L'Oréal-UNESCO Fellow at Politecnico di Milano (Italy).
Research Assistant at the Open University (UK).
Developing hybrid solutions (Knowledge-based + ML-based) to enhance the Visual Intelligence of
service robots.
I am currently funded by the L’Oréal-UNESCO for Women in Science Program to conduct research on visually intelligent robotic applications for precision agriculture.
I am contributing to the EU-funded project GATEKEEPER, which is aimed at developing innovative Robotic and Smart Home solutions to support healthy independent living for the ageing population.
My PhD research has been focused on enhancing the Visual Intelligence of service robots by combining Knowledge-driven technologies with Machine Learning. Specifically, I have developed a prototype of robot assistant which can monitor office environments in search for potentially dangerous situations (e.g., flammable items left by ignition sources, cluttered emergency exits, dangling cables, and others).
I have contributed to the EU-funded project SPICE (Social cohesion, participation, and Inclusion through Cultural Engagement), where I have explored the use of Deep Learning and Neurosymbolic Learning methods to classify artistic subjects from cultural heritage image collections.
I have been part of the organising team of the 1st Smart Cities and Robotics Challenge (SciRoc), which was held in Milton Keynes (UK) on September 2019. This was the first robotic challenge to be held in a public shopping mall, to explore the integration of robots within Smart City infrastructures.
While studying at the Pennsylvania State University, I have been a part of the Human Screenome Project, a collaboration with the Departments of Communication and Medicine at Stanford University. In the Screenomics Lab, I have developed an end-to-end solution to automatically extract the textual content of digital screenshots collected from laptop and smartphone devices, while also providing a customised search engine to navigate the extracted contents.
For an updated list, you can refer to my Google Scholar.
Chiatti, A., Bardaro, G., Matteucci, M., and Motta, E.
Visual Model Building for Robot Sensemaking: Perspectives, Challenges, and Opportunities.
Bridge Session on AI and Robotics of the thirty-seventh AAAI conference on Artificial Intelligence (AAAI-23).
Chiatti, A.
Visually Intelligent Agents: Improving Sensemaking in Service Robotics.
PhD Thesis. The Open University.
Chiatti, A., and Daga, E.
Neuro-symbolic learning for dealing with sparsity in
cultural heritage image archives: an empirical journey.
In Proceedings of the Workshop on Deep Learning for Knowledge Graphs at ISWC 2022 (DL4KG 2022). CEUR.
Chiatti, A., Bardaro, G., Motta, E., and Daga, E.
A Spatial Reasoning Framework for Commonsense Reasoning in Visually Intelligent Agents.
In Proceedings of the 8th International Workshop on Artificial Intelligence and Cognition (AIC 2022). CEUR.
Bardaro, G., Daga, E., Carvalho, J., Chiatti, A., and Motta, E.
Introducing a Smart City component in a Robotic Competition: a field report.
In Frontiers in Robotics and AI - Smart Sensor Networks and Autonomy. 9.
Chiatti, A., Motta, E., and Daga, E.
Robots with Commonsense: Improving Object Recognition through Size and Spatial Awareness.
In Proceedings of the 2022 AAAI Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022). CEUR.
Chiatti, A.
Towards Visually Intelligent Agents (VIA): a Hybrid Approach.
In Proceedings of 2021 European Semantic Web Conference (ESWC 2021) Satellite events. Springer.
Chiatti, A., Motta, E., Daga, E., and Bardaro, G.
Fit to Measure: Reasoning about Sizes for Robust Object Recognition.
In Proceedings of the 2021 AAAI Spring Symposia - Workshop on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021). CEUR.
Chiatti, A., Motta, E., and Daga, E. Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis.
In Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020). IJCAI.
Chiatti, A., Bardaro, G., Bastianelli, E., Tiddi, I., Mitra, P., and Motta, E. Task-agnostic Object Recognition for Mobile Robots
through Few-shot Image Matching.
In Electronics. Special Issue on Big Data Analytics for Smart Cities. 9(3), 380. MDPI.
Reeves, B., Ram, N., Robinson, T.N., Cummings, J. J., Giles,L.,Pan,J., Chiatti, A., Cho, M.J. et al.
Screenomics: A Framework to Capture and Analyze Personal Life Experiences and the Ways that Technology Shapes Them.
In Human Computer Interaction.
Chiatti, A., Bardaro, G., Bastianelli, E., Tiddi, I., Mitra, P., and Motta, E. Exploring Task-agnostic, ShapeNet-based Object Recognition for Mobile Robots.
In Proceedings of the 3rd International workshop on Data Analytics solutions for Real-LIfe APplications (DARLI-AP). CEUR.
Ul Hoque, M.R., Bradley, D., Kwan, C., Chiatti, A., Li, J. and Wu, J.
Searching for Evidence of Scientific News in Scholarly Big Data..
In Proceedings of the 10th International Conference on Knowledge Capture (K-CAP 2019). ACM.
Bardaro, G., Semprebon, A., Chiatti, A., and Matteucci, M.
From Models To Software Through Automatic Transformations: An AADL To ROS End-to-End Toolchain..
In Proceedings of the Third IEEE International Conference on Robotic Computing (IRC), 580-585. IEEE.
Chiatti, A., Cho, M.J., Gagneja, A., Yang, X., Brinberg, M., Roehrick, K., Choudhury, S.R., Ram, N., Reeves, B. and Giles, C.L.
Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository
for Life in Media. .
In Proceedings of the 33rd ACM/SIGAPP Symposium on
Applied Computing (SAC 2018). Pau, France. April 9-13, 2018. ACM.
Chiatti, A., Yang, X., Brinberg, M., Cho, M.J., Anupriya, A., Ram, N., Reeves, B. and Giles, C.L.
Text Extraction from Smartphone Screenshots to Archive in situ Media Behavior. .
In Proceedings of the 9th International Conference on Knowledge Capture (K-CAP
2017). Austin, TX, USA. December 4-6, 2017. ACM.
Wu, J. Choudhury, S.R., Chiatti, A., Liang, C, and Giles, C.L.
HESDK: A Hybrid Approach to Extracting Scientific Domain Knowledge Entities..
In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2017). 241-
244. Toronto, ON, Canada, June 19-23, 2017.
Chiatti, A., Dragisic, Z., Cerquitelli, T., and Lambrix, P.
Reducing the search space in ontology alignment using clustering techniques and topic
identification..
In Proceedings of the 8th International Conference on Knowledge
Capture (K-CAP 2015). Palisades, NY, USA, October 7-10, 2015. ACM.