LinkedIn Profile
Research Interests and Technical Skills
My research focuses on addressing the statistical challenges associated with highly structured data from diverse applications, including imaging and wearable technologies. These data are often discrete samples of complex underlying functional processes, which present dependencies that are difficult to model using traditional statistical approaches. My work aims to develop computationally efficient methods that preserve information across all dimensions of these data while ensuring the results are interpretable and actionable. Additionally, I apply advanced statistical and machine learning techniques, such as mixed effects models, gradient boosting, and tree-based methods, to support research in epidemiology, neurology, and oncology.
Education
Relevant modules: Mathematical Modelling, Data Mining and Neural Networks, Financial Services Information Systems, and Data analytics for Esports.
Relevant modules: Global Marketing Management, Managing Financial Performance, International Operations and Project Management.
Collaborative Research Experience
- Conducting research in machine learning, deep learning, and AI applications in health.
- Utilizing ElectroMap for quantitative cardiac electrophysiology data analysis, advancing cardiac research through optical mapping of heart tissue.
Relevant Work Experience
Awards and Achievements
- Stanford University
- Center for Continuing Medical Education, Stanford University School of Medicine
- Contact: (650) 497-8554
- Ref.: stanfordcme@stanford.edu
- Organiser: University of Leicester, UKRI Funded (UK Research and Innovation)
- Landscape Decisions Programme 2023 Conference
- Multifunctional Landscapes: From Research to Policy
- Ref.: sb999@leicester.ac.uk
- Host: Birmingham City University, Birmingham, UK
- Digital Skills for Smart Manufacturing
- CPD Bootcamp
- Core Module: Machine Learning and Artificial Intelligence Applications.
- Ref.: Andrew.Neilson@bcu.ac.uk
- Host: Harvard University, Boston, USA
- style="color: #007A33;">Harvard University
- AI for Health Care: Concepts and Applications (On Campus) - Fellowship Award–Start: 2/6/2024
- Verification Code: 240T0I6RMEN1, Please verify yourself Verify It
- Innovation with AI in Health Care (On Campus) – Start: 5/7/2024
- Core Module: AI for Health Care: Concepts and Applications - Course Information
- Core Module: Innovation with AI in Health Care - Course Information
- Ref.: kescott@hsph.harvard.edu
- Host: University of Cambridge, Cambridge, UK
- AI and digital transformation in health care (On Campus)
- W35Am29: An introduction to Critical Thinking and W35Pm30: AI and digital transformation in health care
- Sponsor Licence Number: 4NUV7KB58
- Student Number: 24-IH-00235016
- Core Module: AI and digital transformation in health care - Course Information
- Ref.: Sarah.Ormrod@ice.cam.ac.uk
- Host: University of Oxford, Oxford, UK
- Machine learning health and Bio (On Campus)
- MLx Representation Learning & Generative AI (On Campus)
- MLx Fundamentals (access)
- Core Module: Machine learning health and Bio - Course Information
- Ref.: contact@oxfordml.school
Professional Membership
- Royal Statistical Society, Ref. 224591 (Fellow)
- IEEE (Institute of Electrical and Electronics Engineers), Young Professional, Ref. 98514633
- BCS (British Computer Society), Ref. 995126302
- LMS (London Mathematical Society), Ref. (membership@lms.ac.uk)
Peer Review Activities
- Reviewed manuscripts for Soft Computing Journal, Springer 2023
- Ref.: Ms. No. SOCO-D-23-05517
- Title: "Transforming Psychotic Disorder Diagnosis with Deep Learning Model on Timeseries Motor Activity Signals Soft Computing"
- For Reference: em@editorialmanager.com, Mauro Gaggero, Associate Editor, Soft Computing
- IEEE Journal of Biomedical and Health Informatics, 2024, Ref.: JBHI-02923-2024, Reviewed manuscripts.
References
Available on request