2023 Cohort (MRes)
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Nina Baranduin
In 2023 I completed my bachelor's degree in Ecology and Conservation at Nottingham Trent University. Throughout my undergraduate degree I used machine learning to investigate phototaxis in marine plankton, model peat depth, and to track the movement of pollinators. Geographical information systems were also routinely used in many of my other projects such as vegetation and landscape classification surveys, wild boar surveys, and freshwater quality investigations. I am particularly interested in exploring the ways that AI can be used within ecology, especially its use to aid evidence-based conservation efforts. Many of my interests revolve around science communication, as I often make infographics and animations that cover a wide range of topics in nature; as well as volunteering for habitat management groups which include teaching children about plants and invertebrates. |
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Camilla Giulia Billari
Prior to joining AI4ER, I completed a BEng in Design Engineering at Imperial College London. I focused on utilising computational methods and ML to develop sustainable agriculture solutions. This included working on a group project on pest detection in coconut trees, going on a field trip to Sri Lanka to test our solution and conducting technology-enabling workshops for coconut growers. At Imperial, I chaired the Union’s Ethics and Environment Network, campaigning and representing the student body in the College’s sustainability committees. I have also interned at Ocean Infinity, a sustainable marine robotics company, and Minazi Consulting, a non-profit design engineering consultancy working with NGOs around the world. Through this CDT, I will continue my research in AI as a tool to analyse and develop solutions to climate challenges, in particular those faced by countries at highest risk with least support. Outside of my studies, you’ll find me sketching in museums, listening to live music, and exploring the globe. |
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Tom Cowperthwaite
I’m joining the AI4ER CDT after a 4 year stint at Imperial College London, where I gained a master’s degree in theoretical physics. At Cambridge, my main interest is in exploring how AI can be applied to atmospheric and oceanic physics. My ultimate goal is to gain a clearer understanding of the physical aspects of climate change and to hopefully inform our predictions and adaptation strategies for years to come. I am motivated both by a pure scientific interest in climate-related physics and a desire to mitigate and adapt to climate-related risks. During my time at Imperial, I worked on a project with the Grantham Institute, where I investigated what causes variations in the earth's radiative balance, focusing on the effects of cloud feedbacks on shortwave radiation. Outside of my studies, I'm an avid badminton player and enjoy diving into books about AI, physics, and the natural world. At Cambridge, I'm also hoping to finally get the hang of the piano—a challenge I've been taking on for the past couple of years! |
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Emilio Luz-Ricca
Before joining the CDT, I completed my undergraduate degree in data science and mathematics at William & Mary. My research with the William & Mary Institute for Integrative Conservation and U.S. Fish & Wildlife Service towards automated identification of the migratory sandhill crane in thermal aerial imagery prompted my interest in artificial intelligence for the environmental sciences. I also engaged in a number of applied machine learning research projects in fields ranging from physical chemistry to international relations to federated learning; I co-authored three publications in total, two as first author. I am excited to continue to develop and apply AI approaches to improve our understanding of natural systems at scale |
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Sharan Maiya
I’m moving down to Cambridge from sunny Scotland where I’ve been researching the health effects of air pollution exposure at the University of Edinburgh. I’m interested in the intersection of machine learning and causal inference, so my work involved the use of causal discovery algorithms and targeted causal learning to estimate dose-response functions from particulate matter in the air to lung function, using data from cities across the world from London to Delhi. I’m a Glaswegian who has lived in Edinburgh since 2016 when I started my BSc in Computer Science and Mathematics, with a (mostly locked-down) year in-between in London for my MSc in Statistics at Imperial College. I care deeply about environmental risks and I also care deeply about AI Safety, whether that means immediate issues in AI Ethics or long-term catastrophic risks from transformative AI. I also spend a lot of time thinking about meditation (trying not to think), playing my saxophone, reading and coding. |
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Pritthijit Nath
Before commencing my studies as a part of the AI4ER CDT at Cambridge, I completed my MSc specializing in AI and Machine Learning at Imperial College London and my BEng. (Hons) in Computer Science and Engineering at Jadavpur University (JU) in Kolkata, India. At Imperial (in collaboration with the Data Science Institute), my dissertation focused on using state-of-the art diffusion models to forecast tropical cyclones using satellite and atmospheric data. During my UG years at JU, I researched extensively on predictive analytics of pollution levels in major cities in India, creating spatio-temporal maps with AI models using univariate time-series data and satellite measurements such as aerosol optical depth (AOD). At the CDT, I plan to expand my research to tackle problems related to climate change adaptation and decision making on a more global scale, focussing on statistical analysis and AI-based modelling to assess extreme weather related impact on vulnerable communities. Outside of AI research, I possess a keen interest in history. During the weekends, I enjoy visiting museums, art galleries and historic sites, participating in pub quizzes and engaging in discussions that seek to explore historic world events from alternative perspectives. During my MSc, I developed a deep affection for the charismatic streets and transportation of London, strong enough to aspire to take "the Knowledge" one day in the near future. |
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Jakob Poffley
Prior to joining the AI4ER CDT, I graduated from the University of Cambridge with a Natural Sciences degree in which I specialised in Ecology and Conservation via Part II Plant Sciences. Research projects during my degree focussed on tree-wind dynamics, ecological restoration, biodiversity metrics and a novel method for using pattern recognition to monitor amphibian populations. During a summer studentship, I also studied the impacts of climate change on global plant communities. I am passionate about all things sustainability and outside of academia, I am an outdoorsy person who also loves music, cooking and cycling. |
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Thomas Ratsakatika
I am joining AI4ER after ten years of working on sustainable infrastructure, agribusiness and climate change initiatives across multiple countries. As a member of the British Diplomatic Service, I led a range of projects including installing and operationalising 100 solar mini-grids in Sierra Leone, rehabilitating roads in the conflict-afflicted regions of the Democratic Republic of Congo, and supporting Tanzanian agribusinesses to access climate finance. I supported the COP26 climate negotiations and worked with governments to develop supportive policies for renewable energy investment. I previously worked in the UK as an aerospace engineer and investment consultant. I am an engineer by training and hold an MEng in Aerospace Engineering from the University of Bath and an MPhil in Engineering for Sustainable Development from the University of Cambridge. I am excited by this opportunity to build expertise in an emerging technology and apply it to issues I am passionate about. |
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Alex Shinebourne
I am a Computer Scientist and Software Engineer. Before AI4ER, I obtained my bachelor's degree in Computer Science from the University of Warwick. I carried out my thesis on decentralised distributed computing, particularly low-memory techniques for maintaining stable peer-to-peer networks. After graduating, I joined Google as part of their Cloud Technical Residency, where I held a technical consultancy role, advising large and small companies on using Cloud technologies. At Google, I was part of the regenerative agriculture group focused on bringing industry players and farmers together to aid technology development to make farming more sustainable. I am a member of the Global Alliance for Climate Smart Agriculture (GACSA), a UN-hosted alliance of agricultural stakeholders to accelerate the adoption of Climate Smart Agriculture (CSA) practices and have spoken at their annual forum about the use of AI to automate irrigation systems and Large Language Models (LLMs) to democratise access to agronomy information. In parallel, I have been co-developing an LLM-based platform to provide smallholder farmers with accurate, scientifically-backed agronomy information. I am interested in remote sensing and its application to improving biodiversity, AI optimisation and Operational Research (OR) techniques, as well as Decision Support Systems (DSS) to improve the sustainability of agricultural, manufacturing and energy processes. I continue collaborating with FAO, other researchers, agronomists and engineers on introducing data-driven practices and AI in agriculture. |
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Aline Van Driessche
I started off my academic career in the field of architecture, in which I completed my bachelor’s degree in a joint program of Engineering and Architecture, followed by a master’s degree in Urban Design, both at the Katholieke Universiteit Leuven, Belgium. Triggered by the intersection of architecture and the humanitarian field, I wanted to pursue another MSc degree in Sustainable Emergency Architecture at the Universitat Internacional de Catalunya in Barcelona, Spain. Due to COVID-19 I postponed this idea of studying abroad and followed another one-year master program at the KU Leuven, in the field of Artificial Intelligence. When I did join the Emergency Architecture program at the UIC in Barcelona the year after, my newly-found interest in AI got me into writing a thesis about the advantages and bottlenecks of AI-based post-earthquake damage assessments on satellite imagery. Going into industry after 7 years of studying, I joined the early-staged start-up Genvision as machine learning engineer. In this start-up I got the chance to delve into the very challenging problem of digitally measuring the mount of CO2 stored in forests around the world, relying solely on remote sensing data. Outside of academics and work, I am a travel-guide for a Belgian-based youth travel organisation, but I also like to go out for hikes on my own. When I am not outside exploring nature, I can mostly be found at home taking care of my million plants or doing some crocheting with a good cup of coffee. |
