I'm Nour Ghribi, a melody of cultures and experiences - from the vibrant streets of Sfax, Tunisia, to the serene landscapes of Zürich and Lausanne, Switzerland, my journey is a testament to the rich tapestry of my heritage and upbringing. Born in Zürich, Switzerland, and raised in Sfax, Tunisia, my childhood was a beautiful blend of Swiss precision and Tunisian warmth, before returning to Switzerland to pursue my academic and professional ambitions in Lausanne. This unique blend of cultures has not only shaped my worldview but has infused my approach to data science with creativity and resilience. Educated at the prestigious EPFL, I navigated the realms of Communication Systems and Data Science with a minor in Management of Technology and Entrepreneurship. My academic journey was a thrilling adventure, marked by late nights, caffeine-powered study sessions, and a relentless pursuit of knowledge. Alongside, I dove into extracurricular activities, where I honed my leadership and organizational skills, particularly as a co-founder of Applied Machine Learning Days Africa, showcasing my passion for AI and machine learning and as a Communication Manager and Principal Designer at The Consulting Society EPFL. Professionally, I've woven through the complex world of financial data analysis with Compagnie Financière Tradition, where I spearheaded innovative projects as a Data Analyst Intern and later as a Data Scientist Intern. My work in financial analysis, anomaly detection, and business intelligence BI, alongside developing visualization tools and dashboards, has not only enhanced stakeholder decision-making but has also paved the way for innovative perspectives in financial trade data. My passion for entrepreneurship, shaped by my rich cultural background, led me to initiate and oversee an impactful digital AI event across Africa: Applied Machine Learning Days Africa. Our debut, while planned for Tunisia, pivoted online due to the unforeseen COVID-19 pandemic, marking a resilient start. Subsequently, the event found its physical presence in Morocco, and we've had a thrilling third edition in Kenya by 2024. This endeavor not only illustrates my capability to synergize organizational talents with a deep technological insight but also, in tandem with my academic journey, has furnished me with a rare mix of technical prowess and business insight. My work ethic transcends mere task completion; it is fueled by an innate desire to create meaningful impact, weaving together excellence, innovation, and a deep-rooted passion for the arts. This creative spirit, inspired by my love for art, design, and music, drives me to not only meet but surpass the expectations set before me, infusing every project with a unique blend of artistic vision and analytical precision. My commitment is to bring a sense of purpose, dedication, and aesthetic sensibility to every challenge, marrying the analytical with the artistic. This fusion of professional drive and personal passions reflects in my life beyond the workplace, where I pursue balance and growth, akin to the harmony in a meticulously composed piece of music or the balance in a thoughtfully designed piece of art. As I navigate the intricacies of data science, I also seek harmony in life, embracing the serenity and lessons found in artistic expression. My path is one of perpetual learning, fueled by a desire to make a meaningful contribution to society and the planet through the lens of my artistic and technical endeavors.
Transforming financial data into actionable insights through innovative tools and graph theory magic, propelling financial decision-making into the future.
Unlocking the stories hidden in data with advanced analytics, turning unstructured text into strategic decisions.
Pioneering AI in Africa: from concept to reality, leading the charge in organizing a continent-wide machine learning extravaganza.
Shaping the next generation of tech innovators by demystifying object-oriented programming, one Java line at a time.
Designed on Adobe Indesign and available at the Rolex Learning Center at EPFL or by request to TheConsultingSociety.com.
Master Semester Project: Researching Multi-Modal framework for article image retrieval and matching:
- Reviewed and extensively researched State-Of-the-art Machine Learning models to replicate and combine them in a creative setting.
- Concluded the project by implementing a proof-of-concept on fashion data that leverages interactive image retrieval, keyword extraction, and image clustering.
This project was during the course Systems of Data Science which covers the important notions necessary for the deployment of todays state of the art data science solutions. What made me proficient in Scala and Spark without a doubt. I learned how to make use of modern clusters and cloud offerings to scale up to very large workloads and analyze the trade-offs between various approaches to large-scale data management and analytics. This helps to choose the most appropriate existing systems architecture and technology for a task. - Developed a movie recommendation system leveraging user ratings and similarities for clustering from publicly available data and scaled it up to work on large databases using Spark and Scala. - Concluded the project by evaluating different system deployment strategies' price/performance.
Who has ever watched movies or documentaries about the 2008 crisis like "The Big Short" or "Inside Job" and wondered "If only I could predict the stock market crisis and make so much money by betting against the market?"
We did.
- A replication of the paper Predicting the Present with Google Trends followed by a creative extension.
- Leveraged Auto-Regressive models improved by time series data of Google search engine trends Predicting the "Present" with Google Trends to predict index volatility in financial markets.
My team and I used the techniques proposed in the paper to devise a model for market volatility predictions and tested it on the Dow Jones Index to assess it.
Where I found my passion for NLP before the rise of LLM. A practical use of Natural Language Processing is sentiment analysis. This project aims to devise a sentiment classifier for tweets in English. During the project, I implemented different machine learning classifiers (Linear models and Deep Neural Networks) to detect positive and negative sentiments in tweets. GloVe embeddings and famous state-of-the-art NLP models such as ELMo and BERT helped make this project an astounding success. Learned to benchmark different machine learning models to detect positive/negative sentiments in tweets, by using basic linear models and sophisticated deep learning ones. Implemented different machine learning classifiers to detect positive/negative sentiments in tweets. Natural Language Processing: Glove embeddings and famous state-of-the-art NLP model BERT.
Deep Learning: Under the hood of Neural Networks with PyTorch.
Discovered how deep learning framework PyTorch works by re-implementing some modules from scratch and learned the logic used and the power of underlying mechanisms that helped me develop an ease of working in PyTorch and deep learning.
Implemented the modules of pytorch for sequential neural networks from scratch (Linear layers, Activation Functions, Initializers, forward propagation, backpropagation and optimizers).
Benchmarking several neural net architectures combining Siamese Networks with weight sharing and auxiliary losses to predict a comparison between two hand-written digits from the MNIST dataset using PyTorch.
Improved SBB-CFF journey planner by considering historical success rates using statistical modeling.
Dealt with Big Data: all the SBB-CFF lines timeltables, using Spark and Hadoop.
Modelled the data in a graph using NetworkX.
Modelled the delay of each line probabilistically.
Created a routing model that prioritizes success of connection instead of shorter travels.
What if production companies and publishers had a magic recipe for Blockbuster Movies ? Choosing the right cast members and executive team such that they are guarenteed a prominent success? Through thourough analysis of data (Exploratory Data Analysis) and fitting an appropriate ML model, we tried to predict movies success (ratings and revenues) and find the magic recipe for the greatest grossing film.