According to Business Insider, more than 50% of the total costs for delivering goods is attributable to what is known as "last mile delivery" – the point at which the package finally arrives at the buyer's door. In a recent study by Global Industry Analysts, the last mile delivery market worldwide is expected to reach over $35 Billion by 2025. Last mile delivery is the most expensive and time-consuming part of the shipping process, either due to lack of density and long distances in rural areas or traffic congestion in urban ones. The idea of using Unmanned Aerial Vehicles (UAVs) – or drones – for last mile delivery is gaining popularity. The use of drones to deliver parcels has the potential to significantly decrease delivery costs – no driver, truck or congestion – and expand coverage areas.

"I really liked the setup.

At a packed hall in Lagos, a gathering of education and technology enthusiasts cheered for a milestone moment: the launch of Nigeria's first book on artificial intelligence for primary and secondary schools. The eight-chapter book illustrated with animations is written by Olubayo Adekanmbi, convener of Data Science Nigeria (DSN). His organisation has taken an active role in democratizing artificial intelligence application and research in Nigeria. With a suite of hands-on training programmes, toolkits and events, Data Science Nigeria aims to increase Nigeria's presence on the global AI map. "AI is a catalyst for good that creates new frontiers," Adekanmbi said, in his remarks at the launch.

THE THAMES seems to draw people who work on intelligence-gathering. The spooks of MI6 are housed in a funky-looking building overlooking the river. Two miles downstream, in a shared office space near Blackfriars Bridge, lives Arkera, a firm that uses machine-learning technology to sort intelligence from newspapers, websites and other public sources for emerging-market investors. London has the right time zone, between the Americas and Asia. It is a nice place to live.

We introduce a general tensor model suitable for data analytic tasks for heterogeneous data sets, wherein there are joint low-rank structures within groups of observations, but also discriminative structures across different groups. To capture such complex structures, a double core tensor (DCOT) factorization model is introduced together with a family of smoothing loss functions. By leveraging the proposed smoothing function, the model accurately estimates the model factors, even in the presence of missing entries. A linearized ADMM method is employed to solve regularized versions of DCOT factorizations, that avoid large tensor operations and large memory storage requirements. Further, we establish theoretically its global convergence, together with consistency of the estimates of the model parameters. The effectiveness of the DCOT model is illustrated on several real-world examples including image completion, recommender systems, subspace clustering and detecting modules in heterogeneous Omics multi-modal data, since it provides more insightful decompositions than conventional tensor methods.

Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on {\it clinical trial recruitment}, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. We propose doctor2vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials. In particular, doctor2vec utilizes a dynamic memory network where the doctor's experience with patients are stored in the memory bank and the network will dynamically assign weights based on the trial representation via an attention mechanism. Validated on large real-world trials and EHR data including 2,609 trials, 25K doctors and 430K patients, doctor2vec demonstrated improved performance over the best baseline by up to $8.7\%$ in PR-AUC. We also demonstrated that the doctor2vec embedding can be transferred to benefit data insufficiency settings including trial recruitment in less populated/newly explored country with $13.7\%$ improvement or for rare diseases with $8.1\%$ improvement in PR-AUC.

The retail banking services are one of the pillars of the modern economic growth. However, the evolution of the client's habits in modern societies and the recent European regulations promoting more competition mean the retail banks will encounter serious challenges for the next few years, endangering their activities. They now face an impossible compromise: maximizing the satisfaction of their hyper-connected clients while avoiding any risk of default and being regulatory compliant. Therefore, advanced and novel research concepts are a serious game-changer to gain a competitive advantage. In this context, we investigate in this thesis different concepts bridging the gap between persistent homology, neural networks, recommender engines and reinforcement learning with the aim of improving the quality of the retail banking services. Our contribution is threefold. First, we highlight how to overcome insufficient financial data by generating artificial data using generative models and persistent homology. Then, we present how to perform accurate financial recommendations in multi-dimensions. Finally, we underline a reinforcement learning model-free approach to determine the optimal policy of money management based on the aggregated financial transactions of the clients. Our experimental data sets, extracted from well-known institutions where the privacy and the confidentiality of the clients were not put at risk, support our contributions. In this work, we provide the motivations of our retail banking research project, describe the theory employed to improve the financial services quality and evaluate quantitatively and qualitatively our methodologies for each of the proposed research scenarios.

Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the "Coordination Initiator Inference Problem" and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time-series data from interacting entities.

"It is now too late to stop a future collapse of our societies because of climate change." These are not the words of a tinfoil hat-donning survivalist. This is from a paper delivered by a senior sustainability academic at a leading business school to the European Commission in Brussels, earlier this year. Before that, he delivered a similar message to a UN conference: "Climate change is now a planetary emergency posing an existential threat to humanity." In the age of climate chaos, the collapse of civilization has moved from being a fringe, taboo issue to a more mainstream concern. As the world reels under each new outbreak of crisis--record heatwaves across the Western hemisphere, devastating fires across the Amazon rainforest, the slow-moving Hurricane Dorian, severe ice melting at the poles--the question of how bad things might get, and how soon, has become increasingly urgent. The fear of collapse is evident in the framing of movements such as'Extinction Rebellion' and in resounding warnings that business-as-usual means heading toward an uninhabitable planet. But a growing number of experts not only point at the looming possibility that human civilization itself is at risk; some believe that the science shows it is already too late to prevent collapse. The outcome of the debate on this is obviously critical: it throws light on whether and how societies should adjust to this uncertain landscape. Yet this is not just a scientific debate. It also raises difficult moral questions about what kind of action is warranted to prepare for, or attempt to avoid, the worst. Scientists may disagree about the timeline of collapse, but many argue that this is entirely beside the point. While scientists and politicians quibble over timelines and half measures, or how bad it'll all be, we are losing precious time.

Try Hyperloop, rocket travel, and robotic avatars. Hyperloop is currently working towards 670 mph (1080 kph) passenger pods, capable of zipping us from Los Angeles to downtown Las Vegas in under 30 minutes. Rocket Travel (think SpaceX's Starship) promises to deliver you almost anywhere on the planet in under an hour. Think New York to Shanghai in 39 minutes. As 5G connectivity, hyper-realistic virtual reality, and next-gen robotics continue their exponential progress, the emergence of "robotic avatars" will all but nullify the concept of distance, replacing human travel with immediate remote telepresence.