Lucas H. McCabe

Present:

I am a Data Science Fellow at LMI. There, I have served as machine learning lead for the LIGER platform, modeled the climatic and operational impacts of synthetic jet fuel, developed software for maritime logistics, and more.

I am also a computer science PhD candidate at GW, advised by Howie Huang. My work primarily pertains to natural language processing and computational social science; see my research page for more.

I am grateful to have been recognized as a DARPA Riser, Luminary Awardee (LMI), and Bernstein Scholar (Institute for Quantitative Biomedicine).

Past:

I completed my master’s at Johns Hopkins under Tom Woolf. Before that, I studied math and computer science at Rutgers and worked in the Moghe Lab.

selected publications

Appl. Netw. Sci.*
Network Analysis of U.S. Non-Fatal Opioid-Involved Overdose Journeys, 2018-2023

L. H. McCabe, N. Masuda, S. Casillas, N. Danneman, A. Alic, and R. Law

arXiv preprint arXiv:2402.03924, 2024

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We present a nation-wide network analysis of non-fatal opioid-involved overdose journeys in the United States. Leveraging a unique proprietary dataset of Emergency Medical Services incidents, we construct a journey-to-overdose geospatial network capturing nearly half a million opioid-involved overdose events spanning 2018-2023. We analyze the structure and sociological profile of the nodes, which are counties or their equivalents, characterize the distribution of overdose journey lengths, and investigate changes in the journey network between 2018 and 2023. Our findings include that authority and hub nodes identified by the HITS algorithm tend to be located in urban areas and involved in overdose journeys with particularly long geographical distances.
@article{mccabe2024overdosenetworks, title = {Network Analysis of U.S. Non-Fatal Opioid-Involved Overdose Journeys, 2018-2023}, author = {McCabe, L. H. and Masuda, N. and Casillas, S. and Danneman, N. and Alic, A. and Law, R.}, journal = {arXiv preprint arXiv:2402.03924}, year = {2024}, }
EMNLP*
Prompts have evil twins

R. Melamed, L. H. McCabe, T. Wakhare, Y. Kim, H. H. Huang, and E. Boix-Adsera

arXiv preprint arXiv:2311.07064, 2024

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Carefully-designed prompts are key to inducing desired behavior in Large Language Models (LLMs). As a result, much human labor is dedicated to engineering prompts that tailor LLMs to specific use-cases, raising the question of whether this process can be automated. In this work, we propose an automatic prompt optimization framework, PROPANE, which requires no user intervention and solves the inverse problem of finding a prompt that induces semantically similar outputs to a fixed set of examples. We further demonstrate that PROPANE can be used to (a) improve existing prompts, and (b) discover semantically obfuscated prompts that transfer between models.
@article{melamed2023propane, title = {Prompts have evil twins}, author = {Melamed, R. and McCabe, L. H. and Wakhare, T. and Kim, Y. and Huang, H. H. and Boix-Adsera, E.}, journal = {arXiv preprint arXiv:2311.07064}, year = {2024}, }