Data Science and AI in Practice: Research, Innovation and Real-World Applications

Speakers

Sylvia Smit – Founder, AITIS

Tillman Weyde – Data Science Advisor, AITIS; City St George’s, University of London

Jorge Rodriguez – Senior Data Scientist / PhD Researcher

Yumi Heo – Data Scientist

Thanawat Thanaponpaiboon – Senior Data Scientist

Dao Feng – Data Scientist

Anthony Victoros – Tech Developer

Jaeeun Lee – Data Science Intern

Nasos Karas – Data Science Intern

Timi Dapo – Project Manager Intern

Abstract

How can data science and artificial intelligence be translated into practical solutions for real-world challenges in healthcare, construction, and safety?

This Data Bites session will feature AITIS, a technology company working at the intersection of data science, AI, technology, and applied research. The session will provide MSc Data Science students with first-hand insight into real-world data science projects and the different stages involved in taking AI solutions from research and model development through to evaluation, deployment, and practical application.

The AITIS team will showcase a range of projects, including wound image analysis and measurement, smartphone and dermoscopic image analysis for skin-mole modelling, health monitoring and glucose prediction, and construction video monitoring and safety alerts. Speakers will discuss the data science and machine-learning approaches behind these applications, together with practical challenges encountered when developing and applying AI models in real-world settings.

The session will also cover model explanation and robustness evaluation, computer vision and object detection, construction safety monitoring, cloud-based deployment and AWS services, and technology development workflows. Students will hear directly from experienced data scientists, researchers, developers, interns, and project managers about their roles and experiences working on applied AI projects.

A particular focus will be placed on industry–academic collaboration, internships, and research opportunities. The AITIS team will share examples of student involvement in current projects, including work on SHAP-based model explanation and robustness evaluation and the evaluation of YOLO-based computer-vision models for construction safety.

The session will provide an opportunity to learn from practitioners and researchers working on applied data science challenges and to explore potential pathways for internships, research collaborations, and professional development.