About Me
PhD in Human-AI Interaction with experience in designing and evaluating user interaction with machine learning classifiers, large language models and conversational agents.
Skills
Technical
- Machine Learning: LLMs, fine-tuning, text classification, interpretability
- Programming: Python (PyTorch, transformers, scikit-learn, numpy, pandas)
- Development: Web and Desktop applications
Research
- Human-AI Interaction: User studies and experimental design
- Data Analysis: Quantitative and qualitative data analysis
- Academic writing and reviewing: CHI, CSCW, CUI, etc.
Experience
PhD Student
September 2020 - November 2024
University College London
- Research focus on Human-AI Interaction and Explainable AI
- Published papers in top HCI conferences
Postgraduate Teaching Assistant
January 2021 - May 2024
University College London
- For the modules "Data Visualization" and "Affective Interaction" of the MSc in HCI
- Assisted students with understanding the course content and providing feedback on their assignments
Research Assistant
August 2019 - October 2019
University College London
- Research on the effects of reply suggestion buttons and response variability in chatbots on autonomy delegation and trust
Education
PhD in Human-AI Interaction
September 2020 - November 2024
University College London
- Thesis title: "Evaluating Interaction with Machine Learning Classifiers and Interpretability Techniques"
MSc in Human-Computer Interaction
September 2018 - August 2019
University College London
- Graduated with Distinction
- Thesis title: "Investigating the Effects of Reply Suggestions on User Trust in Chatbot Applications"
BSc in Computer Science
September 2015 - July 2018
King's College London
- Graduated with First Class Honours
- Specialization in Software Engineering
- Thesis title: "Getting Things Done: A Context-Aware Android Application for Productivity"
Awards
UCL Faculty of Brain Sciences 2018/2019 Dean's List
December 2019
Academic performance recognised among the top 5% students from across the Faculty of Brain Sciences
TACA: Thematic Analysis Coding Assistant
2020-2024
- A 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). Evaluating Interaction with Machine Learning through a Thematic Analysis Coding Assistant: A User Study
Interpretable Text Classification
2024
- An XGboost and BERT interpretable text classification model using LIME, SHAP and Occlusion values
- Conducted a user study with 128 participants to evaluate model interpretability techniques
Chatbot Social Trading Assistant
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
Literary Corpus Processor
2018
- A tool to enable corpus-based analysis and comparisons of translated literary texts
- Developed for the following publications:
Getting Things Done
2018
- An Android application using context-awareness to implement David Allen's productivity methodology
2025
Understanding Interaction with Machine Learning through a Thematic Analysis Coding Assistant: A User Study
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
- 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
2023
Chatbots as Advisers: the Effects of Response Variability and Reply Suggestion Buttons
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
Paper
Contact
Reach out to me at federicomilana@outlook.com
Or connect with me on LinkedIn