CV

Profile

• Final year PhD student in machine learning (ML) and biomedical engineering
• 4.5 years of experience using Python, PyTorch, Docker, and Git for ML and data-driven research projects during PhD and in industry
• Industry experience across 1.5 years at The Technology Partnership (TTP) and Intel
• Seeking post-PhD role as AI/ML research scientist or engineer starting from July 2024
• Distinction in postgraduate MRes in AI. High 2:1 MEng (Hons) in Electrical and Electronic Engineering from the University of Bath, with a First Class in the MEng project component.

Experience

• University of Bath, Doctoral Researcher, Sept ’20 – Present
  • Interdisciplinary PhD project on developing implantable closed-loop nerve interfaces to improve treatment of various neurological conditions
  • Adapted and implemented CNN-based models such as VAE and U-Net (Noise2Noise) models in PyTorch for efficient ML-based denoising of peripheral nerve data (Paper / Code)
  • Improved previous quadratic programming approach (Two-CAP method) for estimating distributions of fibre conduction velocities in whole nerve electrical recordings (Paper)
  • Recently expanded quadratic programming work with biophysically accurate simulation models and ML-based segmentation of nerve imaging (histology) data (to be published)
  • Planned and carried out in-vivo and ex-vivo animal experiments to collect neuromodulation datasets and promote data-driven and ML-based research (Paper)
  • Leveraged Docker to create reproducible and easy-to-use environments to reduce onboarding time for collaborators
  • Published and collaborated on 4 peer-reviewed journal papers and 5 conference papers, multiple times as first author, and presented research at conferences internationally
• Technology Partnership (TTP), Neurotechnology Consultant, Feb ’22 – Aug ’22
  • Contributed to both internal R&D projects and client-facing projects on software engineering
  • Researched and implemented low-power ML algorithms for implantable cardiac devices, which have since been presented in prototypes at medical device conferences and meetings and electronics for medical devices
• University of Bath, Undergraduate and Postgraduate Teaching Assistant, Sept ’19 – Dec ’22
  • Taught and assessed undergraduate and master’s courses in Computer Science and Electrical and Electronic Engineering at the University of Bath
  • Modules included robotics software, artificial intelligence, and programming
• Intel UK, Digital Design and Verification Intern, Jul ’16 – Aug ’17
  • Worked at Intel’s Communications and Devices Group for a year, specifically on functional verification and Design-For-Test of application-specific ICs (ASICs)
  • Wrote and presented conference paper at Synopsys User Group Conference (SNUG) titled “Automatic Test Point Insertion in MPHY Products” (Technical committee award, honourable mention)

Education

• PhD at Centre for Accountable, Responsible and Transparent AI (ART-AI), 2020 – Present
• MRes in Computer Science, University of Bath (Distinction), 2019 - 2020
  • Modules in machine learning (89%, 87%), statistics (86%), robotics (77%), and social science (64%, 65%)
• MEng in Electronic and Electrical Engineering, University of Bath (High 2.1), 2014 - 2019
  • Masters project in bioelectronics, involving the development of a microfluidic Brain-on-Chip platform for drug discovery.

Skills

• Programming languages: Python, MATLAB, C, SystemVerilog, VHDL, Bash
• Tools & Libraries: AI/ML (PyTorch, Scikit-learn, Numpy, Pandas, Weights & Biases), Parallel Computing (Numba), Version Control (Git, ClearCase), DevOps (Docker), LaTeX
• Data analysis & modelling: Large-scale biomedical data analysis, signal processing, image processing, regression analysis, data visualisation, quadratic programming
• Electrical engineering: Current-voltage characteristics, pulse measurements, impedance spectroscopy
• Natural languages: English (fluent), Portuguese (fluent), German and Spanish (limited working proficiency)

Publications

Ex-vivo systems for neuromodulation: A comparison of ex-vivo and in-vivo large animal nerve electrophysiology

M. Ribeiro, F.R. Andreis, L. Jabban et al., Ex-vivo systems for neuromodulation: A comparison of ex-vivo and in-vivo large animal nerve electrophysiology. Journal of Neuroscience Methods (2024), doi: https://doi.org/10.1016/j.jneumeth.2024.110116

Denoising and decoding spontaneous vagus nerve recordings with machine learning

Ribeiro M, Koh RGL, Donnelly T, Lutteroth C, Proulx MJ, Rocha PRF, and Metcalfe B (2022) "Denoising and decoding spontaneous vagus nerve recordings with machine learning." 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 1-4. doi: 10.1109/EMBC40787.2023.10340443

The assessment list for trustworthy artificial intelligence: A review and recommendations

Radclyffe C, Ribeiro M and Wortham RH (2023) The assessment list for trustworthy artificial intelligence: A review and recommendations. Front. Artif. Intell. 6:1020592. doi: 10.3389/frai.2023.1020592

An in-vitro system for closed loop neuromodulation of peripheral nerves

Ribeiro M, Jabban L, Andreis FR, Nielsen TGNdS, Rocha PRF and Metcalfe B (2022) "An in-vitro system for closed loop neuromodulation of peripheral nerves." 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 2361-2364. doi: 10.1109/EMBC48229.2022.9871933

Adaptation of the Two-CAP Method for Conduction Velocity Distribution Estimation in Multi-Channel Recordings

Ribeiro M, Wozniak K, Andreis FR, Nielsen TGNdS and Metcalfe B (2022) "Adaptation of the Two-CAP Method for Conduction Velocity Distribution Estimation in Multi-Channel Recordings." 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 4109-4114. doi: 10.1109/EMBC48229.2022.9871895

Pig Ulnar Nerve Recording with Sinusoidal and Temporal Interference Stimulation

Jabban L, Ribeiro M, Andreis FR, Nielsen TGNdS and Metcalfe B (2022) "Pig Ulnar Nerve Recording with Sinusoidal and Temporal Interference Stimulation." 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 5084-5088. doi: 10.1109/EMBC48229.2022.9871603

Microfluidics Integration into Low-Noise Multi-Electrode Arrays

Ribeiro M, Ali P, Metcalfe B, Moschou D, Rocha PRF (2020) "Microfluidics Integration into Low-Noise Multi-Electrode Arrays." Micromachines 12:727. doi: 10.3390/mi12060727

An Ultra-Sensitive Biosensor to Investigate Random Telegraph Noise in Human Breast Cancer Cells

Ribeiro M, Rocha P.R.F, Metcalfe B (2020) "An Ultra-Sensitive Biosensor to Investigate Random Telegraph Noise in Human Breast Cancer Cells." MDPI IECB 2020 60:27. doi: 10.3390/IECB2020-07223

Human breast cancer cells demonstrate electrical excitability

Ribeiro M, Elghajiji A, Fraser SP, Burke ZD, Tosh D, Djamgoz MBA and Rocha PRF (2020) "Human Breast Cancer Cells Demonstrate Electrical Excitability." Front. Neurosci. 14:404. doi: 10.3389/fnins.2020.00404

Memberships and Awards

• Institute of Engineering & Technology (IET) Travel Award Recepient, 2023
• University of Bath Doctoral Recognition Award Winner for work in Equality, Diversity, and Inclusion (ED&I) groups, 2022
• Member of Equality, Diversity, and Inclusivity (ED&I) committees for Computer Science and ART-AI at the University of Bath, 2019 – Present
• Technical Committee Honourable Mention at Synopsys User Group Conference, 2017
• Member of the Institute for Electrical and Electronics Engineers (IEEE) and IET, 2019 – Present