Research Interests
Expanding knowledge in the biomedical and clinical sciences Signal Processing Optimisation Artificial Intelligence & Image Processing Natural Language Processing Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience) Cognitive neuroscience Knowledge Representation and Machine Learning Deep learningTeaching Strengths
Ms Farwa Abbas
ARC Grant-Funded Researcher A
School of Pharmacy and Biomedical Sciences
College of Health
Farwa is an ARC grant funded postdoctoral researcher working at the intersection of cognitive and computational neuroscience and healthcare technologies. Her research interests span biomedical and statistical signal processing, convex optimization, machine learning, and deep learning, with a particular focus on interpretable AI for clinical translation. Through this work, she aims to develop healthcare solutions that are trustworthy, usable, and impactful in real-world settings. She completed her doctoral studies in the Department of Electrical and Electronic Engineering at Imperial College London, where she worked in the Communications and Signal Processing Research Group. Her doctoral research focused on advanced signal processing models and learning-based methods for biomedical applications.
Beyond research, Farwa is deeply passionate about science communication, education, and advocating for underrepresented voices in science. She is strongly committed to closing the gender gap in STEM and believes that early exposure to science, particularly for young girls, can be transformative. Through teaching, outreach, and public engagement, she aims to make science more inclusive, engaging, and empowering, while contributing meaningful, data-driven innovations and helping shape a more equitable and diverse scientific community.
Biomedical and Statistical Signal Processing: Development of signal processing methodologies for the analysis of multimodal biomedical data such as EEG, sEMG, MEG, and MRI. This includes statistical signal modeling, source localization, and feature extraction techniques aimed at understanding neural dynamics and identifying clinically relevant biomarkers in neurological and movement disorders.
Computational Modeling of Neural Systems: Mathematical and computational modeling of neural population dynamics using differential equations and stochastic systems, with the goal of linking physiological mechanisms to observable signals and imaging data.
Neuromodulation and Data-Driven Neuroscience: Analysis of non-invasive brain stimulation data (e.g., TMS) to study neuroplasticity and working memory, using signal processing, biophysical modeling, and machine learning techniques to characterize brain-state dependent effects, individual variability and pharmacological effects on neuroplasticity.
Inverse Problems and Imaging: Inverse problems arising in biomedical imaging and neurophysiology, including the reconstruction of latent neural sources and physiological states from indirect, noisy, and high-dimensional measurements. This includes theoretical and computational approaches to ill-posed inverse problems in neuroimaging and related modalities.
Deep Learning for Image and Signal Reconstruction: Strong interest in deep learning based approaches for image and signal reconstruction, particularly hybrid model-based and data-driven methods. This includes neural architectures for accelerated and robust reconstruction in biomedical imaging, as well as learning priors and representations that improve generalization and interpretability in inverse problems.
Convex Optimization and Structured Signal Representations: Convex optimization methods for learning low-rank, sparse, and structured representations of signals and images. This spans optimization algorithms, regularization techniques, and theoretical guarantees for recovering meaningful structure in high-dimensional biomedical data, with applications to imaging, neural signal analysis, and connectivity modeling.
| Date | Position | Institution name |
|---|---|---|
| 2026 - ongoing | ARC Grant Funded Postdoctoral Researcher | University of Adelaide |
| 2023 - 2023 | Lecturer - Computer Science | Oxmedica |
| 2022 - 2022 | Lecturer - Computer Science | Oxmedica |
| 2021 - 2025 | Course Instructor | Imperial College London |
| 2018 - 2020 | Research Associate | Center for Artificial Intelligence and Computational Sciences (CACTuS) |
| Date | Type | Title | Institution Name | Country | Amount |
|---|---|---|---|---|---|
| 2025 | Award | Early Career Development Grant for Women in AI | Imperial College London | United Kingdom | - |
| 2022 | Scholarship | Higher Education Commission of Pakistan Overseas Scholarship | Higher Education Commission of Pakistan | Pakistan | AUD 125586.80 |
| 2021 | Scholarship | Electrical and Electronic Engineering Department Scholarship | Imperial College London | United Kingdom | AUD 59769.46 |
| 2021 | Scholarship | Chief Minister Merit Scholarship | The Punjab Educational Endowment Fund | Pakistan | AUD 267838.01 |
| 2019 | Distinction | High Distinction | Lahore University of Management Sciences | Pakistan | - |
| Language | Competency |
|---|---|
| English | Can read, write, speak, understand spoken and peer review |
| Urdu | Can read, write, speak, understand spoken and peer review |
| Date | Institution name | Country | Title |
|---|---|---|---|
| 2021 - 2025 | Imperial College London | United Kingdom | PhD Biomedical Signal Processing |
| 2017 - 2019 | Lahore University of Management Sciences | Pakistan | MS Electrical Engineering |
| 2013 - 2017 | University of Engineering and Technology Lahore | Pakistan | BSc. Electrical Engineering |
| Date | Title | Institution | Country |
|---|---|---|---|
| 2025 | Postdoctoral Fellowship | University of Adelaide | Australia |
| Date | Title | Institution name | Country |
|---|---|---|---|
| 2024 | AWS Cloud Computing Training | Amazon Web Services | Online |
| Year | Citation |
|---|---|
| 2025 | Abbas, F., McClelland, V., Cvetkovic, Z., & Dai, W. (2025). Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation.. IEEE Trans Biomed Eng, 72(5), 1697-1707. |
| Year | Citation |
|---|---|
| 2025 | Abbas, F., Ahmad, H., & Szabo, C. (2025). SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning. In 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 762-769). IEEE. DOI |
| 2025 | Abbas, F., McClelland, V., Dai, W., & Cvetkovic, Z. (2025). INFR-GC: Interpretable Feature Representations for Granger Causality in Cortico-muscular Interactions. In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE. DOI |
| 2024 | Abbas, F., McClelland, V., Cvetkovic, Z., & Dai, W. (2024). DLGC: Dictionary Learning based Granger Causal Discovery for Cortico-muscular Coupling. In 32ND EUROPEAN SIGNAL PROCESSING CONFERENCE, EUSIPCO 2024 (pp. 1746-1750). FRANCE, Lyon: IEEE. DOI WoS2 |
| 2023 | Abbas, F., McClelland, V., Cvetkovic, Z., & Dai, W. (2023). SS-ADMM: Stationary and Sparse Granger Causal Discovery for Cortico-Muscular Coupling. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE. DOI |
| 2019 | Shamshad, F., Abbas, F., & Ahmed, A. (2019). DEEP PTYCH: SUBSAMPLED FOURIER PTYCHOGRAPHY USING GENERATIVE PRIORS. In 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (pp. 7720-7724). ENGLAND, Brighton: IEEE. DOI WoS37 |
| 2019 | Shamshad, F., Hanif, A., Abbas, F., Awais, M., & Ahmed, A. (2019). Adaptive Ptych: Leveraging Image Adaptive Generative Priors for Subsampled Fourier Ptychography. In 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) (pp. 3834-3843). SOUTH KOREA, Seoul: IEEE COMPUTER SOC. DOI WoS2 |
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Fellowship: ARC Grant Funded Postdoctoral Researcher (Australian Research Council)
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Studentship: Eelectrical and Electronic Engineering Department Scholarship (Imperial College London)
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Studentship: Chief Minister Merit Scholarship (The Punjab Educational Endowment Fund)
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Studentship: Higher Education Commission of Pakistan Overseas Scholarship (Higher Education Commission of Pakistan)
Ms Farwa's teaching is closely informed by her research experience, interdisciplinary perspective, with an emphasis on connecting mathematical and computational foundations to real-world clinical and technological applications.
From 2021 to 2025, Ms Farwa served as a Course Instructor at Imperial College London, supporting undergraduate and master’s level courses across Electrical Engineering, Applied Mathematics, and Data Science. These included
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Topics in Large Dimensional Data Processing
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Mathematics for Engineers II
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Cryptography and Coding Theory
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Topics in Electrical Engineering
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Analysis and Design of Circuits
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Second-year MATLAB online module.
Her responsibilities included developing teaching and assessment materials, grading coursework and presentations, and leading laboratory and tutorial sessions. She focuses on conceptual clarity, practical understanding, and experimental safety, while supporting students with diverse academic backgrounds through one-to-one mentoring and tailored guidance.
In parallel with her university teaching, Ms Farwa has been actively engaged in science communication and outreach. In 2022 and 2023, she was a Lecturer at the Oxmedica Summer Bootcamp in Riyadh, where she delivered a three-week intensive course titled Computer Science for a Digital Future to secondary school girls. She designed interactive, project-based learning activities to encourage creativity, computational thinking, and confidence in problem solving. As part of the programme, she delivered TED-style talks on emerging topics in computer science and artificial intelligence and facilitated career panel discussions highlighting academic and industry pathways in STEM.
She is deeply committed to inclusive education and believes that early exposure to science can be transformative, particularly for young girls. Her teaching philosophy is based on accessibility, interpretability, and real-world relevance, and her research focus on developing trustworthy and impactful data-driven solutions for healthcare. Through teaching, mentorship, and outreach, she seeks to develop technically strong students and to inspire curiosity, confidence, and sustained engagement with science and engineering.
| Date | Title | Engagement Type | Institution | Country |
|---|---|---|---|---|
| 2024 - 2024 | Presented Talk at NeurIT workshop | Scientific Community Engagement | International Symposium on Information Theory (ISIT) | Greece |
| 2024 - 2024 | Presented Talk at Changing the Face of Science Research Symposium (CFoS) | Scientific Community Engagement | Imperial College London | United Kingdom |
| Date | Event Name | Event Type | Institution | Country |
|---|---|---|---|---|
| 2025 - 2025 | International Conference on Tools with Artificial Intelligence (ICTAI) | Conference | IEEE | Greece |
| 2024 - 2024 | European Conference on Signal Processing (EUSIPCO) | Conference | EURASIP | France |
| 2023 - 2023 | International Conference on Acoustics, Speech, and Signal Processing (ICASSP) | Conference | IEEE | Greece |
| 2022 - 2022 | Conference on Learning Theory (COLT) | Conference | Association for Computational Learning | United Kingdom |
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