Highly accomplished Postdoctoral Researcher with over 7 years of academic and industry experience in neuroscience, Brain-Computer Interfaces (BCIs), and advanced engineering. Specializes in developing noise-resilient BCI systems using deep learning and machine learning, adept at experimental protocol design, complex data analysis, and leading multidisciplinary research projects to deliver impactful neurotechnology solutions.
Dublin, Dublin, Ireland
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Summary
Currently serves as a Postdoctoral Researcher at INSIGHT research center at DCU, leading experimental protocol design and multidisciplinary team support for biomedical research projects.
Highlights
Designed and executed experimental protocols for biomedical research projects, ensuring rigorous data acquisition using EEG, GSR, and HRV sensors.
Managed participant recruitment and engagement for multiple studies, ensuring strict adherence to research ethics and GDPR compliance.
Led data preprocessing and analysis workflows, optimizing data integrity and contributing to reliable insights for multidisciplinary research teams.
Facilitated collaborative research initiatives, supporting project milestones and contributing to advancements in neurotechnology applications.
Dublin, Dublin, Ireland
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Summary
Provided academic support and practical instruction to undergraduate engineering students across diverse courses at DCU, enhancing their understanding of complex concepts.
Highlights
Delivered tutorials and guided practical laboratory sessions for a diverse student body across courses including Engineering Mathematics, Digital/Analog Electronics, and Probability & Statistics.
Provided academic support to hundreds of undergraduate students, clarifying complex engineering concepts and fostering a deeper understanding of course material.
Developed and presented supplementary educational content, improving student engagement and comprehension in challenging technical subjects.
Mentored junior students in problem-solving and experimental techniques, contributing to enhanced academic performance and skill development.
Karachi, Sindh, Pakistan
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Summary
Supported research initiatives at The Disrupt Labs by conducting literature reviews, assisting with data collection, and managing project documentation for 16 months.
Highlights
Conducted comprehensive literature studies, providing critical background and insights that informed ongoing research projects.
Assisted in the collection and organization of research data, ensuring accuracy and integrity for subsequent analysis.
Prepared, maintained, and updated project documentation, streamlining information flow and ensuring compliance with research protocols.
Collaborated effectively with multidisciplinary teams on various projects, contributing to overall research progress and timely deliverables.
Seri Iskandar, Perak, Malaysia
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Summary
Facilitated undergraduate learning in Microprocessor, Embedded Systems, and Probability & Statistics courses by conducting tutorials and assisting with laboratory work.
Highlights
Arranged and led tutorials and laboratory sessions for undergraduate students in Microprocessor, Embedded Systems, and Probability & Statistics courses.
Assisted teachers in guiding students through complex laboratory work, contributing to improved practical skills and conceptual understanding.
Supported postgraduate research activities under supervisor's guidance, gaining exposure to advanced research methodologies.
Provided individualized academic support, helping students grasp challenging technical concepts and achieve academic success.
N/A, N/A, Pakistan
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Summary
Managed technical documentation for embedded systems, power electronics, and RF projects, specializing in avionics, and represented the company at project tenders.
Highlights
Managed comprehensive technical documentation for embedded systems, power electronics, and RF projects, specifically in the avionics sector.
Liaised directly with customers to understand requirements and ensure documentation accuracy, improving client satisfaction and project clarity.
Supervised documentation staff, ensuring adherence to quality standards and timely delivery of technical materials.
Represented the company at project tenders, effectively communicating technical capabilities and contributing to successful bid acquisitions.
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PhD. Computer Applications
Brain-Computer Interfaces (BCIs) in real-world settings
Grade: N/A
Courses
Pioneered research in noise-resilient Brain-Computer Interfaces (BCIs) for real-world applications, moving beyond controlled lab settings.
Acquired and analyzed novel EEG datasets under challenging everyday noise conditions (body/head movements, speech) to assess classification performance.
Evaluated and applied advanced machine learning, deep learning, and transformer architectures to enhance BCI robustness and accessibility.
Developed practical neurotechnology applications by addressing real-world signal challenges, contributing to the advancement of BCI utility.
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M.Sc. Electrical & Electronics Engineering (research mode)
Machine Learning-based Electroencephalographic Brain-Computer Interfaces
Grade: N/A
Courses
Conducted extensive research on Brain-Computer Interfaces using diverse machine learning and deep learning techniques.
Managed EEG data collection with various portable and non-portable acquisition systems to support research objectives.
Developed and applied ML algorithms to classify motor imagery patterns for BCI control of wheelchairs, producing an extensive thesis and multiple research articles.
Focused on brain connectivity analysis to enhance BCI performance and understanding in electroencephalographic signal processing.
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B.Sc. Electronic Engineering
Electronic Engineering
Grade: N/A
Courses
Designed and implemented a miniature brain-controlled wheelchair using EEG signals as a capstone project, demonstrating expertise in AI and biomedical engineering.
Developed a system capable of interpreting two states of mind (attention, eye blinks) via a Neurosky MindWave headset to control wheelchair movement.
Awarded 2nd position for the best final year project, recognized for its innovative prototype and comprehensive documentation.
Gained foundational knowledge in electronic engineering principles, preparing for advanced studies in neurotechnology.
Awarded By
Mohammad Ali Jinnah University
Awarded for the innovative design and comprehensive documentation of a miniature brain-controlled wheelchair utilizing EEG signals, showcasing excellence in biomedical engineering.
Published by
7th International Conference on Smart Computing and Informatics, Kuala Lumpur
Summary
Presented at a major international conference, detailing a novel dataset for BCI research in naturalistic settings using Head-Mounted Display-based Rapid Serial Visual Presentation tasks.
Published by
36th Irish Signals & Systems Conference, Letterkenny
Summary
Presented research on improving P300 classification in BCI systems using deep learning, focusing on subject-independent applications within RSVP paradigms.
Published by
30th Annual Conference of the RAMI Section of Bioengineering (BINI 2025), Athlone
Summary
Presented a comparative study on P300 detection accuracy across different display environments, including traditional and head-mounted displays, for BCI applications.
Published by
Journal of Neural Engineering
Summary
Published research investigating the influence of real-world interactions on the performance of RSVP-based Brain-Computer Interfaces, bridging the gap between lab and practical applications.
Published by
Frontiers in Neuroergonomics
Summary
Published a comprehensive dataset (AMBER) designed to advance multimodal BCI research, focusing on enhancing robustness in naturalistic environments.
Published by
10th International BCI Meeting, Brussels
Summary
Presented research on how ecologically valid interactions affect the performance of RSVP-based Brain-Computer Interfaces, emphasizing real-world applicability.
Published by
Springer Lecture Notes in Electrical Engineering
Summary
Published research on classifying two-class motor imagery using sub-frequency bands and Convolutional Neural Networks, contributing to BCI signal processing.
Published by
Proceedings of the Multimedia University Engineering Conference (MECON 2022)
Summary
Published conference proceedings on the application of Partial Directed Coherence (PDC) for classifying motor imagery in Brain-Computer Interfaces.
Deep Learning, Traditional Machine Learning, ML Algorithms, Transformer Architectures, Classification Performance.
Neuroscience, EEG Data Collection, Biomedical Signal Processing, Motor Imagery, Noise-Resilient BCIs, Neurotechnology Applications.
Experimental Protocol Design, Data Acquisition, Data Preprocessing, Data Analysis, Literature Review, Project Documentation, Research Writing.
Python, MATLAB, EEGLAB, Brainstorm, Proteus Design Suite, Latex (Overleaf), Microsoft Office (Word, PowerPoint, Excel), MS Visio.
Engineering Education, Academic Support, Tutorial Delivery, Laboratory Demonstrations, Complex Concept Explanation.
Biomedical Engineering, Brain-Controlled Wheelchair.
Stakeholder Management, Multidisciplinary Team Collaboration, GDPR Compliance, Product Quality Assurance.