Giacomo Iadarola, Ph.D.

Back-end Developer & AI-Cybersecurity Research Fellow

I am a Software Engineer with a Ph.D. in Computer Science, specializing in back-end engineering, scalable software systems, data analysis, and deep learning.
I have experience designing and building reliable, maintainable solutions that serve millions of users, with a particular interest in multidisciplinary projects that bring together diverse teams and domains.
My background includes work in Cybersecurity, Malware Analysis, Formal Methods, and Explainable AI.
Currently, I serve as an Elixir Back-end Developer at Turn.io, where I build chat-based platforms and interactive no-code programming tools using Elixir and Phoenix LiveView for health organizations, governments, NGOs, and mission-driven enterprises. These solutions enable organizations with a positive social impact to engage communities at scale through conversational experiences on messaging platforms.

Passionate about technology and its impact on society, I am dedicated to building software that improves people’s lives and supports thriving communities.

Experience

Projects

Publications

Overall Stats   

Journals: 5+ ; Papers: 25+ ; Book Chapters: 1;
Citations: 450+ ; H-index: 12 ; Scientific peer-reviews: 50+;
Teaching: 50+ hours ; Co-advisor: 10+ thesis;

Updated information and the complete list of my publications are available on Google Scholar, Orcid and IIT-CNR.

Journals   

• Iadarola Giacomo, et al. "Towards an Interpretable Deep Learning Model for Mobile Malware Detection and Family Identification." Computers & Security (2021): 102198. LINK
• Iadarola Giacomo, et al. "Call Graph and Model Checking for Fine-Grained Android Malicious Behaviour Detection." Applied Sciences 10.22 (2020): 7975. LINK
• Casolare Rosangela, et al. "Dynamic Mobile Malware Detection through System Call-based Image representation" Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (2021): 10.22667 LINK

Papers   

• Iadarola, Giacomo, et al. "A Semi-Automated Explainability-Driven Approach for Malware Analysis through Deep Learning." 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. LINK
• Gerardi, Federico, et al. "Perturbation of Image-based Malware Detection with Smali level morphing techniques" 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2021. LINK
• Errica, Federico, et al. "Robust Malware Classification via Deep Graph Networks on Call Graph Topologies." ESANN. 2021. LINK
• Iadarola Giacomo, et al. "Formal Methods for Android Banking Malware Analysis and Detection." 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). IEEE, 2019. LINK
• Crincoli, Giuseppe, et al. "Vulnerable Smart Contract Detection by Means of Model Checking." Proceedings of the Fourth ACM International Symposium on Blockchain and Secure Critical Infrastructure. 2022. LINK
• Iadarola Giacomo, et al. "Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques." IoTBDS. 2020. LINK
• Iadarola Giacomo, et al. "Evaluating Deep Learning Classification Reliability in Android Malware Family Detection." 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE, 2020. LINK
• Di Giammarco, Marcello, et al. "Explainable Retinopathy Diagnosis and Localisation by means of Class Activation Mapping." 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. LINK
• Ciaramella, Giovanni, et al. "Introducing Quantum Computing in Mobile Malware Detection." Proceedings of the 17th International Conference on Availability, Reliability and Security. 2022. LINK
• Casolare, Rosangela, et al. "Mobile Family Detection through Audio Signals Classification." 19th International Conference on Security and Cryptography (SECRYPT). July 2021. LINK

Miscellaneous   

• Master's Thesis - Iadarola, Giacomo. Graph-based classification for detecting instances of bug patterns. MS thesis. University of Twente, 2018. LINK
• Article for ICT Security Magazine (Italian) - Metodi Formali per la rilevazione di collusioni in ambiente Android. ICT Security Magazine. December 2020. LINK
• Webinar for Ludoteca Registro.it (Italian) - La Crittografia. Ludoteca del Registro.it. November 2021. LINK
• Video for Ludoteca Registro.it (Italian) - Cyber Care: La sicurezza in ambiente mobile. Ludoteca del Registro.it. March 2021. LINK
• Video for Ludoteca Registro.it (Italian) - CyberCare: Giacomo Iadarola - Machine Learning. Ludoteca del Registro.it. September 2021. LINK

Education

Interests

I enjoy most of my free time outdoors. I love activities like riding my road bike, running, hiking, and playing futsal and basketball.

I have a passion for traveling, exploring new places, and experiencing different cultures and meeting new people. At this stage of my life, I prefer to spend several weeks or months in the same place and frequently change locations rather than living in the same city and engaging in many short, quick travels. I am particularly drawn to the lifestyle of smaller to medium-sized countries and cities, preferably in warm climates near the sea. I'm a big fan of remote work that allows me to combine work and exploration seamlessly.

When I'm at home, I like to spend my free time reading classical novels and historical fiction. I also work on computer science side-projects, which may involve learning a new programming language or developing something on my Raspberry Pi. Additionally, I am a novice bass player who enjoys playing and listening to rock, prog, and funky music.

I proudly support my hometown's (Livorno) soccer and basketball sports teams.