Real-time mapping of changes in species extinction risks / Jan 2024
This is an idea proposed as a Cambridge Computer Science PhD topic, and is currently being worked on by Emilio Luz-Ricca. It is supervised by Andrew Balmford and Anil Madhavapeddy.
Loss of habitat represents the most significant threat to wildlife overall, but advances in satellite sensing have enabled the assessment of habitat extent with comprehensive spatial coverage and reasonable temporal resolution. To address rising demand for metrics to quantify biodiversity, we have developed the LIFE metric (see Mapping LIFE on Earth) that models the effect of landuse changes on species extinction risk as a function of Areas of Habitat (AoH).
This PhD work explores how to deal with the anthropogenic threats beyond simple habitat loss, including hunting, agricultural practices, and the introduction of invasive species. These additional threatening processes degrade habitat quality and lower species occupancy, but are extremely difficult to observe directly via remote sensing. This project will therefore involve a combination of modelling, machine learning and remote sensing data analysis to understand the impact of these additional anthropogenic threats on habitat quality on a per-species basis.
ideasPrivacy preserving emissions disclosure techniques / Jan 2024
This is an idea proposed as a Cambridge Computer Science PhD topic, and is currently being worked on by Jessica Man. It is supervised by Martin Kleppmann and Anil Madhavapeddy.
Customers of online services may want to take carbon emissions into account when deciding which service to use, but are currently hindered by a lack of reliable emissions data that is comparable across services. Calculating accurate carbon emissions across a cloud computing pipeline involves a number of stakeholders, none of whom are incentivised to accurately report their emissions for competitive reasons.
This PhD explores mechanisms to support verifiable and privacy-preserving emissions reporting across a chain of energy suppliers, cloud data centres, virtual machine hosting services providers and cloud services providers, which are ultimately passed through to APIs used by customers. We hypothesise that adding verifiable and composable emissions transparency to cloud computing architectures enables providers to compete on the basis of sustainability, resulting in demand-side pressure on cloud services to shift to renewable energy sources.
We published a workshop paper on this topic in Emission Impossible: privacy-preserving carbon emissions claims.
Low-power sensing infrastructure for biodiversity / Jan 2024
This is an idea proposed as a Cambridge Computer Science PhD topic, and is currently being worked on by Josh Millar. It is supervised by Hamed Haddadi and Anil Madhavapeddy.
In-situ sensing devices need to be deployed in remote environments for long periods of time, and minimizing their power consumption is vital for maximising both their operational lifetime and coverage.
We are exploring the construction of a versatile multi-sensor device (initially based around the ESP32 chipset) and designing an exceptionally low power consumption model by using an on-device reinforcement learning scheduler that can learn to cooperate with other nearby devices.
Our prototype device setup for learning schedules for biodiversity monitoring does pretty well against a number of fixed schedules; the scheduler captures more than 80% of events at less than 50% of the number of activations of the best-performing fixed schedule. You can read more about this in Terracorder: Sense Long and Prosper.
Foundation models for complex geospatial tasks / Jan 2024
This is an idea proposed as a Cambridge Computer Science PhD topic, and is currently being worked on by Onkar Gulati. It is supervised by Sadiq Jaffer, Anil Madhavapeddy and David A Coomes.
Self-supervised learning (SSL) represents a shift in machine learning that enables versatile pretrained models to leverage the complex relationships present in dense–oftentimes multispectral and multimodal–remote sensing data. This in turn can accelerate how we address sophisticated downstream geospatial tasks for which current methodologies prove insufficient, ranging from land cover classification to urban building segmentation to crop yield measurement and wildfire forecasting.
This PhD project explores the question of how current SSL methodologies may be altered to tackle remote sensing tasks, and also how to make them amenable to incremental time-series generation as new data regularly comes in from sensing instruments.
The role of urban vegetation in human health / Jan 2023
This is an idea proposed as a Cambridge Computer Science PhD topic, and is currently being worked on by Andres Zuñiga-Gonzalez. It is supervised by Ronita Bardhan and Anil Madhavapeddy.
Cities around the globe have experienced unprecedented growth in recent years, becoming centres of economic, cultural, and social hubs for human activity. Rapid urbanisation has transformed the physical landscape and significantly altered local climates, with predictions stating that cities will harbour more than 70% of the population by the middle of the 21st century. This has also changed the climatic variables to which humans are most directly exposed. Combining global climatic changes with localised human activities has created new challenges that cities must solve to be sustainable homes for humanity in the coming decades.
Given the complexity of building sustainable cities and the breadth and variety of available information, it is crucial to use data-driven approaches for urban planning. Urban environments have become humanity's home in the past century, and they will play a key role in shaping the culture, environment and society of the 21st century. Moreover, due to how cities have been built historically and how their urban structure reflects social and economic conditions, it is essential to address the challenge of shaping cities into a more sustainable and equal future regarding the environment and human health. In particular, green spaces and trees have been regarded as one of the most crucial interventions in cities because of their ecosystem services. […281 words]
Scheduling for Reduced Tail Latencies in Highly Utilised Datacenters / Jan 2023
This is an idea proposed as a Cambridge Computer Science PhD topic, and has been completed by Smita Vijayakumar. It was supervised by Evangelia Kalyvianaki and Anil Madhavapeddy.
Modern datacenters have become the backbone for running diverse workloads that increas- ingly comprise data-parallel computational jobs. Due to the ease of use and diversity of resources they host there has been an exponential rise in the demand for datacenters leading to high volume of traffic. Datacenters execute thousands of jobs by scheduling billions of tasks every day. To meet these demands, datacenters providers operate their clusters at levels of high utilisation. We show that under such conditions existing scheduling designs impose large wait times on tail tasks. This leads to large tail task completion times and consequently elevated job completion times that can potentially cost datacenter providers millions of dollars in terms of total cost of operations of these datacenters.
This PhD explores a new decentralised scheduling model, Murmuration, that uses multiple communicating scheduler instances to ensure tasks are scheduled in a manner that reduces their total wait times. It achieves this by scheduling all tasks of a job such that their start times are as close together as possible, thereby ensuring small tail task completion times and better average job completion times.
Meta Properties of Financial Smart Contracts / Jan 2023
This is an idea proposed as a Cambridge Computer Science PhD topic, and has been completed by Derek Sorensen. It was supervised by Anil Madhavapeddy and Srinivasan Keshav.
Financial smart contracts routinely manage billions of US dollars worth of digital assets, making bugs in smart contracts extremely costly, and are also increasingly being used in other areas of endeavour such as carbon credit tracking. Because of this, much work has been done in formal verification of smart contracts to prove a contract correct with regards to its specification. However, financial smart contracts have complicated specifications, and it is not all straightforward for humans to write one which correctly captures all of its intended high-level behaviors.
To mitigate this challenge, this PhD explores the development of formal tools to target meta properties of smart contracts, which are properties of a contract that are intended by, but out of scope of, its specification. The targeted properties include the economic behaviors of the contract, properties relating to its upgradeability features, and the intended behaviors of systems of contracts. The formal tools presented are written in Coq.
Interspatial Networking with DNS / Jan 2023
This is an idea proposed as a Cambridge Computer Science PhD topic, and is currently being worked on by Ryan Gibb. It is supervised by Anil Madhavapeddy and Jon Crowcroft.
The existing Internet architecture lacks support for naming locations and resolving them to the myriad addressing mechanisms we use beyond IP. While there have been many advances in addressing locations via multiple routing schemes, it remains difficult to refer to location-based services via logical names. This in turn makes it difficult to deploy network services that can be referred to by a stable name that specifies a given location, and that resolves to the addresses of the devices in that space. This matters because there are a broad class of network-connected devices with a physical presence to which location is an intrinsic part of their identity. A networked speaker in, say, the Oval Office is defined by its location: it is simply the Oval Office Speaker! If the specific device moves location its identity should change with its new location, and if the device is replaced then the replacement should assume the function of its predecessor. […218 words]
Computational Models for Scientific Exploration / Jan 2023
This is an idea proposed as a Cambridge Computer Science PhD topic, and is currently being worked on by Patrick Ferris. It is supervised by Anil Madhavapeddy and Srinivasan Keshav.
The modern scientific method has become highly computational, but computer science hasn't entirely caught up and is sometimes hindering research progress.
We use climate science and ecology computation needs as a case study, we are conducting a systematic study in the sources of uncertainty in these fields. We are also designing and implementing a specification language and hermetic computation environment that empowers climate scientists and ecologists to create less ambiguous, more precise and testable scientific methodologies and results, while preserving the ability to explore and introspect intermediate results. […125 words]
Secure Programming with Dispersed Compartments / Jan 2022
This is an idea proposed as a Cambridge Computer Science PhD topic, and has been completed by Zahra Tarkhani. It was supervised by Anil Madhavapeddy.
This PhD project proposes novel approaches and mechanisms for application compartmentalization and isolation to reduce their ever-growing attack surfaces.
Our approach is motivated by the key observation that while hardware vendors compete to provide security features (notably memory safety and privilege separation) existing systems software like commodity OSs fail to utilize such features to improve application security and privacy properly.
We propose a novel principled approach to privilege separation and isolation, enabling application security to be designed and enforced within and across different isolation boundaries, and yet remain flexible in the face of diverse threats and changing hardware requirements. […186 words]