​​1- ​University timetabling for maximizing the probability of attendance 

Problem:  Universities usually design timetables without any account of the behaviour of students in terms of attendance.

Objective: To analyze the attendance behavior of students from historical data using machine learning techniques (e.g. clustering and classification) and to develop an advanced timetabling system for universities that maximizes the probability of student attendance​ using mixed integer convex programming

Members:

Dr. Souad Larabi, Dr. Jaber Jamei, Dr. Ali AlMatouq​, Dr. Mohammed Al-Tounisi, Dr. Dhafer AlMukhlas and Dr. Bandar AlKhayyal


2-  Blind estimation of glucose insulin model using continuous glucose monitors ​​

Problem: Type 1 diabetes require patient specific dynamic models that can predict the trajectory of blood glucose during meals in order to effectively maintain a healthy glucose level.  Currently, developing such models is very 
expensive and the life span of the model is very short (parameter uncertainties).

Objective: To obtain a glucose/insulin dynamic model of the patient using continuous glucose monitors, meal information and insulin infusion recordings for type 1 diabetes while exploiting emerging regularization techniques in system identification.

Members
Dr. Ali AlMatouq, Dr. Mohammed AlShahrani and Dr. Taous-Meriam Laleg​


Other projects to come later...