Lecturer and Head of International Programs
École centrale d'électronique (ECE), Data Management, Artificial Intelligence and Generative AI programs
01
Profile
AI Trainer · Consultant · Researcher
Taieb Touati
Paris 1 Panthéon Sorbonne · Independent Data & AI Practice
Trained at the crossroads of applied mathematics, engineering and innovation management, with a PhD from Sorbonne, dual Master's and Statistician Diploma from ISUP, I operate where rigorous science meets real-world execution.
Today my work splits across three complementary activities. As associate researcher at Paris 1 Panthéon Sorbonne, I contribute to modern martingale theory and statistical learning. As an independent Data & AI consultant, I design generative-AI and predictive-modeling solutions for real-estate analytics, industrial IoT and energy intelligence. Finally, as a corporate AI trainer, I help BtB organizations turn generative AI, agentic AI and workflow automation into measurable business outcomes. Where I once taught calculus and statistics to undergraduates, I now train both BtB audiences (executive teams, managers and product leaders integrating AI into their workflows and decision processes) and BtC audiences (professionals navigating career reconversion, individuals building AI literacy and acculturation to today's tools). These three streams continuously reinforce each other: research keeps my methods rigorous and current, consulting grounds them in real industrial constraints and ROI logic, and training transforms both into transmissible expertise that compounds across organizations and individuals. It is a continuous feedback loop where client engagements sharpen my pedagogy, cohorts surface new research questions, and papers feed the next generation of business solutions.
n8n
Workflow
Gemini
LLM
Agentic AI
Autonomous
RAG
Retrieval
Academic Positions
-
20242025
-
2022
Visiting Assistant Professor
Mediterranean Institute of Technology (MedTech), 96 hours of teaching: Pre-engineering
-
20202021
Temporarily lecturer and researcher
Paris 1 Panthéon Sorbonne, 96 hours of teaching: L1 and L2 Level
-
20192020
Temporarily lecturer and researcher
Paris 1 Panthéon Sorbonne, 96 hours of teaching: L2 Level
-
20162019
Teaching Assistant
Sorbonne University, 192 hours of teaching on various levels from L1 to Master 1.
-
2016
Data Scientist
IFSTTAR, Data mining for profile extraction usage and forecasting in the field of energy.
-
2015
Research engineer
EDF R&D, Functional Data Analysis in an industrial context, analysis of a welding process.
Education & Training
-
Ph.D. 10/2016 – 12/2020
PhD of Applied Mathematics
Sorbonne University
-
Master 09/2015 – 06/2016
Master 2 of Innovation Management
Sorbonne University
-
Master 09/2015 – 06/2016
Master 2 of Mathematics
Sorbonne University
-
Statistician Diploma 09/2014 – 09/2016
Statistician Diploma
ISUP: French Grande Ecole
-
Master 1 09/2013 – 06/2014
Master 1 of Mathematics
Sorbonne University
-
Bachelor 09/2010 – 06/2013
Bachelor of Applied Mathematics
Paris Dauphine University
Awards
-
2018Winner of Data Challenge 2018 organized by EDF and SFDS.
-
2016Valedictorian at Sorbonne University among 55 students, the only student to publish two articles before starting the PhD.
-
2014Youngest contributor in the R Package Sensitivity in 2014.
-
2015Youngest Poster Presenter during the Annual conference of the GDR MASCOT NUM 2015.
02
02 — Research
Research Summary
Touati's current research revolves around probability theory, statistics, machine learning and renewable energy engineering.
Interests
- Applied Mathematics
- Concentration inequalities
- Martingales
- Elephant and Minimal Random Walks
- Online learning
- Engineering
- Renewable Energy
- Energy Analytics
- Sensitivity Analysis
03
03 — Scientific Production
Publications
Filter by type
Sort by year
New insights on concentration inequalities for self-normalized martingales
PublicationDOI: 10.1214/19-ECP269
Abstract
We propose new concentration inequalities for self-normalized martingales. The main idea is to introduce a suitable weighted sum of the predictable quadratic variation and the total quadratic variation of the martingale. It offers much more flexibility and allows us to improve previous concentration inequalities. Statistical applications on autoregressive process, internal diffusion-limited aggregation process, and online statistical learning are also provided.
Hourly Solar Irradiance Forecasting Based on Machine Learning Models
PublicationDOI: 10.1109/ICMLA.2016.0078
Abstract
In recent years, many research studies are conducted into the use of smart meters data for developping decision-making tools including both analytical, forecasting and display purposes. Forecasting energy generation or forecasting energy consumption demand are indeed central problems for urban stakeholders (electricity companies and urban planners). These issues are helpful to allow them ensuring an efficient planning and optimization of energy resources. This paper investigates the problem for forecasting the hourly solar irradiance within a Machine Learning (ML) framework using Similarity method (SIM), Support Vector Machine (SVM) and Neural Network (NN). These approaches rely on a methodology which takes into account the previous hours of the predicting day and also the days having the same number of sunshine hours in the history. The study is conducted on a real data set collected on the Paris suburb of Alfortville. A comparison with two time series approaches namely Naive method and Autoregressive Moving Average Model (ARMA) is performed. This study is the first step towards the development of the hourly solar irradiance forecasting hybrid models.
Abstract
In this communication, the extension of the Martinez method to non Gaussian distribution is studied. Indeed, non Gaussianity can distort the Fisher's confidence interval, and the outcome can be quite misleading. The two following points will be discussed, the R implementation of the method is available in the Package Sensitivity).
04
04 — Pedagogy
Teaching
List of courses
-
2M26177 Hours
Integer series, integral depending on a parameter, applications to differential equations
Sorbonne Université. Bachelor of Mathematics, second Year.
-
3M10021 Hours
Initiation to Python
Sorbonne Université. Bachelor of Mathematics, Third Year.
-
1M00138 Hours
Algebra and Calculus for Science
Sorbonne Université. Bachelor of Science, First Year.
-
4M01524 Hours
Statistics
Sorbonne Université. Master of Mathematics, First Year.
-
1M...21 Hours
Mathematical Statistics
Sorbonne Université. Master of Actuary, First Year.
-
3M10110 Hours
Applied Mathematics Project
Sorbonne Université. Bachelor of Mathematics, Third Year.
-
2M...96 Hours
Numerical Methods
Sorbonne Université. Bachelor of Applied Mathematics, Second Year.
-
2M...96 Hours
Calculus 1 and Real Analysis
Paris 1 Sorbonne. Bachelor of Applied Mathematics, First and Second Year.
-
Math24342 Hours
Discrete Mathematics
MedTech. Freshman and Sophomore Level.
-
Math244 Hours
Probability and statistics
MedTech. Freshman and Sophomore Level.
05
05 — Consulting
Consulting Experience
Data & AI consulting missions across real estate, energy and IoT sectors — from technological watch and architecture design to predictive modeling and platform deployment.
Partnered with ECOBAU to develop an innovative decision-making platform tailored for the real estate sector. The solution integrates generative AI and predictive modeling to enable continuous, accurate assessments of transaction volumes and rental prices. Sophisticated algorithms identify high-potential areas, providing targeted recommendations for real estate developers and investors focused on optimizing their asset management strategies.
As ECOBAU transitions into a digital-first model, it positions itself as a disruptive alternative to traditional real estate agencies. The launch of a transformative startup, supervised by the parent company, is set to occur in the coming months.
Started with a technological watch following the company's desire to access the energy market and develop an intelligent solution allowing the optimization of electricity consumption and the reduction of the carbon footprint. OzoneConnect offers advanced connectivity, industrial computing, and network infrastructure IoT solutions to help industrial IoT project owners succeed, providing data acquisition solutions that revolutionize remote data collection.
The mission applied the CRISP-DM protocol and the AGILE method for the development of the solution, in collaboration with the MILLENIA group (specialized in information management). The project was divided into two complementary stages: (1) the transmission of data from smart meters to a database, and (2) the modeling and forecasting of energy consumption, identification of the optimal model, then deployment and implementation of an architecture dedicated to an energy efficiency platform.
A capstone project was attached to this mission, including the supervision of Sarra Toumi, an engineer in Energy Engineering.
End-to-end energy data architecture
06
06 — Credentials
Certificates
I have obtained following certificates.
Certificates
- Creation of written and visual content through the responsible use of generative artificial intelligence — RS6776 INKREA FORMATIONS Certificate
- RAG and Agentic AI Professional: IBM Certificate (In preparation)
07
07 — Writing
Blog
Notes and articles on AI, statistics, and applied machine learning.
No articles published yet — check back soon.
08
07 — Reach out
Contact
The best way to reach me is by email.
- +33 7 82 09 17 74
- Taieb.Touati@univ-paris1.fr
- Google Scholar
- Linkedin Profile
- Researchgate
At My Office
C20-10, at the SAMM Department
Université Paris I, Panthéon Sorbonne
Centre PMF
90 Rue de Tolbiac
75013, Paris, France.