Berardino Barile PhD

Education

• Double PhD in Computer Science - KU Leuven & Université Claude Bernard Lyon 1 (2019-2022)
  • Thesis: Machine Learning Methods for Multiple Sclerosis Classification and Prediction Using MRI Brain Connectivity
  • - Research focused on developing advanced machine learning models for medical imaging and neuroscience applications.
  • - Worked on Probabilistic Deep Learning, Bayesian Inference, and Causal ML.
  • - Collaborated with top researchers and contributed to multiple peer-reviewed publications.

• Thesis: Short and Long Structural Effects of the International Bank System: A Structural-VAR Approach
• - Grade: 110/110 with honors

• Thesis: Purchase Power Parity (PPP) and Equilibrium Exchange Rate in the Financial Market
• - Grade: 110/110 with honors

Professional Experience

• Research Scientist - McGill & Concordia University - Mila AI Institute (2023 - Present)

Developed advanced deep learning models for computer vision and causal ML, focusing on longitudinal modeling and trajectory prediction. Extensive experience using Large Language and Vision Models for zero- and few-shot learning.

• Senior Data Scientist - Verti Spa, Milan (2022 - 2023)

Developed machine learning and statistical models for pricing optimization, churn prediction, and customer retention using regression analysis, gradient boosting, and neural networks. Built automated ML pipelines with AWS SageMaker for scalable deployment and implemented constrained optimization models for risk assessment and profitability.

• Big Data Scientist - Isiway Srl, Rome (2017 - 2019)

Implemented ML solutions for big data consulting projects, including deep learning models for human trajectory prediction and customer behavior analysis. Developed NLP-based sentiment analysis using web scraping, word embeddings, and RNN/LSTM models for improved prediction accuracy.

• Data Analyst - Invitalia SpA, Rome (2014 - 2017)

Analyzed economic and financial indicators for investment risk assessment and portfolio management. Developed econometric models for public policy evaluation, applying survival analysis, clustering, and counterfactual simulation techniques.

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Skills & Tools

• Programming & Development: Python, R, Stata, Matlab, SQL, LaTeX, Bash, Java, GitHub.

  Used for developing machine learning models, statistical analysis, and data processing in research and industry projects.

• Machine Learning & AI: PyTorch, TensorFlow, scikit-learn, NumPy, Pandas, SciPy, CUDA.

  Implemented deep learning models for computer vision, probabilistic modeling, and time-series forecasting.

• Cloud & MLOps: AWS SageMaker, Docker, Linux.

  Deployed ML pipelines for large-scale training, model serving, and workflow automation in cloud environments.

• Mathematical & Statistical Methods: Bayesian Inference, Probabilistic Modeling, Causal Inference, Optimization.

  Designed models for counterfactual analysis, risk evaluation, and decision-making in medical imaging and finance.

• Computer Vision & AI: Deep Learning, Generative AI, Image Processing.

  Developed generative models for medical imaging, trajectory prediction, and biomarker discovery.

Scientific Contributions

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