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X-WR-CALNAME:Making the most of your Healthcare Data for Reliable AI
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UID:2026-05-21-dr-aahlad-puli@cml.ics.uci.edu
DTSTAMP:20260521T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20260521T130000
DTEND;TZID=America/Los_Angeles:20260521T140000
SUMMARY:[CML Seminar] Dr. Aahlad Puli: Making the most of your Healthcare D
ata for Reliable AI
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Dr. Aahlad Puli\, Faculty Fellow\, Center for Data Science at N
YU\n\nTitle: Making the most of your Healthcare Data for Reliable AI\n\nAb
stract: Scaling has become the dominant recipe for building AI systems tha
t perform reliably across settings. In healthcare\, however\, data cannot
be scaled in the same way as on the internet: it is expensive to collect\,
hard to share\, and constrained by the availability of human expertise. T
he central challenge\, then\, is how to build reliable healthcare AI by ma
king the most of the data we already have. In this talk\, I present a new
CHD risk score learned from EHR data\, then turn to the problem of interpr
etation and trustworthiness. I introduce encoding as a fundamental obstacl
e to interpreting black-box predictions with feature attributions\, and pr
esent the first general formalization of this phenomenon. I then show that
the CHD risk score exhibits poor transportability in certain settings\, a
nd describe an approach to improve transportability without additional sup
ervision. I conclude by highlighting opportunities for AI to improve evalu
ation and decision-making in healthcare\, with a particular focus on adapt
ing LLMs and world models to clinical settings.\n\nhttps://cml.ics.uci.edu
/seminars/2026-05-21-dr-aahlad-puli
X-ALT-DESC;FMTTYPE=text/html:Dr. Aahlad Puli\, Faculty F
ellow\, Center for Data Science at NYU
Title: Making the mos
t of your Healthcare Data for Reliable AI
Abstract: Scaling
has become the dominant recipe for building AI systems that perform reliab
ly across settings. In healthcare\, however\, data cannot be scaled in the
same way as on the internet: it is expensive to collect\, hard to share\,
and constrained by the availability of human expertise. The central chall
enge\, then\, is how to build reliable healthcare AI by making the most of
the data we already have. In this talk\, I present a new CHD risk score l
earned from EHR data\, then turn to the problem of interpretation and trus
tworthiness. I introduce encoding as a fundamental obstacle to interpretin
g black-box predictions with feature attributions\, and present the first
general formalization of this phenomenon. I then show that the CHD risk sc
ore exhibits poor transportability in certain settings\, and describe an a
pproach to improve transportability without additional supervision. I conc
lude by highlighting opportunities for AI to improve evaluation and decisi
on-making in healthcare\, with a particular focus on adapting LLMs and wor
ld models to clinical settings.
https://cml.ics.uci.edu/seminars/2026-05
-21-dr-aahlad-puli
URL:https://cml.ics.uci.edu/seminars/2026-05-21-dr-aahlad-puli
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