Jeudi 2 Mars
Heure: 
12:30  13:30 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
On big data, optimization and learning 
Description: 
Prof. Andrea Lodi In this talk I review a couple of applications on Big Data that I personally like and I try to explain my point of view as a Mathematical Optimizer  especially concerned with discrete (integer) decisions  on the subject. I advocate a tight integration of Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decisionmaking in Data Science. For such an integration I try to answer three questions: 1) what can optimization do for machine learning? 2) what can machine learning do for optimization? 3) which new applications can be solved by the combination of machine learning and optimization? 
Heure: 
14:00  15:00 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
Reachability Analysis of Pushdown Systems with an Upper Stack 
Description: 
Adrien Pommellet Pushdown systems (PDSs) are a natural model for sequential programs, but they can fail to accurately represent the way an assembly stack actually operates. Indeed, one may want to access the part of the memory that is below the current stack or base pointer, hence the need for a model that keeps track of this part of the memory. To this end, we introduce pushdown systems with an upper stack (UPDSs), an extension of PDSs where symbols popped from the stack are not destroyed but instead remain just above its top, and may be overwritten by later push rules.
We prove that the sets of successors post* and predecessors pre* of a regular set of configurations of such a system are not always regular, but that post* is contextsensitive, so that we can decide whether a single configuration is forward reachable or not. In order to underapproximate pre* in a regular fashion, we consider a boundedphase analysis of UPDSs, where a phase is a part of a run during which either push or pop rules are forbidden. We then present a method to overapproximate post* that relies on regular abstractions of runs of UPDSs. Finally, we show how these approximations can be used to detect stack overflows and stack pointer manipulations with malicious intent. 

