About
Martin is an AI Research Engineer at Helsing, London, UK working on AI Assurance. Martin obtained a PhD in Electronic and Electrical Engineering from University College London, London, UK in 2024. Prior to that, Martin obtained an MEng in Electronic and Information Engineering from Imperial College London, London, UK in 2019. He has hands-on experience from industrial placements spanning ARM, Deeplite, Amazon and private consultancy projects in different countries. His research interests include Bayesian inference, deep learning, hardware acceleration and reliable, safe and secure artificial intelligence.
Download my resumé/CV.
Interests
- Machine Learning
- Uncertainty Quantification
- Hardware Optimisation
- Real-world Deployment of Machine Learning Systems
- Edge AI
- Computer Vision
- Confidence Calibration
- AI Assurance
- Safety-Critical AI
Education
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PhD in Electornic and Electrical Engineering, 2019-2024
University College London
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MEng in Electronic and Information Engineering, 2015-2019
Imperial College London
Industrial Experience
AI Research Engineer
Verification, validation, assurance and confidence quantification and calibration of machine learning models
Machine Learning Consultant
Designed and delivered a learnable recommendation system for a customer and business-agnostic discount optimisation
Applied Science Research Intern
Continual learning, in the domain of computer vision and natural language processing, to reduce resource costs in model retraining on new data
Research Scientist Intern
Structured neural network compression for real-world deployment on embedded devices for computer vision applications through causal inference
Machine Learning Consultant
Delivered an end-to-end machine learning pipeline for CT scans segmentation to an industrial healthcare client
Machine Learning/AI Advisor
Advising and evaluating implementations of time-series data classification and regression from medical records
Machine Learning Researcher
Investigation into combining variational inference with quantisation for hardware-efficient Bayesian neural networks
AI Toolchain Researcher
Developed a verification platform for hardware implementation of convolutional neural networks on FPGAs
Software Engineer & Consultant Summer Intern
Automated processing of data from smart-electrometers into SAP systems that improved the initially manual processing time from hours to seconds
Teaching/Service
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Teaching
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Teaching Assistant in Integrated Machine Learning Systems: Emerging Topics at UCL, Department of Electronic and Electrical Engineering
- Spring 2024: link
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Co-Lecturer in Foundations of Machine Learning and Data Science at UCL, Department of Information Studies
- Spring 2021: link
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Co-Lecturer in Introduction to Machine Learning, a Masterclass at UCL, Department of Pharmacology
- Spring 2020
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Teaching Assistant at the Defence Science and Technology Laboratory, on behalf of UCL and the Alan Turing Institute
- February 2020/November 2020
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Teaching assistant at OxML Summer school
- Summer 2020
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Postgraduate Teaching Assistant for Introduction to Machine Learning at UCL, Department of Information Studies
- Spring 2020: link
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Postgraduate Teaching Assistant for Programming 2 at UCL, Department of Information Studies
- Autumn 2019: link
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Undergraduate Teaching Assistant for Introduction to Computer Architecture at Imperial College London, Department of Electrical and Electronic Engineering
- Autumn 2017/Autumn 2018: link
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Teaching assistant at PAPAA Summer School
- Summer 2018
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Service
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Reviewer:
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Journals:
- IEEE Transactions for Neural Networks and Learning Systems (TNNLS)
- IEEE Journal of Selected Topics in Signal Processing (JSTSP)
- IEEE Transactions on Circuits and Systems I (TCAS-I)
- IEEE Transactions on Image Processing (TIP)
- IEEE Solid-State Circuits Letters (SSC-L)
- IEEE Transactions on Computers (TC)
- IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
- IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI)
- Transactions on Machine Learning Research (TMLR)
- PLOS ONE
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Conferences/Workshops:
- ACL 2025
- AISTATS 2024
- UAI 2024
- IEEE Mediterranean Conference on Control and Automation 2023 (MED)
- Efficient Deep Learning for Computer Vision (CVPR Workshop) 2023
- Workshop on Structured Probabilistic Inference & Generative Modeling (ICML Workshop) 2023
- Workshop on Spurious Correlations, Invariance, and Stability (ICML Workshop) 2023
- Workshop on Causal Representation Learning (NeurIPS Workshop) 2023
- IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip 2023
- International Conference on Artificial Neural Networks 2023/22/21 (ICANN)
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Mentor:
- Growni: Remote counselling to self-driven high-school students from university alumni
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Organiser:
- Weekly Machine learning reading group at UCL EE Department (~25 people) 2023
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Accomplishments
Awards and Honours
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2023
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Heidelberg Laureate Forum (HLF) Participant
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G-Research Grant supporting HLF Participation
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SAP Grant supporting HLF Participation
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2022
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2021
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2021 Študenti do sveta Scholarship
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2021 MITACS Research Collaboration Award
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2022 Alan Turing Institute PhD Enrichment Student
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2021 G-Research grants for PhD students and postdocs in quantitative fields
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2021 Semi-finalists in the Deepnote publishing Competition
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2021 Runner up in a 3-Minute PhD Pitch Competition
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2020
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2020 Talenty Novej Európy Scholarship
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2020 Finalists at the AIHack held at Imperial College London
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2019
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2018/19 Dean’s List, Imperial College London
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2019 Talenty Novej Európy Scholarship
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2019 Študenti do sveta Scholarship
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2018
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2017/18 Dean’s List, Imperial College London
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2018 Semifinalists at Shell Ideas360
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2018 Talented Youth of Bratislava
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2018 First prize at FEIstival
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2017
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2017 Faculty of Engineering Undergraduate Research Opportunities Programme Award, Imperial College London
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2017 Študenti do sveta Scholarship
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2017 Forbes Slovakia 30 under 30
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2016
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2016 Winners of PA Consulting Group Video Challenge
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2016 Finalists of Social Impact Award
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2016 Finalists of HSBC Grow Your Future
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2016 First prize at 2016 CISCO Switch-up Challenge
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2015
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2015 National Representative at Expo in Milan, Italy
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2015 Third place at Unilever Sustainability Challenge
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2014
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2014 Diploma of St. Gorazd
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2014 National Representative at Expo Sciences Europe
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2014 Gold Medal and a Special Award at I-SWEEEP
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2014 Ambassador of Youth
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2014 Award of the Rector of Paneuropean University in Bratislava
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2014 Stredoškoláci do sveta Scholarship
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2014 Green Fond Project Grant
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2013
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2013 Award of the Comenius University for the Best Science Project
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2013 Diploma of St. Gorazd
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2013 Silver medal at the Austrian Young Physicist Tournament
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2013 Third place at CEMACH
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Media appearances
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2024
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2023
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2022
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2021
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2020
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2019
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2018
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2017
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2016
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2015
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2014
Publications
Quickly discover relevant content by filtering publications.
(2025).
Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede’s Cultural Dimensions.
Proceedings of the 31st International Conference on Computational Linguistics.
(2024).
Online End-to-End Learning-Based Predictive Control for Microgrid Energy Management.
IEEE Transactions on Control Systems Technology.
(2024).
Smart Laser Sintering: Deep Learning-Powered Powder Bed Fusion 3D Printing in Precision Medicine.
International Journal of Pharmaceutics.
(2024).
Learning-based MPC with uncertainty estimation for resilient microgrid energy management.
2024 IFAC Safeprocess.
(2024).
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks.
Transactions on Machine Learning Research.
(2024).
SAE: Single Architecture Ensemble Neural Networks.
35th British Machine Vision Conference 2024, BMVC 2024, Glasgow, UK, November 25-28, 2024.
(2024).
Large language models surpass human experts in predicting neuroscience results.
Nature human behaviour.
(2024).
LLM Alignment Using Soft Prompt Tuning: The Case of Cultural Alignment.
Workshop on Socially Responsible Language Modelling Research.
(2023).
An Online Learning Method for Microgrid Energy Management Control.
2023 31st Mediterranean Conference on Control and Automation (MED).
(2023).
Impact of Noise on Calibration and Generalisation of Neural Networks.
ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability.
(2023).
MIMMO: Multi-Input Massive Multi-Output Neural Network.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
(2023).
Renate: A library for real-world continual learning.
2023 Continual Learning in Computer Vision (CLCV) workshop at CVPR 2023.
(2022).
Simple Regularisation for Uncertainty-Aware Knowledge Distillation.
ICML 2022 Workshop on Distribution-Free Uncertainty Quantification.
(2022).
Accelerating Bayesian Neural Networks via Algorithmic and Hardware Optimizations.
IEEE Transactions on Parallel and Distributed Systems.
(2022).
FPGA-based Acceleration for Bayesian Convolutional Neural Networks.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
(2022).
Enabling fast uncertainty estimation: accelerating bayesian transformers via algorithmic and hardware optimizations.
Proceedings of the 59th ACM/IEEE Design Automation Conference.
(2022).
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator.
2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC).
(2021).
High-Performance FPGA-based Accelerator for Bayesian Neural Networks.
2021 58th ACM/IEEE Design Automation Conference (DAC).
(2021).
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator.
2021 International Conference on Field-Programmable Technology (ICFPT).
(2021).
Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs.
IEEE Transactions on Neural Networks and Learning Systems.
(2021).
VINNAS: Variational Inference-based Neural Network Architecture Search.
arXiv pre-print.
(2021).
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation.
Artificial Neural Networks and Machine Learning – ICANN 2021.
(2021).
Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators.
Electronics.
(2021).
On the effects of quantisation on model uncertainty in Bayesian neural networks.
Uncertainty in Artificial Intelligence.
(2021).
On Causal Inference for Data-free Structured Pruning.
The AAAI-22 Workshop on Information-Theoretic Methods for Causal Inference and Discovery.
(2020).
Optimizing FPGA-Based CNN Accelerator Using Differentiable Neural Architecture Search.
2020 IEEE 38th International Conference on Computer Design (ICCD).
(2020).
Improving Performance Estimation for FPGA-Based Accelerators for Convolutional Neural Networks.
Applied Reconfigurable Computing. Architectures, Tools, and Applications.
(2019).
Static Block Floating-Point Quantization for Convolutional Neural Networks on FPGA.
2019 International Conference on Field-Programmable Technology (ICFPT).
(2019).
F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition.
2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP).
(2018).
A Real-Time Object Detection Accelerator with Compressed SSDLite on FPGA.
2018 International Conference on Field-Programmable Technology (FPT).
Open Source Projects
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YAMLE - Creator
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YAMLE: Yet Another Machine Learning Environment facilitates rapid prototyping and experimentation with machine learning models and methods
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Renate - Founding Contributor
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Continual learning with hyperparameter tuning, model selection after each update, and a variety of continual learning strategies
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OL-EMS - Founding Contributor
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Online learning control algorithms specialising in energy management systems
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Contact
- martin [dot] ferianc [dot] 19 [at] ucl [dot] ac [dot] uk
- 2 Malet Pl, London WC1E 7JE, London, WC1E 7JE
- @MartinFerianc
- Martin Ferianc
- martinferianc
- martinferianc