Pages tagged: Algorithms
Professor Nick Reed:
Nick joined the Human Factors and Simulation group at TRL in January 2004 following post-doctoral work in visual perception at the University of Oxford and in 2014 became director TRL’s Academy co-ordinating scientific activities across the business. He has led a wide variety of research studies using the full mission, high fidelity car and truck simulators with a number of published articles, conference papers, and appearances in national and international media. Nick also championed work in the area of vehicle automation at TRL, culminating in technical leadership of the GATEway (Greenwich Automated Transport Environment) project – a flagship UK Government project to investigate the implications of the introduction of automated vehicles in the urban environment. In 2015, he was awarded a visiting professorship in the Engineering and Physical Sciences faculty at the University of Surrey.
At Decoded, Greg demonstrates the value of cutting-edge machine learning technologies to business leaders. Lifting the veil on data science, machine learning and artificial intelligence, he seeks to help others question their data and identify opportunities for intelligent solutions.
After working in AdTech, Greg returned to academia to investigate the state of machine learning and artificial intelligence. He completed his Master's Degree in Machine Learning at the University of London, which he holds in complement to his Bachelor's Degree in Economics from Harvard University. His work focused on developing artificially intelligent programs capable of learning and solving a range of different problems and games.
Mitchel Ondili is a Lawyer and Tech Policy professional, and recognized as Women Deliver Young Leader, a New Emerging openAIR researcher and a member of the Feminist AI Research Network. Her primary areas of interest are the impact of internet communications on democratic practices, Open Data, Artificial Intelligence, and the public digital sphere.
At Eticas, Mitchel leads the Observatory of Algorithms with Social Impact (OASI) and provides support to different projects looking at AI bias and discrimination, with a focus on gender.