Suleman Rasheed is a Data Science Expert (PhD) with 5 years of academic and 2+ years of professional experience in data analytics, statistical modelling, and machine learning. He applies Python, R, SQL, and Power BI to build data-driven solutions, deliver insights, and support evidence-based decisions across research and industry settings.
Suleman is seeking job opportunities as a Data Scientist, Data Analyst, Research Scientist, or Machine Learning Engineer, where he can apply analytical and computational methods to solve real-world problems.
Education
PhD – Engineering and IT
The University of Melbourne | 2021 – 2025
Thesis: Decoding Eye Movements using an Endovascular Brain–Computer Interface
BS – Electrical Engineering
Pakistan Institute of Engineering and Applied Sciences | 2015 – 2019
Thesis: Driver Drowsiness Detection using Lightweight Deep Convolutional Neural Networks
Experience
Data Scientist – School of Sufi Teachings (Pro Bono)
Melbourne, Australia | Mar 2025 – Present
- Analysed wearable (ECG, fitness tracker, sleep), survey, and interview data across international cohorts.
- Generated findings on meditation and wellbeing that informed competitive grant proposals ($625k, Qatar Spark Award & Templeton Foundation).
Data Scientist Intern – Synchron Inc.
Melbourne, Australia | Mar 2023 – Mar 2024
- Developed a real-time data acquisition pipeline integrating brain, eye-tracking, and camera signals, enabling multimodal ML experimentation beyond existing software capabilities.
- Designed a cardiac artefact removal algorithm that filtered noise from brain signals, improving classification accuracy by 18%. Adopted internally and published in IEEE BCI Conference 2024.
Research Assistant – National University of Sciences & Technology (NUST)
Islamabad, Pakistan | Mar 2020 – Apr 2021
- Reviewed EEG denoising algorithms, identifying challenges and establishing community guidelines for neurotechnology applications, published in Biomedical Signal Processing and Control.
- Built models predicting imagined hand movements in stroke patients from brain signals, achieving 85% accuracy and demonstrating potential for rehabilitation, published at IEEE ICAI 2021.
Skills
| Area | Tools & Technologies |
|---|---|
| Programming | Python, R, C/C++, MATLAB, SQL |
| Machine Learning | Scikit-learn, PyTorch, TensorFlow, Keras, Pandas, NumPy |
| Deployment | Git, CI/CD, Docker, Kubernetes, FastAPI, MLflow |
| Data Visualisation | Power BI, Plotly, R Shiny, Excel, Matplotlib |
| Cloud Platforms | Azure, AWS, Databricks |
| Computer Vision | Face Detection, Object Tracking, Facial Landmarks Detection, OpenCV |
| Hardware | Raspberry Pi, Arduino, Eye Trackers, EEG, ECG, Fitness Trackers |
| Qualitative Research | Interviews (Zoom, Meet, Teams), Transcription (Otter.ai), Analysis (NVivo) |
| Compliance | ICH-GCP, ISO 13485, IEC 62304 |
Portfolio Projects
Showcasing end-to-end data science and analytics projects across healthcare, environment, and brain–computer interface applications.
Each project highlights the use of Python, Power BI, R, and cloud-based analytical tools for real-world data-driven impact.
1️⃣ Monash Health Surgery Wait Times Dashboard
A Power BI dashboard comparing elective surgery wait times by urgency (2018–2023) at Monash Health.
It enables real-time tracking of compliance rates, volumes, and median wait durations for operational planning.
Tools: Power BI, Excel, DAX, Data Modeling
2️⃣ Victoria EPA Air Quality Dashboard
This interactive R Shiny app visualises EPA Victoria’s 2024 air quality data, enabling users to select a suburb and pollutant to explore daily concentration levels across the year. Data are presented in a calendar-style heatmap designed to enhance environmental awareness and public health insight.
Tools: R, Shiny, lubridate, openair
3️⃣ Healthcare Financial Dashboard
An interactive Power BI dashboard visualising healthcare billing, treatment costs, insurance coverage, and demographic disparities across Australia.
Developed to help health economists and hospital analysts explore cost variability and resource efficiency.
Tools: Power BI, Excel, DAX, Power Query
4️⃣ Predicting Hand Movements of Stroke Patients from Brain Signals
Benchmarked EEG-based Brain–Computer Interface (BCI) pipelines for decoding motor imagery in stroke patients. Implemented and compared five algorithms in Python, ranging from traditional feature extraction methods (CSP, FBCSP, PSD, Wavelets) to deep learning models such as EEGNet.
Findings:
- FBCSP achieved the highest within-subject accuracy (~84.8%)
- EEGNet provided the strongest cross-subject generalisation (~77.0%)
This benchmark supports the development of assistive robotic and rehabilitation BCIs.
Tools: Python, NumPy, SciPy, Scikit-learn, MNE-Python, Matplotlib
Publications
Journal Articles
-
Suleman Rasheed, James Bennett, Peter Yoo, Anthony Burkitt, David Grayden.
Decoding Saccadic Eye Movements from Brain Signals Using an Endovascular Neural Interface.
Journal of Neural Engineering, 2025 -
Wajid Mumtaz, Suleman Rasheed, Alina Irfan.
Review of Challenges Associated with EEG Artefact Removal Methods.
Biomedical Signal Processing and Control, 2021
Conference Papers
-
Suleman Rasheed, James Bennett, Peter Yoo, Nicholas Opie, Anthony Burkitt, David Grayden.
Comparing Cardiac Artefact Removal Algorithms for Endovascular BCI Recordings.
IEEE Winter BCI Conference, 2024 -
Suleman Rasheed, Wajid Mumtaz.
Classification of Hand-Grasp Movements of Stroke Patients using EEG Data.
IEEE International Conference on Artificial Intelligence (ICAI), 2021
Abstracts / Posters
- Predicting Eye Movement Intentions from Brain Signals. ICNS NeuroEng Workshop, 2025
- Decoding Eye Movements from Brain Signals. IEEE EMBC, 2023
- Removing Cardiac Artefacts from Endovascular Interface Data. ICNS NeuroEng Workshop, 2023
Honours & Awards
- PhD Write-up Award, The University of Melbourne (2025)
- Australian Research Council Industrial Transformation Training Centre (ARC ITTC) Travel Grant, The University of Melbourne (2024)
- Faculty of Engineering and IT Conference Travel Grant, The University of Melbourne (2023 & 2024)
- BME GR Conference Attendance Award, The University of Melbourne (2022)
- ARC ITTC Top-up Scholarship, The University of Melbourne (2021)
- Melbourne Research Scholarship, The University of Melbourne (2021)
- First Rank, National Testing Service (NTS) Graduate Entrance Exam, Pakistan (2021)
- Gold Medal & Cash Prize, Distinction in HSSC Exams, Read Foundation College, Pakistan (2015)
- Laptop & Gold Medal, Distinction in SSC Exams, Government of Pakistan (2013)
Professional Memberships
- NeuroEng Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
- Brain–Computer Interface Society (BCI)
- Institute of Electrical and Electronics Engineers (IEEE)
- IEEE Engineering in Medicine and Biology Society (EMBS)
- Pakistan Engineering Council (PEC)
- Engineers Australia