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Yenni Villa Acuña

Faculty of Civil Engineering and Geosciences Yenni completed her master’s degree in Applied Earth Sciences. Her thesis work is part of the IDEA-League Master Program and is done as an internship at Aramco Overseas Company BV in Delft. Minimisation and optimisation are at the heart of many challenges oil & gas companies face in structural imaging of the subsurface in oil & gas exploration. Many algorithms and ideas struggle to find robust solutions because of the scale and complexity of the subsurface. During a period of 5 months, Yenni created new ideas to make a general optimisation method significantly faster and more robust. She also demonstrated the applicability of the new method to a number of synthetic data sets that are well-known in the field of numerical optimization, as well as to seismic data processing algorithms that are used by many oil & gas companies. The quality of the results achieved are extraordinary. Yenni’s thesis was rewarded with an impressive 9.5 and her work has already been recognized as state of the art in current seismic processing software. “Yenny worked very independently on her thesis while taking the suggestions of her supervisors seriously. She managed to defended her thesis as good as perfectly. Besides her thesis work, she also did her course work very well, with excellent marks in all 3 high-standard European universities (ETH, TUD and RWTH).” Graduation committee - Dr G.G. Drijkoningen, Dr Yimin Sun, Dr Florian Wellman. Thesis synopsis Since its inception in 1975, Genetics Algorithms (GA) have been successfully used as a tool for global optimization of non-convex problems in several real world applications. Its creation was inspired by the neo-Darwinian theory of evolution, where the goal is to evolve an initial population of candidate solutions using the artificial operators of selection, crossover and mutation. An advanced Genetic Algorithm (aGA) was developed by AOC* to find the global maximum of n-th dimensional non-convex functions. However, as computational time is a key factor when it comes to scalability, the objective of this project is to improve the convergence speed of this currently available aGA by simultaneously enhancing both its global and its local search capabilities. To this end, two solutions were proposed. The first is a modified version of the well-known Island model GAs and the second is a Self-Adaptive Differential Evolution (SADE) fine tuning scheme. After a successful demonstration of its improved performance on multi-modal test functions, my enhanced Genetic Algorithm (eGA) is used to tackle two common non-linear Geophysical problems: static correction and Common Reflection Surface (CRS) stacking, where promising results were obtained. *AOC: Aramco Overseas Company

Rhythima Shinde

Faculty of Technology, Policy and Management Rhythima Shinde completed two master’s programmes: Engineering and Policy Analysis (EPA, with honours), including field work in India, and Computer Science, including a graduate project at ETH Zurich. Her career as an Honours student at Delft furthermore included an active board membership of the Energy Club, the publication of several journal papers and a book chapter, various student assistantships to support cybersecurity and open-data research, and the development of MOOCs. She also co-founded a start-up company ‘Energy Bazaar’, where she now puts her research findings and recommendations to immediate use. Rhythima graduated from MSc EPA on electrifying rural India through institutional innovation. Her thesis was rewarded with a grade 9.5. She executed empirical research in India and developed an institutional innovation framework to analyse her empirical findings, furthering Nobel prize winner Elinor Ostrom’s work. She built various agent-based computational models to verify her framework and then, through this framework and her models, she identified feasible and durable system designs and policy alternatives for electrifying rural India. She more than proved her mastery of all skills that make her a true Delft-trained policy analyst: providing computationally strong, empirically-sound engineering solutions for solving society’s critical issues. “Rhythima’s career in Delft is nothing short of impressive and she may be the ultimate example of a scientifically driven, intelligent, broadly-interested, entrepreneurial, socially responsible engineer, with a demonstrable impact on science and society.” Graduation committee – Prof. Paulien Herder, Dr Amineh Ghorbani, Dr Martijn Warnier Thesis synopsis Around 100 million households live without electricity in India. At the same time, there is a booming solar panel market in India. This gives an opportunity for escalating the reach through peer to peer (p2p) electricity exchange. The thesis explored the potential and challenges of this p2p solution with an extensive field study, development of theoretical framework to understand diffusion of emerging technologies considering community benefits (e.g. role of cooperative shops, etc.) and finally proposing socio-tech policies (e.g. hybrid microgrid-p2p solutions, anonymization of network) for making such projects a success for energy companies and consumers. The results of this thesis were successfully implemented in a computer science thesis to facilitate AI based optimization platform. The cumulative knowledge thus gained has lead to start-up 'Energy Bazaar' to implement the results in India. If implemented at scale, these solutions would accelerate complete household electrification of India by 2025.

Charlotte Koster

Faculty of Applied Sciences Charlotte obtained her master’s degree in Life Sciences & Technology. She chose to focus on the generation and analysis of industrially relevant yeast hybrids. She developed a fast and efficient method to generate new genetic variants of yeasts by ‘crossing’ parental strains with specific desired characteristics. The method that Charlotte designed, tested and optimized is highly relevant for industrial application. She applied this method to produce a new hybrid yeast that can be used in the beer brewing industry. Her thesis formed the basis for a patent application, on which she is one of the inventors. Charlotte received an impressive 9.5 for her thesis. Besides her excellent study achievements, she has also been an active member of the TU Delft student team that won a gold medal in the 2016 International Genetically Engineered Machine competition (iGEM). Another illustrative example of Charlotte’s drive to seek challenges is her internship performed at Ginkgo Bioworks, a high-profile Boston-based company active in the frontline of synthetic biotechnology. The top scientists who supervised Charlotte at Ginkgo awarded her a 9.5 for her internship. “During her graduation project, Charlotte was not only scientifically, but also socially a ‘pacemaker’, with a very positive impact on our research group. As evident from discussions in the lab and her activities on social media, she has an active interest in the societal impacts of science.” Graduation committee – Prof. Jack Pronk, Arthur Gorter de Vries, Dr Jean-Marc Daran, Dr Peter-Leon Hagedoorn Thesis synposis With ongoing climate change, we need to make our industrial processes more sustainable. In biotechnology, micro-organisms like yeast are used as ‘cell factories’ to produce products like biofuels, medicine and bioplastics in a sustainable way. However, this often requires genetic modification of the organisms, which is still a controversial issue. The use of hybrid yeasts can form a non-GMO alternative. Like some animals, different yeasts can mate with each other, forming hybrids that inherit qualities from each parent. For instance, mating a bioplastic-producing yeast with a plant-consuming yeast could result in a hybrid capable of making bioplastics from plant waste, without using genetic modification. However, such mating is rare, so hybrids are difficult to obtain. Therefore, I developed a method to obtain hybrid yeasts, based on color-coding and optical sorting of different yeasts, providing a promising approach to develop new non-genetically modified yeasts for the biotech industry.

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New LDE trainee in D&I office

Keehan Akbari has started since the beginning of September as a new LDE trainee in the Diversity and Inclusion office. What motivated him to work for the D&I office, what does he expect to achieve during this traineeship? Read the short interview below! What motivated you to pursue your LDE traineeship in Diversity and Inclusion office of the TU Delft? I completed both bachelor's and master's degrees in Cultural Anthropology and Development Sociology at Leiden University. Within these studies, my main area of interest was in themes of inclusion and diversity. After being hired as a trainee for the LDE traineeship, and discovering that one of the possible assignments belonged to the Diversity and Inclusion office, my choice was quickly made. I saw this as an excellent opportunity to put the theories I learned during my studies into practice. What specific skills or experiences do you bring to the D&I office that will help promote inclusivity on campus? I am someone who likes to connect rather than polarize, taking into account the importance of different perspectives and stakeholders. I believe that this is how one can achieve the most in fostering diversity and inclusion. You need to get multiple parties on board to get the best results. What are your main goals as you begin your role here, and how do you hope to make an impact? An important goal for me this year is to get students more involved in diversity and inclusion at the university. One way I will try to accomplish this is by contributing to the creation of D&I student teams. By establishing a D&I student team for faculties, it will be possible to deal with diversity- and inclusion-related issues that apply and relate to the specific department. How do you plan to engage with different (student) communities within the university? Since I am new to TU Delft, the first thing I need to do is expand my network here. Therefore, I am currently busy exploring the university and getting to know various stakeholders. Moreover, I intend to be in close contact with various student and study organizations to explore together how to strengthen cooperation on diversity and inclusion. Welcome to the team Keehan and we wish you lots of success with your traineeship!

Researchers from TU Delft and Cambridge University collaborate on innovative methods to combat Climate Change

For over a year and a half, researchers from TU Delft and the Cambridge University Centre for Climate Repair have worked together on groundbreaking techniques to increase the reflectivity of clouds in the fight against global warming. During a two-day meeting, the teams are discussing their progress. Researchers at Cambridge are focusing on the technical development of a system that can spray seawater, releasing tiny salt crystals into the atmosphere to brighten the clouds. The team from TU Delft, led by Prof. Dr. Ir. Herman Russchenberg, scientific director of the TU Delft Climate Action Program and professor of Atmospheric Remote Sensing, is studying the physical effects of this technique. Prof. Russchenberg emphasizes the importance of this research: "We have now taken the first steps towards developing emergency measures against climate change. If it proves necessary, we must be prepared to implement these techniques. Ideally, we wouldn't need to use them, but it's important to investigate how they work now." Prof. Dr. Ir. Stefan Aarninkhof, dean of the Faculty of Civil Engineering and Geosciences, expresses pride in the team as the first results of this unique collaboration are becoming visible. If the researchers in Delft and Cambridge can demonstrate the potential of the concept, the first small-scale experiments will responsibly begin within a year. This research has been made possible thanks to the long-term support from the Refreeze the Arctic Foundation, founded by family of TU Delft alumnus Marc Salzer Levi . Such generous contributions enable innovative and high-impact research that addresses urgent global challenges like climate change. Large donations like these enable the pursuit of innovative, high-impact research that may not otherwise be feasible, demonstrating how our collective effort and investment in science can lead to real, transformative solutions for global challenges like climate change. Climate-Action Programme

How system safety can make Machine Learning systems safer in the public sector

Machine Learning (ML), a form of AI where patterns are discovered in large amounts of data, can be very useful. It is increasingly used, for example, in chatbot Chat GPT, facial recognition, or speech software. However, there are also concerns about the use of ML systems in the public sector. How do you prevent the system from, for example, discriminating or making large-scale mistakes with negative effects on citizens? Scientists at TU Delft, including Jeroen Delfos, investigated how lessons from system safety can contribute to making ML systems safer in the public sector. “Policymakers are busy devising measures to counter the negative effects of ML. Our research shows that they can rely much more on existing concepts and theories that have already proven their value in other sectors,” says Jeroen Delfos. Jeroen Delfos Learning from other sectors In their research, the scientists used concepts from system safety and systems theory to describe the challenges of using ML systems in the public sector. Delfos: “Concepts and tools from the system safety literature are already widely used to support safety in sectors such as aviation, for example by analysing accidents with system safety methods. However, this is not yet common practice in the field of AI and ML. By applying a system-theoretical perspective, we view safety not only as a result of how the technology works, but as the result of a complex set of technical, social, and organisational factors.” The researchers interviewed professionals from the public sector to see which factors are recognized and which are still underexposed. Bias There is room for improvement to make ML systems in the public sector safer. For example, bias in data is still often seen as a technical problem, while the origin of that bias may lie far outside the technical system. Delfos: “Consider, for instance, the registration of crime. In neighbourhoods where the police patrol more frequently, logically, more crime is recorded, which leads to these areas being overrepresented in crime statistics. An ML system trained to discover patterns in these statistics will replicate or even reinforce this bias. However, the problem lies in the method of recording, not in the ML system itself.” Reducing risks According to the researchers, policymakers and civil servants involved in the development of ML systems would do well to incorporate system safety concepts. For example, it is advisable to identify in advance what kinds of accidents one wants to prevent when designing an ML system. Another lesson from system safety, for instance in aviation, is that systems tend to become more risky over time in practice, because safety becomes subordinate to efficiency as long as no accidents occur. “It is therefore important that safety remains a recurring topic in evaluations and that safety requirements are enforced,” says Delfos. Read the research paper .