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Prior to joining AI4ER, I spent a year in Cranfield for the MSc in Environmental Engineering and the year before I graduated from Cranfield as well in MSc in Applied Artificial Intelligence. My master’s thesis was on improving global gridded precipitation using NWP models and CNN.

The MSc in IA was part of double degree programme with IPSA, a French aeronautics and space engineering MSc from which I graduated last year. I majored in embedded systems, and space launchers and satellites.

In my spare time, I like to read and play music. I recently started play ice hockey.

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I completed two undergraduate degrees, one in Marine Sciences (2016) and the other in Environmental Engineering (2019), both at the Federal University of São Paulo (Brazil). In my first undergraduate degree, my research interests were related to the effects of a multi-impacted coastal area on mollusks shells (https://doi.org/10.1016/j.envpol.2017.04.045). Then, in my second degree, I developed a study concerning the role of São Paulo’s protected areas in preserving the Guarani Aquifer’s recharge zones.  

In 2020, I started my master’s degree in Remote Sensing at the Brazilian National Institute for Space Research (INPE). I worked in the last couple of years focused on estimating water quality parameters from orbital and field remote sensing data. My dissertation aimed to retrieve Phycocyanin (a photosynthetic pigment with a major presence in Cyanobacteria) concentrations using hyperspectral orbital remote sensing data and machine learning algorithms. My study was part of the MAPAQUALI project, which aims to create an online platform for monitoring the water quality of Brazilians' inland and coastal waters. I was also involved in other studies, such as estimating the Secchi Disk Dept for Brazilian waters from Sentinel-2/MSI images (https://doi.org/10.1016/j.isprsjprs.2021.10.009), deriving a method for correcting the adjacency effect in orbital images for inland waters (https://doi.org/10.3390/rs14081829), and mapping the phytoplankton biodiversity in the Amazon floodplain. 

At Cambridge, I will be part of the 2022 AI4ER cohort and from the Cambridge Centre for Carbon Credits (4C). I would like to continue my studies regarding the application of hyperspectral orbital images for deriving biophysical parameters and improve my skills in Artificial Intelligence and computer sciences. Biodiversity indexes, deforestation, and carbon credits are among my research interests for my MRes project. Spending time in natural landscapes, watching volleyball games, and practicing sports are my favorite activities besides studying the environment. 

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Prior to joining the AI4ER course at Cambridge, I completed my BSc degree in Geophysics from Imperial College London. At Imperial's Grantham Institute, I researched the application of country-specific Social Cost of Carbon in quantifying the effects of climate change and as well as statistical analysis on temperature climate risk in Bangladesh.  

I am specifically interested in the intersection of policy and environmental science and predicting climate risk in vulnerable regions. As an active member of the European Youth Parliament, I have chaired and presided sessions, focussing amongst other things on the Nord Stream 2 pipeline and sustainable shipping. At the CDT I aim to acquire specific practical skills that help research and effectively predict environmental impact in order to enable informed decision-making. 

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I graduated with a Masters of Science in Civil and Environmental Engineering and a Master of Arts in Public Policy from Stanford University as a Knight-Hennessy scholar, and BSc in Civil Engineering (magna cum laude) from the University of the Philippines Los Baños. Through engineering, policymaking, and AI, I aspire to shape an equitable disaster-resilient future to face the global crisis caused by the pressing need to adapt to intensifying effects of climate change and other natural hazards. I previously worked for Stanford LBRE and the local government of Quezon City (Philippines) to support data-driven policies to manage their regional earthquake risks. At Stanford, I worked with US Geological Survey to use machine learning on NASA satellite imagery products to improve PAGER, a global real-time earthquake hazard and loss estimation system. I have also spent time at Arup, Stanford Urban Resilience Initiative, and FM Global Engineering & Research. I enjoy spending time with my dogs, hiking, and playing ukulele. 

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Prior to joining the 2022 AI4ER cohort, I completed my undergraduate studies at the University of Tokyo, where my senior thesis focused on developing a novel system for investigating the quantum mechanical phenomena dictating environmentalmagnetoreception in avian species.

Afterwards, I spent the better part of a year working in private equity in Tokyo, with a particular focus on secondary funds and impact investments. I hope to tackle complex issues at the CDT from both a statistical and economic viewpoint. In my spare time, you'll find me exploring new varieties of coffee, hosting horror movie nights, or tutoring just about anything.  

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Before coming to Cambridge, I completed my BSc in Mathematics with Computing at the University of Essex, and MSc in Machine Learning in Science at the University of Nottingham, where my thesis involved using contrastive learning and Bayesian inference in an astrophysics context. I am particularly interested in how deep learning and computer vision methods with earth observation data can be applied to climate change mitigation, and how these insights can be made more actionable by utilising uncertainty quantification and interpretable machine learning. 

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I completed my undergraduate studies in 2022 at Michigan State University, where I earned degrees in Computer Science, Advanced Mathematics, and Statistics as an Alumni Distinguished Scholar and Goldwater Scholar. I have joined the CDT as a Marshall Scholar and Gates Cambridge Scholar interested in using deep learning to more accurately model spatiotemporal extremes in earth and climate science. Outside of research, I am passionate about teaching and broadening access to computer science through outreach. Outside of academics, I am an avid skier, runner, hiker, and outdoorsperson with a passion for travelling and coffee.  

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I'm joining AI4ER after nine years in the tech industry. I worked on a wide variety of software systems, from low-latency trading platforms for banking to mobile-cloud distributed systems for the automotive sector at companies across the size spectrum. My most recent position was as a tech lead for Google's Android Automotive personalization team. 

I left the industry driven by curiosity and looking to apply my experience and skill to some of society's most pressing and challenging problems - understanding our environment and our sustainable place in it. In no small part, I'm motivated by my passion for backpacking, wildlife, and landscape photography. I would prefer that we still have those in 50 years. 

I moved from Serbia to the US to study at MIT, where I completed my undergraduate and master's degrees in Electrical Engineering and Computer Science.

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My previous career largely focused on the operational assessment of wind and solar plants (including energy yield projections and SCADA analysis); and I have also supported a number of R&D/digital projects and community energy initiatives. I am always keen to cross traditional subject boundaries and tackle problems as holistically as possible, and I believe the CDT will provide me with the perfect environment in which to do this. Outside of work I enjoy making music, and I plan to make good use of the creative opportunities available at Cambridge. 

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Before starting at Cambridge, I completed my BSc with Honors in Geographic Information Science at Nanjing University in 2019, and an MSc in Geomatics at ETH Zurich in 2021 fully funded by ETH’s Excellence Scholarship. Since my undergraduate, I have been fascinated by computational methods ubiquitous in extracting information despite its various manifestations, which I further pursued at ETH by studying machine learning and computer vision, often under the context of geoinformation. While AI has refreshed our imagination in other disciplines such as Go or Biology, I felt clearly its potential in environmental studies, where the novel setting and abundant data will also challenge the existing benchmarks of AI. I look forward to developing such a co-evolved relationship by pursuing my PhD at the AI4ER CDT. 

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I graduated with a Bachelor's degree in Geology from the University of Birmingham in 2016. Since then, I have spent the past six years working at the European Centre for Medium-Range Weather Forecasts (ECMWF), providing users with numerical weather data and coordinating operational projects. I am returning to academia, joining the AI4ER 2022 cohort, to develop technical skills and contribute to the implementation of ML tools into operational workflows.  

I am excited by the potential that AI offers for enhancing the predictability of extreme weather events, and have a particular interest in enhancing the uptake of increasing volumes of EO data. I also believe AI has a role to play in more human-centred approaches to tackling the climate crisis; in my spare time, I collaborate with the Focus Group on AI for Natural Disaster Management, reviewing applications of AI for disaster communication.  

My goal is to develop further partnerships and expertise to help inform strategic decisions. I also hope to make environmental data science more accessible to the broader community, mentoring with CodeFirst: Girls, and using my access to academia to advocate for communities that are disproportionately impacted by the climate crisis. 

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I steered my Physics BSc at the University of Bristol towards all things related to AI applications in climate science, specialising in how machine learning can improve climate forecasting. 
At Cambridge I look forward to investigating a range of computational methods with the aim of making a tangible impact to real-world sustainability efforts.

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I completed my bachelor's degree in Geophysics and master's degree in Geography at the China University of Geosciences (Wuhan). Before joining AI4ER, I tried to solve paleoclimate problems. My work was to use various statistical and analytical methods to investigate Dansgaard-Oeschger events, Antarctic isotope maximums, ice core water isotope, and ice core atmospheric CO2 records (publications: 10.1029/2021GL093868; 10.1175/JCLI-D-21-0713.1). Some long-standing questions in these topics are still not solved yet. Artificial Intelligence (AI) methods provide new possibilities for paleoclimate research. During my PhD, I will try to use AI methods to solve more paleoclimate questions so that eventually, we can better know the climate in the past to understand modern and future climate risks.  

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Before joining the AI4ER CDT, I obtained a double major in Biology and Microbiology and a Master's in Biological Sciences from Universidad de Los Andes in Colombia. There, I worked in Palaeoecology on projects related to ancient and environmental DNA from tropical high-mountainous ecosystems to reconstruct past environments. During my master's, I was a teaching assistant in the biostatistics and informatics courses. After graduation, I worked as a data scientist in a real estate company developing machine learning models for client and property classification and maintaining databases of web scraped data using cloud technologies. 

Joining the CDT will allow me to work with outstanding scientists with the common goal of using computation as a powerful means for creating a sustainable future for our planet. Particularly, I am interested in using technology for climate justice, especially in underdeveloped –and understudied- places. 

Aside from my academic career and interests, I enjoy playing football, basketball, tennis and video games and pretending to play piano and keyboards.