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DTSTART;TZID=Europe/Amsterdam:20260527T130000
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DTSTAMP:20260418T094611
CREATED:20260417T080624Z
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UID:10894-1779886800-1779903000@mondai.tudelftcampus.nl
SUMMARY:Bloopers of Brilliance: When Science Goes Sideways
DESCRIPTION:Mondai | House of AI is happy to host the next edition of the AI PhD & Postdoc Spring Symposium\, together with the TU Delft AI Initiative and the AI PhD Committee! \nBloopers or Brilliance: When Science goes Sideways (De voertaal van dit event is Engels) \n\nAre you a PhD or postdoc researcher at TU Delft working on AI-related topics? You are cordially invited!  \n\nHosted on May 27th (13:00 – 17:30) at Panorama XL@Mondai | House of AI\, this year’s event is going to shake things up and focus on a less-talked-about side of science – embracing scientific failures! The event includes poster pitches\, an interactive panel discussion on the importance of negative results\, errors\, and failures in science\, keynotes from final year PhDs\, and the ever-important borrel. It’s an excellent opportunity to present your research (particularly what did not go to plan) and network with fellow AI-focused scholars across campus. \nProgramme (preliminary) 13:00 – 17:30 \n\nKeynotes & talks from final year PhDs working in/with AI at TU Delft\nPanel discussion on embracing scientific failure\nPoster market: You are invited to contribute to this event with your own poster and/or abstract! See poster requirements below. Final deadline for participating with hand in: May 18th\n\nThe afternoon session will end in a casual manner with drinks\, refreshments and an opportunity for networking. More details to be announced so keep an eye on this page for more updates on speakers and panellists! \nPosters and/or ‘failure’ abstract Researchers at all stages of their PhD and working in all different areas of AI are welcome: \n\nMachine learning and foundational AI techniques\nHuman-centered AI systems\nApplication of AI\nFairness\, bias\, legal\, and ethical considerations of AI\nEducation and AI\nDesign with AI\nReflexive and critical research on AI\nAnd more…\n\nPrizes for best poster and ‘failure abstracts’ to be announced! \nRequirements \nYou can submit either A) a poster with a short description of failure or B) a ‘failure abstract’\, aka a short description of the research you wanted to do\, but it didn’t quite work out. \n\nA) If you’re bringing a printed poster\, we request a short description of the efforts that went awry before the successful work. What went wrong in the project before it succeeded? It can be a misstep\, a small mistake\, or a significant error—it can be any way to show that the research is rarely a smooth process.\n\nB) Alternatively\, you can submit a short description explaining the intended goal and how it did not go to plan. Here\, you don’t have to have succeeded; it can be an idea that you eventually abandoned!  \n\n\nFor A) Poster A1 or A0 size \n\nPrinting is available via the AI Initiative for new posters in A0 format. Send in as PDF\, JPEG\, or PNG (portrait mode\, 300 DPI). \n\n\nFor A) and B)\, we expect abstract submissions of up to 300 words for both types. Send in as PDF or DOC(X).\nSubmissions can be made via the registration form available on this page\n\n\n\nRegister & submit your poster and/or ‘failure abstract’ by Monday\, 18 May. \nAny questions? Please contact the AI PhD Committee at AI-PhD-Committee@tudelft.nl \nSpeakers Keynotes & talks from final year PhDs \nModeling Discretization Error with the Bayesian Finite Element Method for Better Parameter Estimates by Anne Poot\nKeynote by Anne Poot (SLIMM Lab\, CEG)\nCan computation itself be probabilistic? In this talk\, I will give a crash course on the finite element method\, demonstrate the issue of discretization error\, and describe how this error can be modeled probabilistically. We will see that by reinterpreting the finite element method from a Bayesian point of view\, we can get better performance in downstream applications such as parameter estimation in inverse problems.\n \nCan neural networks design better structures faster? Neural parameterizations in topology optimization by Surya Manoj Sanu\n \nLayman talk by Surya Manoj Sanu (MACHINA Lab\, ME)\nAs engineers\, we constantly simplify problems so we can solve them faster. That’s why it sounds counterintuitive to add complexity to an already well-defined structural optimization problem. Why make things more complicated? In this talk\, we explore exactly that idea. We introduce an unsupervised neural network — the “extra complexity” — into a traditional topology optimization pipeline — the “simple” engineering workhorse. And surprisingly\, this added layer of intelligence can improve how we design structures. But\, as in all good science\, there’s not only the good. There’s also the bad — and sometimes\, the ugly and we will try to unpack all of this! \nSearch Machines for Architects by Casper van Engelenburg\nKeynote by Casper van Engelenburg (AiDAPT Lab\, A+BE)\nWhile image-based retrieval has drastically diversified the use cases of modern-day search engines\, their relevance judgments are far from optimal for disciplines like architecture\, which heavily rely on visual data that are fundamentally different from the natural photos most search engines are trained on. Where natural photo understanding focuses on appearance mainly (color\, texture)\, architectural drawing understanding is about understanding graphic-like drawings—floor plans\, sections\, axonometric projections\, etc.—that emphasize the composition and organization of the spaces that we live in. Therefore\, to accurately judge relevance between architectural drawings\, we must rethink what it means to be similar and explore how to train domain-specific models or fine-tune pretrained large vision models on architectural data. In this talk\, I will present several of our recent works that highlight advancements in floor plan representation learning and the necessity of building high-quality architectural datasets. \nTensor decompositions for the analysis of functional ultrasound data by Sofia Kotti\nLayman talk by Sofia Kotti (DeTAIL Lab\, EEMCS)\nFunctional ultrasound indirectly measures brain activity through changes in cerebral blood flow. Tensor decompositions provide a natural framework for analysing the acquired data by exploiting their multidimensional structure and expressing them in terms of latent components. This can help identify underlying spatial and temporal patterns in brain activity\, supporting improved interpretation of functional ultrasound measurements. \nPanel: Embracing Scientific Failure \nHow can we think about and practically approach our failures in science? And how can we make them visible through\, for instance\, documentation? \n\n\nThis panel explores what scientific failure really means across different disciplines\, from rejected papers and failed grant applications\, to broader personal and professional setbacks. Speakers reflect on how failure is defined within their fields\, share their own experiences at both an individual and disciplinary level\, and discuss how these moments have shaped their work. By examining not just the challenges but also the lessons learned\, the panel aims to highlight how failure can be an essential and productive part of the scientific process. \nDuring this panel discussion\, Elvire Landstra (Tilburg University)\, Agostino Nickl (A+BE)\, Nazli Cila (IDE)\, and Megha Khosla (EEMCS) will shed light on different definitions of ‘scientific failure’ and how to deal with them.
URL:https://mondai.tudelftcampus.nl/event/ai-phd-postdoc-symposium-bloopers-brilliance/
LOCATION:Panorama @Mondai | House of AI – Delft\, Molengraaffsingel 29\, Delft\, 2629 JD\, Netherlands
ATTACH;FMTTYPE=image/jpeg:https://mondai.tudelftcampus.nl/wp-content/uploads/sites/7/2025/10/TU250612_4059_0283_lowres.jpg
ORGANIZER;CN="Mondai | House of AI":MAILTO:mondai@tudelft.nl
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