Quentin Ferry

Prospective ML Researcher/ Engineer

Profile

Originally trained as an engineer, I have since worked in a wide range of fields, including machine learning (MSc), synthetic biology/genome engineering (PhD), cognitive neuroscience, and artificial intelligence (postdoc). Motivated by the desire to understand intelligence, my current research combines analysis of animal models and Deep Learning agents to characterize the formation, storage, and generalization of knowledge in biological and artificial brains. Moving forward, I wish to pursue a career in mechanistic interpretability.

🎓 Education

[2013-2017] – PhD in Genomic Medicine and Statistics, University of Oxford, UK, Awarded 01/2018.
[2011-2012] – MSc in Biomedical Engineering, University of Oxford, UK, Awarded with Distinctions & best overall research project.
[2009-2012] – Undergraduate Studies in Engineering, Ecole Centrale de Nantes, France.

📚 Courses & Accreditations relevant to ML research

[2023 credit] – MIT MicroMasters Program in Satistics and Data Science, MIT, edX. Included: Fundamentals of Statistics, Probability - The Science of Uncertainty and Data, Data Analysis: Statistical Modeling and Computation in Applications, and Machine Learning with Python-From Linear Models to Deep Learning.
[2022 audit] – Reinforcement Learning Series 2021, Google DeepMind.
[2022 audit] – Designing, Visualizing and Understanding Deep Neural Networks, UC Berkeley.
[2019 credit] – Deep Learning Specialization, DeepLearning.AI. Included: Neural Networks and Deep Learning, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, Structuring Machine Learning Projects, Convolutional Neural Networks, Sequence Models, and Deep Learning.

🔬 Research Experience

[2013-2017] – The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, US.
- Role: Postdoctoral Associate
- Focus: (i) Combined animal behavior, biological techniques, and machine learning to characterize the formation, storage, and generalization of knowledge in the brain. (ii) Performed similar analyses on trained deep neural networks to understand abstraction in artificial brains.
- Adviser: Prof. Susumu Tonegawa
- Fields: Cognitive Neuroscience, Deep Learning, Mechanistic Interpretability