Women in AI Dinner schedule

Quantifying the Connection Between Scenic Beauty and Our Wellbeing

Intuitively, we often seek out beautiful scenery when we want a respite from our busy lives, but do such settings actually help to boost our wellbeing? While architects and policymakers have puzzled over this question for centuries, quantitative analyses have been held back by a lack of data. Now, vast volumes of online data alongside developments in deep learning are opening up new opportunities to analyse the beauty of our environment. In this talk, I will explain how I used over 1.5 million ratings of over 200,000 images covering Great Britain from a website called Scenic-Or-Not to find answers to this age-old question.

Chanuki Illushka Seresinhe is a data science researcher at the Alan Turing Institute and the Lead Data Scientist at Popsa (using AI to automatically curate photo content into beautifully designed physical products). She formerly worked as a Senior Data Scientist at Channel 4.

Royal Mail’s Estimated Delivery Window – Another Successful Data Science Story

One of Royal Mail’s latest initiatives to improve customer experience and convenience was launched in April this year. Customers are now receiving information about their parcel deliveries a day in advance and also get shorter estimated delivery windows, down to a time frame of two hours. I will present some of the data science behind the project, as well as how we managed to make this project another data science success story for Royal Mail.

Following an Academic career in Evolutionary Genetics and Bioinformatics (University of Zurich - PhD; University of Bristol - Senior research scientist), I have switched to professional Data Science. Since 2016, I have been working on several projects for Royal Mail, supporting the business in making smarter, data-driven decisions. I have supported or lead several ambitious predictive analytics projects for Royal Mail, such as:

(I) Estimated parcel delivery times: Lead a team of 7 (data scientists and data engineers) to successfully implement the technical part for one of Royal Mails capital projects regarding predicting delivery windows. Resposible for all technical data-sciencerelated aspects of the project, interacting with multiple teams within the business and communicating with major stakeholders. (Talk) (II) Developed and implemented Royal Mails traffic forcast. Finding an end-to-end solution that is running live for two years, supporting Royal Mail's daily resource planning. (III) Lead, developed and implemented a predictive project with a savings potential in the millions for Royal Mail. Responsible for end-to-end solution, including deployment via webapp and usage evaluation.

Solving Real-World Black Box Optimisation

Optimisation problems are ubiquitous in the field of AI. The talk will focus on optimisation of operational decisions to maximise production. We will explore modern black box optimisation techniques like Bayesian Optimisation and Genetic algorithms as well as the value of thinking about complex systems as graphs. Finally, we will discuss the importance of multi-disciplinary teams as organisation apply AI in the real world.

Maren is an experienced Data Scientist with Maths background and PhD in Probability.

How Data Analytics Can Help Us Build A More Empathetic Internet?

The internet has became as essential as electricity and water to more than 4 billion people present online today. However, over 40% of the world population is still offline and a significant percentage of the connected population, particularly in the developing world, suffers from a low quality of experience translating into an inability to access many websites and apps, and significant delays and stalls when accessing sensitive content such as video. In this talk, we start by explaining the basics of how the internet works. We then dig deeper into how techniques like operations research and game theory can inform a enable internet players with different resources and interests to cooperate, thus improving the state of internet connectivity and helping to bring more people online to a better internet.

Dr. Ghida Ibrahim works as a quantitative engineer/data scientist in the edge infrastructure team at Facebook London, where she builds data driven tools and models, and performs in depth analysis to drive the expansion and optimise the operation of one of the largest and most complex networks forming the internet, with the goal of bringing more people online to a better internet. In her spare time, Ghida built Rafiqi, a platform that leverages artificial intelligence (AI) for connecting refugees to life opportunities, that has been recognized by TechCrunch as one of the most innovative new projects using tech to help refugees, and was a finalist of many awards including Techfugees Global Challenges competition and the Tech for Good UK awards.