Tommaso Calò

Tommaso Calò

I conduct research at the intersection of human-computer interaction (HCI) and artificial intelligence (AI).

Welcome to my academic website! I am Tommaso Calò, a PhD student at Politecnico Di Torino, under the supervision of Luigi De Russis. My research focuses on the intersection of human-computer interaction (HCI) and artificial intelligence (AI), aiming to create intelligent systems that seamlessly integrate with user workflows and enable more efficient and effective interactions with complex interfaces.

Research Interests

My primary research interests encompass the development of algorithms and methods for automated sketch-to-code and mockup-to-code conversion, understanding UI components and their semantics, and designing human-centered methods to improve UI design, navigation and automation.

Current Research Projects

During my PhD journey, I have been working on several exciting research projects, such as:

Style-Aware Sketch-to-Code Conversion for the Web: This project aims to develop a deep learning-based system that can convert hand-drawn UI sketches into functional code while allowing users to specify design styles. The method automatically injects reference styles into sketch components and generates accurate code snippets that adhere to the user’s desired style.

Creating Dynamic Prototypes from Web Page Sketches: This project focuses on supporting the creation of dynamic prototypes from sketches by introducing a set of symbols that designers can use on their sketches to model dynamic behaviors. The implementation generates dynamic prototypes based on these symbols.

Visual Programming Tools to Create Deep Learning Models: The DeepBlocks project presents a visual programming tool that allows deep learning developers to design, train, and evaluate models without relying on specific programming languages. DeepBlocks enables developers to visually design complex deep learning architectures.

Planned Future Work

As I continue my research journey, I plan to explore the following areas:

Human-Centered Methods for UI Navigation and Automation: Designing novel interaction techniques that enable users to navigate complex UIs more efficiently and intuitively. This will include researching user behavior and preferences in order to create personalized and adaptive UI systems.

Evaluation of AI-Generated UI Designs: Establishing methods and metrics for evaluating and improving the quality, usability, and effectiveness of AI-generated UI designs, ensuring that the resulting interfaces meet the needs and expectations of users.