Highly motivated and experienced academic Data Scientist with two year industry experience, able to grasp new concepts and technologies quickly and effectively. Working as a Postdoctoral Research Associate at the University of Rochester, and prior expericne working as a Senior Clinical Researcher at Neuromod, with a doctorate in Cogntitive Neuroscience and 8+ years of experience. Solid numerical, analytical and problem-solving skills established through education (B.Sc., M.Sc., Ph.D.). Thriving as team player, ambitious, methodical, self-motivated with a business focus. Able to work under pressure withon a fast paced environement and without strong supervision. Passionate about technology, data & analytics and adaptable.

Strong analytical, quantitative and qualitative skills: Solid experience in Python (pandas, numpy, scipy, scikit-learn, matplotlib, nltk, plotly, dash, shapely, keras (novice)), LabVIEW (novice), SQL (MySQL, PostgreSQL). Currently develeoping skills in programming with R (tidyverse, plotly, tidytext). Further IT skills include; Tableau, PowerBI and MS Office,

Self-motivated: Continued self-improvement by following courses and training throughout education and professional career (internal offered by the University of Glasgow & Institute of Neuroscience & Psychology, or externals (Udemy, EDX, Coursera) in Statistics, Machine Learning, Deep Learning, Project Management, Data Science

Excellent written and oral communication skills: Excellent internal and external stakeholder management skills, able to present complex subjects to both technical and non-technical audiences using appropriate language and avoiding unnecessary jargon.

Strong team player: Interface efficiently and accurately with multidisciplinary teams, such as Management and Executive teams, engineers, medical and clinical doctors, and scientists with diverse backgrounds. Mentored junior members (B.Sc, M.Sc and Ph.D levels) in technical aspects and day-to-day activities

Working as a Senior Academic Data Scientist in the Deptartment of Neuroscience. My role involves research, neuroimaging database management and curation, data cleaning and analyses using computational modelling of multidimensional data, project supervision, and dissemination of results in forms of stakeholders, publications, and conferences. This includes:

• Database construction and multisite curation of large (TB+) multidimensional neuroimaging data.
• Developed code to standardise data across multisite lab hardware, data preprocessing and computational analyses of over 300 multidimensional neuroimaging datasets.
• Prepared database for Open Science publication with coding best practices and data repository sharing to conform to international standards (BIDS).
• Developed custom code for analysing clinical animal models and statistically compared model parameter outputs with human models.
• Successfully demonstrated an improved method to optimise clinical neuroimaging analyses in RETT and Batten Disease patients thought means of machine learning applications (ridge regression) to model neurodegeneration in the brain to incoming stimuli.
• Being proficient in various technologies: Matlab, Github, Bash, Signal processing.
• Intermediate: Python, Pandas, Numpy, Scikit-learn. | Novice: R, SQL
  Code: Code

  Paper: Preprint

Working as a Senior Academic Data Scientist in the Deptartment of Biomedical Engineering. My role involved four core aspects: Research, Data collection using state of the art biomedical imagine technologies, Data cleaning and analyses using computational modelling of multidimensional data, and dissemination of results in forms of stakeholders, publications, conferences, and teaching. This includes:

• Successfully demonstrated evidence to support the hypothesis that the brain uses top-down predictive coding to improve modeling of linguistic features of incoming complex natural speech using ML applications.
• Improved existing models descripting hierarchical processing of complex natural language processing from low-level acoustic input in human neuroimaging data using various models based on GloVe, NLP, Mutual Information and Entropy.
• Extensive Experience of neuroimaging data collection, data cleaning and analyses using complex computational modelling and statistics based on machine learning approaches and statistics of model parameters using bootstrapping and non-parametric Monte Carlo permutation statistics.
• Developed custom code for analysing clinical animal models and statistically compared model parameter outputs with human models.
• Used various predictive analytics such as speech-to-text algorithms and Montreal Forced Phoneme Alignment to parametrise speech used to predictive linguistic coding in the brain.
• Being proficient in various technologies: Matlab, Excel, Git / Github, Bash, Signal processing.
• Intermediate: Python, Pandas, Numpy, Scikit-learn. | Novice: R, Cloud services (AWS).
  Code: Code

  Paper: Journal of Neuroscience

Working as a Clinical Data Scientist in the Neuromod Devices. My role at Neuromod, a medical device clinical startup company, was to coordinate and lead one branch of the company’s pilot project, which covers various core responsibilities from:

• Successfully demonstrated the feasibility of using a decoder algorithm to model objective audiometry in hearing loss patients, laying down the foundation to explore a self-tuning hearing aid.
• Managed streamlined collaboration across multidimensional teams, including software engineers, design, a clinical ENT and audiologist and research staff.
• Prepared documents for hospital ethical approval and attended hospital research training standards.
• Responsible for data collection, data cleaning and analyses, and dissemination of results to stakeholders and collaborators. Including preparing dashboard reports (Tableau and equivalent custom software).
• Designed and coded a Matlab GUI to administer pure tone audiometry using audiometric grade insert Etymotic earphones. Signed off by a professional audiologist and is now used at Trinity College Dublin.
• Being proficient in various technologies: Matlab, Excel, Git / Github, Bash, Signal processing.
• Novice, infrequent use: Python

Alongside my PhD, I was the lead site collaborator on the UK’s first national database of MEG neuroimaging data at MEGUK. My role included:

• Maintained effective communication and database collaboration with external members across eight UK universities.
• Lead on data collection, database curation and ensured partnership protocols in collecting and analysing high density neuroimaging machines (fMRI, MEG) where adhered to.
• Processed large (TB+) multidimensional data and applied invers modelling techniques (LCMV and DICS beamforming) and computational modelling using machine learning applications (SVMs, MVPA,LDA, Mutual Information) to assess neural sensory functioning in humans.
• Demonstrated skills in technologies: Matlab, Excel, Git / Github, Python (novice).

Doctoral candidate on the MRC UK MEG Partnership Grant at Centre For Cognitive Neuroimaging (CCNI). My role included:

• My PhD used a combination of brain imaging, psychophysics and machine learning to build predictive models of the brain. I collected large (TB+) datasets from high density neuroimaging machines (fMRI, MEG) and used various data cleaning (principal component analysis) and machine learning techniques (SVMs, MVPA, LDA, Mutual Information) to try and classify whether human beings were seeing images or hearing sounds while their brain data was being recorded. All coding work was completed in Matlab, FieldTrip Toolbox.

• Designed and taught Research Methods, Brain Imaging Methods, and Biological Psychology on a Certificate of Higher Education (Cert HE) course titled "Introduction to Psychology". This course focused on teaching students the basics of brain science as well as practical data science topics relevant to psychology masters research students, including experimental design, statistics, data cleaning, data wrangling and machine learning. Also taught programming, data analysis, and practical skills labs to Undergraduate and M.Sc. students.

Clinical Research Assistant at Dorset Council, St. Annes’s Psychiatric Hospital . Psychiatric clinical research assistant in collaboration with UCL and Bournemouth University. This role involved:

• Successfully offered insight & adaptation into a new mental health delivery and recovery plan, STAR.
• Completed various psychiatric hospital research training courses, including, Mental Health Research, Breakaway and Emergency Management training.
• Creating visualisations and reports to Dorset Council and external stakeholders

Cognitive Neuroscience
Thesis title: "Electrophysiological and behavioural consequences of cross-modal phase resetting"

Research supported by 3-year MRC UK MEG Partnership and Doctoral Training Grant: MEG UK .

Thesis title: "Consistent Connectivity in the Visual Word Processing Network"

Grade: Distinction (Best Project Prize Award)

Courses included: Magnetoencephalography physics, Neuroimaging, Neuroscience, Signal Processing, Brain Connectivity Analyses, photonics

Psychology & Computing

Grade: 2:1

Specialisation in Biopsychology and Neurophysiology, Eye tracking

Nuffield Foundations Undergraduate Bursary

I volunteered as a demonstrator at the Trinity College Dublin Summer School Programme in the Biomedical Engineering. My role was to introduce the labs existing work by means of demonstrating the correction biotechnology in hearing aid developments and language processing capabilities. As well as provide hands on experience in coding experiments and analysing data using Matlab and in-house custom software.

Level 4 first aid and over 300+ hours service