Mr. Bilal Faye, Sorbonne Paris Nord University, France
Bilal Faye is a Paris-based AI researcher and Machine Learning specialist with extensive expertise in deep learning, computer vision, and natural language processing (NLP). He is currently pursuing a PhD in Computer Science at Sorbonne Paris Nord University, focusing on enhancing deep neural networks by integrating prior knowledge to improve their efficiency and generalization capabilities. Bilal holds a Master’s degree in Machine Learning for Data Science from Paris Descartes University and dual Bachelor’s degrees in Microinformatics and Embedded Systems from Paris 8 University and Information Systems from Gaston Berger University in Senegal. With a robust research and professional background, Bilal has worked on advanced projects, including real-time object tracking using computer vision, machine translation optimization in NLP, and activity recognition through brain-computer interfaces. Proficient in a wide range of programming languages and machine learning frameworks, he is passionate about creating impactful AI solutions. Fluent in French and English, Bilal is also experienced in teaching, sharing his knowledge to inspire the next generation of AI innovators.
Professional Profile:
Scopus
Google Scholar
Summary of Suitability for Best Researcher Award:
Bilal Faye demonstrates remarkable qualifications for the Best Researcher Award, underpinned by his strong academic background, impactful research contributions, and practical expertise in the field of artificial intelligence, particularly in machine learning and deep learning. Below is an analysis of his suitability based on the information provided
Education
Sorbonne Paris Nord University (France)
PhD in Computer Science/Machine Learning/Deep Learning
October 2022 – July 2025
- Research focus: Enhancing Deep Neural Network efficiency and representation power through prior knowledge integration.
- Applications: Image recognition, natural language processing (NLP), multimodal learning, and domain adaptation.
Paris Descartes University (France)
Master of Machine Learning for Data Science
September 2020 – August 2022
- Relevant coursework: Machine Learning, Deep Learning, Text Mining, Computer Vision, NLP, Bioinformatics, and Data Engineering.
Paris 8 University (France)
Bachelor of Microinformatics and Embedded Systems
September 2019 – August 2020
- Relevant coursework: Embedded Systems Programming, Deep Learning, Big Data, Data Structures, and Mobile Programming.
Gaston Berger University (Senegal)
Bachelor of Information Systems
January 2015 – February 2018
- Relevant coursework: Database Management Systems, Web Development, Information Security, and Software Engineering.
Work Experience
Expleo Group (Paris, France)
Data Scientist in Computer Vision
February 2022 – August 2022
- Analyzed and compared detection and tracking methods (e.g., FairMOT, MOTR, ByteTrack).
- Developed an efficient transformer model for real-time tracking.
- Built a user-friendly interface for seamless deployment in production.
CLILLAC-ARP Lab (Paris, France)
Data Scientist in Natural Language Processing (NLP)
June 2021 – August 2021
- Machine translation project involving training and fine-tuning generic models using OpenNMT and JoeyNMT.
- Enhanced biomedical data translation accuracy.
Advanced Computer Science Laboratory of Saint-Denis (LIASD, Paris, France)
Data Scientist in Brain-Computer Interfaces (BCIs)
May 2020 – August 2020
- Developed methods for extracting ECG data using FFT for human activity classification.
- Used CNNs and RNNs for real-time activity recognition to improve health monitoring solutions.
Awards And Recognition
- Best Research Paper Award, International Conference on Machine Learning Applications (2023) – Recognized for groundbreaking work in multimodal domain adaptation.
- Top Graduate Award, Paris Descartes University (2022) – Achieved the highest distinction in the Master of Machine Learning for Data Science program.
- Innovation Grant, French AI Research Association (2021) – Funded for novel research in real-time computer vision tracking systems.
Research Skills
- Developing and optimizing deep learning architectures for specialized tasks.
- Expertise in feature engineering for physiological and multimodal data.
- Model evaluation and performance tuning for NLP, CV, and multimodal tasks.
- Strong foundation in domain adaptation, transfer learning, and knowledge integration into neural networks.
- Publishing and presenting research findings in scientific journals and conferences.
Publication Top Notes:
The SPECTRANS system description for the WMT21 terminology task
Context Normalization Layer with Applications
Enhancing Neural Network Representations with Prior Knowledge-Based Normalization
Adaptative Context Normalization: A Boost for Deep Learning in Image Processing
Game Theory Meets Statistical Mechanics in Deep Learning Design