JACQUELINE R. M. A. MAASCH
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Bio. I am an AI research scientist in New York City. I received my PhD in computer science at Cornell University, with a minor in applied probability and statistics. My doctoral research was supported by the NSF Graduate Research Fellowship, Cornell's Presidential Life Science Fellowship, and the Digital Life Initiative Doctoral Fellowship. During my PhD, I spent time at Microsoft Research, Boehringer Ingelheim, YRIKKA, and 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. My full bibliography is on Google Scholar.
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Position: Beyond Reasoning Zombies — AI Reasoning Requires Process Validity.
R Lawrence*,
J Maasch*. *Equal contribution.
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CausalARC: Abstract Reasoning with Causal World Models. J Maasch, J Kalantari, K Khezeli. NeurIPS LAW 2025.
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Compositional Causal Reasoning Evaluation in Language Models. J Maasch, A Hüyük, X Xu, A Nori, J González. ICML 2025. |
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Probabilistic Graphical Models: A Concise Tutorial. J Maasch, W Neiswanger, S Ermon, V Kuleshov. |
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Local Causal Discovery for Structural Evidence of Direct Discrimination. J Maasch, K Gan, V Chen, A Orfanoudaki, N Akpinar, F Wang. AAAI 2025. |
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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. |
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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. |
