DSTI School of Engineering
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Overview Teaching approach Teaching areas Academics and researchers Practitioners and contributors Profile notes Next step
Faculty

Academics and practitioners, teaching the real field.

DSTI’s faculty combines university academics, researchers, engineers, data and AI specialists, cyber security practitioners, business and legal contributors, and internal teaching leadership. The objective is simple: connect strong foundations with the systems, tools and professional judgement students need after graduation.

01 — Teaching approach

A deliberately mixed faculty.

DSTI brings together complementary teaching profiles: academics and researchers for the scientific foundations, practitioners for implementation and systems work, and professional contributors for the wider environment in which technology is designed, deployed and governed.

Science

Foundations and methods

Mathematics, statistics, modelling, optimisation, machine learning and scientific reasoning are supported by academics and researchers from universities, research institutes and scientific organisations.

Technology

Systems and implementation

Software, data platforms, cloud, cyber security, networks and operational systems are taught with input from people who build, secure, manage and use these systems professionally.

Professional context

Judgement beyond tools

Project management, law, ethics, communication, sustainability and business applications help students understand how technical work is used in organisations and society.

02 — Teaching areas

Teaching areas across the school.

Faculty profiles summarise broad subject areas so readers can see the expertise represented across DSTI. Exact teaching assignments are confirmed for each intake, following programme structure and academic planning.

Mathematics, statistics and modellingFoundations, inference, time series, optimisation, survival analysis and applied mathematics.
AI, machine learning and data scienceMachine learning, deep learning, neural networks, computer vision and model operations.
Data engineering and analyticsSQL, data wrangling, pipelines, NoSQL, graph databases, warehousing, reporting and visualisation.
Software, systems and cloudProgramming, software engineering, operating systems, cloud platforms, DevOps and infrastructure.
Cyber security and secure systemsNetworks, penetration testing, secure code, formal verification, governance, risk and compliance.
Professional environmentProject management, business applications, legal context, ethics, communication and sustainable digital systems.
03 — Academics and researchers

Academic depth where it matters.

Academic and research profiles bring disciplinary depth in mathematics, statistics, computer science, AI, systems, cyber security, law, history and related fields. Where academic titles are displayed, the page uses a concise Dr / Pr style.

Academic / researcher

Pr Catherine Faron

Université Côte d’AzurTechnology
Teaching areas
  • data engineering, analytics and platforms
Courses taught
Data Pipeline — Part 1 DA DE Exec
Courses supported
Semantic Web Technologies DA DE DS Exec
Academic / researcher

Dr Christophe Becavin

Université Côte d’AzurScience & technology
Teaching areas
  • statistics, modelling and optimisation
  • AI, machine learning and model operations
Courses taught
Foundations of Statistical Analysis and Machine Learning — Part 1 BSc DA DE DS Exec
Inferential Modelling DA
Courses supported
Python Machine Learning Labs DA DE DS Exec
Academic / researcher

Pr Didier Auroux

Université Côte d’AzurScience & technology
Teaching areas
  • foundations, modelling, optimisation, inverse problems and data assimilation
Courses taught
Mathematics Harmonisation BSc
Mathematics for Applied Engineering BSc
Mathematics for Data Science DA DE DS Exec
Continuous Optimisation DS
Inverse Problems & Data Assimilation DS
Courses supported
Foundations of Statistical Analysis and Machine Learning — Part 1 BSc DA DE DS Exec
Foundations of Statistical Analysis and Machine Learning — Part 2 DS
Academic / researcher

Dr Georgiy Bobashev

RTI InternationalScience & technology
Teaching areas
  • statistics, modelling and optimisation
Courses taught
Agent-Based Modelling DA DS
Academic / researcher

Hanna Abi Akl

DSTIScience & technology
Teaching areas
  • AI, machine learning and model operations
  • data engineering, analytics and platforms
  • software, systems, cloud and infrastructure
Courses taught
Relational Database Management Systems BSc
The Relational Model and SQL BSc
Data Wrangling with SQL BSc DA DE DS Exec
Data Management DA DE DS
AI Awareness DA DE DS
Networking DA DE DS
Clean IT DA DE DS
Python Machine Learning Labs DA DE DS Exec
Warm Up Exec
Academic / researcher

Pr Julien Jacques

Université Lumière Lyon 2Science
Teaching areas
  • statistics, modelling and optimisation
Courses taught
Time-Series Analysis DA DS
Academic / researcher

Pr Marina Teller

Université Côte d’AzurProfessional environment
Teaching areas
  • data law, ethics and regulation
Courses taught
Data Laws and Regulations — Philosophies, Geopolitics and Ethics BSc CY DA DE DS Exec
Academic / researcher

Martin Van Waerebeke

INRIAScience & technology
Teaching areas
  • statistics, modelling and optimisation
  • AI, machine learning and model operations
Courses taught
Foundations of Statistical Analysis and Machine Learning — Part 1 BSc DA DE DS Exec
Academic / researcher

Valeriya Strizhkova

INRIAScience
Teaching areas
  • statistics, modelling and optimisation
Courses supported
Time-Series Analysis DA DS
04 — Practitioners and professional contributors

Professional practice in the classroom.

These profiles bring industrial, technical, consulting and operational experience into the programmes.

Industry practitioner

Albert Konrad

BlaBlaCarScience & technology
Teaching areas
  • AI, machine learning and model operations
  • data engineering, analytics and platforms
Courses taught
Data Structure and Machine Learning using Python & R DA DE DS
Industry practitioner

Ana Escobar

TinderScience & technology
Teaching areas
  • data engineering, analytics and platforms
Courses taught
Graph Databases — NoSQL Part 1 DA DE DS Exec
Industry practitioner

Assan Sanogo

Datajam.aiScience & technology
Teaching areas
  • AI, machine learning and model operations
Courses supported
Python Machine Learning Labs DA DE DS Exec
Industry practitioner

Benoit Mialet

ValowayScience & technology
Teaching areas
  • AI, machine learning and model operations
Courses taught
Artificial Neural Networks DE DS Exec
Deep Learning DE DS
Industry practitioner

Clément Ziane

ConsultantScience & technology
Teaching areas
  • data engineering, analytics and platforms
  • software, systems, cloud and infrastructure
Courses taught
Operating Systems BSc
Web Application Systems BSc
Software Engineering BSc
Web Technologies for Data-Centric Applications & Services BSc
Operating Systems Reliant and Interacting Code CY
Computer Systems Labs DA DE DS
Document Databases — NoSQL Part 2 DA DE DS
Software Engineering — Part 1 DA DE DS
Software Engineering — Part 2 DA DE DS
Cloud Computing — Microsoft Azure DE
Web Engineering DE
Industry practitioner

David Worms

AdaltasTechnology
Teaching areas
  • data engineering, analytics and platforms
  • software, systems, cloud and infrastructure
  • cyber security, networks and secure code
Courses taught
Securing Data Banks CY
Industry practitioner

Eliott Morcillo

ADALTASTechnology
Teaching areas
  • data engineering, analytics and platforms
  • cyber security, networks and secure code
Courses taught
Securing Data Banks CY
Industry practitioner

Estelle Auberix

IOKELATechnology
Teaching areas
  • software, systems, cloud and infrastructure
  • cyber security, networks and secure code
  • project, business and professional practice
Courses taught
Microsoft Office Specialist: Associate preparation BSc
Microsoft Certified: Windows Server Hybrid Administrator Associate preparation BSc
Preparation for Microsoft Information Protection and Compliance Administrator CY
Security Operations Architecture & Detection Engineering CY
Industry practitioner

Fabien Massol

AmadeusTechnology
Teaching areas
  • cyber security, networks and secure code
  • project, business and professional practice
Courses taught
IT Project Management: Traditional and Agile Approaches BSc DA DE DS Exec CY
Safe Behaviour Awareness CY
Industry practitioner

Dr Jannic Cutura

European Central BankTechnology
Teaching areas
  • data engineering, analytics and platforms
  • software, systems, cloud and infrastructure
Courses taught
Data Pipeline — Part 2 DE Exec
Industry practitioner

Jean-François Derenty

ConsultantScience & technology
Teaching areas
  • data engineering, analytics and platforms
Courses taught
SAS Base DS
Industry practitioner

Joe Sanchez

AdaltasTechnology
Teaching areas
  • AI, machine learning and model operations
  • data engineering, analytics and platforms
  • software, systems, cloud and infrastructure
Courses taught
DevOps by Adaltas BSc DE
Big Data Ecosystem by Adaltas BSc CY DE DS
MLOps by Adaltas DS
Industry practitioner

Luke Radivoev

Nordic Guarantee GlobalScience
Teaching areas
  • data engineering, analytics and platforms
Courses taught
Data Analytics Domain Applications DA Exec
Industry practitioner

Mori Huang

AdaltasTechnology
Teaching areas
  • AI, machine learning and model operations
  • data engineering, analytics and platforms
  • software, systems, cloud and infrastructure
Courses taught
DevOps by Adaltas BSc DE
Big Data Ecosystem by Adaltas BSc CY DE DS
MLOps by Adaltas DS
Industry practitioner

Nelson Lopez

PrestashopTechnology
Teaching areas
  • data engineering, analytics and platforms
Courses taught
Data Warehousing & ETL DA DE
Industry practitioner

Octave Prevot

AdaltasTechnology
Teaching areas
  • data engineering, analytics and platforms
  • cyber security, networks and secure code
Courses taught
Securing Data Banks CY
Industry practitioner

Stéphane Mollard

AmadeusTechnology
Teaching areas
  • cyber security, networks and secure code
  • project, business and professional practice
Courses taught
IT Project Management: Traditional and Agile Approaches BSc DA DE DS Exec CY
Safe Behaviour Awareness CY
Professional contributor

Suzanne Simonet

FreelanceProfessional environment
Teaching areas
  • project, business and professional practice
Courses taught
General and Cost Accounting BSc
DSTI faculty

Sébastien Corniglion

DSTITechnology
Teaching areas
  • AI, machine learning and model operations
  • data engineering, analytics and platforms
  • software, systems, cloud and infrastructure
Courses taught
AI Awareness DA DE DS
Computer Architecture DA DE DS
Analysis & Design of Information Systems DA DE Exec
Warm Up Exec
Industry practitioner

Valery Zuniga Kondrashov

IxpantiaScience & technology
Teaching areas
  • AI, machine learning and model operations
  • data engineering, analytics and platforms
Courses taught
Data Structure and Machine Learning using Python & R DA DE DS
Industry practitioner

Yannick Ramond

PMO DataTechnology
Teaching areas
  • data engineering, analytics and platforms
Courses taught
Excel Basics DA DE DS
Advanced Excel for Data Analytics DA Exec
Industry practitioner

Yong Yao

ConsultantTechnology
Teaching areas
  • data engineering, analytics and platforms
Courses taught
CRM Data Management BSc DA DE Exec
Reporting & Visualisation DA Exec
Industry practitioner

Alexis Marié

AdaltasTechnology
Teaching areas
  • data engineering, analytics and platforms
  • AI, machine learning and model operations
Courses taught
MLOps by Adaltas DS
05 — Profile notes

Understanding the profiles.

This page gives a clear public view of DSTI’s teaching team and the expertise it brings. The team develops with the programmes.

How to read these profiles. Programme names follow DSTI’s current structure, and the topic summaries describe each member’s main contribution areas. A professor named for a course teaches it; where several are named, they co-teach it. Exact teaching assignments for a specific cohort are confirmed at each intake.
06 — Next step

Understand the academic environment around the programmes.

The faculty page is one part of the picture. Programme structure, academic framework, Direction of Studies and internships explain how students move from teaching to professional experience.