Master-level scholarship,  IA & Process Mining

We are currently seeking applicants for a Master-level scholarship related to Fair Predictive Process Mining  (detailed description below)

The internship will take place at LAMSADE (Université Paris-Dauphine, France) or remotly  starting from end of April 2021 for 5 to 6 months paid 560 euros per month.

Applicants should have: 
- Good knowledge on Business Process Management and Data Mining.
- Some notion of Machine Learning 
- Good programming skills.

To apply. 
Applicants about to complete their Master 2 level degree (or equivalent engineering school degree) must send in a single PDF the following documents to < > and < > : 
- fully detailed CV,  
- academic records (master's degree or equivalent),  
- recommendation(s) and supporting letter(s).  
Process mining is a recent research topic that applies artificial intelligence and data mining techniques to process modelling and analysis [1,2]. The main idea is to extract knowledge from events recorded in an events log to discover, monitor and improve processes. Event logs stores activities related to process instances, as well as additional information such as the resources executing the activities, data produced or used, timestamps, or costs. 
Process mining approaches allow the discovery of the process model or its variants (a.k.a. discovery), the detection of deviations between the real process and the designed model (a.k.a. conformance checking), and the improvement of the process model based on the observed events (a.k.a. enhancement). Predictive process monitoring is a subfield of process mining that deals with predicting outcome for running instances [3,4,5]. However, these techniques do not consider fairness criteria. Fairness has only been studied in [6] for process analysis. 
The aim of this internship is to  
- To review existing approaches on predictive monitoring and discrimination-aware data mining 
- Propose fairness criteria adapted to predictive process mining context. 
- Propose and implement a technique for including fairness criteria in predictive monitoring. 

[1] Van Der Aalst, W. (2016). Data science in action. In Process mining. Springer, Berlin, Heidelberg.    
[2] Beheshti, S.M.R., Benatallah, B., Sakr, S., Grigori, D., Motahari-Nezhad, H. R., Barukh, M. C., Gater, A., & Ryu, S. H. (2016). Process Analytics: concepts and techniques for querying and analyzing process data. Springer.  
[3] Taymour, F., La Rosa, M., Dumas, M., & Maggi, F. M. (2019). Business Process Variant Analysis: Survey and Classification. arXiv preprint arXiv:1911.07582.  
[4] Rama-Maneiro, E., Vidal, J. C., & Lama, M. (2020). Deep Learning for Predictive Business Process Monitoring: Review and Benchmark. arXiv preprint arXiv:2009.13251.  
[5] Lin, L., Wen, L., & Wang, J. (2019). MM-pred: A deep predictive model for multi-attribute event sequence. In Proc. of the SIAM Int. Conf. on Data Mining (pp. 118-126).   
[6] Qafari, Mahnaz Sadat, and Wil van der Aalst. "Fairness-aware process mining." OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". Springer, Cham, 2019.