AI Research
I am a Ph.D., Senior AI Research Scientist & Technical Lead with expertise in deep learning, computer vision, multimodal perception, Transformers, meta-learning, LLMs, and NLP. At Cosmo Intelligent Medical Devices (Cosmo IMD), Milan — part of Cosmo Health Confidence, I lead the design, prototyping, and deployment of AI systems for medical imaging, vision–language models, and multimodal perception. I manage cross-functional teams of researchers and engineers, ensure rigorous model evaluation and compliance, and work closely with leadership to align technical roadmaps with strategic goals.
A central part of my work has been leading the development of AI projects integrated into the GiGenius medical device platform, which is MDR- and FDA-approved and distributed worldwide. This role involves bridging advanced AI research with real-world impact, ensuring that innovative solutions meet clinical, regulatory, and industry standards.
From 2017 to 2020, I was a Senior Researcher (Postdoctoral) at the University of Glasgow, under Professor Alessandro Vinciarelli. Over these four years, I conducted research on deep learning and attention models and played a major role in teaching. In the 2019–2020 academic year, I taught one-third of the undergraduate Artificial Intelligence course, delivering frontal lectures and mentoring students. I also worked in close collaboration with Heriot-Watt University, taking part in hybrid weekly research meetings connecting Glasgow and Edinburgh, which fostered interdisciplinary progress in AI.
From 2020 to 2022, I led AI research at MindVisionLab (a UCL spin-off) in partnership with Toyota Research Institute, London. There, I managed a research team, coordinated collaborative milestones, and contributed hands-on technical solutions in computer vision, Transformers, and autonomous systems.
I have published in ICCV, ECCV, TOG, T-PAMI, CHI, and MICCAI, with around 2,500 citations. My contributions include open-source software such as FSLib (a widely used MATLAB toolbox for feature selection) and a private LLMSuite in PyTorch for large language models. Recognition for my work includes the CVPR Outstanding Reviewer Award, an NVIDIA GPU Grant, and an invitation to the MathWorks Research Summit.
Google Scholar
Recent News
Talk on Vision Transformers
Here are the slides from the vision transformers talk: Vision Transformers Slides
Recent AI Talks
  1. LLMs Applications (RAG, PAL, CoT, ReAct) [Download slides]
  1. Transformers, Computational Costs (FSDP, ZeRO), Datasets [Download slides]
  1. LLM Fine-Tuning, PEFT, LoRA, RLHF, ReST [Download slides]
  1. Advancements in Video Understanding: TSM Temporal Shift Module
  1. Non-local Neural Networks
  1. Interpretability & Attribution Methods ​
SELECTED PUBLICATIONS
Feature Selection, Computer Vision, and Deep Learning:
Relevant to: Automated Medical Imaging Analysis
Relevant to: Content Recommendation,Autonomous Systems, AI-Driven Marketing Analytics, Intelligent Customer Service ChatBots
Relevant to: Content Recommendation,Autonomous Systems, AI-Driven Marketing Analytics, Intelligent Customer Service ChatBots
  • Infinite Feature Selection
    G. Roffo, S. Melzi, and M. Cristani
    Presented at the IEEE International Conference on Computer Vision (ICCV), 2015
Relevant to: Content Recommendation,Autonomous Systems, AI-Driven Marketing Analytics, Intelligent Customer Service ChatBots
Relevant to: Intelligent Customer Service ChatBots, Educational Tools,Virtual Try-On for Retail
Shape Analysis and Matching:
Relevant to: Virtual Try-On for Retail,Marketing Analytics
Natural Language Processing - Chats/Instant Messaging:
G. Roffo, M. Cristani, L. Bazzani, H. Q. Minh, V. Murino, IEEE International Conference on Computer Vision Workshops (ICCV) 2013.
Relevant to: Enhanced Security Systems.
M. Cristani, G. Roffo, C. Segalin, L. Bazzani, A. Vinciarelli, V. Murino, ACM International Conference on Multimedia (ACMM) 2012. Top 1% Computer Science - Scimago Journal & Country Rank.
Relevant to: Intelligent Customer Service ChatBots.
OTHER PROJECTS
  • Infinite Feature Selection: Developed in Matlab, this tool introduces an innovative graph-based feature filtering approach, which has been a cornerstone of Roffo’s research.
  • Large Language Models (LLMSuite): A toolbox designed for evaluating large language models, contributing tools and methodologies to assess the effectiveness and efficiency of these models in various applications.
In addition to his primary research, Giorgio has been involved in several educational projects, particularly focusing on teaching Python at the university level. These projects include practical implementations and templates that serve as educational tools for students studying various aspects of machine learning and neural networks:
  • Auto-Encoders: A recommender system project that predicts ratings from 1 to 5 using the MovieLens 1M Dataset, implemented using PyTorch and autoencoders.
  • Restricted Boltzmann Machines: An implementation of a recommender system based on Restricted Boltzmann Machines, again using the MovieLens 1M Dataset.
  • Artificial Neural Network: A simple implementation of an ANN in Python, using Keras to demonstrate fundamental neural network concepts in business analytics.
DATASET & CODE
Open-Source Datasets
I maintain and contribute to several open-source computer vision datasets, enabling research and development in the field.
GitHub Repositories
My GitHub profile hosts a range of code repositories, from innovative Large Language Models to feature selection algorithms.
Reproducible Research
I strive for transparency and reproducibility in my work, providing detailed documentation and code to support my published research.
CONTACT
Giorgio Roffo can be reached through his profiles on major academic and professional networking sites. For insights into his publications, ongoing projects, or to connect professionally, please visit the following links:
AWARDS & RECOGNITION
  • Giorgio Roffo has received several notable awards and recognitions that underscore his contributions to the fields of computer vision and machine learning:
  • NVIDIA GPU Grant (2017): Roffo was awarded this grant which provided crucial computational resources, aiding in advanced AI research.
EDUCATION
1
Ph.D., Doctor Europaeus in Computer Science
University of Verona, Italy (2014-2017)
Thesis: "Ranking to Learn and Learning to Rank"
Awarded with the prestigious Doctor Europaeus certification, recognizing his research's adherence to the high international standards and its broad academic significance.
Postdoctoral Researcher
University of Glasgow, UK (2017-2020)
2
Master Level I in Computer Game Development (MGDev)
University of Verona, Italy (2013)
Specialized program concentrating on game development technologies and methodologies, enhancing his skills in complex software system design.
3
MS, Master's Degree in Computer Science and Engineering
University of Verona, Italy (Completed 2011)
Focused on advanced computational techniques and software engineering principles.
4
BS, Bachelor Degree in Computer Science and Engineering
University of Verona, Italy (Completed 2009)
His undergraduate studies laid the groundwork for his deep technical expertise in algorithms, programming, and system design.
Research and Work Experience
  • AI Research Scientist, COSMO Intelligent Medical Devices (IMD), Milan, Italy
    (2022 - Present)
    At COSMO IMD, Roffo focuses on leveraging deep learning and computer vision to innovate in medical applications. His work includes developing AI systems and novel deep learning architectures that improve the effectiveness and accessibility of medical diagnostics and treatments.
  • Research Scientist, Mind Vision Labs, London, UK
    (2020 - 2022)
    Collaborated with UCL London and Toyota Research Institute (TRI) Brussels on autonomous driving research. Specialized in vision transformers and advanced computer vision algorithms to enhance the reliability and safety of autonomous vehicle technologies.
  • Research Associate, University of Glasgow, School of Computing Science, UK
    (2017 - 2020)
    Contributed to the EPSRC Project: Multimodal Deep Learning. Focused on attention models and transformer networks, developing solutions that are not only technically robust but also interpretable and scalable across different applications.
  • Ph.D. Student, University of Verona, Italy
    (2014 - 2017)
    Conducted seminal research on "Ranking to Learn and Learning to Rank" in pattern recognition, which has been recognized with a Doctor Europaeus certificate, indicating the high international standards of his work.
  • Research Assistant, Italian Institute of Technology, Pattern Analysis and Computer Vision department
    (2012)
    Worked on advanced computer vision algorithms and pattern analysis, contributing to foundational research that supports automated visual understanding technologies.
  • Intern, University of Glasgow, School of Psychology
    (2012, Summer)
    Explored the intersection of technology and psychology, particularly focusing on how machine learning can be applied to understand and predict human behavior based on visual and interactive cues.
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