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Research results
Ph.D de GHUFRAN Mohammad
GHUFRAN Mohammad
Group :

Digital Identity Discovery and Reconciliation for Human Resources Management

Starts on 01/04/2014
[QUERCINI Gianluca]

Funding : CIFRE
Affiliation : Centrale Supélec
Laboratory :

Defended on 27/11/2017, committee :
- Patrick MARCEL – François-Rabelais Université

- Mathieu ROCHE – CIRAD

- Nacéra SEGHOUANI BENNACER – LRI, CentraleSupélec

- Gianluca QUERCINI – LRI, CentraleSupélec

- Dario COLAZZO – Paris-Dauphine Université

- Nicolas SABOURET – Paris-Sud Université

- Florent ANDRÉ – MindMatcher

Research activities :

Abstract :
Finding the appropriate individual to hire is a crucial part of any organization. With the number of applications increasing due to the introduction of online job portals, it is desired to automatically match applicants with job offers. Existing approaches that match applicants with job offers take resumes as they are and do not attempt to complete the information on a resume by looking for more information on the Internet. The objective of this thesis is to fill this gap by discovering online resources pertinent to an applicant. To this end, a novel method for extraction of key information from resumes is proposed. This is a challenging task since resumes can have diverse structures and formats, and the entities present within are ambiguous. Identification of Web results using the key information and their reconciliation is another challenge. We propose an algorithm to generate queries, and rank the results to obtain the most pertinent online resources. In addition, we specifically tackle reconciliation of social network profiles through a method that is able to identify profiles of individuals across different networks. Moreover, a method to resolve ambiguity in locations, or predict it when absent, is also presented. Experiments on real data sets are conducted for all the different algorithms proposed in this thesis and they show good results.

Ph.D. dissertations & Faculty habilitations
In this thesis, we propose a formal energy model which allows an analytical study of energy consumption, for the first time in the context of population protocols. Population protocols model one special kind of sensor networks where anonymous and uniformly bounded memory sensors move unpredictably and communicate in pairs. To illustrate the power and the usefulness of the proposed energy model, we present formal analyses on time and energy, for the worst and the average cases, for accomplishing the fundamental task of data collection. Two power-aware population protocols, (deterministic) EB-TTFM and (randomized) lazy-TTF, are proposed and studied for two different fairness conditions, respectively. Moreover, to obtain the best parameters in lazy-TTF, we adopt optimization techniques and evaluate the resulting performance by experiments. Then, we continue the study on optimization for the poweraware data collection problem in wireless body area networks. A minmax multi-commodity netow formulation is proposed to optimally route data packets by minimizing the worst power consumption. Then, a variable neighborhood search approach is developed and the numerical results show its efficiency. At last, a stochastic optimization model, namely the chance constrained semidefinite programs, is considered for the realistic decision making problems with random parameters. A novel simulation-based algorithm is proposed with experiments on a real control theory problem. We show that our method allows a less conservative solution, than other approaches, within reasonable time.