aahlad puli


CV (as of April 2021)

PhD student, Computer Science, NYU
Email: aahlad [at] nyu [dot] edu



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About Me

I am a PhD candidate in Computer Science at the Courant Institute, NYU. I am fortunate to be advised by Prof. Rajesh Ranganath. I am supported by the Apple Scholars in AI/ML PhD fellowship.

At present, I’m working on building models that are robust across populations using insights and techniques from causal inference. Earlier, I worked on causal effect estimation when standard assumptions like ignorability and positivity/overlap do not hold and improving calibration in survival models. My primary applied interest is ML for healthcare in particular and science in general.

I’m eternally excited about new ideas and finding good applications for my work! Shoot me an email if you want to chat!


  1. July 2022: Organized the SCIS workshop at ICML 2022; website.

  2. March 2022, Very happy to be a recipient of the Apple Scholars in AI/ML PhD Fellowship! announcement

  3. March 2022, Updated version of NuRD on arxiv with code and improved results! link

  4. January 2022, NuRD published at ICLR 2022 and work led by Mark Goldstein published at CLeaR 2022; link.

  5. Oct’ 21, Named Rising Star by the Trustworthy ML initiative

  6. June’ 21, New work on arxiv: What sort of predictive models come with performance guarantees under spurious correlations induced by a relationship between the label and some nuisance variables that are correlated on the covariates? Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations

  7. Apr’ 21, Link to work at AISTATS, 2021; Led by Mukund Sudarshan: A new contrarian test statistic to use in CRTs to improve robustness to mis-specified covariate distributions. CONTRA:Contrarian statistics for controlled variable selection

  8. Nov’ 20. Links to my work at NeurIPS 2020 along with punchlines (shoot me an email if these interest you!):


I was an intern in the summer of 2019 in Adobe Research, San Jose working on bayesian attribution models for ad targeting. Previously, I worked as a Software Developer at DBMI, Columbia University. In 2017, I completed my MS in CS, also at NYU. I was introduced to causal inference in the Clinical Machine Learning group, where I worked with two amazing mentors, Prof. Uri Shalit and Prof. David Sontag. My fateful but fun undergrad was from IIT Madras, where I was enrolled in the EE department.