Seminario pubblico di Simone Agostinelli (Procedura valutativa per n.3 posti di Ricercatore a tempo determinato tipologia A - PNRR PE1 Spoke 5 SC 09/H1 SSD ING-INF/05) - Martedì 21/03/2023 ore 10.00, Aula Magna
Tuesday, 21 March, 2023 - 10:00
Aula Magna, DIAG
Andrea Marrella (firstname.lastname@example.org)
Care colleghe e colleghi,
con la presente vi informo che in ottemperanza ai requisiti previsti dalla procedura valutativa per n.3 posti di Ricercatore a tempo determinato tipologia A - PNRR PE1 Spoke 5 SC 09/H1 SSD ING-INF/05 - Dipartimento di Ingegneria Informatica Automatica e Gestionale "A. Ruberti", Codice Bando: 2023RTDAPNRR001, pubblicato su Gazzetta Ufficiale N. 5 del 20.01.2023, si terrà in Aula Magna alle ore 10:00 il seminario di Simone Agostinelli che illustrerà le sue attività di ricerca svolte e in corso di svolgimento. Il seminario sarà anche trasmesso in modalità telematica su Zoom.
Per partecipare da remoto connettersi al seguente link: https://uniroma1.zoom.us/j/97463105561?pwd=RE5reUxMZ2tlWWdnZ2VuaHFmTDFUdz09
Meeting ID: 974 6310 5561
Titolo: AI-empowered Hybrid Robotic Process Automation
Robotic Process Automation (RPA) is a fast-growing technology in the field of Business Process Management (BPM) that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (or simply routines) performed by human users in their applications’ user interfaces (UIs). Successful usage of RPA requires strong support by skilled human experts, from the detection of the routines to be automated to the development of the executable scripts required to enact SW robots. In this talk, I discuss how process mining and automated planning techniques can be leveraged to minimize the manual and time-consuming steps required to create SW robots, enabling new levels of automation and support for RPA. In particular, I introduce a pipeline of processing steps that enable to (1) semi-automatically discover the anatomy of a routine directly from the UI logs, and (2) automatically develop executable scripts for performing SW robots at run-time. Finally, I discuss how the proposed techniques can be leveraged to tackle relevant challenges in the AI-augmented BPM field.
Simone Agostinelli received his Ph.D. in Engineering in Computer Science from Sapienza Università di Roma in 2022. He is currently a postdoctoral research fellow in Engineering in Computer Science at Sapienza Università di Roma. In 2019, he received the best forum paper award at the 31st International Conference on Advanced Information Systems Engineering (CAiSE’19). His main research interest concerns theoretical, methodological, and practical aspects in different areas of Computer Science, including Business Process Management (BPM); Robotic Process Automation (RPA); Human-Computer Interaction (HCI); Process Mining; Model Learning; Automated Planning in the field of BPM; Big Data Pipelines Discovery; and Blockchain Technologies. Such topics are challenged in the application domains of smart manufacturing, IoT-based environments, and healthcare. He has published many papers on the above topics in top-tier conferences and journals, including BPM, ICSOC, Information Systems, and Computer in Industry.
gruppo di ricerca: