Theoretical physicist by training, working on modelling and machine learning in astrophysics.
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Pre-trained transformer inference for gravitational-wave time series
Foundation-style model for inference of gravitational wave signals with domain adaptation, accelerated training and performance.
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GPU-based Bayesian inference for gravitational waves
A GPU-accelerated code for Bayesian analysis of gravitational-wave time series (simulation and inverse modelling)
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Deep learning for chaos detection
Anomaly detection model for chaotic dynamics in dynamical systems with convolutional networks.
https://github.com/ippocratiss/Deep-classifier-for-chaos-and-order
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Generative-adversarial-network-for-complex-systems
https://github.com/ippocratiss/Generative-adversarial-network-for-complex-systems
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Deep-learning-inference-of-the-neutron-star-equation-of-state
Bayesian deep learning pipeline to solve the inverse problem of inferring the state of dense matter in neutron stars
https://github.com/ippocratiss/Deep-learning-inference-of-the-neutron-star-equation-of-state
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ML for weather forecasting
https://github.com/ippocratiss/minRNNs-for-weather-prediction
- Python (PyTorch, TensorFlow), Fortran
- High-performance & GPU computing
- Bayesian inference, time series, transformers, LLMs, generative modelling, diffusion models, graph neural networks
A full list of publications and research statistics can be found through the following link: