Issa Bamia is a mathematician and data scientist with a deep passion for advancing research in adversarial machine learning and AI security. His expertise spans data engineering, digital health solutions, and cloud-based pipeline architecture, with a focus on addressing real-world issues in healthcare and telecommunications. With significant hands-on experience, Issa has optimized data collection processes, improved decision-making tools, and contributed to impactful projects that prioritize AI safety. His work as a data engineer for Muso Health demonstrates his commitment to using data-driven insights for tangible improvements in public health. Furthermore, he has a strong foundation in advanced data science and machine learning techniques, including proficiency with large language models (LLMs), security frameworks, and virtualization. This experience, combined with his commitment to ongoing research and development, positions Issa as a promising figure in the fields of AI safety and adversarial machine learning.

Professional Profile

• Google Scholar Profile

Education

Issa Bamia holds a Master’s in Mathematical Sciences with a specialization in Data Science from the African Institute for Mathematical Sciences (AIMS), an institution renowned for its focus on African mathematicians and scientists. His education at AIMS included a rigorous curriculum that equipped him with the analytical and technical skills needed for advanced data science research and practical applications. He gained specialized knowledge in AI and adversarial machine learning, which he applied in his professional projects to develop data-driven solutions that impact digital health. Before this, he completed a Bachelor’s degree in Electronic Information Engineering from Tianjin University, where he gained foundational knowledge in data management and engineering principles. Issa’s educational background is complemented by certifications, including a professional certification in Large Language Models (LLMs) from Databricks, which has further refined his ability to work with complex AI models and large datasets. His diverse academic and practical training has laid a strong foundation for his research and professional pursuits in data science and AI security.

Professional Experience

Issa Bamia has a diverse professional background spanning data engineering, software development, and account management. Currently, he works as a data engineer for Muso Health, where he streamlines data collection, optimizes cloud-based data pipelines, and develops dashboards for real-time healthcare data analysis. His work here has been instrumental in improving medication stock management and reducing stockouts, enhancing healthcare delivery for underserved populations. Prior to this, Issa worked as an account manager with Huawei Technologies, where he customized technological solutions to meet telecom operators’ needs, ensuring smooth service delivery and strong client relations. Earlier, he was a software engineer with Whale Cloud Technologies, where he worked on the deployment and maintenance of cloud-based software products and managed system and database maintenance. Throughout these roles, Issa demonstrated an ability to handle complex data infrastructures and security protocols, showcasing his expertise in data science and its applications in both healthcare and telecommunications.

Research Interest

Issa Bamia’s primary research interests lie in adversarial machine learning, AI safety, and the development of secure, resilient AI models. His focus is on understanding and mitigating vulnerabilities in AI systems, particularly those posed by adversarial attacks, which can manipulate machine learning models to produce inaccurate or biased outcomes. He is passionate about exploring solutions that bolster the security and reliability of AI, especially in applications related to digital health, where data integrity is critical for decision-making. Issa is also interested in the ethical and practical implications of AI security, as well as the ongoing evolution of AI governance and control frameworks. Additionally, he seeks to apply his expertise in large language models (LLMs) to further enhance AI’s adaptability and reliability. His dedication to AI safety underscores a commitment to building AI systems that prioritize both performance and ethical responsibility, which is particularly significant in fields like healthcare, where secure and trustworthy AI systems are essential.

Research Skills

Issa possesses a robust set of research skills that are integral to his work in adversarial machine learning and AI security. He is proficient in cloud-based technologies and data pipeline design, with extensive experience in platforms such as Google Cloud Platform (GCP) and Apache Airflow. His technical repertoire includes advanced machine learning frameworks and tools for large language models (LLMs), containerization through Docker, and security protocols that support secure data architectures. In addition to data engineering skills, he has a strong command of SQL, NoSQL, Linux, and various programming languages including Python and JavaScript. Issa is adept at working with virtualization, networking, and incident response, which are crucial in managing and securing complex data systems. His hands-on experience with tools like Looker, Spark, and Hadoop further enhances his capability to analyze, optimize, and visualize large datasets, supporting his research pursuits in AI and data security. His skills in agile project tracking and stakeholder engagement also enable him to lead projects effectively and ensure that his research aligns with organizational goals.

Awards and Honors

Throughout his career, Issa has earned recognition for his contributions to data science and digital health innovation. His academic achievements include a Master’s degree in Mathematical Sciences (Data Science) from the African Institute for Mathematical Sciences (AIMS), an honor that highlights his academic commitment to data science research. While at AIMS, Issa developed a data-driven solution for medication stock management at Muso Health, a project that successfully reduced stockouts and improved patient care outcomes, marking a significant professional achievement in public health. His commitment to professional growth is also evident in his completion of the Databricks Professional Certificate in Large Language Models (LLMs), which reflects his proficiency in implementing, fine-tuning, and managing LLMs in various AI applications. This certification is a testament to his dedication to staying updated with advancements in AI, particularly in AI security, which is a key area of his research focus. These achievements underscore Issa’s commitment to both academic excellence and impactful, socially relevant research.