§ About · curriculum vitæ

Curriculum vitae

I develop machine-learning methods for atomistic simulation and pharmaceutical development. My work spans interatomic potentials, crystal-structure prediction, and AI-assisted scientific workflows. I trained in materials science at EPFL, with research experience at MIT, and now work at Roche in Basel.

§ 01

Experience

ML Research Scientist — atomistic simulation and pharmaceutical development
Oct 2024 - Present

Data and Digitalisation Senior Scientist

Roche · Basel, Switzerland
  • Lead of the Data, Digitalization & AI Cross-Functional Circle (D2AI) at pCMC in Small Molecule Research
  • Developed APPA (Agentic Preformulation Pathway Assistant), an LLM-based agentic system that automates multi-step drug preformulation workflows
  • Built ML reranking pipeline for crystal structure prediction, reducing polymorph search space by orders of magnitude (published CrystEngComm 2024)
  • Driving adoption of ML potentials and descriptor-based models across formulation and solid-state chemistry teams
Mar 2023 - Oct 2024

Data and Digitalisation Scientist

Roche · Basel, Switzerland
  • Benchmarked molecular descriptors (SOAP, MBTR, Coulomb matrices) for solubility and lipophilicity prediction across pharmaceutical compound libraries
  • Developed solute-lipid interaction models connecting atomistic descriptors to formulation performance
  • Built interactive web dashboards (React, Next.js) for real-time visualisation of molecular property landscapes
  • Co-authored publications in Molecular Pharmaceutics and CrystEngComm
Jan 2021 - Mar 2023

Roche Postdoctoral Fellow

Roche · Basel, Switzerland
  • Applied high-dimensional ML potentials and committee-of-experts models to pharmaceutical crystal polymorph screening
  • Developed Bayesian NMR crystallography methods for solid-form identification under uncertainty
  • Published work on ML-accelerated crystal structure prediction and molecular crystal energy ranking
  • Bridged academic ML method development with industrial drug development pipelines
Oct 2020 - Jan 2021

Economic Analyst

Pictet Group · Geneva, Switzerland
  • Built automated data pipelines for Pictet Asset Management's database infrastructure upgrade
  • Applied quantitative analysis methods to financial time-series data
May 2016 - Jul 2020

Doctoral Assistant — PhD in Materials Science

EPFL (École polytechnique fédérale de Lausanne) · Lausanne, Switzerland
  • Designed divide-and-conquer committee-of-experts architecture for high-dimensional machine learning potentials (published Digital Discovery 2024)
  • Developed automatic feature selection methods for atomic fingerprints (SOAP, ACSF) with extrapolation diagnostics
  • Built Python/C++ frameworks for training and deploying neural-network potentials on condensed-matter systems
  • Contributed to the open-source librascal library for atomistic ML representations
  • Thesis: ML architectures for characterisation and classification of molecular materials
Mar 2015 - Oct 2015

Visiting Research Graduate Student

Massachusetts Institute of Technology (MIT) · Cambridge, MA
  • Developed molecular dynamics simulations of ethanol nanofiltration through nanoporous graphene membranes
  • Built computational models for selective molecular transport in 2D materials
  • Collaborated with Prof. Grossman's group on computational nanomaterials research
Sep 2014 - Jan 2015

Special Research Student

Tohoku University · Sendai, Japan
  • Simulated carrier mobility variations in vertical MOSFET architectures using TCAD tools
  • Optimised semiconductor device geometries through systematic computational parameter sweeps
§ 02

Education

2016 - 2020

Doctor of Philosophy — PhD, Materials Science

EPFL (École polytechnique fédérale de Lausanne) · Lausanne, Switzerland
  • Thesis: Machine learning architectures for characterisation and classification of molecular materials
  • Developed committee-of-experts potentials and automatic descriptor selection for atomistic ML
  • Published in Digital Discovery, Physical Chemistry Chemical Physics, Acta Crystallographica
  • Advisor: Prof. Michele Ceriotti (Laboratory of Computational Science and Modelling)
2013 - 2015

Master of Science — MS, Nanotechnology applied to ICTs

Politecnico di Torino · Turin, Italy
  • Final grade: 110/110 cum laude
  • Research stays at MIT (7 months) and Tohoku University (5 months)
  • Focus on computational modelling and simulation of nanoscale systems
§ 03

Capabilities

Machine Learning PotentialsCrystal Structure PredictionGraph Neural NetworksPyTorchPythonC++Molecular DynamicsAtomistic Representations (SOAP, ACSF)LLM AgentsBayesian MethodsActive LearningComputational ChemistryDrug Discovery & FormulationReact / Next.jsScientific Computing