Postdoctoral Researcher
🌀 Bagot Lab, McGill University🌀 Mila, Quebec AI Institute
Past:Ph.D with Doina Precup 🌀 RL Lab, McGill UniversityResearch Scientist Intern 🌀 Google DeepMind(London & Montreal)
meditation enthusiast | yogi (200h RYT)
Hi 🖖🏽
Don’t hesitate to reach out if you want to
chat about research!
Bio
Broadly, I am interested in the study of cognition in humans 🧠 and machines 🤖, aiming both to enhance scientific knowledge and understanding, as well as to assist in the development of technology enhancing human health and well-being, and minimizing suffering.My research spans cognitive computational neuroscience, Reinforcement Learning (RL) and sequential decision making.I use tools and perspectives from mathematics, statistics, optimization, machine/deep learning, numerical methods, dynamical systems, as well as knowledge and insights from neuroscience and psychopharmacology.Research questions
🧠 theoretical, computational & behavioural neuroscience-study and understanding of the mechanisms underlying sensory, motor or cognitive computations, neuroplasticity, psychedelic researchFan of research mysteries 🔮 and fundamental questions 🦄
Appreciate research that offers a new understanding insight or connection across branches of research or fields in a unifying truth
Interests
- computational neuroscience
- psychedelic research
- reinforcement learning & decision-making
- continual learning
Education
PhD in Computer Science (AI/RL/neuro), 2019-2025
McGill University / Mila Quebec AI Institute
M.Sc in Computer Science (AI), 2016-2018
University Politehnica of Bucharest
B.Sc in Computer Science (math/CS), 2009-2013
University Politehnica of Bucharest
News
Last updated:
- - Excited to start a postdoc in Rose Bagot's lab at McGill to study the neural mechanisms of psychedelics 🧠 🍄 ✨🐁🌀
- - I'm officially a Dr. ✨ yay 🥳
- - Successfully defended my Ph.D 🥹 phew...
- - Presenting "Mood as an Extrapolation Engine for Adaptive Learning & Decision-Making" at COSYNE 2025. It's my first neuroscience conference and I couldn't be more excited 😬
Industry experience
Research Scientist Intern 🌀
DeepMind Montreal
April 2022 –
April 2023
Research Scientist Intern 🌀
DeepMind London
February 2021 –
June 2021
Machine Learning Engineer 🌀
Apsisware (‘17-‘18) / Sparktech Software (‘16)
January 2016 –
January 2018
Bucharest, Romania
Software Engineer 🌀
Deutsche Bank (‘15)/ Misys (‘14)/ Cronian Labs (‘13)
January 2013 –
January 2015
Bucharest, Romania
Academic awards
Bourses de doctorat en recherche
IVADO’s PhD Excellence scholarship
Borealis AI Fellowship
Excellence Scholarship
Merit Scholarship
Featured Publications & Preprints
(2024).
Functional Acceleration for Policy Mirror Descent.
arXiv preprint.
(2024).
Recurrent Policies Are Not Enough for Continual Reinforcement Learningt.
preprint.
(2024).
A dual-receptor model of serotonergic psychedelics: therapeutic insights from simulated cortical dynamics.
bioRxiv preprint.
(2022).
Selective Credit Assignment.
arXiv preprint.
(2020).
Forethought and Hindsight in Credit Assignment.
Advances in Neural Information Processing Systems.
Recent Publications & Preprints
Quickly discover relevant content by filtering publications.
(2023).
Acceleration in Policy Optimization.
arXiv preprint.
(2022).
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions.
AAAI Conference on Artificial Intelligence.
(2022).
Actor-critic as a joint maximization problem.
RLDM.
(2021).
Learning Expected Emphatic Traces for Deep RL.
AAAI Conference on Artificial Intelligence.
(2020).
Lambda Successor Return Error.
Biological and Artificial Reinforcement Learning Workshop at Neural Information Processing Systems (NeurIPS).