee Campus
        University of Nottingham
        Wollaton Road
        Nottingham NG8 1BB
        U.K.