I'm a passionate Computer Science student diving deep into the world of Data Science and AI by "Tinkering" with projects! I love exploring fresh ideas and constantly learning—currently leveling up through course.fast.ai. Strong skills in AI, deep learning, and data analytics with 2+ years of experience. Built a deepfake detector (93%+ accuracy) using PyTorch and ResNet. Completed an Internship at Webacy and applied ML to smart contract risk profiling. Passionate about AI ethics and innovation. Currently, I am revolving around AI ethics and how to mitigate it, as mentioned above while working on detecting deepfakes. I’m excited to dive deeper into AI safety, learn more about cutting-edge models, collaborate on impactful AI projects, and build solutions that are not just powerful, but responsible and trustworthy for the future. Let’s build the future with AI!

Let's turn Data into Impactful Stories 🚀 I’m currently a Data Science Intern at Capital Fund Management (CFM), where I’m architecting a multi-agent system to automatically resolve alerts in CFM’s equities data-referential pipeline—a critical component of the firm’s intraday trading workflow. This role builds directly on my Erasmus Mundus Master’s in Big Data Management and Analytics (BDMA) at CentraleSupélec, during which I also studied at Université libre de Bruxelles and Universitat Politècnica de Catalunya. Previously, I developed and integrated spatiotemporal libraries for MobilityDB a PostgreSQL extension, and contributed to the open-source JMEOS project. I also researched gait biometric detection via translating foot pressure maps to human-pose estimation at the University of New Brunswick under a Mitacs Globalink Fellowship, where I honed my ability to translate complex sensor data into reliable, production-ready solutions. I am a Result-driven data scientist with big data, machine learning, deep learning, and GEN-AI expertise. Proficient in Python, PyTorch, and LLMs with a strong background in developing data-driven solutions across various domains, including healthcare, e-commerce, social media, and IoT. Proven ability to design and implement data analysis, NLP, KG, DL, and CV models. Demonstrated success in hackathons winning multiple events achieved prestigious scholarships, published papers, and contributed to open-source, showcasing strong problem-solving, collaborative, and analytical skills with a commitment to innovate and impact using data science.