JACQUELINE R. M. A. MAASCH


                    



Bio. I am a fifth-year PhD candidate in computer science at Cornell Tech and the Weill Cornell Medicine Institute of AI for Digital Health. My doctoral research has been supported by the NSF Graduate Research Fellowship, Cornell's Presidential Life Science Fellowship, and the Digital Life Initiative. I am advised by Dr. Fei Wang, Dr. Volodymyr Kuleshov, and Dr. Kyra Gan. Previously, I was a research intern at Microsoft Research Cambridge. For the spring of 2026, I will be a resident at the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge.



Research focus. I am interested in machine learning for reasoning and decision-making under uncertainty. This includes open problems in AI reasoning: building reasoning machines, what that requires in theory and practice, and its consequences for society. I often approach these problems through the theoretical frameworks of probabilistic and causal graphical modeling. A full list of my skills and interests can be found in my CV.

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

CausalARC: Abstract Reasoning with Causal World Models. J Maasch, J Kalantari, K Khezeli. NeurIPS LAW 2025 ⚡︎ Spotlight.

arxiv  website  Amazon Trusted AI  🤗 hf - 600+ downloads

Compositional Causal Reasoning Evaluation in Language Models. J Maasch, A Hüyük, X Xu, A Nori, J González. ICML 2025.

arxiv  website  code  poster  slides  🤗 hf - 2.3k+ downloads

Position: Beyond Reasoning Zombies — AI Reasoning Requires Process Validity. R Lawrence*, J Maasch*. *Equal contribution.

preprint  website  code

PROBABILISTIC & CAUSAL GRAPHICAL MODELING.

Probabilistic Graphical Models: A Concise Tutorial. J Maasch, W Neiswanger, S Ermon, V Kuleshov.

arxiv  website

Local Causal Discovery for Structural Evidence of Direct Discrimination. J Maasch, K Gan, V Chen, A Orfanoudaki, N Akpinar, F Wang. 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. UAI 2024.

arXiv  code  poster  slides

AI FOR SCIENCE.

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. J Maasch*, M Torres*, M Melo, C de la Fuente. Cell HOST & MICROBE 2023. *Equal contribution.

paper  code  npr  nature news  smithsonian  cnn  vox



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

Pronouns. I use they (or she). 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/'