Martin Ferianc

Martin Ferianc

PhD student at University College London

University College London


Martin Ferianc is a PhD candidate in the Department of Electronic and Electrical Engineering at University College London. His research interests include neural architecture search, Bayesian neural network, deep learning and hardware acceleration of neural networks. Martin has obtained an MEng in Electronic and Information Engineering from Imperial College London.

Download my resumé.

  • Machine Learning
  • Uncertainty Quantification
  • Hardware Optimisation
  • Real-world Deployment of Machine Learning Systems
  • Edge AI
  • PhD in Electornic and Electrical Engineering, 2019-

    University College London

  • MEng in Electronic and Information Engineering, 2015-2019

    Imperial College London


The main condition about any project and work that I do is that I do something novel. I am not interested in repeating solutions developed by the others, I am more interested in researching my own solutions. I am not afraid of reading papers and independently conducting research, I actually love it!

Artificial Intelligence

Probably not the first time you’re reading ‘Artificial Intelligence’ (AI). But hey, the buzzword is absolutely worth the hype. AI is bigger than recognizing cars or cats or road signs- think bigger. What about neural networks that can assemble themselves, and then adapt and learn from the assembly process?

Team Work

Its not only about the technologies, but mainly about the people behind them. (Until FPGA driven robots povered by AI will be able to write code…) I am always excited to work in a team. Working with others generates new ideas, strengthens existing friendships and is just much more fun which can be shared, right?


Industrial Experience

Applied Science Research Intern
Jul 2022 – Nov 2022 Berlin, Germany
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
Jul 2021 – Nov 2021 Montreal, Quebec, Canada
Structured neural network compression for real-world deployment on embedded devices for computer vision applications through causal inference
Technical Consultant
Jun 2020 – Jun 2020 London, United Kingdom
Delivered an end-to-end machine learning pipeline for CT scans segmentation to an industrial healthcare client
Machine Learning/AI Advisor
Nov 2019 – Jan 2021 London/Bratislava, United Kingdom/Slovakia
Advising and evaluating implementations of time-series data classification and regression from medical records
Machine Learning Researcher
Jul 2020 – Oct 2020 (Virtual) Cambridge, United Kingdom
Investigation into combining variational inference with quantisation for hardware-efficient Bayesian neural networks
AI Toolchain Researcher
Apr 2018 – Oct 2018 Shenzhen/London, China/United Kingdom
Contributed to Tensorflow-to-Verilog compiler for a streaming engine on an FPGA to accelerate convolutional neural networks for face detection
Software Engineer & Consultant Summer Intern
Jul 2016 – Sep 2016 Bratislava, Slovakia
Automated processing of data from smart-electrometers into SAP systems that improved the initially manual processing time from hours to seconds


  • Teaching

    • Co-Lecturer in Foundations of Machine Learning and Data Science at UCL, Department of Information Studies

    • Co-Lecturer in Introduction to Machine Learning, a Masterclass at UCL, Department of Pharmacology

      • Spring 2020
    • Teaching Assistant at the Defence Science and Technology Laboratory, on behalf of UCL and the Alan Turing Institute

      • February 2020/November 2020
    • Teaching assistant at OxML Summer school

      • Summer 2020
    • Postgraduate Teaching Assistant for Introduction to Machine Learning at UCL, Department of Information Studies

    • Postgraduate Teaching Assistant for Programming 2 at UCL, Department of Information Studies

    • Undergraduate Teaching Assistant for Introduction to Computer Architecture at Imperial College London, Department of Electrical and Electronic Engineering

      • Autumn 2017/Autumn 2018: link
    • Teaching assistant at PAPAA Summer School

      • Summer 2018
  • Service

    • Reviewer:

      • 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)
      • International Conference on Artificial Neural Networks 2021 (ICANN)
      • IEEE Mediterranean Conference on Control and Automation 2023 (MED)
      • Efficient Deep Learning for Computer Vision (CVPR Workshop) 2023
    • Program committee member:

      • International Conference on Artificial Neural Networks 2022 (ICANN)
    • Mentor:

      • Growni: Remote counselling to self-driven high-school students from university alumni
    • Organiser:

      • Weekly Machine learning reading group at UCL EE Department (~25 people, 2023)


Awards and Honours

  • 2016

    • 2016 Winners of PA Consulting Group Video Challenge

    • 2016 Finalists of Social Impact Award

    • 2016 Finalists of HSBC Grow Your Future

    • 2016 First prize at 2016 CISCO Switch-up Challenge

  • 2015

    • 2015 National Representative at Expo in Milan, Italy

    • 2015 Third place at Unilever Sustainability Challenge

  • 2014

    • 2014 Diploma of St. Gorazd

    • 2014 National Representative at Expo Sciences Europe

    • 2014 Gold Medal and a Special Award at I-SWEEEP

    • 2014 Ambassador of Youth

    • 2014 Award of the Rector of Paneuropean University in Bratislava

    • 2014 Stredoškoláci do sveta Scholarship

    • 2014 Green Fond Project Grant

  • 2013

    • 2013 Award of the Comenius University for the Best Science Project

    • 2013 Diploma of St. Gorazd

    • 2013 Silver medal at the Austrian Young Physicist Tournament

    • 2013 Third place at CEMACH

Media appearances

Recent Publications

Quickly discover relevant content by filtering publications.
(2022). Simple Regularisation for Uncertainty-Aware Knowledge Distillation. ICML 2022 Workshop on Distribution-Free Uncertainty Quantification.

PDF Cite Code

(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.

PDF Cite

(2022). Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator. 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC).