Emilie Yu
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emxtyu.bsky.social
Emilie Yu
@emxtyu.bsky.social
100 followers 98 following 15 posts
Postdoctoral researcher - Expressive Computation Lab @ UCSB

http://em-yu.github.io
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emxtyu.bsky.social
them: so why use CAD software

me: have you thought about the use case of making your own mushroom hat for Halloween
Fusion 360 model of a mushroom hat made of planar slices Step by step process of making a cardboard paper mâché mushroom hat Person wearing mushroom hat and collar
November 2, 2025 at 2:47 AM
emxtyu.bsky.social
Super proud to have been part of this work led by Sam Bourgault and Alejandro Aponte @ expressive computation lab

With WORM, we propose a new way to program cobot manufacturing actions through skilled manipulation and procedural repetitions

Checkout the paper and demo at #UIST2025
September 29, 2025 at 4:47 AM
emxtyu.bsky.social
Today we’ll be showing clay printing and artifacts from our research again at our course on Computational Craft 2-5pm @ #SIGGRAPH2025
If you can’t make it to the whole thing, demos will be 4.30-5pm
August 13, 2025 at 3:32 PM
emxtyu.bsky.social
Packed this little guy up for #SIGGRAPH2025
Come to our course on Computational Craft, Wed 2-5 to see it in action!

s2025.conference-schedule.org/presentation...
August 8, 2025 at 10:24 PM
emxtyu.bsky.social
We are looking forward to opening up the discussion to the community!

Please join us in Vancouver >> s2025.conference-schedule.org/presentation...
RECOMMENDATIONS For Authors: Report hardware used in papers. Consider mid-to-low-end hardware as further opportunities to optimize algorithms. For Institutions: Develop performance metrics that incentivize using low-end hardware. Develop supra-local testing infrastructures with a range of heterogeneous, multi-generational hardware. OPEN DISCUSSION By developing and disseminating methods optimized to work on new hardware, are we actively encouraging premature hardware obsolescence among end-users? By necessitating compute clusters to run our algorithms (the 2nd most-cited GPU is a data center GPU), are we consolidating the dependency of individual users on privatized “cloud” services?
June 1, 2025 at 2:20 AM
emxtyu.bsky.social
Our quantitative analysis of papers let us question what GPUs people use for graphics research, and how that compares to general consumers' hardware.
We find that GPUs used in research papers (white dots in graph) are consistently higher-end GPUs. 87% of GPUs reported in research papers (above blue line) are available to less than 20% of the consumer-level user base at publication time. A stacked bar chart shows GPU usage among the general user-base, and white dots represent GPUs used in research papers.
June 1, 2025 at 2:16 AM
emxtyu.bsky.social
Ever wondered how badly we're all addicted to buying new GPUs in graphics labs?

Come see our talk at #SIGGRAPH2025 to discuss how we can collectively move "Towards a sustainable use of GPUs in Graphics Research"

with @elie-michel.bsky.social @axelparis.bsky.social Octave Crespel and Felix Hähnlein
MOTIVATION Graphical Processing Units (GPUs) are at the core of Computer Graphics research. These chips are critical for rendering images, processing geometric data, and training machine learning models. Yet, the production and disposal of GPUs emits CO2 and results in toxic e-waste [1]. METHOD We surveyed 888 papers presented at SIGGRAPH (premier conference for computer graphics research), from 2018 to 2024, and systematically gathered GPU models cited in the text. We then contextualize the hardware reported in papers with publicly available data of consumers’ hardware [2, 3]. REFERENCES [1] CRAWFORD, KATE. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, 2021. [2] STEAM. Steam Hardware Survey. https://store.steampowered.com/hwsurvey [3] BLENDER. Blender Open Data. https://opendata.blender.org
June 1, 2025 at 2:12 AM