Free University of Bozen-Bolzano
Faculty of Engineering
Dominikanerplatz - Piazza Domenicani 3
Floor 2, Office 2.11
39100 Bozen-Bolzano, Italy
Greetings, and thank you for visiting my profile.
As a highly skilled postdoctoral researcher at the esteemed Free University of Bozen-Bolzano, I am privileged to collaborate with professor Johann Gamper in the Database Systems Group. In my role, I am specifically tasked with contributing to Research Topic 4 (RT4 - Artificial Intelligence and Data Science) of Spoke 3 (Green and digital transition for advanced manufacturing technologies) of the Piano Nazionale di Ripresa e Resilienza (PNRR) Interconnected Nord-Est Innovation Ecosystem (iNEST) project. This noteworthy project is funded by the Next Generation European Union (NextGenerationEU), and seeks to promote a digital and environmentally conscious transformation in manufacturing. My research activities involve research and/or experimental development, focusing on data collection, management, and analysis. By harnessing the latest Data Science, Machine Learning, and Artificial Intelligence technologies, I strive to support the realization of a green and digital transition in manufacturing.
My work is characterized by rigorous attention to detail and a commitment to achieving the highest levels of scientific excellence. As a professional of exceptional caliber, I am deeply committed to advancing the state of the art in my field. My work is characterized by a relentless pursuit of knowledge and a dedication to innovation. Through the application of cutting-edge research and the adoption of best practices, we can create a better world for all.
My inclination towards intellectual inquisitiveness and a desire for comprehensive comprehension has culminated in pursuing a PhD in Mathematics following my prior educational background in Computer Science. My doctoral thesis delves into the realm of Modal Symbolic Learning, which represents the confluence of Machine Learning and Modal Logic. This work systematically utilizes inductive biases to develop a mathematical framework that facilitates symbolic learning from unstructured data. As a result, it has the potential to enhance the efficiency and efficacy of learning algorithms significantly.
My proclivity towards interdisciplinary fields and passion for inspiring motivation in others fuels my interest in collaborating with individuals from diverse research backgrounds, fostering the exchange of knowledge and ideas.
In addition to my academic pursuits, I am an avid animal lover, particularly of dogs, and enjoy engaging in physical activities such as working out and snowboarding.
I am constantly seeking opportunities to expand my professional and personal networks, and I welcome the opportunity to connect with like-minded individuals over a virtual coffee.
|Mar 12, 2023||I have added the cluster map to this site.|
|Mar 1, 2023||I have started my postdoc at the Free University of Bozen-Bolzano, Faculty of Engineering, under the supervision of prof. Johann Gamper.|
|Feb 27, 2023||I have successfully defended my PhD thesis!|
- Foundations of Modal Symbolic LearningUniversity of Parma, 2023
- The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forestsArtificial Intelligence in Medicine, 2023
- FSSFuzzy Halpern and Shoham’s interval temporal logicsFuzzy Sets and Systems, 2023
- On coarser interval temporal logicsArtificial Intelligence, 2019