Tatiana Adele D’Onofrio

Worked on several ML and DL projects, developing supervised and unsupervised models including parameter-efficient fine-tuning. Fabric consumption project: built an ML pipeline to predict fabric consumption for a luxury fashion company, exceeding baseline by 58%; performed EDA and data preparation; used clustering to extract salient information and compared regression models; conducted requirement meetings and project updates. Athlete tracking project: developed a solution to track marathon athletes using images and timing data; optimized performance for fast runtimes and tested pipelines on client systems. Perinatal depression prediction project: created an ML pipeline to identify pregnant women at risk of depression, achieving 53% detection using first-trimester data; applied SVM for classification, ElasticNetCV for regression, and reduced input features for usability. Delivered a web app for practitioners to estimate depression risk.

Streamlined revenue monitoring processes, reducing manual workload by 50%. Evaluated market dynamics to identify trends, growth opportunities and risks. Analyzed revenue forecasts and sales trends (KPIs), presenting insights and results to key stakeholders.