Hi, I'm Antonio đź‘‹
I am an aspiring scientist, a builder at heart, and passionate learner, currently pursuing a Machine Learning master's at the University of Cambridge. I aim to explore the computational principles of intelligence—both natural and artificial—decode the language of life, and contribute to building a future where we can cure disease, enhance cognition, and make biology programmable. My biggest driver is the desire to help unlock unprecedented eras of abundance through science, intelligence, and technology.
When I'm not researching, I enjoy spending time outdoors running, surfing, rock climbing, and boxing. I also love making and playing music, traveling, reading, hanging out with friends, and sharing meaningful moments with my wonderful girlfriend.
Featured Projects
Accelerating Molecular Dynamics with Deep Generative Models
Designing neural samplers to accelerate molecular simulation by learning efficient approximations to the Boltzmann distribution. Leveraging diffusion models and energy-based methods to improve sampling of equilibrium configurations.
In progress →gRNAdeX: eXpressive, Biologically-eXtensible gRNAde
Improved the gRNAde model for RNA inverse folding by refining geometric expressivity, pooling mechanism, sampling robustness, and incorporating biologically inspired priors.
View project →Few-Shot Relation Classification with DistilBERT & CAVIA
Few-shot learning for relation classification on the FewRel dataset by combining a frozen DistilBERT encoder with the CAVIA meta-learning framework. Enables fast N-way K-shot adaptation via task-specific context vectors and includes configurable training and evaluation scripts.
View project →Reinforcement Learning in Grid-World Environments
Implemented a comprehensive suite of RL algorithms—ranging from dynamic programming (Policy Iteration, Value Iteration) to model-free methods (SARSA, Q-learning, Expected SARSA)—across multiple grid-world scenarios.
View project →Bayesian Inference in High Dimensions
Bayesian inference in high-dimensional parameter spaces, combining variational approximation techniques with dimension reduction approaches to address scalability challenges in complex statistical models.
View project →TIMIT Speech Recognition Using CTC
Implemented an end-to-end automatic speech recognition system using Connectionist Temporal Classification (CTC) on the TIMIT speech corpus, achieving competitive phone error rates through architectural innovations in the acoustic model and improved alignment strategies.
View project →