Martin Ferianc
Machine Learning Engineer @G-Research
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WORK HISTORY
Machine Learning Engineer @G-Research
London, GB
Core Topic: Large-scale ML infrastructure and optimisation for quantitative finance- Developing custom distributed training libraries for large-scale financial data, enabling faster model iteration for quantitative researchers- Optimising inference pipelines for trading systems, reducing model latency to meet production deployment requirements.Core Skills: Python, PyTorch, Distributed Training, Model Optimisation, Inference Optimisation, High-Performance Computing, Profiling, Time-Series Analysis, Financial ML, Production ML Systems, Quantitative Research Tools
EDUCATION
INRIA
Summer School: Paris artificial intelligence summer school, Artificial Intelligence
The University of Hong Kong
Summer School: Performance-Aware Programming with Application Accelerators, Computer Science
International Baccalaureate
High School Diploma, Physics, Mathematics, English B, Slovak A, History, Computer Science
Technical University of Munich
Summer School: Finding Nano, Nanotechnology & Nano-electronics
University of Oxford
Summer School: The Oxford Machine Learning Summer School, Machine Learning
Max Planck Institute for Intelligent Systems
Summer School: The Machine Learning Summer School, Machine Learning
UCL
Doctor of Philosophy - PhD, Machine learning, Bayesian neural networks
Mila - Quebec Artificial Intelligence Institute
Summer School: CIFAR DLRL, Machine Learning
Technical University of Munich
Summer School: Innovations in IT, Information Technology
Norwegian University of Science and Technology (NTNU)
Summer School: ProbAI 2021, Machine Learning
Imperial College London
MEng. Electronic and information Engineering, Computer Engineering
Telkom University
Summer School: The Machine Learning Summer School, Machine Learning
ABOUT MARTIN FERIANC
Martin is a Machine Learning Engineer at G-Research, London, UK. 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, software and hardware acceleration of machine learning models and reliable, safe and secure artificial intelligence.
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