Cedric Yu

I am a data scientist based in London, UK. I am a natural problem solver: I enjoy solving interesting problems on different topics. I turn abstract, complex real-world problems into concrete problem statements and solve them. I aspire to help extract useful information from the ocean of data, with which to assist people in making well-informed decisions. I hope to help advance the responsible and equitable use of trustworthy data science and artificial intelligence.

I have rich data science project experience on industrial IoT time series and computer vision datasets, collaborating with large-enterprise clients, developing and deploying edge-compute data science solutions with federated learning, and cloud-compute solutions. I have helped customers predict industrial machine failures, customer churns, HSE compliance and event risks, and evaluate ROI. I am also experienced in implementing and benchmarking Trustworthy AI federated learning features, with short prototype-to-product turnover. I have also designed, implemented, and deployed an RAG-LLM infrastructure.

I had also been a part of the product development team in my company. I helped construct a scalable, federated learning, ML-OPS platform for IoT edge devices. I helped deliver the first version and 2 feature releases under tight time and resource constraints. I acquired experience in the CI/CD Git process, as well as feature integration, codebase maintenance, and continuous testing.

I have regular customer-facing experience, giving business-oriented presentations of data insights with visualisations, and hosting customer onboarding & enablement sessions. I also help prepared product demo videos, and data science write-ups for company blog posts.

My strong analytical skills are also evident from the 4 published papers in top physics journals during my PhD years.