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Professor Prof.dr. Georg Vrachliotis G.Vrachliotis@tudelft.nl Bio Georg Vrachliotis is a Professor for the Design, Data and Society Group at TU Delft. From 2016 Georg was dean of the Karlsruher Institut für Technologie (KIT) Faculty of Architecture and Chair for Architecture Theory (2014-2020). Previously he conducted research at the Institute for the History and Theory of Architecture at ETH Zürich. He studied architecture at the Berlin University of the Arts and did his PhD at the ETH Zürich in 2009. Besides that he was a visiting researcher at the Center for Cognitive Science of the University Freiburg, at the Spatial Cognition Center of the University Bremen, and at the UC Berkeley Department of Architecture in California. From 2006 to 2010 he was a guest lecturer on architecture theory at the TU Wien in Vienna. Georg Vrachliotis is member of the advisory board of the magazine ARCH+ and external examiner at Bartlett School of Architecture, UCL London. He curated the exhibition “Fritz Haller. Architect and Researcher” at the S AM Swiss Architecture Museum in 2014 in collaboration with the Institute for History and Theory (gta) at ETH Zurich (catalogue published by gta Publisher in 2014, with Laurent Stalder), the exhibition “Sleeping Beauty. Reinventing Frei Ottos Multihalle” (catalogue published by Spector Books in 2018) on the occasion of the 16th International Architecture Exhibition of the Venice Biennale in 2018. Most recently he curated the exhibition "Models, Media and Methods. Frei Otto's Architectural Research" at the School of Architecture at Yale University (2020). More information Design, Data and Society Group (tudelft.nl) “My vision is to intellectually strengthen the discipline of architecture for the emerging age of artificial intelligence and to work towards a more social environment.” - Georg Vrachliotis Staff Dara Ivanova d.v.ivanova@tudelft.nl Bio Dara Ivanova is an Assistant Professor in Architectural Design for Healthcare at the Design, Data and Society Group, with a research focus in designing better care places for health and well-being. She is a qualitative researcher with an interest in the interconnections between place, care and emerging digital technologies. Her work on care-in-place carved out a new ‘place-perspective’ on healthcare, introducing a spatial approach to digital care practices. Dara obtained her MSc in cultural anthropology (cum laude, 2013) at Utrecht University and holds a PhD (cum laude, 2020) in Science and Technology Studies from the Erasmus University Rotterdam. Prior to her post at the TU Delft, Dara has worked at the Erasmus University Rotterdam and the Radboud University’s iHub Interdisciplinary hub and Political Philosophy Department, where she focused on the ethics of digitalization and AI. She has also held a Council position at the European Association for the Study of Science and Technology (2016-2020) and is a member of the WTMC Graduate School (Netherlands Graduate Research School of Science, Technology and Modern Culture). Seyran Khademi S.Khademi@tudelft.nl Bio Since April 2021, Seyran Khademi is an Assistant Professor at the Design, Data and Society Group and the co-director of AiDAPT lab (AI for Design, Analysis, and Optimization in Architecture and the Built Environment). Her research interest lies at the intersection of Artificial Intelligent, Computer vision, and Deep learning in the context of visual data for Architectural Design. In 2017 she was appointed as an interdisciplinary postdoctoral researcher between the Computer vision lab and Architecture faculty working on the ArchiMediaL project, regarding the automatic detection of buildings and architectural elements in visual data focusing on Computer Vision and Deep Learning methods for archival data and street-view imagery. Seyran received her Ph.D. in signal processing and optimization in 2015 from TU Delft, supervised by Professor Alle-Jan van der Veen, followed by postdoctoral research on Intelligent Audio and Speech algorithms. She received her MSc. degree in Signal Processing from the Chalmers University of Technology in Gothenburg, Sweden, in 2010 and her BSc degree in telecommunications from the University of Tabriz in Iran. Marija Mateljan M.Mateljan@tudelft.nl Bio Marija Mateljan is a lecturer and a PhD researcher within the 'Design, Data and Society' Group at the Department of Architecture, TU Delft. At the intersection of Architecture, Computer vision and Media studies, her PhD research explores interfaces between design representation and description, visual reasoning, design methods, and the digitalisation of cultural techniques. Recognizing the critical role of architectural representation in design thinking and communication, she investigates how architectural visual data—such as drawings, diagrams, renders, and photographs—could be repurposed through data-driven technologies (AI) to develop new architectural design methods employing data circularity. Between 2015 and 2021, Marija worked at KAAN Architecten on various Dutch and international projects, including the Museum Paleis Het Loo and the New Schiphol Airport Terminal. She is experienced in design development and interdisciplinary coordination across various project phases. Her practical experience sparked an interest in AI technologies and their potential to leverage domain-specific knowledge and visual cognitive capabilities. Before joining KAAN, Marija worked as a freelance architect in Rotterdam. In 2014, she obtained her MSc degree in Architecture with honors from the Faculty of Architecture and the Built Environment at TU Delft. Prior to her studies in Delft, she completed her undergraduate studies at the Faculty of Architecture in Zagreb. Angela Rout A.E.Rout@tudelft.nl Bio Angela Rout, joined the Faculty of Architecture and Built Environment (ABE) at TU Delft as an Assistant Professor, and recipient of the distinguished Delft Technology Fellowship (DTF) for Top Female Academic Scientists. Her research investigates methods and implications for leveraging emerging data resources for societal benefit, within the discipline of architecture. Previous to her post at TU Delft Angela was appointed as a Postdoctoral Research Fellow at the University of British Columbia, where she was team lead for a two year research collaboration exploring opportunities for sensor data to inform resilient and equitable community design. In 2020 she received her Ph.D. from the University Calgary in Computational Media Design, where she developed approaches for leveraging spatio-temporal data from smartphones to aid in master planning processes and design practice. Julien Vuillamy j.j.g.vuillamy@tudelft.nl Bio Julien Vuillamy is a computer scientist originally based in Aix-en-Provence, France. He has joined the AiDAPT Lab last June as a postdoctoral researcher to explore AI applications in Architecture. His doctoral research, conducted with INRIA Sophia-Antipolis and Dassault Systèmes, focused on developing efficient 3D surface reconstruction algorithms for large-scale urban scenes captured through aerial photogrammetry. With a background in 3D computer vision, machine learning, and computational geometry, he is currently working with the Computer Graphics and Visualization Group at TU Delft on estimating architectural floorplans from indoor imagery. PhD candidates Jess Chang h.chang-1@tudelft.nl Bio Hsiu-Ju (Jess) Chang is a PhD student in the Design, Data and Society Group and the Interiors Buildings Cities Group. She is attentive to contemporary social and environmental phenomena encompassing care, digital technologies, gender, politics, and their intricate influence on spatial configurations across different scales. Her current research focuses on the convergence of technology, nursing, and architecture regarding historical, present, and future perspectives. Jess earned her Master's degree in Architecture cum laude from TU Delft in 2022. Her thesis project, "Blijkeuken," examined how the kitchen design, the care-providing space at home, intertwined with socio-political ideologies. Her attention to domestic spaces and the housing market also led to her contribution to the publication "Cooperative Conditions" (forthcoming 2024). Casper van Engelenburg C.C.J.vanEngelenburg@tudelft.nl Bio Architects communicate their designs through various visual abstractions of the physical space; including orthographic drawings, photos, and 3D models. Semantic similarity learning for architectural drawings is a PhD project of Casper van Engelenburg that started in October 2021, focusing on understanding visual patterns in floorplan image data. He develops deep contrastive learning frameworks that enable us to learn low-dimensional, task-agnostic representations of architectural drawings. This research line builds a foundation for large quantitative analysis of archival and linked visual data. Besides theoretical work, his aim is to connect it to the practice by enhancing Architectural-specific search engines. Matiss Groskaufmanis m.groskaufmanis@tudelft.nl Bio Matīss Groskaufmanis is a PhD researcher at the Design, Data and Society Group at TU Delft. He also holds the position of teaching assistant professor at the Aarhus School of Architecture and serves as the operations director at mgr-dev, a shell organization for design projects and collaborations. In his work, he examines architecture’s relationship to post-extractive building practices, managerial desires, and technology. His current research project is titled "Spreadsheets Take Command: Grids, Formulas, and an Architectural History of Data Management." From 2019 to 2020, Matīss was the Sanders Fellow at the University of Michigan's Taubman College of Architecture and Urban Planning. In 2018, he served as a curator of the Latvian Pavilion at the Venice Architecture Biennale, exploring housing as a means of nation-building. An alumnus of the Strelka Institute for Media, Architecture, and Design in Moscow, he holds a master’s degree in architecture (with distinction) from TU Delft and a bachelor's degree in architecture from Glasgow School of Art. Previously, Matīss has worked on research, publishing, and building projects as part of Rotterdam-based architecture practices MVRDV and OMA/AMO. Linda Kronmüller L.Kronmuller@tudelft.nl Bio Linda Kronmüller is a PhD Candidate at the Design, Data and Society Group at TU Delft. Her research, Networks of Care: The Architecture of Future Healthcare Environments, investigates how emerging technologies—such as digital health tools, cyborg design and robotics—are transforming healthcare environments. She explores how these technologies impact the design of healthcare settings across various scales and digital platforms, evolving from traditional facilities to integrated networks that blend urban, private and digital spaces. By emphasizing empathic design, Linda’s work aims to enhance patient wellbeing through a nuanced understanding of the relationship between the human body, data collection and digital environments. Linda completed her Master of Architecture at TU Delft in 2021, supported by a DAAD scholarship from the German Academic Exchange Service. Her master’s thesis, How to Live with Nature: Towards Another Scale of Architecture, examined scale as an empathic design method for both humans and other species. She has several years of practical experience as an architect at Dutch offices NL Architects and Mecanoo Architecten, contributing to projects nationally and internationally and researching healthier urban environments. Fatameh Mostafavi F.Mostafavi@tudelft.nl Bio Fatemeh Mostafavi is a PhD researcher at the Design, Data, and Society Group at TU Delft. She is a member of AiDAPT lab, where data-driven intelligence and model-based engineering come together to support structural and architectural choices, towards a sustainable built environment. Her research proposes a machine learning (ML) framework to learn environmental features from the large-scale existing architectural floor plan data to augment the architect’s intuition towards microclimatic-oriented building design. The goal of her research is to develop an AI-based decision-making model to assist in the analysis and generation of floor plans while taking environmental factors into consideration. Fatemeh obtained her master’s degree in Building Science - Architecture and Energy at Shahid Beheshti University of Tehran in 2021. Formerly, she got her bachelor’s degree in Mechanical Engineering at Shiraz University in 2017. As a researcher, her interests lie in building energy efficiency, carbon emissions, visual, thermal, and acoustic comfort, and integrated renewable energies in the built environment. By leveraging that foundation, she is aiming to integrate AI technologies into the environmental building design process in order to speed up the analysis and generation of big architectural data. Gent Shehu G.Shehu@tudelft.nl Bio Gent Shehu is a PhD Candidate between the Section of Urban Design and the Design, Data and Society Group at TU Delft. His research investigates the typological transformations and overall spatial and cultural implications of using digital technologies in horticulture. His focus is on the contemporary Glasshouse: that finely attuned nineteenth century building type, inside which human, plants, architecture, and AI interchange information—across scales and interfaces—to meet the cultural and societal demands of the twenty-first century. Gent holds a master’s degree of Architecture and Urban Design, with high honours, from Polis University (2018), and a post-master’s degree, cum laude, from The Berlage Center for Advanced Studies in Architecture and Urban Design, TU Delft (2021). Since 2018, he has worked as a freelance architect from his eponymous studio in Skopje, and prior to joining The Berlage, he collaborated on an architectural and urban design project with an Austrian firm. His research and design projects have appeared in The Plan Journal, Forum A+P, among others, and have been exhibited in various venues in Europe. Gent has contributed to numerous publications from The Berlage Center for Advanced Studies in Architecture and Urban Design, namely: Facades for a Canal House (2020), The Asset Class No.1 (2022), Travel Agency (forthcoming 2022), Architecture on Display: On the History and Contemporary Approaches to Exhibiting Architecture (forthcoming 2023), and Project Global — Power (forthcoming 2023).

Doctoral Defence S.T. Hung

Doctoral Defence S.T. Hung 02 October 2024 15:00 till 30 September 2024 15:30 - Location: AULA, Senaatszaal - By: DCSC | Add to my calendar Super-resolution microscopy in tissue through illumination engineering and aberration correction Promotor: Prof.dr.ir. Michel Verhaegen Co-promotor: Dr.ir. Carlas Smith, Prof.dr. Nynke Dekker Abstract: Super-resolution microscopy has been demonstrated to surpass the diffraction limit, delivering high-resolution images. State-of-the-art super-resolution research continually strives to improve imaging resolution using existing super-resolution technology. This thesis aims to enhance super-resolution microscopy resolution through engineered illumination and establish a solid theoretical foundation to support resolution improvements. In Chapter 2, I discuss the enhancement of resolution in single-molecule localization microscopy for thick sample imaging. In thick samples, the issue of high background noise can degrade the localization precision of single-molecule localization. We introduce Single Objective Lens Lightsheet Microscopy (SOLEIL) as a solution to mitigate the high background photon issue, thereby improving the resolution of single-molecule localization microscopy. Furthermore, in Chapter 3, I propose an imaging solution to address the high background issue and sample-induced aberration in thick sample imaging. In this chapter, I suggest combining SOLEIL microscopy with a sensorless Adaptive Optics (AO) method to address these issues. In Chapter 4, I propose a comprehensive PSF model theory for Image Scanning Microscopy (ISM) that fully considers vectorial effects, aberrations, Stokes shifts, and Fresnel coefficients at media interfaces. This approach allows for the accurate modeling of excitation and emission PSFs, providing precise references for selecting hardware parameters and assessing image quality.

Legal protection

This section contains regulations about objections and complaints for students and, in some cases, for third parties. The Higher Education and Research Act prescribes the establishment of an accessible facility, also referred to as a desk, where students can file complaints, objections or appeals. Regulations concerning the Central Objections Committee for TU Delft students The Higher Education and Research Act prescribes the establishment of an arbitration committee that deals with objections from students (other than appeals to the Examination Appeals Board (CBE), for which separate regulations have been established). This is a either a committee in accordance with the Dutch General Administrative Law Act or an objections committee. The regulations concerning the Central Objections Committee for TU Delft students regulate the organisational aspects of this committee, such as composition, appointment, support and compensation. Furthermore, the regulations contain procedural provisions in addition to the procedure described in the General Administrative Law Act. The regulations are also included as Appendix 4 of the EMR. For more information, see the objection and appeal page . TU Delft Examination Appeals Board Regulations Pursuant to Article 7.62 of the Higher Education and Research Act (WHW), the Examination Appeals Board (CBE) must draw up rules of procedure. The Examination Appeals Board Regulations contain rules about the organisational aspects of this board, such as composition, appointment, support and compensation. In addition, the regulations contain a procedural provision on amicable settlement. In other respects, the procedural provisions described in the General Administrative Law Act and the Higher Education and Research Act apply. For more information, see the objection and appeal page . TU Delft Student Complaints Regulations The TU Delft Student Complaints Regulations allow students to submit complaints to the Executive Board about the way in which an administrative body of TU Delft or a person associated with the university has acted towards them or another person. The Higher Education and Research Act prescribes the establishment of a ‘complaints desk’. TU Delft also has a Student Ombudsman who deals with complaints and takes any further action required. The regulations mainly contain organisational aspects; the complaint-handling procedure is described in the General Administrative Law Act. The regulations are also included as Appendix 4 of the EMR. For more information, see the Central complaints page . TU Delft Academic Integrity Complaints Regulation All parties involved in research at TU Delft are individually responsible for maintaining research integrity, which includes observing the general principles of professional research conduct. One means of assessing research integrity is the right of complaint if university staff members have violated the precepts of research integrity (or are suspected of having done so). The TU Delft Regulation on Complaints about Research Integrity includes rules for the purpose of achieving this right of complaint. Provision is made for the appointment of a confidential counsellor and the Research Integrity Committee (CWI), which investigates the complaints and provides recommendations to the Executive Board. The regulation has been adapted following the new Netherlands Code of Conduct for Research Integrity 2018. The regulation refers to this Code for the violations of research integrity included therein. The regulation is based on the national model, coordinated by the Association of Universities in the Netherlands (VSNU). On the VSNU website, you can find the Netherlands Code of Conduct for Research Integrity 2018, as well as the pronouncements issued nationwide following advice from a CWI. For more information, see the Research integrity page . TU Delft Regulations for Complaints about Undesirable Behaviour This regulation gives a person who is confronted by the undesirable behaviour of an employee or student in a work or study situation the right to speak to a confidential adviser or submit a complaint to the complaints committee for undesirable or inappropriate behaviour. The TU Delft Regulations for Complaints about Undesirable Behaviour describe the procedure that must be followed here. For more information, see the Social integrity page . TU Delft Regulations for Reporting Misconduct (Whistleblower Regulations) In the context of good governance and an honest organisation, TU Delft considers it desirable to have regulations that set out how everyone can report irregularities confidentially. The Whistleblower Regulations set out the procedure on the basis of which a report of an irregularity or misconduct should be made. First of all, this should be reported internally, to the supervisor, but you can also contact the ‘whistleblower contact person’ (this contact person has been appointed by the Executive Board under the title ‘confidential adviser for administrative integrity’). As a last resort, a matter can be reported to the external contact point, which in the case of TU Delft is the Association of Netherlands Municipalities’ Whistleblowers Committee. The Whistleblower Regulations are the final piece of TU Delft integrity policy. For more information, see the Organisational integrity page . Privacy Statement The TU Delft handles personal data with care and acts within the limits of the law, including the General Data Protection Regulation (GDPR). The committees also follow the applicable privacy rules. For further details, we refer you to the Privacy Statement of TU Delft. In this Privacy Statement, you are informed about the purposes for which TU Delft processes personal data and about the exercise of your privacy rights.

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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 .