INDRO SPINELLI

Data Science and Computer Science
Fundamentals/Foundation of Data Science (FDS) , a.y. 2026-27
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Applied Computer Science and Artificial Intelligence
Deep Learning (DL) , a.y. 2026-27
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Informatica
Machine Learning (ML) , a.y. 2026-27
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AAF 1149: Requirements for Earning 3 Credits
To obtain the 3 credits for AAF activities, students must complete two components:

1. One Training Camp (compulsory; no substitutions permitted)
2. Elective Component (select one)

• A Hackathon
• A second Training Camp
• An internship/stage of at least 60 hours (Note: The host company must be different from the one selected for your thesis internship)

Submission Procedure When registering for the exam, send me an email with the subject line [AAF] that includes:

• A list of your completed qualifying activities
• All relevant completion certificates attached

Students who do not follow this procedure will not be registered.

Indro Spinelli is an Assistant Professor (RTDA) at Sapienza University of Rome, affiliated with the PinLab research group and the ELLIS Society. He is an NVIDIA Academic Grant recipient and Principal Investigator of multiple research initiatives.

He received his Ph.D. in Information and Communication Technologies (2023) and M.Sc. in Artificial Intelligence and Robotics (2019) from Sapienza University of Rome. He was also a visiting researcher at the Arctic University of Norway and the Technical University of Munich.

Dr. Spinelli serves on the committees of top-tier AI conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR), earning the NeurIPS 25 Top Reviewer Award. He is the Associate Editor for The Visual Computer and has chaired several successful workshops, including Beyond Euclidean: Hyperbolic & Hyperspherical Learning for Computer Vision (co-located with ECCV24 and ICCV25). Moreover, he is an Associate Chair for VISAPP and served as Area Chair for both ICIAP25 and NLDL25.

His research has focused on trustworthy representation learning and generative models for human-centered applications. Building on this foundation, his current work integrates AI, 3D vision, and robotics to develop photorealistic simulators from videos. His goal is to close the sim-to-real loop, improving learning, benchmarking, and natural language-based debugging. His long-term goal is to scale these methods to reconstruct entire cities from satellite videos.