Associate Scientist || Valence Labs
05/2023 - Now
Following the acquisition of Valence Discovery by Recursion, Valence Labs continues as a semi-autonomous R&D engine with a mission of harnessing computation to radically improve lives.

Application Scientist, Computational Chemistry || Valence Labs
08/2022 - 05/2023
Valence Discovery was a biotechnology company built on advances in deep learning. Valence was spun out of Mila, the world's largest deep learning research institute, before being acquired by Recursion Pharmaceutical (NASDAQ: RXRX) in May 2023.

1. Developing infrastructure features to support internal Machine Learning models
2. Planning medium and long-term evolution of the platform infrastruc- ture using Machine Learning models
3. Supporting custom and existing implementation of new Machine Learning methods
4. Designing and implementing hands-on a fine-grained infrastructure security policy
5. Designing and implementing hands-on a platform-wide monitoring strategy
6. Assisting and supporting end-users
7. Drug metabolism and pharmacokinetics (DMPK) prediction with state-of-the-art Graph Neural Network Models
8. Free energy molecular dynamics calculation on protein-ligand complexes
9. Structure-based approaches for Drug Discovery in low data regime via state-of-the-art physical fingerprints (SOAP, ACE)
10. Equivariant Machine Learning Interaction Potentials from Quantum Mechanical calculations
11. Chemistry focused Large Language Models

Software Engineer || SMS Group, R&D Modeling & Digitalization
10/2021 - 04/2022

1. Signal processing and modeling of a bayesian exponential degradation model for the predictive maintenance of bearing machinery
2. Development of machine learning models for EAF maintenance
3. Time-Series forecasting using Theta, ARIMA and LSTM models
4. Correlation analysis and models for EAF and CCM data
5. Development and implementation of finite element thermoelastic linear models for continuos casting
6. Development and deployment of custom grafana interfaces

Researcher || University of Trieste (UNITS)
01/2021 - 05/2022

1. Created BayGPGO: aframework to perform multi-objective optimization of Coarse-Grainded force fields by using bayesian optimization on gaussian process regression model
2. Investigation of the influence of the peripheral bulk of the monolayer on the solvent accessible area by Umbrella Sampling, Voronoi Tassellation and other more techniques
3. Developed and implemented routines to model AuNPs by correctly simulating the pKa-influenced behaviour in explicit solvents. Replica Exchange used to improve the Monte-Carlo protonation
4. Created machine learning approaches to analyze and predict the similarity between local environments of different AuNPs by exploiting SOAP fingerprints, dimensionality reduction (PCA), Gaussian Mixtures clustering, kernels product and ESR measurements
5. Development of computational models of a variety of SAM-AuNPs via the integration of many technique (QM, MD and CG dynamics)
6. Free energy calculations via Umbrella sampling and Free energy perturbation
7. Self-Assembly of molecules on h-BN/Graphene monolayer and binding free energy investigation by FEP

Researcher || University of Jan Evangelista (UJEP)
02/2020 - 05/2020

1. Researched machine learning models (DL and other supervised regression models) used for nanomaterials simulations
2. Designed and implemented machine learning approaches to coarse-grain water solvents
3. Implemented different atomistic and coarse-grained simulations to build datasets for testing different machine learning approaches

Technical system engineer - Internship || Fincantieri S.p.A.
09/2019 - 12/2019

1. Designed and implemented the automatic creation of complex 2D and 3D objects from tabular data by using parametric modeling
2. Developed new internal procedures to to decrease man-hours on modeling