François Chung, Ph.D.

About me

François Chung Choong LonCurrently, I am Functional Analyst at Zetes (BE). Prior to that, I have been working as a Scientific Software Engineer at CISTIB from UPF (ES), R&D Engineer in Intelligent Systems at Inspiralia (ES), Consultant in Energy Efficiency at E²=MC (BE), Consultant in Software Implementation at Sofico (BE), Business Analyst at FIS (BE), and Digital Business Analyst at Fujitsu (BE).

Since January 2011, I am Ph.D. in Computer Science from Mines ParisTech (FR). My Ph.D. was done at Asclepios, INRIA Sophia-Antipolis (FR). My thesis mainly deals with the analysis of medical images and took place within the framework of the EU Marie Curie project 3D Anatomical Human (3DAH).

Before starting my Ph.D., I took courses in computer vision and robotics at ViCOROB, UdG (ES). In 2005, I graduated Industrial Engineer in Computer Science (Ing./M.Sc.) from ISIB (BE).

My professional experience

Artificial Intelligence

Financial Services

Fleet Management

Energy Efficiency

Image Processing

Medical Imaging

Where I work

Where I worked

Where I studied

Positions I held

Business Analyst

Consultant

R&D Engineer

Software Engineer

My diplomas

Ph.D.

Ing./M.Sc.

For whom I work (and worked)

https://www.francoischung.com/wp-content/uploads/2020/11/Zetes-1-80x80.jpg
Fujitsu
FIS
Sofico
E² = MC
Inspiralia
UPF
Inria

Where I studied

Inria
Mines ParisTech
UdG
ISIB

Fun Facts

Years Abroad

8

Spoken Languages

5

Publications

22

Visited Countries

52
Identity proofing

Identity proofing

Zetes project @Brussels, Belgium (2021). Identity proofing consists in verifying for a given level of assurance that a person, who is claiming an identity, is indeed the correct person. This identity proofing process can be performed manually by a human operator, either on site (through physical presence) or online (remotely through videoconference), but also automatically (e.g. fully automated online or in a controlled environment).

At the European level, the European Telecommunications Standards Institute (ETSI) is working on technical specification ETSI TS 119 46 to lay the foundations on a new identity proofing standard, whose aim is to be applicable in areas such as the issuance of electronic identity (eID) and Know Your Customer (KYC) processes, with several person types considered: natural person, legal person, and natural person representing a legal person.

One of the objectives of this specification is to provide controls against two main identity proofing threats:

  • Falsified evidence: A person claims an incorrect identity using forged evidence;
  • Identity theft: A person uses valid evidence associated with another person.

Therefore, implementing identity proofing requires a risk-based and outcome-based approach where requirements can be tuned up to a desired level of assurance (i.e. degree of certainty) of the result, depending on the context (e.g. purpose of the identity proofing, regulatory environment, acceptable risk regarding the result of the process).

In this project, my tasks are related to the analysis of technical specification ETSI TS 119 46 so as to investigate what parts of the identity proofing process are already developed and available at Zetes (and therefore could be reused), how the missing parts can be implemented in practice and what are the possible impacts of the implementation on the existing products and solutions.

References

Project

Related article

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ETSI - European Telecommunications Standards Institute

Business process and decision modeling

Business process and decision modeling

HPI training, MOOC (2021). This online training introduces concepts of business process modeling using the Business Process Model and Notation (BPMN) industry standard. Based on a thorough understanding of BPMN, the last part of the training covers decision models using the Decision Model and Notation (DMN). Decision models complement process models by representing concrete, operational decisions, both with their structure and their decision logics.

Week 1: Introduction to business process management

Main topics:

  • Defining business processes;
  • Business process models;
  • Interacting business processes;
  • Models and instances;
  • Business process lifecycle.

Week 2: Basic business process modeling

Main topics:

  • Process activities;
  • Exclusive and parallel gateways;
  • Inclusive gateways and loops;
  • Start, intermediate and end events;
  • Concurrency.

Week 3: Analyzing the behavior of process models

Main topics:

  • Process behavior;
  • Structural soundness;
  • Simulating business processes;
  • Petri nets and process analysis;
  • Checking soundness.

Week 4: Advanced business process modeling

Main topics:

  • Sub-processes and boundary events;
  • Activity modifiers;
  • Event-based gateway;
  • Modeling organizations;
  • Resource allocation patterns.

Week 5: Data in business process models

Main topics:

  • Organizing process models;
  • Data and data flow;
  • Data execution semantics;
  • Structured data and sub-processes;
  • Object lifecycle conformance.

Week 6: Business decision modeling

Main topics:

  • Implementation of decisions;
  • Decision requirements diagrams;
  • Semantics of decision tables;
  • Analysis of decision tables;
  • Consistency of processes and decisions.

References

Related article

UML class diagrams (edX training)

Learn more

BPMN - Business Process Model and Notation
DMN - Decision Model and Notation
openHPI - Hasso Plattner Institute

PKI for identity documents

PKI for identity documents

Zetes project @Brussels, Belgium (2021). A Public Key Infrastructure (PKI) is a set of physical components (e.g. computers and hardware), human procedures (e.g. checks and validation) and software (e.g. system and applications) intended to manage the public keys of the users of a system. The objective is the secure electronic transfer of information for a range of online activities, such as e-commerce and electronic identification (eID).

In the case of electronic identity documents, such as the identity card, a PKI makes it possible to bind public keys to the identity of citizens, whose personal information is not only printed on the identity card, but also stored in the identity card chip. This system not only allows citizens to use their card to identify themselves online (authentication), but also to sign digital documents using a Qualified Electronic Signature (QES).

A PKI can also be used in an international scheme, such as for the verification of passports at country borders. In that case, a country emits passports for its citizens and also puts in place a PKI to allow other countries to check those passports. This means that, when a citizen presents a passport at the border control, the inspection system checks the identity information both printed on the passport and stored in the passport chip.

As a Functional Analyst and Product Owner within Zetes People ID’s development team, my tasks are related to the analysis of PKI software needs, whether internal or from the customer (e.g. requirement gathering and product presentation), PKI software implementation (e.g. software releases and documentation) and project management (e.g. project coordination during change requests).

References

Related article

Identity proofing (Zetes project)

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UML class diagrams

UML class diagrams

edX training, MOOC (2020). This online computer science training from KU Leuven (BE) provides an in-depth understanding of Unified Modeling Language (UML) class diagrams, which are used to visually represent the conceptual design of a system. The training presents UML class diagrams and explains how they are used to map out the structure of a business domain by showing business objects, their attributes and associations.

Week 1: Introduction

Main topics:

  • Why does data modelling matter?
  • Modelling languages.

Week 2: UML basics

Main topics:

  • Attributes and data types;
  • Class definitions;
  • Unary and ternary association;
  • Aggregation;
  • Derived and implicit association;
  • Parallel paths.

Week 3: UML advanced

Main topics:

  • Superclass, subclass and inheritance;
  • Generalisation sets;
  • Constraints on generalisation and specialisation;
  • Inherited associations;
  • AssociationClass;
  • Association reification.

References

Related article

Learn more

UML - Unified Modeling Language
edX

Deep learning and TensorFlow

Deep learning and TensorFlow

Cognitive Class training, MOOC (2020). This learning path presents the basic concepts of deep learning and TensorFlow with hands-on experience in solving problems. Throughout the training, TensorFlow is used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the deep learning world where TensorFlow is applied for backpropagation to tune the weights and biases while the neural networks are being trained.

Course 1: Deep learning fundamentals

Main topics:

  • Introduction to deep learning;
  • Deep learning models;
  • Additional deep learning models;
  • Deep learning platforms and libraries.

Course 2: Deep learning with TensorFlow

Main topics:

  • Introduction to TensorFlow;
  • CNN - Convolutional Neural Network;
  • RNN - Recurrent Neural Network;
  • Unsupervised learning.

References

Training

Deep learning fundamentals (course certificate)
Deep Learning Essentials (certification badge)
Deep learning with TensorFlow (course certificate)
Deep Learning using TensorFlow (certification badge)

Related articles

Learn more

ITIL Foundation

ITIL Foundation

QRP International training, Belgium (2020). This online training introduces participants to the IT service management (ITSM) and prepares them for the ITIL Foundation exam. The training provides an understanding of the common language and key concepts of ITIL (Information Technology Infrastructure Library) and shows how ITSM professionals can improve their work and the work of their organisation thanks to the ITIL 4 guidance.

Module 1: Introduction

Main topics:

  • Guide for the ITIL Foundation exam;
  • ITIL repository and documents;
  • Successive versions of ITIL.

Module 2: Definitions and key concepts

Main topics:

  • Service management;
  • Provision of services;
  • Client, user and sponsor;
  • Services and products;
  • Offer of services;
  • Service relations;
  • Consumption of services;
  • Deliverables and results;
  • Utility and warranty.

Module 3: The 7 guiding principles of ITIL

Main topics:

  • Focus on value;
  • Start where you are;
  • Progress iteratively with feedback;
  • Collaborate and promote visibility;
  • Think and work holistically;
  • Keep it simple and practical;
  • Optimize and automate.

Module 4: The 4 dimensions of service management

Main topics:

  • Organizations and people;
  • Information and technology;
  • Partners and suppliers;
  • Value streams and processes.

Module 5: The service value chain

Main topics:

  • Plan;
  • Engage;
  • Design and transition;
  • Obtain and build;
  • Deliver and support;
  • Improve.

Module 6: General management practices

Main topics:

  • Continual improvement;
  • Information security management;
  • Relationship management;
  • Supplier management.

Module 7: Service management practices

Main topics:

  • Change control;
  • Incident management;
  • IT asset management;
  • Monitoring and event management;
  • Problem management;
  • Release management;
  • Service configuration management;
  • Service desk;
  • Service level management;
  • Service request management.

References

Training

Certification (PeopleCert)

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First aid trainer

First aid trainer

Red Cross training, Belgium (2020). This 10-day training to first aid trainers covers the technical gestures and the teaching methods needed to conduct first aid training sessions for the BEPS (brevet européen de premiers secours). The trainer teaches the participants the saving gestures, who thus acquire the right reflexes that will make them an essential link in the rescue chain.

Day 1: Technical module

Main topics:

  • Positioning of the victim;
  • Cardiopulmonary resuscitation (CPR);
  • Automated external defibrillator (AED).

Day 2: Theoretical reinforcement

Main topics:

  • The human cell;
  • The human body;
  • Pathologies.

Day 3: Organization of trainings

Main topics:

  • Make-up;
  • Cleaning the manikin;
  • Legal framework.

Day 4: First aid reinforcement (I)

Main topics:

  • First aid material;
  • Essential rules of intervention;
  • Approaching an unconscious victim.

Day 5: First aid reinforcement (II)

Main topics:

  • Approaching a conscious victim;
  • Skin lesions;
  • Musculoskeletal injuries.

Day 6: Pedagogy

Main topics:

  • Methodology;
  • Educational goals;
  • Management of an animation.

Day 7: Specific module (I)

Main topics:

  • Electrical accident;
  • Car accident;
  • CO poisoning.

Day 8: Specific module (II)

Main topics:

  • Recovery position;
  • CPR and AED;
  • Airway obstruction.

Day 9: Specific module (III)

Main topics:

  • Wound with foreign body;
  • Head trauma;
  • Severe thermal burn.

Day 10: Specific module (IV)

Main topics:

  • Ankle sprain;
  • Hemorrhage;
  • Chest pain.

References

Related articles

Certified first response (Red Cross training)
First aid (Red Cross training)

Learn more

BEPS – Brevet européen de premiers secours
Animateur.rice premier secours (first aid trainer)
Croix-Rouge de Belgique (Belgian Red Cross)

Google Cloud: big data and machine learning

Google Cloud: big data and machine learning

Coursera training, MOOC (2020). This online training introduces the big data and machine learning (ML) capabilities of Google Cloud Platform (CGP). Through a combination of presentations, demos and hands-on labs, the training gives an overview of Google Cloud and a detailed view of the data processing and ML solutions, such as BigQuery, Cloud SQL, Dataproc, Pub/Sub, Dataflow and Data Studio.

Week 1: Big data and ML fundamentals

Main topics:

  • Exploring a BigQuery public dataset;
  • Choosing the right solution approach;
  • Recommending products using Cloud SQL and Spark;
  • Predicting visitor purchases using BigQuery ML.

Week 2: Modern data pipeline challenges

Main topics:

  • Real-time IoT dashboards;
  • Creating a streaming data pipeline;
  • ML on unstructured datasets;
  • Classifying images with pre-built ML models.

References

Spark fundamentals

Spark fundamentals

Cognitive Class training, MOOC (2020). This learning path addresses the fundamentals of Apache Spark, an open source engine for large scale data processing that is revolutionizing the analytics and big data world. This training is an opportunity to learn from industry leaders about Spark, which is built around speed, ease of use and analytics, and provides hands-on opportunities and projects to build confidence with the Spark toolset.

Course 1: Spark fundamentals I

Main topics:

  • Introduction to Spark;
  • Resilient Distributed Dataset (RDD) and DataFrames;
  • Spark application programming;
  • Introduction to Spark libraries;
  • Spark configuration, monitoring and tuning.

Course 2: Spark fundamentals II

Main topics:

  • Introduction to notebooks;
  • RDD architecture;
  • Optimizing transformations and actions;
  • Caching and serialization;
  • Developing and testing.

Course 3: Spark MLlib

Main topics:

  • Spark MLlib data types;
  • Review of algorithms;
  • Decision trees and random forests;
  • Spark MLlib clustering.

Course 4: Exploring GraphX

Main topics:

  • Introduction to Graph-Parallel;
  • Exploring graph operators;
  • Visualizing and modifying GraphX;
  • Aggregation and caching.

Course 5: Big data in R using Spark

Main topics:

  • Introduction to SparkR;
  • Data manipulation in SparkR;
  • Machine learning in SparkR.

References

Training

Spark fundamentals I (course certificate)
Spark – Level 1 (certification badge)
Spark fundamentals II (course certificate)
Spark MLlib (course certificate)
Exploring GraphX (course certificate)
Big data in R using Spark (course certificate)
Spark - Level 2 (certification badge)

Related articles

Hadoop fundamentals (Cognitive Class training)
Data science specialization (Coursera training)

Learn more

Hadoop fundamentals

Hadoop fundamentals

Cognitive Class training, MOOC (2020). This learning path presents Hadoop, which is an open source framework for distributed storage and processing of big data. The training covers content that is critical to anyone's success in this realm by explaining the Hadoop conceptual design, introducing MapReduce, YARN (Yet Another Resource Negotiator) and Hive, then explaining how to use Hadoop and manipulate data without the use of complex coding.

Course 1: Hadoop 101

Main topics:

  • Introduction to Hadoop;
  • Hadoop architecture and HDFS;
  • Hadoop administration;
  • Hadoop components.

Course 2: MapReduce and YARN

Main topics:

  • Introduction to MapReduce and YARN;
  • Limitations of Hadoop v1 and MapReduce v1;
  • YARN architecture.

Course 3: Moving data into Hadoop

Main topics:

  • Load scenarios;
  • Using Sqoop;
  • Flume overview;
  • Using Data Click.

Course 4: Accessing Hadoop data using Hive

Main topics:

  • Introduction to Hive;
  • Hive DDL - Data Definition Language;
  • Hive DML - Data Manipulation Language;
  • Hive operators and functions.

References

Training

Hadoop 101 (course certificate)
Hadoop Foundations – Level 1 (certification badge)
MapReduce and YARN (course certificate)
Hadoop Programming – Level 1 (certification badge)
Moving data into Hadoop (course certificate)
Hadoop Administration – Level 1 (certification badge)
Accessing Hadoop data using Hive (course certificate)
Hadoop Data Access – Level 1 (certification badge)
Hadoop Foundations – Level 2 (certification badge)

Related articles

Spark fundamentals (Cognitive Class training)
Data science specialization (Coursera training)

Learn more