Resume

• 2012

  The Company has been founded in december 2011 in Montreal

  Canada. It is operated by a set of 5 Managers. The firms is located in three different countries which are Brazil

  Canada and Taiwan.\n\nAs a dynamic team with strong ambition and desire
 to achieve

  Guarana Technologies aim to be a creative start-up bringing innovation and creativity in a field we bet to be the future of computing.

  Advisor of the Board of Directors

  Montreal

  Canada Area

  Guarana Technologies Inc.

  Arabic

  Spanish

  Italian

  English

  French

  Tunisian Academic Excellence Awards

  The Tunisian Ministry of Education and Training

  Tunisian Academic Excellence Awards

  The Tunisian Ministry of Education and Training

  UQAM foundation: Academic Excellence Awards: LGS group award.

  Université du Québec À Montréal

  Tunisian Academic Excellence Awards

  The Tunisian Ministry of Education and Training

  Tunisian Academic Excellence Awards.

  The Tunisian Ministry of Education and Training

• 2011

  Certificate in Agile Project Management

  Scrum Master

  Professional Scrum Master

  Agile Project Management - Scrum Master

  Professional Scrum Master

• 2010

  Master

  Business Administration (MBA)

  Université du Québec à Montréal

• 2008

  GÉNINOV

  WADA

  Guarana Technologies Inc.

  - Establishment of the Information Systems Department and the implementation of a quality management system ISO9001:2000 ;\n- Function as the main point of contact for managing all technology internal and external projects\n- Provide IT leadership

  project management

  relationship partnering

  budget planning strategic and tactical technology direction\n- Establish Scrum project management process and support multidisciplinary project teams\n- Lead human resources activities

  including hiring

  developing training plans and conducting training for employees\n- Prepare the files for tax credits and government grants submission for research and development projects\n- Ensure domain best practice and technology watch\n\nKey achievements :\n- Financial Data warehouse. The Research Institute of the McGill University Health Centre. Canada (Business intelligence

  Data warehouse

  Data mining

  JAVA and Oracle DB). 2010-2011\n- R&D - Decision support system for airborne systems and equipment certification. NRC Canada - IRAP (Knowledge Management and Discovery

  Text mining

  Microsoft SQL server DB and .NET). 2009-2011\n- Financial Oracle Database. BRH – Bank of the Republic of Haiti. 2010\n- R&D - Mobile Application prototype for Wine Recommendation (IPhone

  Android

  PhoneGap). 2010\n- Enterprise resource planning. Ed’H - Haitian electric power company (SAP ERP). 2009-2010\n- Education census information system. CIDA Canada (Business intelligence

  Data warehouse

  Data mining

  Servers and network infrastructure

  WebLogic

  J2EE and Oracle DB). 2009-2010\n- Professional training information and eLearning platform (INFP). IDB Bank / Haitian government (Servers and network infrastructure

  WebLogic

  J2EE et Oracle). 2009-2011\n- Education information system. IDB Bank / Haitian government (Servers and network infrastructure

  WebLogic

  J2EE and Oracle). 2008-2010\n- R&D - Optimization planning tools for 3G/4G mobile networks. NRC Canada - IRAP (Optimization

  Microsoft SQL server DB and .NET). 2008-2010

  GÉNINOV

• 2007

  Jeppesen

  Heron Solution

  Main responsibilities :\n- Carry out Agile coaching and transition project at the clients' site;\n- Help team implement Agile development approaches;\n- Coach future Scrum Master to help him familiarize with his role and responsibilities;\nDevelop and analyze performance indicators;\n- Remain at the leading-edge of Agile development practices and contribute to their spreading inside the company.

  Heron Solution

  Guarana Technologies Inc.

  Main responsibilities :\n- Implementation of a quality management system ISO9001:2000 ;\n- Participate in the definition of deliverables and project risk; \n- Coordinate

  delegate and organize the work to be done;\n- Identify needs in terms of human resources and participate in the recruitment process;\n- Define and develop working methods of the team (including team structure) that are best suited to meet the needs of the project;\n- Participate in the selection of team members and attend interviews with external candidates;\n- Coordinate tasks that involve multiple teams with project managers of other trades.\n- Create a motivating work environment for the team encouraging creativity and self-development;\n- Encourage collaboration and knowledge sharing within the team and with teams from other projects;\n- Support the career development activities and provide training and development of interpersonal skills and technical team members;\n- Prepare the files for tax credits and government grants submission for research and development projects.\n- Carry out project monitoring and communicate important information to steering committees.

  Director of Information Systems Projects

  Montreal

  Canada Area

  Main responsibilities :\n- Assemble project team

  develop scope

  develop cross-project relationships

  assign individual responsibilities

  identify appropriate project resources

  and provided guidance and direction to project team members;\n- Remove impediments preventing the team from achieving the iteration’s objectives; Facilitate team meetings; Support the team during the iteration; \n- Measure the team’s velocity; Coach the team in estimating items and breaking them down into tasks;\n- Coach the Product Owner in maintaining and ordering the product backlog; Help the Product Owner carry out the delivery plan;\n- Cooperate with colleagues to maximize synergy opportunities and ensure optimal collaboration enter teams;\n- Develop effective and robust technology based solutions satisfying internal and external customer expressed needs and technological orientations; \n- Ensure solutions integrity from beginning to end with all technology partners;\n- Provide executive leadership and detailed project and budget follow up to steering committees and teams; Ensure domain best practice and technology watch.\n\nKey achievements :\n- Web applications for flight crew schedule bidding system (Servers and network infrastructure

  WebSphere / Tomcat

  J2EE

  .NET and Oracle / SQL server DB). 2007-2008.\n- R&D - Business aviation flight planning optimization solutions (C/C++

  Python and Oracle DB). 2007-2008.

  Jeppesen

• 2002

  Ph.D

  Specialized subjects: Collaborative Filtering

  Search Engine

  Optimization

  Artificial Intelligence

  Recommender Systems

  Database

  Data Mining.

  Computer Science : Speciality Cognitive Science and Artificial Intelligence

  IEEE

  ACM

  LATECE Laboratory

  MyBlogLog

  Facebook

  Xing

  Feel free to connect with me on Xing at https://www.xing.com/profile/Zied_Zaier.

  Université du Québec à Montréal

• 2001

  Main responsibilities :\n- Take part in teaching courses;\n- Supervise and guide student on their projects;\n- Collaborate with professors and internal research teams and with researchers from within the industry.\n\nKey courses :\n- INF1130 - Mathematics for Computer Science (Problems Analysis and Solving). 2012.\n- INF5180 - Database Design and Implementation (Database Design

  SQL

  PL-SQL

  Triggers

  Business intelligence

  Data warehouse

  Data mining

  JAVA

  Hibernate

  etc.). 2011.\n- INF7215 - Information systems analysis and design (Customer requirements analysis

  Classical and agile development approaches – RAD

  XP

  Scrum –

  UML

  etc.). 2011.\n- INF7115 - Databases (SQL

  PL-SQL

  Triggers

  Business intelligence

  Data warehouse

  Data mining

  ERP

  etc.). 2010.\n- INF5280 - Advanced databases (Business intelligence

  Data warehouse

  Data mining

  etc.). 2001-2002.\n- INF4470 - Information and Network Security (Auditing

  approaches

  protocols

  hardware and software security systems

  etc.). 2001- 2002.

  Université du Québec à Montréal

  WADA

  Assist the CTO

  in the day-to-day operations of ADAMS as required \nAssist and coordinate International Federations in the implementation of ADAMS.\nKeep abreast with Anti-Doping operations of WADA and stakeholders \nAssist and coordinate creating templates for repetitive inquiries; looping back to the documentation;\nManage communication with the ADAMS Testing Group.\nAssist and coordinate creating

  updating and maintaining organizational and user profiles for WADA stakeholders in ADAMS.\nAnalyze and model the current anti-doping processes used by stakeholders;\nAssist and coordinate training new and existing users in proper use of the ADAMS system\nOngoing requirements gathering;\nExplore and collaborate on solution design; Elicit the requirements of the software providers;\nAssist and coordinate performing in functional testing; Participate in user acceptance testing;

  IT Manager

  Montreal

  Canada Area

• 2000

  Zied

  Zaier

  PhD

  EMBA

  CSM

  Université du Québec à Montréal

  Main responsibilities :\n- Supervise and guide research team members and the necessary activities to carry out innovative research projects;\n- Collaborate with professors and internal research teams and with researchers from within the industry.\n- Lead and mobilize team members and resources to achieve project goals. \n- Estimate and establish project budget

  schedule

  and goals and ensure resource expenses follow-up.\n\nKey achievements :\n- R&D - Distributed Multi-agents collaborative filtering recommender system (Business intelligence

  Data warehouse

  Data mining

  P2P

  Tomcat

  J2EE and Oracle DB). 2002-2007.\n- R&D - Movie recommender system (Business intelligence

  Data warehouse

  Data mining

  P2P

  Tomcat

  J2EE and Oracle DB). 2002-2007.\n- R&D – Text language Identification Tools (MATLAB

  C/C++ and Neural Networks). 2003-2004.\n- R&D – Text Subjects Identification Tools (MATLAB

  C/C++ and Neural Networks). 2003-2004.\n- R&D – Text subject Identification Tools (MATLAB

  C/C++ and Neural Networks). 2001.\n- R&D - Web search engine (Java

  JRules - Rules based system and Oracle DB). 2000-2001.\n- R&D – Meta-search engine (Java

  JRules - Rules based system and Oracle DB). 2000-2001.

  Project Manager R&D - Research Assistant

  Montreal

  Canada Area

  Université du Québec à Montréal

• 1999

  Master

  Specialized subjects: Collaborative Filtering

  Search Engine

  Database

  Data Mining.

  Computer Science : Speciality Information Systems and Information Management

• 1995

  Bachelor

  Specialized subjects: Search Engine

  Database.

  Computer Science : Speciality Information Systems and Information Management

  Institut Supérieur de Gestion de Tunis

• Analysis

  Machine Learning

  Databases

  Recommender Systems

  Teamwork

  Data Mining

  Project Coordination

  Information Retrieval

  Programming

  Requirements Analysis

  Software Development

  Business Intelligence

  Software Project Management

  Team Management

  Optimization

  Team Leadership

  Knowledge Management

  Artificial Intelligence

  Business Analysis

  Software Engineering

  Recommendation Quality Evolution Based on Neighborhood Size

  Luc Faucher

  Robert Godin

  Automated recommender systems play an important role in e-commerce applications. Such systems recommend items (movies

  music

  books

  news

  web pages

  etc.) that the user should be interested in. These systems hold the promise of delivering high quality recommendations. However

  the incredible growth of users and applications poses some challenges for\nrecommender systems. One of the concerns for current recommenders is that the quality of recommendations is strongly dependant on the size of the user’s population. In this paper we investigate

  with the scaling of neighborhood size

  the evolution of different recommendation techniques performance

  the increase\nof the coverage

  and the quality of prediction. We also identify which recommendation method is the most efficient given reasonably small training datasets.

  Recommendation Quality Evolution Based on Neighborhood Size

  Implemented a search engine application that sends the user queries to several other search engines and returns the results from each one and apply an alternative search algorithms based on user profile.

  Metasearch engine for Information Retrieval

  Luc Faucher

  Robert Godin

  An “Automated Recommender System” plays an essential role in e-commerce applications. Such systems try to recommend items (movies

  music

  books

  news

  etc.) which the user should be interested in. The spectrum of proposed recommendation algorithms are based on information including content of the items

  ratings of the users

  and demographic information\nabout the users. These systems hold the promise of delivering high quality recommendations. However

  the incredible growth of users and applications bring some key challenges for recommender systems. One of the concerns in current recommenders is that the quality of recommendations is strongly dependant on the neighborhood size and quality. In this paper

  we\npropose a new peer-to-peer architecture based on prior selection of the neighbors. We investigate the evolution of different recommendation techniques performance

  coverage and quality of prediction. Also

  we identify which recommendation method would be the most efficient with this new peer-to-peer architecture.

  Recommendation Quality Evolution Based on Neighbors Discrimination

  Recommender systems are considered an answer to the information overload in a web environment. Combining ideas and techniques from information filtering

  personalization

  artificial intelligence

  user interface design and human-computer interaction

  recommender systems provide users with proactive suggestions that are tailored to meet their particular information needs and preferences. Indeed recommender system plays an essential role in e-commerce applications. However

  this type of system has been largely confined to a centralized architecture. Recently

  distributed architectures are becoming more and more popular (as witnessed by peer-to-peer

  Grid computing

  semantic web

  etc.) and try replacing classical client/server approach. Recommender system could likewise profit from this architecture. Indeed

  novel decentralized recommender systems are emerging. In this thesis

  we investigate the challenges that decentralized recommender systems bring up and propose a new peer-to-peer collaborative filtering architecture based on prior selection of the neighbors. We investigate the evolution of different recommendation techniques performance

  coverage and quality of prediction. Also

  we identify which recommendation method would be the most efficient for this new peer-to-peer architecture. While this thesis mainly concentrates on decentralized collaborative filtering recommender system domain

  our contributions are not only confined to this research domain. Indeed

  many of these contributions address issues relevant to other research domains (multi-agent systems

  user profile management

  computational complexity reduction

  collecting preference information

  PageRank

  etc.).

  Distributed Recommender System

  Developed an artificial intelligence based multi-agent Information retrieval algorithm using a text mining algorithm for information and knowledge discovery.

  Artificial Intelligence Contribution for Information Retrieval

  Luc Faucher

  Robert Godin

  Collaborative filtering recommender systems are gaining popularity in a variety of E-commerce applications. Such systems attempt to present information items (movies

  music

  etc.) that are likely of interest to the user. Most proposed recommendation algorithms are based on information such as content of the items

  ratings of the users and users’ demographic data with purpose of delivering high-quality recommendations. However

  with the incredible growth of users and applications

  new challenges are presented to recommender systems. One issue is the presence of various languages and different kinds of textual errors. As recommender systems must work reliably on all inputs

  they must tolerate such kinds of problems. This paper proposes a new hybrid recommendation algorithm

  which is based on n-grams instead of keywords. Additionally

  simulations are carried out to demonstrate its effectiveness

  of which results show that our proposal delivers better performance than collaborative filtering and hybrid collaborative filtering.

  New n-gram-based hybrid collaborative filtering recommender system

  Luc Faucher

  Robert Godin

  Recommender systems are considered as an answer to the information overload in a web environment. Such systems recommend items (movies

  music

  books

  news

  web pages

  etc.) that the user should be interested in. Collaborative filtering recommender systems have a huge success in commercial applications. The sales in these applications follow a power law distribution. However

  with the increase of the number of recommendation techniques and algorithms in the literature

  there is no indication that the datasets used for the evaluation follow a real world distribution. This paper introduces the long tail\ntheory and its impact on recommender systems. It also provides a comprehensive review of the different datasets used to evaluate collaborative filtering recommender systems techniques and\nalgorithms (EachMovie

  MovieLens

  Jester

  BookCrossing

  and Netflix). Finally

  it investigates which of these datasets present a distribution that follows this power law distribution and which\ndistribution would be the most relevant.

  Evaluating Recommender Systems