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.

Maren Eckhoff is a principal data scientist at QuantumBlack, where she leads the analytics work on client projects, working across industries on predictive, explanatory, and optimization problems. Her role includes defining the analytical approach, developing the code base, building models, and communicating the results. Maren also leads the technical training program for QuantumBlack’s data science team and arranges bespoke trainings, seminars, and conference attendance. Previously, Maren worked in demand forecasting. She holds a PhD in probability theory from the University of Bath.

An Intro To AIOps: How To Scale IT Operations With AI

In this talk, the speaker covers how to leverage AI and quantitative techniques in order to scale and optimize large scale product infrastructure in the cloud or on the edge. In particular, they will explain how techniques like time series forecasting, operations research, and statistical and causal inference could be leveraged to optimize infrastructure investments and resource allocations, enable predictive maintenance, and allow building infrastructure that is more aware of the needs of products such as video, real time messaging and the metaverse

Speaker bio:

Ghida is a lead quantitative engineer at Meta (previously Facebook) where she uses automated decisioning and advanced analytics to help scale and optimize Meta internal cloud and edge infrastructure, used to serve billions of people across Meta family of apps and products. Prior to joining Meta, Ghida worked for 6+ years in the Telco and media industries in multiple analytics and engineering roles, mainly focusing on optimizing large scale distributed systems. She holds a PhD and master’s (Diplome d’Ingénieur) in computer engineering from Institut Polytechnique de Paris.

Ghida also teaches a course on using AI for scaling IT operations at the university of Oxford. She is a TED speaker and an Expert of the World Economic Forum, providing expertise on the future of computing. In the past, Ghida prepared and delivered the first online course on data science in Arabic attracting 30k+ learners, and built an award-winning platform for connecting refugees to opportunities, among others.

Ghida's expertise is at the intersection of computing infrastructure, data engineering and AI. It covers Edge Computing, Cloud Computing, Content Delivery Networks, time series analysis, operations research, statistical inference, ETL, expert systems, recommender systems, machine learning and federated learning, among others.