Post-Doctoral Researcher at University of Leeds, United Kingdom📖
David O’Reilly, PhD, MSc, BSc, is a Post-doctoral Research Scientist at the University of Leeds specializing in Computational Neuroscience and Machine Learning. His work focuses on neurophysiological signal analysis, with applications in clinical and engineering fields. He is known for his contributions to muscle synergy analysis and brain-computer interfaces. David is committed to research and development, consistently applying his skills in data analysis, neural signal processing, and software development. He is passionate about the restoration and augmentation of human movement, particularly in the context of clinical rehabilitation.
Profile
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Education Background🎓
Professional Experience🌱
- Post-doctoral Research Scientist (March 2024-Present) – University of Leeds
Awarded a BBSRC grant for the project “The mechanistic architecture of human action across the lifespan.” Responsibilities include concept development, research, data analysis, programming, software development, and reporting.
- AI Prompt Trainer (July 2024-Present) – Outlier AI
Involved in generating and fine-tuning prompts for training generative AI models.
- Research Assistant (March 2023-March 2024) – University of Leeds
Lead experimentalist and analyst for a project studying the effects of a supplement on cognitive performance and brain activity, including EEG and behavioural data collection and analysis.
- Statistics Tutor (March 2022-2023) – University of Leeds Library
Provided tutoring in statistics to university students.
- Freelance Research & Data Analyst (April 2021-2022) – PeopleperHour.com
Offered freelance services in data analysis, writing, programming, and more.
- Research Assistant (Nov 2019-July 2020) – University College Cork/Trinity College Dublin
Assisted with research activities including literature reviews, data collection, and statistical analysis.
David’s primary research interests include computational neuroscience, neurophysiological signal processing, and brain-computer interfaces. His work focuses on understanding muscle synergies in movement and motor control, particularly in clinical populations such as stroke survivors and individuals with cerebral palsy. He also explores applications of machine learning in neural signal analysis, cognitive performance, and brain activity.
Author Metrics
Publications Top Notes 📄
1. Identifying differences in gait adaptability across various speeds using movement synergy analysis
- Authors: D Ó’Reilly, P Federolf
- Journal: PLoS One
- Year: 2021
- Volume: 16
- Issue: 1
- Article ID: e0244582
- Summary: This paper focuses on the differences in gait adaptability at varying speeds, using movement synergy analysis to explore how human movement adapts across different walking conditions.
2. Letter to the editor regarding “The assessment of center of mass and center of pressure during quiet stance: Current applications and future directions”
- Authors: P Federolf, RM Angulo-Barroso, A Busquets, B Ferrer-Uris, Ø Gløersen, …
- Journal: Journal of Biomechanics
- Year: 2021
- Volume: 128
- Article ID: 110729
- Summary: This is a letter to the editor addressing the assessment of the center of mass and center of pressure during quiet stance, offering insights into current applications and potential future directions in this field.
3. Dissecting muscle synergies in the task space
- Authors: D O’Reilly, I Delis
- Journal: eLife
- Year: 2024
- Volume: 12
- Article ID: RP87651
- Summary: This paper investigates muscle synergies and how they operate within a task space framework, focusing on their role in motor control and movement execution.
4. Feature selection for the classification of fall-risk in older subjects: a combinational approach using static force-plate measures
- Authors: D Ó’Reilly
- Journal: BioRxiv
- Year: 2019
- Article ID: 807818
- Summary: This study develops a feature selection method to classify fall-risk in older adults, integrating static force-plate measurements to predict fall risk based on biomechanical data.
5. A network information theoretic framework to characterise muscle synergies in space and time
- Authors: D Ó’Reilly, I Delis
- Journal: Journal of Neural Engineering
- Year: 2022
- Volume: 19
- Issue: 1
- Article ID: 016031
- Summary: This paper presents a framework based on network information theory to analyze muscle synergies in both space and time, enhancing understanding of the dynamic nature of motor control.
Conclusion
Dr. David O’Reilly is a highly deserving candidate for the Best Researcher Award due to his substantial contributions to computational neuroscience, neurophysiological signal processing, and clinical rehabilitation. His work is pioneering, and he demonstrates a clear passion for improving human movement through innovative approaches and interdisciplinary research. While there is room for broadening the practical applications of his findings and extending his research into more diverse clinical populations, his commitment to both advancing knowledge and ensuring its impact on real-world challenges positions him as a leader in his field. This combination of scientific rigor, innovation, and clinical relevance makes Dr. O’Reilly an outstanding choice for the award.