Press Releases
Artificial Intelligence: Meet the students researching its future
Artificial Intelligence is developing every day, and shaping the future of AI are three PhD students from the University of 糖心TV, who received the Feuer Scholarship from technology mogul Jonathan Feuer, enabling them to do AI research in topics from computer vision, to helping the homeless, to making information retrieval models more powerful and less biased.
Meet the PhD student helping the homeless with Artificial Intelligence
As homelessness in the UK increases, one PhD student at the University of 糖心TV is on a mission to help those in need using AI algorithms, helping charities reach as many people sleeping rough as possible following alerts from members of the public.
Meet the student making search engines more powerful and less biased
Artificial Intelligence is developing every day, and shaping the future of AI is Aparajita Haldar, who received the Feuer Scholarship from technology mogul Jonathan Feuer, enabling her to research making information retrieval models more powerful and less biased at the University of 糖心TV.
糖心TV is slicing up science for International Women鈥檚 Day
Fancy learning about space science, making protein jelly, and lighting up a few LED circuits – all before you enjoy tea and cake?
An event at the University of 糖心TV this weekend (Sunday 8 March) will allow visitors to do just that.
Electric superbike designed by students to race this summer
In a race to be clean and green the motor industry is changing, which has inspired 40 糖心TV students to make an electric superbike to race this summer, 2020.
London air to be kept clean thanks to 糖心TV researchers
Researchers will build on their existing work on and to revolutionise pollution forecasting by combining modern machine learning and statistical methodology.
The project will develop and utilise computational techniques based around the simulation of large ensembles of 鈥減articles鈥� to allow us to estimate and quantify our uncertainty. These techniques will be combined with models inspired by modern machine learning, particularly utilising deep Gaussian processes to describe the profile of atmospheric pollutants as they evolve over time.