JACQUELINE R. M. A. MAASCH

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I am a PhD student in Computer Science and NSF Graduate Research Fellow at Cornell Tech. I work at the intersection of machine learning and causal inference.

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. The first year of my PhD was internally funded by the Presidential Life Science Fellowship. Formally, my concentrations are in artificial intelligence, scientific computing, and applied probability and statistics.

       

Jacqueline Maasch at Cornell Tech

CURRENT WORK

My current work mainly centers on causal structure learning and causal reasoning. My research is motivated by urgent biomedical problems, including drug discovery and fairness in healthcare. As a Research Intern in Machine Intelligence at Microsoft Research Cambridge, I explored causal reasoning evaluation and elicitation in LLMs.

My research interests include:

  • Probabilistic and causal graphical modeling.
  • AI reasoning evaluation and elicitation.
  • The challenges that arise in low-data settings.
  • The automation and acceleration of scientific discovery and clinical research.
A subset of my recent works are highlighted below. The rest of my publications can be accessed through Google Scholar.

Compositional Causal Reasoning. Under review. 2024. J Maasch, A Huyuk, X Xu, A Nori, J Gonzalez.

[arXiv coming soon]

[tl;dr] We explore the ability of AI agents to infer how causal quantities compose and propagate through causal graphs.

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

[ arXiv ] [ code ] [ slides ]

[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.

A preliminary version of this work was presented as an invited talk at INFORMS 2024.

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs. Uncertainty in Artificial Intelligence. 2024. J Maasch, W Pan, S Gupta, V Kuleshov, K Gan, F Wang.

[ arXiv ] [ code ] [ poster ] [ slides ]

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

Preliminary versions were presented as a poster at the NeurIPS 2023 Causal Representation Learning Workshop and as an invited talk at POMS 2024.

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. Cell Host & Microbe 31. 2023. J Maasch*, M Torres*, M Melo, C de la Fuente. *Equal contribution.

[ paper ] [ code ] [ npr ] [ nature news ] [ cnn ] [ vox ]

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

PROFESSIONAL ACTIVITIES

INVITED TALKS

10.24   INFORMS Annual Meeting | Seattle, WA | Local Causal Discovery for Structural Evidence of Direct Discrimination
07.24   Microsoft Research Machine Intelligence Core | Cambridge, UK
06.24   University of Cambridge Statistical Laboratory | Cambridge, UK | Local causal discovery for effect estimation
04.24   34th Annual POMS Conference | Minneapolis, MN | Local Discovery by Partitioning

ACTIVITIES   In 2023, I co-organized Cornell's interdepartmental Causal Reading Group, a student-led discussion group on causality and causal inference. I co-developed CS 6006: Succeeding in the Graduate Environment, the first-year PhD seminar at Cornell Computer Science. With students at Cornell, MIT, and NJIT, I co-organized the 2024 NYC meetup for the Learning on Graphs Conference, Learning Meets Geometry, Graphs, and Networks. I am a student leader for the PhD admissions and recruitment process at Cornell Tech.

PEER REVIEW   [Computing] International Conference on Artificial Intelligence and Statistics (AISTATS); Association for Computational Linguistics (ACL) Rolling Review; ICML Workshop on Structured Probabilistic Inference & Generative Modeling (SPIGM); NeurIPS Women in Machine Learning Workshop (WiML). [Life sciences] Communications Biology (Nature Portfolio); Bioinformatics (Oxford Academic); ACS Infectious Diseases (American Chemical Society).


PRIOR WORK

As a master's student at Penn Engineering, I used machine learning to investigate the therapeutic potential of antimicrobial peptides. My master's research was supported by the University of Pennsylvania's GAPSA-Provost Fellowship for Interdisciplinary Innovation and the Reproducible Research Fellowship, funded by the Open Knowledge Foundation and Alfred P. Sloan Foundation.

Prior to this, I was a research assistant in various clinical and experimental biology labs. As an experimentalist, I helped develop molecular diagnostics for neglected tropical diseases in academia and for viral pathogens in industry.


CONTACT

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

Pronouns: they / them.
If you are new to non-binary 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 October 2024. This website was adapted by J. Maasch from this source code.