JACQUELINE R. M. A. MAASCH

       

I am a fourth-year PhD candidate in computer science at Cornell Tech. I work at the intersection of machine learning and causal inference. My doctoral research has been supported by the NSF Graduate Research Fellowship and Cornell's Presidential Life Science Fellowship.

I am advised by Dr. Fei Wang, Dr. Volodymyr Kuleshov, and Dr. Kyra Gan. I am affiliated with the Weill Cornell Medicine Institute of AI for Digital Health. Formally, my concentrations are in artificial intelligence, scientific computing, and applied probability and statistics. Previously, I explored causal reasoning evaluation and elicitation in LLMs as a research intern at Microsoft Research Cambridge.

I will be entering the job market in Fall 2025 / Spring 2026. If you are interested in collaborating, please reach out.

Jacqueline Maasch at Cornell Tech

CURRENT WORK


My work centers on AI reasoning and causal methods for improved decision-making. I am motivated by urgent societal problems, including drug discovery and fairness in healthcare. Recent contributions include:



PUBLICATIONS


A subset of my lead-author papers are highlighted below. My full bibliography is on Google Scholar.

Compositional Causal Reasoning Evaluation in Language Models. J Maasch, A Hüyük, X Xu, A Nori, J González. Under review.

[tl;dr] A novel evaluation framework reveals taxonomically distinct error patterns when LLMs reason over the composition of causal measures.

arxiv  slides

Probabilistic Graphical Models: A Concise Tutorial. V Kuleshov*, J Maasch*, S Ermon. Coming soon.

[tl;dr] This tutorial introduces the formalisms, methods, and applications of representation, learning, and inference in probabilistic graphical modeling.

arxiv coming soon  website coming soon

Local Causal Discovery for Structural Evidence of Direct Discrimination. J Maasch, K Gan, V Chen, A Orfanoudaki, N Akpinar, F Wang.

[tl;dr] Local structure learning facilitates causal fairness analysis in complex decision systems, as illustrated by a real-world case study on organ transplant allocation.

AAAI 2025  ✴︎  arXiv  code  poster  slides

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs. J Maasch, W Pan, S Gupta, V Kuleshov, K Gan, F Wang.

[tl;dr] Local causal discovery enables efficient automated covariate selection under limited prior knowledge with guarantees on correctness.

UAI 2024  ✴︎  arXiv  code  poster  slides

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. J Maasch*, M Torres*, M Melo, C de la Fuente.

[tl;dr] Computational proteolysis guides bioinspired prospection for encrypted antimicrobial peptides that display in vitro and in vivo activity with low host toxicity.

Cell Host & Microbe 2023  ✴︎  paper  code  npr  nature news  cnn  vox

CONTACT

Preferred contact: In general, I can be reached through LinkedIn.

Pronouns: they / them.
If you are new to nonbinary pronouns, here are some examples for how to use them in a grammatical way from Merriam Webster, the MLA Style Guide, and the APA Style Guide. ☺ 

echo @ | sed 's/^/maasch/' | sed 's/$/cs.cornell.edu/'

Updated February 2025. This website was adapted by J. Maasch from this source code.