• May 2019-June 2023

    Smart Efficient Energy Centre (SEEC)

    SEEC is an interdisciplinary “big data” research centre, working across low-carbon energy sectors. One of these sectors is Marine Renewable Energy. SEEC takes a pioneering approach in utilizing advanced engineering, computer science, and modelling to address significant challenges related to enhancing the sustainability of energy supply and utilization. The focus is on minimizing adverse environmental effects, particularly in reducing net carbon emissions. SEEC is part-funded by the European Regional Development Fund, administered through the Welsh Government.
    As a researcher in Machine Learning and Data Science, my contribution to the SEEC project within the Marine Renewable Energy sector has been focused on investigating and leveraging advanced techniques to address data-driven key challenges. By integrating predictive modelling and optimization approaches, I've worked on cutting-edge ML algorithms to analyse large-scale data sets, uncovering valuable insights and patterns related to energy production, environmental impacts, and system performance while enhancing the sustainability of energy supply and utilization.

    Status: Finished

  • Aug 2022-Aug 2026

    Ecological implications of accelerated seabed mobility around windfarms (EcoWind-ACCELERATE)

    Accelerating the shift from fossil fuels, offshore wind farms are being rapidly developed. However, their impact on the seabed and marine ecosystem needs urgent attention. The presence of wind turbines and anchors alters sea currents, causing sediment movement and reduced water clarity. These changes affect the seabed shape, composition, biodiversity, and ecosystem services like fishing and carbon storage. Disruptions in prey species impact deep-diving predators, including protected seabirds. This project evaluates the combined effects of wind farm expansion and climate change on the seabed, quantifying implications for biodiversity, ecosystem services, habitats, and seabird-food interactions.
    ML-based Digital Twin (DT) is one of my research interests. DTs have the potential to revolutionize decision-making across science, technology, and society. A DT is a collection of virtual information constructs that mimic the structure, context, and behaviour of a physical asset. These constructs are dynamically updated with data from its physical twin throughout its lifecycle, enabling informed decisions that yield value. Currently, I am working on a DT for the NERC EcoWind project, focusing on understanding, and predicting the impacts of windfarms on the physical and ecological functioning of coastal seas.

    Status: Active

  • April 2022-January 2024

    Morlais Demonstration Zone - Environmental Monitoring and Mitigation Programme

    A tidal energy project located offshore Anglesey in Wales is set to advance the development of tidal power generation technologies. The project, known as Morlais, aims to establish grid connectivity and enhance the infrastructure for tidal power generation. The Welsh Government has emphasized the importance of environmental monitoring and mitigation package (EMMP) for the project. This includes monitoring interactions with sensitive species and testing monitoring technologies, addressing critical data gaps and challenges in the tidal sector. These efforts contribute to the sustainable progress of the Morlais project and the wider advancement of tidal energy technology.
    In this project, my role is to utilize machine learning to analyse and address data-driven challenges, focusing on the behaviour of sensitive species, such as marine mammals. Machine learning techniques can play a crucial role in analysing diverse and voluminous data sets, particularly in cases where labelled data is limited. By leveraging machine learning, we aim to gain a deeper understanding of the behaviour patterns of these species, facilitating the development of effective strategies within the EMMP framework. [ offsure energy].

    Status: Finished

  • April 2022-April 2023

    MEECE R&D project - APT wind farm constraints tool

    Producing risk maps for wind farm site selection in the Celtic Sea requires analysis and assessment of a range of GIS (Geographic Information System) data (e.g., seabed bathymetry and substrate, shipping route density, military danger and exercise areas, UK oil and gas infrastructure, UK wind, wave, and tidal designated areas), is a complex and challenging task.
    In this project, I am working on a novel integrated evaluation framework based on the rule-based fuzzy inference system. This framework brings together (GIS) and spatial multi-criteria decision analysis, to provide a trustable decision tool for windfarm site placement. Embedding this framework in a fuzzy platform not only addresses uncertainty in the decision-making process but also enhances the interpretability of complex information, an essential requirement for this sector.

    Status: Finished

  • January 2022-March 2022

    Development of a cetacean classifier to support the study of Passive Acoustic Monitoring data used in the marine renewables sector in Wales - Wales Data Nation Accelerator (Welsh Government) - Sprint Award

    Tidal stream turbines have the potential to play a significant role in the generation of marine renewable energy. However, the risk of large marine mammals encountering these turbines has raised concerns among regulators and conservationists. Passive acoustic monitoring is an effective and established method to remotely monitor marine mammals in situ, over extended periods of time and at high frequency, allowing the capture of episodic events. Analysis of the large resulting datasets is a significant challenge.
    In this project, I've analysed the effective application of two fundamental structures of deep neural networks, CNN and RNN, for auto-classifying delphinid clicks. I've illustrated how to tweak models' structure to study their abilities in analysing both the raw waveform and the spectrogram of delphinid clicks. Through the application of deep learning techniques, our research has demonstrated the feasibility of identifying dolphins based on their unique biological clicks, even in the presence of significant background noise. Building upon this discovery, our focus now shifts to developing robust tracking algorithms specifically tailored to different dolphin species. These algorithms will enable us to gain valuable insights into the behaviour patterns of these species, further enhancing our understanding of their ecological dynamics.

    Status: Finished

  • April 2019-June 2019

    Predicting failures in medium voltage lines from a sequence of SCADA events

    One of the goals of reliability is to identify and manage the risks around assets that could fail and cause unnecessary and expensive downtime. Organizations know it is important to identify areas of potential failures and rate them in terms of likelihood and consequence. ENEL distribution must manage a very complex reality, several control centres (STUX and STM Systems), more than 2200 primary substations (HV/MV) and more than 100000 remote-controlled secondary substations (MV/LV) In substation automation systems, SCADA performs the operations like bus voltage control, bus load balancing, circulating current control, overload control, transformer fault protection, bus fault protection, etc. The main idea investigated in this project was to apply predictive maintenance to the medium voltage lines using only SCADA events messages (each of which is coded as a unique string), in order to predict component failures in the distribution grid.
    In this project, a diverse range of methods was employed to identify patterns and signals in the data, specifically focusing on classifying anomalies within SCADA events that result in faults in medium voltage lines. The initial phase involved a comprehensive analysis of the data to gain insights into how to frame the problem effectively. Subsequently, a combination of supervised and unsupervised techniques was utilized to uncover patterns and identify event sequences that were associated with anomalies. The problem was approached from two angles which are sentiment analysis (supervised) and anomaly detection (unsupervised) using natural language processing (NLP) techniques. This was based on the notion that, when considering the sequence of SCADA events leading to faults or non-faults, it could be likened to a sentence composed of words. To extract meaningful information, various embedding methods were applied, including CNN for text classification, RNN-based structures such as RNN, LSTM, and GRU, as well as customized waveNet, VAE, and GAN models. To further enhance the predictive capabilities, attention mechanisms were employed to extract significant SCADA events that were crucial to the occurrence of failures. By aggregating the representations of these informative events, a more comprehensive understanding of the underlying factors contributing to faults was achieved.

    Status: Finished

  • October 2017-January 2018

    Zillow Prize - Improving Zestimate Home Valuation Accuracy (Kaggle Competition)

    In this project, I participated in the renowned Zillow Prize competition, which aimed to enhance the accuracy of Zillow's Zestimate home valuation. With a prize of 1.2 million dollars, this competition presented a significant opportunity for the data science community to make a substantial impact on the real estate industry. The winning algorithms had the potential to influence the home values of over 110 million properties across the United States.
    During the competition, I worked diligently as part of a competitive environment, leveraging my expertise in DS and ML to develop innovative approaches for improving the Zestimate model. Collaborating with a diverse group of data scientists and researchers, I employed advanced techniques in data analysis, feature engineering, and model optimization to create predictive models with enhanced accuracy.
    By exploring various algorithms, experimenting with feature selection, and refining the model architecture, I aimed to uncover insights and patterns that would contribute to superior home valuation predictions. Additionally, I employed rigorous evaluation methodologies to assess model performance and iteratively refine my approach.
    My efforts in the Zillow Prize competition resulted in achieving a commendable rank of 71 out of 3,775 participating teams. This accomplishment demonstrates my ability to tackle real-world challenges, apply cutting-edge techniques, and deliver competitive results within a highly competitive setting.

    Status: Finished

  • September 2008-Auguest 2010

    Hybrid optimization of fuzzy systems - research project - KNTU ISLAB

    A fuzzy neuro system, also known as a fuzzy neural network or neuro-fuzzy system, is a hybrid computational model that combines the principles of fuzzy logic and artificial neural networks. It integrates the ability of fuzzy logic to handle uncertainty and imprecise information with the learning and adaptive capabilities of neural networks. Incorporating fuzzy sets, fuzzy rules, and fuzzy inference mechanisms into the neural network architecture, enables the model to handle uncertain or imprecise data, perform fuzzy reasoning, and make accurate predictions or decisions based on the learned patterns. ANFIS and LLNFS are two popular models in this category.
    In this research project, my focus has been on developing a framework that facilitates the application of hybrid optimization methods for training fuzzy neuro systems. The framework I have been working on aims to harness the advantages of both gradient-based and evolutionary-based techniques to optimize the training process of fuzzy neuro systems. Hybrid optimization methods leverage the strengths of both gradient-based and evolutionary-based techniques, providing enhanced convergence, robustness, scalability, and flexibility in the training process. They are particularly valuable in complex optimization scenarios with multimodal non-convex landscapes, noisy data, and large-scale problems.

    Status: Finished

  • September 2007-Auguest 2008

    Multi-objective optimaization - research project - KNTU ISLAB

    Multi-objective optimization problems involve finding solutions that optimize multiple conflicting objectives simultaneously. These problems are often challenging because improving one objective may lead to a degradation in others, creating a trade-off situation. This concept is known as the "Pareto optimality" principle. The goal is to identify a set of Pareto frontier solutions that represent the best trade-offs among the objectives. Various algorithms explore the solution space to find a diverse set of options, allowing decision-makers to choose the most suitable solution based on their preferences or requirements.
    In this project, I conducted a thorough examination of evolutionary algorithms and their capabilities in addressing multi-objective optimization problems. Evolutionary algorithms, being population-based, possess the unique ability to identify the Pareto frontier. These algorithms leverage the principles of natural evolution, including selection, reproduction, and mutation, to iteratively improve the population of candidate solutions. By employing various mechanisms such as fitness assignment, selection operators, and genetic operators, evolutionary algorithms are able to offer a balance between exploration and exploitation, to search and converge towards the Pareto-optimal solutions.

    Status: Finished