Scholar
Martin Ferianc
Google Scholar ID: itcRKZQAAAAJ
AI Research Engineer at Helsing
Citations & Impact
Citations
778
H-index
15
i10-index
19
Publications
20
Co-authors
28
list available
Publications
Resume (English only)
Academic Achievements
- No specific academic achievements such as publications or awards are listed
Research Experience
- - AI Research Engineer, Helsing, Sep 2024 – Present, Verification, validation, assurance, and confidence quantification and calibration of machine learning models
- - Machine Learning Consultant, UCL Consultants, Oct 2023 – Jan 2024, Designed and delivered a learnable recommendation system for discount optimisation
- - Applied Science Research Intern, Amazon, Jul 2022 – Nov 2022, Continual learning to reduce resource costs in model retraining on new data
- - Research Scientist Intern, Deeplite, Jul 2021 – Nov 2021, Structured neural network compression for real-world deployment on embedded devices for computer vision applications
- - Machine Learning Consultant, UCL Consultants, Jun 2020 – Jun 2020, Delivered an end-to-end machine learning pipeline for CT scans segmentation
- - Machine Learning/AI Advisor, Powerful Medical, Nov 2019 – Jan 2021, Advising and evaluating implementations of time-series data classification and regression from medical records
- - Machine Learning Researcher, ARM, Jul 2020 – Oct 2020, Investigation into combining variational inference with quantisation for hardware-efficient Bayesian neural networks
- - AI Toolchain Researcher, Corerain Technologies, Apr 2018 – Oct 2018, Developed a verification platform for hardware implementation of convolutional neural networks on FPGAs
- - Software Engineer & Consultant Summer Intern, Západoslovenská energetika, a.s. - Skupina ZSE, Jul 2016 – Sep 2016, Automated processing of data from smart-electrometers into SAP systems
Education
- PhD in Electronic and Electrical Engineering, 2019-2024, University College London; MEng in Electronic and Information Engineering, 2015-2019, Imperial College London
Background
- Research interests include Bayesian inference, deep learning, hardware acceleration, and reliable, safe, and secure artificial intelligence. Has hands-on experience from industrial placements spanning ARM, Deeplite, Amazon, and private consultancy projects in different countries.
Miscellany
- Interests include Machine Learning, Uncertainty Quantification, Hardware Optimisation, Real-world Deployment of Machine Learning Systems, Edge AI, Computer Vision, Confidence Calibration, AI Assurance, Safety-Critical AI
