10/ Also, I will be sticking around in San Diego for NeurIPS afterwards to present SSME. See my co-author’s thread on that here:
bsky.app/profile/dmsh...

9/ This was joint work with
Sophia Lin, @bergerlab.bsky.social , and @emmapierson.bsky.social ! A special shoutout to Sophia Lin who was a high school student when we started this project.

Arxiv: arxiv.org/pdf/2511.11684
Code: github.com/shuvom-s/fun...

8/ We find that the estimated mortality risk required to admit male patients into the ICU (4.5% is lower than that for female patients (5.1%). We find a similar disparity in hospital admissions as well.

7/ We then apply our model to MIMIC-IV, a dataset of medical records from a Boston-area emergency department. We model the flow of patients from the ED to the hospital to the ICU using our funnel model.

6/ We introduce a funnel model that predicts both the ground truth label and the human decisions, while accounting for unobserved covariates that affect both the label and the decisions.

5/ This censoring of the ground truth happens over multiple stages and isn't random. Patients who make it to later stages tend to be at higher risk of cancer. If we train only on the population with observed labels, we’ll be learning from a biased subset of the population.

4/ We only observe the ground truth outcome (biopsy result) for patients who make it to the last stage.

3/ For instance, in a breast cancer diagnosis, a clinician may first administer a breast exam, then order a mammogram for patients with concerning exams, before ordering a biopsy for patients with concerning mammograms.

2/ In many decision-making settings we only observe ground truth labels only after a sequence of human decisions.

Relatedly, @dmshanmugam.bsky.social is on the academic job market and I strongly recommend working with her! She has an impressive array of reliable ML work, often with applications in biomedical/health settings, and I'd recommend talking to her if you can.

congrats! sad I missed it :/

It's interesting because "Shuvom" is definitely the highest signal for gender out of the list mentioned, but it probably just doesn't know this, since my name is pretty rare.

Some past writing I like:

Democracy and the CLT: shuvom-s.github.io/blog/2020/de...

Privacy vs collaboration in genomics: shuvom-s.github.io/blog/2023/ov...

Please check out the others too if interested! [3/3]

Relatedly, I finally consolidated my scattered writing into a blog: shuvom-s.github.io/blog/

For now, I'm hosting on my (rebuilt) personal website, but curious if anyone has recs on a platform to blog (Medium? Substack? Personal site?). I've turned on comments with giscus. [2/3]

context: https://www.nyc.gov/assets/oti/html/nyc-core-framework/subway-icons.html

@dmshanmugam @manish_raghavan @mit_caml @lab_berger @2plus2make5 Labeled examples give access to samples from (s,y) but unlabeled examples still provide samples from the marginal for s. We should use those samples! By turning evaluation into a semi-supervised problem, we can obtain more reliable ...