About me
I'm pursuing a joint JD-PhD degree at Harvard Law School and the MIT
Media Lab. My work focuses on Computational Law: leveraging
computation to analyze, improve, and extend the study and practice
of law.
My research aims to bridge the gap between technology and law to
surface new quantitative insights into jurisprudential systems and
to identify computationally-enabled approaches to the practice of
law. By formalizing research around computational law, I aim to
deepen our understanding of legal processes, while building tools
that improve legal practice, expand access to justice, and increase
judicial efficacy.
I also study the technological transformation of the legal
profession and its effect on how organizations manage legal services
and risks. To this end, I collaborate with private and public
entities around the world to prototype practice-oriented
computational legal solutions, deploying and studying computational
law in the real world.
Computational law leverages advanced computational techniques—like network science, natural language processing, and machine learning to help us better understand how legal systems function.
Universal citation dynamics across knowledge systems and a community-based citation model that better predicts future citation trajectories.
We use judges' early career citations to capture their idiosyncracies and we find that a significant minority of judges appear to routinely rely on extreneous factors.
Widely used law firm rankings focus on reputation and we show they have little to do with trial outcomes. We present a data-driven ranking approach that captures law firm's actual performance.
Grounded in empirical data, we explore how litigation finance affects the way litgants and law firms behave.
Legal practice is deeply rooted in written language and so stands to benefit immensely from the thoughtful and responsible application of machine learning and NLP. Computational law brings a practice-oriented perspective to the development of these technologies in an effort to use technology to further access to justice and sustainably transform legal services.
The slow uptake of NLP in legal practice may be exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers.
A massive collection of U.S. federal judicial citations to precedent in context that facilitates work on legal passage prediction, a challenging practice oriented legal retrieval and reasoning task.
Introducing Legal Precedent Prediction (LPP), the task of predicting relevant passages from precedential court decisions given the context of a legal argument.
LLM generated can help individuals understand complex legal concepts by generating an illustrative story. This especially helps non-native English speakers in their understanding.
Computational law enables new approaches to regulation and provide valuable information to regulators and market participants. Computational approaches can be used to express regulations in new ways and provide insights that allow lawmakers to regulate new technologies like AI more effectively.
Exploring the role of human creativity in generative AI.
A large-scale audit of AI training data including data licenses, sources, and provenance.
Individuals are starting to form connections with AI companions. Although this can can harm users, this area is difficult to regulate. Regulation by Design and Legal Dynamism might offer solutions.
Advances in computational law allow us to re-examine and update regulation in real time, recasting laws as dynamic systems rather than as static rules.
Designing central bank digital currencies to stifle money laundering.
Responding to unique challenges of a data-driven marketplace by expanding monopoly power to include data ownership and enhancing premerger review with network science.