New faculty Q&A: Uthsav Chitra Get to know Uthsav Chitra, who joins Johns Hopkins as an assistant professor of computer science.

Read our Q&A with @uthsav.bsky.social to learn about his research in computational biology, the courses he’s teaching, and his hobbies! 🧗‍♂️

Nine new tenure-track faculty join Johns Hopkins Computer Science Their research spans social computing and human-computer interaction to the theoretical foundations and real-world applications of machine learning models.

The Department of Computer Science is pleased to welcome nine new tenure-track faculty to its ranks this academic year! Featuring @anandbhattad.bsky.social, @uthsav.bsky.social, @gligoric.bsky.social, @murat-kocaoglu.bsky.social, @tiziano.bsky.social, and more:

10 new CS professors! 🥳

@anandbhattad.bsky.social @uthsav.bsky.social @gligoric.bsky.social @murat-kocaoglu.bsky.social @tiziano.bsky.social

NSF just cancelled ALL grants to Harvard researchers. That’s right - physics, astronomy, bio, CAREER - ALL. Professors won’t get paid. Postdocs won’t get paid. PhD students won’t get paid. This is insane!

If they can do this to Harvard, they can do this to your school.

Wow. "NIH" canceled my co-mentored (with Dave Sulzer) PhD student's F31 funding. His work is on understanding the genetics and neuroscience of language learning disorders. F31 provides no indirect $ to Columbia, just pays his salary. Not that it should matter, but he's an American citizen. W.T.F.

Computer Science Seminar Series. Tuning Our Algorithmic Amplifiers: Embedding Pro-Social Values Into Online Platforms. March 13, 2025, 228 Malone Hall. Refreshments available 10:30 a.m. Seminar begins 10:45 a.m. Tiziano Piccardi, Stanford University.

CS & BME Seminar Series: Machine Learning for Spatial and Network Biology. March 13, 2025, 12 p.m. 228 Malone Hall. Refreshments available at noon. Seminar begins 12:15 p.m. Uthsav Chitra, Eric and Wendy Schmidt Center at Broad Institute.

2⃣ seminars coming up on Thursday with @tiziano.bsky.social and @uthsav.bsky.social! Check them out at bit.ly/3Fv5n7C and bit.ly/3FssGPr

Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spa…

Finally, if you've gotten this far: this work builds on our earlier work with @congma.bsky.social using ~complex analysis~ (conformal mapping) to model spatial variation in ST data: www.sciencedirect.com/science/arti...

Check out the paper for more details and neat biological applications! For example, modeling spatial gradients → much more accurate SVG identification

Thanks to great collaborators: Brian @hrksrkr.bsky.social Kohei @congma.bsky.social Sereno @braphael.bsky.social

GASTON algorithm: parametrize functions h,d with neural nets and learn from data!

Fun back-story: @braphael.bsky.social and I derived most of this model at the bar near an NCI workshop 🥂😅

Model implicitly accounts for sparsity by “pooling” measurements across locations (x,y) with equal isodepth d(x,y).

These locations look like contours of equal height on an elevation map, hence the “topographic map” analogy.

We prove that f(x,y) = h(d(x,y)), i.e. gene expression f(x,y) is function of a *single* spatial coordinate d(x,y) rather than 2 spatial coordinates x,y

-> Spatial dimensionality reduction! 🚀

We call d(x,y) the "isodepth" - it characterizes spatial gradients ▽f_g

We handle sparsity w/ two assumptions:

(1) genes have *shared* gradient directions, i.e. each gradient ▽f_g(x,y) is proportional to shared vector field v(x,y)
(Equivalent to Jacobian of f being rank-1 everywhere)

(2) vector field v has no “curl”, so v=▽d is gradient of "spatial potential" d

You can view ST data as samples of function f: R^2 → R^G, where f(x,y) is (high-dim) gene expression vector at location (x,y).

Spatial gradients are gradients ▽f_g of each component (gene)

Unfortunately, large data sparsity means naive estimation of gradient ▽f_g is very noisy 😱

GASTON, our method to learn “topographic maps” of gene expression, is out now @naturemethods.bsky.social!

IMO the coolest part is a new model of *spatial gradients in sparse data*.
As is typical for bio papers, it’s buried in Methods, but see below for a quick outline on the math 👇

Distinguishing real from invented problems with the NIH How does the NIH work and where does it work well?

A must-read article by @sashagusevposts.bsky.social (hopefully by those whose voice will have an impact). Personally, I'd advocate for a fruit-based distribution system of funds.

theinfinitesimal.substack.com/p/distinguis...