Federico Milana

2025 - under development

• Fine-tuned LLaMA-3-8B-Instruct on personal WhatsApp group chats using Quantized Low-Rank Adaptation (QLoRA) for efficient training on consumer hardware
• Implemented parameter-efficient fine-tuning techniques to maintain model performance while reducing computational requirements
• Engineered a communication pipeline using the Telegram API for seamless integration, allowing the model to analyze conversations and generate contextually appropriate responses that mimic a specific user's writing style

2025

• An Electron application developed to visualize and compare manual and AI-generated annotations of English translations of "Conversations on the Plurality of Worlds" by Bernard Le Bovier de Fontenelle, 1686
• Extracted, aligned and segmented text to use with the Anthropic API
• Developed for the forthcoming publication:
• Anna Maria Cipriani, Federico Milana (2025, Forthcoming). AI-Powered Corpus Translation Studies

2023 - 2024

• An XGboost and BERT interpretable text classification model using LIME, SHAP, Occlusion values and experimental LLM summarization of confusion matrices
• Conducted a user study with 128 participants to evaluate current model interpretability techniques

2021 - 2024

• An Interactive Machine Learning (IML) tool for thematic analysis of qualitative data, enabling iterative XGBoost model training and customization
• Conducted a user study with 20 participants to evaluate interaction with IML systems
• Developed for the following publication:
• Federico Milana, Enrico Costanza, Mirco Musolesi and Amid Ayobi (2025, Forthcoming). Understanding Interaction with Machine Learning through a Thematic Analysis Coding Assistant: A User Study

2019

• A simulated social trading platform for investment decisions with chatbot guidance
• Conducted a user study with 64 participants to evaluate effects of UI and response variations on user trust
• Developed for the following publication:
• Federico Milana, Enrico Costanza and Joel E Fischer (2023). Chatbots as Advisers: the Effects of Response Variability and Reply Suggestion Buttons

2018

• A tool to enable corpus-based analysis and comparisons of translated literary texts
• Developed for the following publications:
• Anna Maria Cipriani (2019). The Impact of Censorship on Translating and Publishing Virginia Woolf during the 1930s in Italy: The Case of To the Lighthouse
• Anna Maria Cipriani (2022). The translator’s presence in (re)translations of To the Lighthouse into Italian: a corpus-driven study

2018

• An Android application using context-awareness to implement David Allen's productivity methodology

Federico Milana, Enrico Costanza, Mirco Musolesi and Amid Ayobi

ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW)

• Conducted a user study with 20 participants exploring how non-experts interact with Interactive Machine Learning (IML) systems
• Developed TACA, an IML tool for thematic analysis, enabling iterative model training and customization
• Found that users gained new analytical insights and adapted their interpretative approaches through IML interaction
• Identified key challenges in user understanding of ML concepts and proposed design recommendations for IML systems

Federico Milana, Enrico Costanza and Joel E Fischer

ACM conference on Conversational User Interfaces (CUI)

• Investigated how chatbot design features influence users' likelihood to follow AI advice in decision-making contexts
• Designed and conducted an incentivized study examining effects of response variability and reply suggestion buttons
• Built a simulated social trading platform where participants made investment decisions with chatbot guidance
• Found that both response variability and reply suggestions significantly increased users' trust and adherence to chatbot advice