Jeremias Sulam
@jsulam.bsky.social
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Assistant Prof. @ JHU 🇦🇷🇺🇸 Mathematics of Data & Biomedical Data Science jsulam.github.io
Deadline for CPAL coming up on Dec 5! Submit your best work on Parsimony and Learning and come join us in Tübingen in March!
cpal.cc
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Conference on Parsimony and Learning (CPAL) - Addressing the low-dimensional structures in high-dimensional data that prevail in machine learning, signal processing, optimization, and beyond.
https://cpal.cc
3 months ago
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The Biomedical Engineering Department at @JohnsHopkins is hiring! Do you work on data science and machine learning for biomedical problems? Consider applying - deadline for full consideration *Dec 5t*
www.bme.jhu.edu/careers-indu...
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Join Hopkins BME - Johns Hopkins Biomedical Engineering
Hopkins BME is not only a great place to learn, it’s also a great place to work. Browse the listing of postdoctoral, research, and faculty openings.
https://www.bme.jhu.edu/careers-industry/join-hopkins-bme/
3 months ago
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Check this out 📢 Score-based diffusion models are powerful—but slow to sample. Could there be something better? Drop the scores, use proximals instead! We present Proximal Diffusion Models, providing a faster alternative both in theory* and practice. Here’s how it works 🧵(1/n)
7 months ago
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Awesome to see our cover in
@cp-patterns.bsky.social
finally out! And kudos go to Zhenzhen Wang for her massive work on biomarker discovery for breast cancer
www.cell.com/patterns/ful...
add a skeleton here at some point
11 months ago
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Today, on
#WomenInScience
day, this paper on biomarker discovery for breast cancer, by my amazing student Zhenzhen, has just appeared in
@cp-patterns.bsky.social
🎉 Her work shows how to construct fully interpretable biomarkers employing bi-level graph learning!
@jhu.edu
@hopkinsdsai.bsky.social
about 1 year ago
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Nice write-up by @JHUCompSci about @JacopoTeneggi's work. Puch-line: interpretability of opaque ML models can be posed as hypothesis tests, for which online (efficient) testing procedures can be derived!
www.cs.jhu.edu/news/wanna-b...
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Wanna bet? Testing conceptual importance for more explainable AI
Johns Hopkins researchers used betting strategies to help clarify AI models’ decision-making processes.
https://www.cs.jhu.edu/news/wanna-bet-testing-conceptual-importance-for-more-explainable-ai/
about 1 year ago
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📣 What should *ML explanations* convey, and how does one report these precisely and rigorously?
@neuripsconf.bsky.social
come check Jacopo Teneggi's work on Testing for Explanations via betting this afternoon! I *bet* you'll like it :)
openreview.net/pdf?id=A0HSm...
@hopkinsdsai.bsky.social
about 1 year ago
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reposted by
Jeremias Sulam
Adam Charles
about 1 year ago
NeurIPS paper: Excited for our work (with Iuliia Dmitrieva+Sergey Babkin) on "realSEUDO for real-time calcium imaging analysis"
arxiv.org/abs/2405.15701
to be presented tomorrow (Thu 4:30-7:30PM). realSEUDO is a fully on-line method for cell detection and activity estimation that runs at >30Hz.
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realSEUDO for real-time calcium imaging analysis
Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing experimental time. A cr...
https://arxiv.org/abs/2405.15701
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