Bardh Prenkaj

Bardh Prenkaj

Computer Scientist, PhD

Technical University of Munich

Biography

Currently a postdoc in AI at the Technical University of Munich trying to understand What Makes us (or not) Human and What Actually is Machine Intelligence with Gjergji Kasneci.

From 10/2022 to 10/2024, I was a postdoc at Sapienza in anomaly detection and explainability. During this period (June-September 2023), I spent time at my current research group, RDS working on graph counterfactual explainability.

I received my PhD in Computer Science from the Sapienza University of Rome in 2022 with a thesis on “Latent Deep Sequential Learning of Behavioural Sequences”. In Rome, I worked in the AIIM - formerly IIM - group advised by Paola Velardi and Giovanni Stilo. I then worked as a senior researcher at the Department of Computer Science in Sapienza in anomaly detection for social isolation disorders (February - September 2022).

I made BetterScholar with Antonio Norelli, an alternative to Google Scholar profiles. Read more here: Why BetterScholar?

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

 
 
 
 
 
Sapienza University of Rome
Postdoctoral Researcher
Sapienza University of Rome
Oct 2022 – Present Rome, Italy
Researching on Anomaly Detection and Counterfactual Explainability. I actively participate in e-health and anomaly detection interdisciplinary projects.
 
 
 
 
 
Technical University of Munich
Visiting Researcher
Technical University of Munich
Jun 2023 – Sep 2023 Munich, Geramny
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.
 
 
 
 
 
Sapienza University of Rome
Senior Research Fellow
Sapienza University of Rome
Dec 2021 – Sep 2022 Rome, Italy
Coordinated research and implementation of innovative deep learning algorithms to predict anomalous events in patient behavioural time series.
 
 
 
 
 
George Mason University
Visiting PhD Student
George Mason University
Apr 2021 – Jun 2021 Farifax, VA, USA
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.
 
 
 
 
 
George Mason University
Junior Researcher
George Mason University
Mar 2020 – Dec 2021 Faifax, VA, USA (remote)
Worked alongside Dr Sarvari and Prof Domeniconi on boosting-based anomaly detection models.
 
 
 
 
 
Sapienza University of Rome
Student Research Assistant
Sapienza University of Rome
Jul 2017 – Oct 2018 Rome, Italy
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

 
 
 
 
 
Heimerer College
Assistant Professor
Jul 2022 – Present Pristina, Kosovo (remote)
I’m head of the M.Sc. in Digital Healthcare. I teach Bioinformatics and AI for Health.
 
 
 
 
 
Pricewaterhouse Coopers, Digital Innovation Team
Software Engineer
Dec 2021 – Jun 2022 Rome, Italy
I focused on software prototyping and development activities. In particular, I optimized back-end services and developed highly-maintainable and efficient API services.
 
 
 
 
 
E Software Solutions
Senior Software Consultant
Sep 2020 – Mar 2021 Rome
I designed and maintained the CMS for electric vehicle leasing in the UK, Gridserve

Honors & Awards

Sapienza University of Rome
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
NeurIPS XAIA 2023 Workshop
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 🤨
University of L'Aquila
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
Sapienza University of Rome
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 certificate
Sapienza University of Rome
Scholarship 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
IRDTA
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
LazioDiSU, Ente per il Diritto agli Studi Universitari nel Lazio
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 certificate
LazioDiSU, Ente per il Diritto agli Studi Universitari nel Lazio
Merit 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

Recent Publications

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(2024). Agnostic Visual Recommendation Systems: Open Challenges and Future Directions. In IEEE TVCG.

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(2024). Robust Stochastic Graph Generator for Counterfactual Explanations. In AAAI 2024.

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(2023). A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges. In ACM CSUR.

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(2023). Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes. In ECML-PKDD 2023.

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(2023). Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection. In ICCV 2023.

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(2023). Are we certain it is anomalous?. In CVPR 2023 Workshops.

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(2023). Developing and Evaluating Graph Counterfactual Explanation with GRETEL. In WSDM 2023.

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(2022). Ensemble approaches for Graph Counterfactual Explanations. In XAI.it 2022.

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(2022). A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences. In Artificial Intelligence in Medicine.

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(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.

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(2021). Latent and sequential prediction of the novel coronavirus epidemiological spread. In ACM SIGAPP Applied Computing Review.

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(2021). Unsupervised boosting-based autoencoder ensembles for outlier detection. In Pacific-Asia Conference on Knowledge Discovery and Data Mining 2021.

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(2021). CoRoNNa: a deep sequential framework to predict epidemic spread. In SAC 2021.

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(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.

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(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.

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(2020). A survey of machine learning approaches for student dropout prediction in online courses. In ACM Computing Surveys, Volume 53, Issue 3.

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(2019). MIMOSE: multimodal interaction for music orchestration sheet editors. In Multimedia Tools and Applications.

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(2018). House in the (biometric) cloud: a possible application. In IEEE Cloud Computing.

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(2017). A smart peephole on the cloud. In International Conference on Image Analysis and Processing.

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