Dr. Louis-Félix Nothias is a CNRS junior group leader based at the University Côte d’Azur & the Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d’Azur, France. In 2023, he founded the Holobiomics Lab, which develops experimental and computational methods for studying host/microbial metabolism in holobionts and their microbiomes. The lab is pioneering advanced mass spectrometry-based metabolomics techniques in multi-omics studies using artificial intelligence. Prior to this, Dr. Nothias was a postdoctoral researcher at the University of California San Diego with Prof. Pieter Dorrestein (2016-2021), where he contributed to the GNPS ecosystem GNPS ecosystem, and a research associate at the University of Geneva with Prof. Jean-Luc Wolfender (2021-2023). He has contributed to both experimental and computational methods for MS-based metabolomics annotation (collaboration with the SIRIUS team/Boecker lab, see CASMI 2022) and has pioneered integrative multi-omics studies for studying microbiota metabolites in ecosystems, including through the collaborative Earth Microbiome Project.

Dr. Tao Jiang is a passionated computational chemist and CNRS engineer with extensive expertise in applying machine learning to chemical research. He earned his Ph.D. in Computational Chemistry, followed by several postdoctoral positions, before joining the Centre National de la Recherche Scientifique (CNRS) as an Ingénieur d'Études (IE) in 2017. His initial role was at the Chemistry Laboratory of École Normale Supérieure de Lyon, where he has been actively involved in projects leveraging machine learning for chemical applications. His work took an international turn during his assignment at the CNRS-Syensqo Joint Lab, the Eco-Efficient Products and Processes Laboratory (E2P2L) in Shanghai. There, he focused on the machine learning-driven development of biodegradation processes for specialty polymers. In September 2024, Dr. Jiang joined the Holobiomic Lab at the University Côte d'Azur, where he supports the lab’s research initiatives. He is currently engaged in multiple projects, including ScienceXGuide and the application of machine learning to mass spectrometry data. His work continues to advance the integration of computational tools and artificial intelligence in chemical and biochemical research.

Madina Bekbergenova has Master in Applied Mathematics: Stochastic Modelling in Neuroscience, Erasmus Mundus InterMaths program (University of L'Aquila, University of Hamburg, University of Côte d'Azur). She is currently undertaking a joint international PhD program in Chemistry and Computer Science at the University of Côte d'Azur and University of Antwerp, under the supervsion of Dr. Nothias and Dr. Wout Bittremieux, and supported by Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d'Azur. Her research focuses on artificial intelligence for mass spectrometry, including the development of agentic framework for mass spectrometry interpretation and the use reinforcement learning techniques to optimize data acquisition processes in mass spectrometry.

Yousouf Tagzhouti has a PhD degree in Computer Science (École des Mines, Saint-Etienne, FR) and has strong expertise in semantic web technologies. He has joined the HolobiomicsLab and the Wimmics team at the INRIA Sophia-Antipolis. since November 2024, with funding from the UniCA JEDI Initiative d'Excellence (IdEx) UniCA JEDI Initiative d'Excellence (IdEx) of the Université Côte d'Azur. He contributes to the development of an AI-framework for knowledge graph for omics research.

Matthieu Ferraud is an AI research engineer at CNRS, specialized in Artificial Intelligence and graduated from Polytech Sophia-Antipolis, France, with academic and professional background in artificial neural networks and semantic knowledge integration and development in the fields of biology and chemistry. He has joined the HolobiomicsLab since October 2024, supported by funding from the Université Côte d'Azur JEDI Initiative d'Excellence (IdEx). His work focuses on the development of methods at the intersection between metabolomics, knowledge graphs and artificial intelligence, contributing to the integration and analysis of metabolomics data for biological research. He is actively involved in the MetaboLinkAI project, developing multi-omics applications for marine holobiont research in collaboration with Eric Rottinger (IRCAN).

Martin Legrand is a Machine Learning Engineer and AI researcher with expertise in multi-agent systems, Natural Language Processing, and audio recognition. He holds a Master of Computer Science from Epitech with specialization in AI, complemented by international experience at Feng Chia University in Taiwan. Currently an AI research engineer at HolobiomicsLab CNRS, his research focuses on autonomous AI systems and multi-agent frameworks for scientific discovery. He is dedicated to advancing next-generation tools that bridge artificial intelligence and metabolomics research, with notable open-source contributions including agenticSeek. Martin's work aims to push the boundaries of how AI can accelerate and automate complex scientific processes. He is actively contributing to the MetaboLinkAI project, developing innovative AI-driven solutions for metabolomics research.

Since October 2025, Thaiz has been a Postdoctoral Fellow in HolobiomicsLab at the Institut de Chimie de Nice, under an IdEx Excellence fellowship of the Université Côte d'Azur. Within the RAPID-NP framework, she develops innovative purification strategies to accelerate the discovery of antimicrobial peptides encoded by marine biosynthetic gene clusters — in collaboration with Vincent Libis (INSERM / ELiS Lab, Paris, https://www.elis-labs.org/libis/) — tackling urgent challenges in antimicrobial resistance with sustainable use of marine biodiversity.

Thaiz Rodrigues Teixeira completed her PhD in Pharmaceutical Sciences (2016–2022) at the University of São Paulo (FCFRP-USP), with a sandwich period at the Scripps Institution of Oceanography / University of California San Diego under the joint supervision of Dr. Hosana Debonsi and Prof. William H. Gerwick. She then conducted postdoctoral research at UC San Diego under Prof. Conor Caffrey, focusing on metabolomics and high-throughput screening of natural and synthetic compounds against neglected tropical disease parasites.

Thaiz brings her expertise in mass spectrometry, NMR spectroscopy, molecular biology, and microbial cultivation. Her publication record includes advances in natural product discovery, antiparasitic drug leads, and computational metabolomics. She is passionate about bridging synthetic biology, analytical chemistry, and sustainable drug discovery.

Benjamin Navet joined the HolobiomicsLab in July 2025 after being recruited for the Associated Researcher (CNRS) position within the MetaboLinkAI project. He is a research engineer and doctoral researcher working on knowledge graph-based memory for LLM-based scientific assistants. His PhD, supervised by Fabien Gandon and Louis-Félix Nothias, explores a neuro-symbolic architecture integrating large language models and knowledge graphs as external memory, validated within the MetaboLinkAI platform using metabolomics data.

Before joining the lab, Benjamin worked as a data scientist engineer in the 3IA Techpool on automation and machine learning projects, including NLP applications, action detection in videos, and software leveraging LLMs to query biological graph databases. He holds a Master's degree in Computer Science with a specialization in software engineering from Université de Rennes 1.

Aleksandra Izdebska is a postdoctoral researcher at HolobiomicsLab since October 2025, contributing to the ANR POPS "DeciPhering the rOle of PolyamineS in Bacterial Virulence" project (ANR-24-CE44-1190). She obtained her PhD in Analytical Chemistry from Université Pau et des Pays de l'Adour (IPREM), where she developed an online 2D reverse-phase HPLC method hyphenated with ICP-MS and high-resolution ESI-MS to detect elusive arsenic species — including As(III)–phytochelatins, arseno-thiols, and arsenolipids — in plant matrices.

The POPS project investigates how polyamines modulate bacterial virulence in Pseudomonas aeruginosa and Staphylococcus aureus 🦠. By combining controlled modifications of polyamine biosynthetic pathways with integrated omics approaches, the project aims to uncover the molecular mechanisms driving pathogenicity. Within WP3 of POPS, Aleksandra designs and implements semi-automated extraction and pre-enrichment workflows, optimizes multi-mode LC separations, and leads deep iterative LC–MS/MS (MSⁿ) acquisitions on an Orbitrap IQ-X. Her work focuses on comprehensive metabolite profiling and confident annotation in bacterial pathogens, particularly in relation to polyamine response, using advanced experimental and computational metabolomics from multi-modal analysis collected with state-of-art equipment.

Dina Boukhajou is a final-year Bioinformatics student at Polytech Nice Sophia and joined the HolobiomicsLab from February 2026 to August 2026 as a master's intern through the dual master's program of IdEx Academy 1 at Université Côte d'Azur. In close collaboration with Matthieu Ferraud and Eric Rottinger (IRCAN, UniCA), she works on AI methods for integrating transcriptomics and metabolomics with knowledge graphs in a marine holobiont model based on sea anemones.

Before joining the lab, Dina completed a four-month internship at the Karolinska Institute, where she developed an internal R package to facilitate translational efficiency analyses, and a two-month internship at IRCAN in Nice. She brings strong training in bioinformatics and programming, with experience in Python, R, Java, SQL, HTML, CSS, PHP, and Matlab.

Deniz Ertuğrul is a Master's student in Bioinformatics at the University of Bologna and joined the HolobiomicsLab from February 2026 to August 2026 as an assistant engineer master 2 intern. He works on next-generation epigenetics data analysis pipelines assisted by artificial intelligence for clinical data.

Before joining the lab, Deniz completed research internships in computational biology at Bogazici University and Sabanci University, where he developed experience in virtual screening, molecular dynamics, structural bioinformatics, and machine learning for mutation pathogenicity prediction. He combines strong bioinformatics and programming skills, including Python, PyTorch, scikit-learn, Biopython, SQL, Linux, and Bash.