Biography
I obtained my M.Sc. and PhD in Computer Science from the Sapienza University of Rome in 2018 and 2022, respectively. I then worked as a senior researcher at the Department of Computer Science in Sapienza. Here, I led a team of 4 junior researchers and 6 software engineers in devising novel anomaly detection strategies for social isolation disorders. Since October 2022, I’ve been a postdoc at Sapienza in eXplainable AI and Anomaly Detection.
I am part of and actively collaborate with Italian and international research groups (RDS of TU Munich, PINlab of Sapienza, AIIM of L’Aquila). I serve as a Program Committee (PC) member for conferences such as ICCV, CVPR, KDD, CIKM, and ECAI. I actively contribute as a reviewer for journals, including TKDE, TKDD, VLDB, TIST, and KAIS.
In June 2023 I joined the Chair of Responsible Data Science at TU Munich lead by prof. Kasneci to work on counterfactual explainability (external researcher).
Download my curriculum vitae.
Interests
- Anomaly Detection
- eXplainable AI
Education
PhD in Computer Science, 2022
Sapienza University of Rome
MSc in Computer Science (Grade 110/110 with honors; GPA 4.00), 2018
Sapienza University of Rome
BSc in Computer Science (Grade 110/110; GPA 4.00), 2016
Sapienza University of Rome
Research Experience
Postdoctoral Researcher
Sapienza University of Rome
Researching on Anomaly Detection and Counterfactual Explainability. I actively participate in e-health and anomaly detection interdisciplinary projects.
Visiting Researcher
Technical University of Munich
Visiting period at the Responsible Data Science research group with head Prof Kasneci, School of Social Sciences and Technology. Research on explainability in dynamic data landscapes and graph learning.
Senior Research Fellow
Sapienza University of Rome
Coordinated research and implementation of innovative deep learning algorithms to predict anomalous events in patient behavioural time series.
Visiting PhD Student
George Mason University
Visiting period at Prof Domeniconi’s Data Mining and Machine Learning lab. I worked on deep learning applications and anomaly detection in e-learning and learning analytics.
Junior Researcher
George Mason University
Worked alongside Dr Sarvari and Prof Domeniconi on boosting-based anomaly detection models.
Student Research Assistant
Sapienza University of Rome
Extended the UCrawler framework for crawling and scraping content of research articles and citation graphs on DBLP and SemanticScholar. During this period, I also completed my master’s thesis.
Other Experience
Assistant Professor
I’m head of the M.Sc. in Digital Healthcare. I teach Bioinformatics and AI for Health.
Software Engineer
I focused on software prototyping and development activities. In particular, I optimized back-end services and developed highly-maintainable and efficient API services.
Senior Software Consultant
I designed and maintained the CMS for electric vehicle leasing in the UK, Gridserve
Honors & Awards
Subject Expert
(“Cultore della Materia” Art. 42 del R.D. 04/06/1938, n. 1269) Award for highly-skilled researcher and technician on Machine Learning course in M.Sc. Computer Science, Department of Computer Science
Highlighted Reviewer
I reviewed three workshop papers thoroughly and provided useful hints to make the submissions better. The Program Chairs emailed me that I received the Highlighted Reviewer acknowledgement. Unfortunately, I did not receive a certificate 🤨
Subject Expert
(“Cultore della Materia” Art. 42 del R.D. 04/06/1938, n. 1269) Award for highly-skilled researcher and technician on Deep Neural Networks course in M.Sc. Computer Science, Department of Information Engineering, Computer Science, and Mathematics
Scholarship in AI & Computer Science
Winner of the BS-S 6/2021 Open Competition published on September 15th 2021 (num: 1207, rep: 326, class: VII/1) on research project “000090 19 RS VELARDI - RICERCA ATENEO 2019 - MEDI PROGETTI-VELARDI (Responsabile Scientifico, VELARDI P.)”
See certificateScholarship in AI
Winner of the Avvio alla Ricerca 2020 - Tipo I, prof. Num: AR120172A8B35EEA on the research project “Personalized e-Learning Solutions to Improve the Efficacy of Learning Outcomes in Computer Science e-Courses”. I devised an autonomous model to detect students prone to drop out of university in online computer science courses, and provide them with personalized feedback and learning pathways to support their academic journey. With this money (€ 1000.00 ), I bought my PC (now pretty slow 🤣🙄) and published the paper Prenkaj et al. “Hidden Space Deep Sequential Risk Prediction on Student Trajectories”, In Future Generation Computing Systems, vol. 125, pp. 532-543, 2021.
See certificate
3rd International Summer School on Deep Learning
Research training event about the most recent advances in the critical and fast developing area of deep learning
Graduation Prize (B.Sc.)
Winner of the Premio di Laurea distributed from LazioDiSU to excellent bachelor degree students. Winner num: 899, grade: 110/110, Sum: € 2,559.18
See certificateMerit Scholarship in Computer Science
Winner of the LazioDiSU Study Scholarship for B.Sc. (3 years) and M.Sc. (2 years). Yearly sum: € 5,118.36
Invited Talks, Tutorials & Conference Presentations
Courses
Featured Publications
Recent Publications
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(2024).
Agnostic Visual Recommendation Systems: Open Challenges and Future Directions.
In IEEE TVCG.
(2024).
Robust Stochastic Graph Generator for Counterfactual Explanations.
In AAAI 2024.
(2023).
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges.
In ACM CSUR.
(2023).
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes.
In ECML-PKDD 2023.
(2023).
Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection.
In ICCV 2023.
(2023).
Are we certain it is anomalous?.
In CVPR 2023 Workshops.
(2023).
Developing and Evaluating Graph Counterfactual Explanation with GRETEL.
In WSDM 2023.
(2022).
Ensemble approaches for Graph Counterfactual Explanations.
In XAI.it 2022.
(2022).
A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences.
In Artificial Intelligence in Medicine.
(2021).
Hidden space deep sequential risk prediction on student trajectories.
In Future Generation Computer Systems, Special Issue on Advances in Intelligent Systems for Online Education.
(2021).
Latent and sequential prediction of the novel coronavirus epidemiological spread.
In ACM SIGAPP Applied Computing Review.
(2021).
Unsupervised boosting-based autoencoder ensembles for outlier detection.
In Pacific-Asia Conference on Knowledge Discovery and Data Mining 2021.
(2021).
CoRoNNa: a deep sequential framework to predict epidemic spread.
In SAC 2021.
(2020).
A reproducibility study of deep and surface machine learning methods for human-related trajectory prediction.
In Proceedings of the 29th ACM International Conference on Information & Knowledge Management.
(2020).
Challenges and solutions to the student dropout prediction problem in online courses.
In Proceedings of the 29th ACM International Conference on Information & Knowledge Management.
(2020).
A survey of machine learning approaches for student dropout prediction in online courses.
In ACM Computing Surveys, Volume 53, Issue 3.
(2019).
MIMOSE: multimodal interaction for music orchestration sheet editors.
In Multimedia Tools and Applications.
(2018).
House in the (biometric) cloud: a possible application.
In IEEE Cloud Computing.
(2017).
A smart peephole on the cloud.
In International Conference on Image Analysis and Processing.
Contact
- prenkaj[AT]di[dot]uniroma1[dot]it
- Via Salaria 113, Rome, Lazio 00198
- Enter the Faculty Building and take the lift to Office 316 on Floor 3
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Monday 09:00 to 17:30
Tuesday 09:00 to 17:30
Wednesday 09:00 to 17:30
Thursday 09:00 to 17:30
Friday 09:00 to 17:30 - Book an appointment
- DM Me