David Attard

Developed an innovative Influencer Identification product by leveraging advanced Natural Language Processing (NLP) techniques, enhancing marketing analytics capabilities and identifying over 200 key influencers.

Implemented Named Entity Recognition (NER) systems for automated content analysis, improving data extraction accuracy and efficiency for over 10,000 social media posts daily.

Designed and optimized Large Language Model (LLM) prompting strategies, achieving 90% precise text classification for large-scale social media datasets.

Processed and analyzed extensive social media datasets to identify key influencers and trends, providing actionable insights for three strategic marketing campaigns.

Collaborated effectively with cross-functional teams to deliver data-driven insights, supporting key business decisions and product development within a fast-paced startup environment.

Led engaging workshops for undergraduate students in Foundational Mechanics, Foundational Mathematics, and Mathematical Methods 3 modules, successfully supporting over 50 students per semester.

Conducted comprehensive marking for student assignments and exams, providing constructive feedback that contributed to improved student understanding and academic progression.

Facilitated interactive learning and problem-solving sessions, enhancing comprehension of complex mathematical and physics concepts for diverse student groups.

Led development of GPU systems for cosmological simulations, notably contributing to the `pyc2ray` open-source project, enhancing computational efficiency for large-scale astrophysical models.

Programmed complex scientific models using Python, Bash, and Fortran90, optimizing code for parallel computing environments to process terabytes of data.

Analyzed large-scale astrophysical datasets using Python libraries (NumPy, Pandas) to extract critical insights into cosmic phenomena, contributing to ongoing PhD research.

Participated in specialized workshops on Machine Learning and Statistical Analysis, applying advanced techniques to research data for robust model validation.

Developed and optimized inference solutions for FastCaloGAN utilizing ONNX and SOPHIE, achieving enhanced performance for high-energy physics applications within a two-month intensive program.

Implemented robust solutions using Python and C++ programming languages for complex data processing tasks, improving system throughput by an estimated 15%.

Conducted rigorous testing of inference libraries, including ONNX and SOFIE, ensuring accuracy and efficiency of computational models for critical physics experiments.

Performed advanced data analysis techniques on N-Body simulations, leveraging statistical tools like KS-tests and Gaussian Mixture Modelling to derive significant astrophysical insights from multi-gigabyte datasets.

Developed High-Performance Computing (HPC) code, contributing to efficient processing of large astronomical datasets and improving simulation runtime by 10%.

Utilized a range of statistical tools, including KS-tests and Gaussian Mixture Modelling, to validate and interpret simulation results, ensuring data integrity and research accuracy.

Organized and led weekly scout group meetings for children aged 7-11, fostering teamwork, leadership, and outdoor skills development.

Provided instrumental bagpipe lessons, enabling children to join the scout pipe band and participate in community events.

Contributed to the positive development of young individuals through engaging activities and mentorship.

Performed as a bagpipe player in numerous special occasions and events, showcasing musical talent and cultural heritage.

Participated in prestigious international events, including the Basel Military Tattoo in Switzerland, representing Wallace Pipes and Drums Malta.

Contributed to the band's reputation through consistent performance and dedication to musical excellence.