Giorgia Adorni

Ph.D. Student
Intelligent Tutoring and Assessment Systems in Education

About

I am a Ph.D. student at The Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence), under the supervision of Luca Gambardella and Alberto Piatti.I am conducting an exploratory study in the cantons of St. Gallen, Vaud and Ticino focused on assessing the development of computational thinking skills through Intelligent Tutoring Systems.My research interests are: Intelligent Tutoring Systems, AI for Autonomous Robotics, Computational Thinking, Educational Robotics, Computer Science Education, Learning Technologies, Human-Robot Interaction, Robotics and Machine Learning.In 2018, I achieved a B.Sc. degree in Computer Science at University of Milano-Bicocca (UNIMIB) and in 2020 I obtained a Double M.Sc. degree in Informatics between Università della Svizzera Italiana (USI) and UNIMIB.Always up for a good challenge. Nothing thrills me quite like looking for opportunities to learn, grow and make a difference.Get in touch!

Projects

2021–2022

gansformer-reproducibility-challenge
Replication of the novel Generative Adversarial Transformer.
(Advanced Topics in Machine Learning course @USI)

2019–2020

lstm-for-text-prediction
Implementation of a text generator based on Long Short-Term Memory (LSTM).
(Deep Learning Lab course @USI)

deep-q-network-atari-games
Implementation of a Deep Q-Network agent, based on Mnih et al., 2015.
(Deep Learning Lab course @USI)

ros-turtlesim-controller
A simple ROS node that controls a turtle in turtlesim.
(Robotics course @USI)

mighty-controller
An open loop controller that moves a Thymio in Gazebo.
(Robotics course @USI)

learning-relative-interactions-through-imitation
Learn to perform specific interactions between a marXbot robot and objects in the environment, using the simulator Enki.
(Robotics course @USI)

learning-robot-swarm-controllers
Master’s thesis project that simulates robot swarms for learning communication-aware coordination using imitation learning approaches.
(@USI)

2018–2019

mosquito-disease-spread-rate-prediction
A supervised classification model capable of predicting whether or not WNV virus will be detected in a certain trap every week.
(Data Technology and Machine Learning course @UNIMIB)

viterbi-algorithm-for-hmm
An implementation of the Viterbi Algorithm for training Hidden Markov Models.
(Probabilistic Models for Decisions course @UNIMIB)

likelihood-weighting-sampling
A Python implementation of the likelihood weighting approach for Bayesian Network sampling.
(Probabilistic Models for Decisions course @UNIMIB)

misspelling-corrector
Misspelling corrector based on Hidden Markov Models and Noisy Channel Model.
(Probabilistic Models for Decisions course @UNIMIB)

dct2-compression
DCT2 implementation with Faster-than-light Fourier Transform.
(Methods of Scientific Calculation course @UNIMIB)

comparison-of-cholesky-decomposition-implementations
A study of the implementation of the Choleski method for the resolution of linear systems for sparse, symmetric and positive definite matrices. Comparison based on different open source programming environments and MATLAB implementation.
(Methods of Scientific Calculation course @UNIMIB)

human-disease-network
Human Disease Network: clustering and performance evaluation.
(Data Analytics course @UNIMIB)

2017–2018

neigh
Neighbour Joining algorithm for the creation of Phylogenetic Trees.
(Bioinformatics course @UNIMIB)

chess-recognition
A MATLAB library for recognition of La Settimana Enigmistica chess games.
(Image Processing course @UNIMIB)

learning-personality
Bachelor internship project that uses Neural Networks to predict personality traits from text written in natural language.
(@UNIMIB)

Thesis

Master thesis:
Simulation of robot swarms for learning communication-aware coordination

Bachelor thesis:
Neural networks for learning personality traits from natural language

Research

Recent publications

S. Corecco, G. Adorni* & LM. Gambardella. (2023). Proximal Policy Optimization-Based Reinforcement Learning and Hybrid Approaches to Explore the Cross Array Task Optimal Solution. Machine Learning and Knowledge Extraction, vol. 5, no. 4, pp. 1660-1679.G. Adorni, F. Mangili, A. Piatti, C. Bonesana & A. Antonucci. (2023). Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment. Journal of Communications Software and Systems, vol. 19, no. 1, pp. 52-64.F. Mangili, G. Adorni, A. Piatti, C. Bonesana & A. Antonucci. (2022). Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach. International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1-6.A. Antonucci, F. Mangili, C. Bonesana, & G. Adorni. (2022). Intelligent Tutoring Systems by Bayesian Nets with Noisy Gates. The International FLAIRS Conference Proceedings, vol. 35.A. Piatti, G. Adorni*, L. El-Hamamsy, L. Negrini, D. Assaf, LM. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, vol 5, 100166.A. Antonucci, F. Mangili, C. Bonesana, & G. Adorni. (2021) A New Score for Adaptive Tests in Bayesian and Credal Networks. European Conference on Symbolic and Quantitative Approaches with Uncertainty. Springer, Cham. pp. 399-412.* corresponding author