About Computational Legal Studies Blog
The Computational Legal Studies Blog was founded on March 17, 2009. The CLS Blog is an attempt to disseminate legal or law related studies that employ a computational or complex systems component. We hope this venue will serve as a coordinating device for those interested in using such techniques to consider the development of legal systems and/or implementation of more reasoned public policy.
To contact us, feel free to email firstname.lastname@example.org.
Daniel Katz + Mike Bommarito + Jon Zelner
About the Authors
Daniel Martin Katz – Professor Katz is a scientist, technologist and law professor who applies an innovative polytechnic approach to teaching law, meshing litigation and transactional knowledge with emerging software and other efficiency-enhancing technologies to help create lawyers for today’s challenging legal job market. Both his scholarship and teaching integrate science, technology, engineering, and mathematics.
Professor Katz’s forward-thinking ideas helped to earn him acknowledgement among the Fastcase 50, an award which “recognizes 50 of the smartest, most courageous innovators, techies, visionaries, and leaders in the law.” He was also named to the American Bar Association Journal’s “Legal Rebels,” a prestigious group of change leaders in the legal profession.
Professor Katz teaches Civil Procedure, E-Discovery, and Entrepreneurial Lawyering at Chicago-Kent and spearheads new initiatives to teach law students how to leverage technology and entrepreneurship in their future legal careers. He joined Chicago-Kent in 2015 from Michigan State University College of Law, where he co-founded the ReInvent Law Laboratory, an innovative multi-disciplinary center that focused on the intersection of entrepreneurship, informatics, programming and design thinking to better understand, analyze and design the law.
Professor Katz has published or forthcoming work in a wide variety of academic outlets, including the Emory Law Journal, Ohio State Law Journal, Iowa Law Review, Illinois Law Review, Virginia Tax Review, Cornell Journal of Law & Public Policy, Journal of Law & Politics, Journal of Legal Education, Artificial Intelligence & Law, and Physica A.
His work has been highlighted in a number of media outlets, including the New York Times, The Wall Street Journal, Wired, Vox, National Public Radio, Slate Magazine, Huffington Post, 538, Bloomberg Businessweek, ABA Journal, Law Technology News and The American Lawyer.
In 2014, Professor Katz was named to the external affiliated faculty at CodeX—The Stanford Center for Legal Informatics. In addition to teaching and researching, Professor Katz serves as an editor of the International Journal of Law and Information Technology (Oxford University Press) and as a member of the Editorial Board of the Journal of Artificial Intelligence & Law (Springer Scientific). He serves on the Editorial Advisory Board for Law Technology News and is a member of the ABA Task Force on Big Data and the Law.
Professor Katz is actively involved in the rapidly growing legal technology industry. He is the Co-Founder & Chief Strategy Officer of LexPredict (a Legal Analytics company). He also serves as a formal and informal advisor to a large number of legal startups. In addition, he is a member of the advisory board of NextLaw Labs – a global collaborative innovation ecosystem organized with Dentons (the world’s largest law firm).
Professor Katz received his Ph.D. in political science and public policy with a focus on complex adaptive systems from the University of Michigan. He graduated with a Juris Doctor cum laude from the University of Michigan Law School and simultaneously obtained a Master of Public Policy from the Gerald R. Ford School of Public Policy at the University of Michigan. During his graduate studies, he was a fellow in Empirical Legal Studies at the University of Michigan Law School and a National Science Foundation IGERT fellow at the University of Michigan Center for the Study of Complex Systems.
Michael J. Bommarito II, founds, builds, operates, consults for, and advises businesses in legal and financial services, technology, and logistics. He is the CEO of LexPredict (a Legal Analytics company) and Bommarito Consulting. His experience spans technology, business, and operations, ranging from top Am Law firms and $B+ AUM investment firms to idea-stage startups. He brings over a decade of experience in software, data, infrastructure, operations, and business strategy to any team, and frequently serves in outsourced executive roles.
Outside of for-profit activities, he is passionate about education and community-building. In the community, he organizes the Michigan Python Developer Group, Detroit useR Group and the Chicago Legal Innovation and Technology Meetup. He is also an Adjunct Professor at the University of Michigan Center for the Study of Complex Systems and a Fellow at CodeX – The Center for Legal Informatics @ Stanford University.
Mike has published work in a wide variety of academic outlets including Quantitative Finance, Virginia Tax Review, Journal of Legal Education, Artificial Intelligence & Law and Physica A. His work has been highlighted in a number of media outlets including the New York Times, Wired, Vox, Wall Street Journal, Slate Magazine, 538, Huffington Post and ABA Journal.
Mike holds an M.S.E in Financial Engineering, M.A. and B.S. in Mathematics from the University of Michigan, Ann Arbor, where he was an NSF-IGERT fellow at the Center for the Study of Complex Systems.
Jon Zelner – I am a Health and Society Scholar at the Columbia University site of the Robert Wood Johnson Foundation Health and Society Scholars Program. I received my PhD in Sociology and Public Policy from the University of Michigan in 2011. From 2011 until September 2014, I was a postdoc in the lab of Prof. Bryan Grenfell in the Department of Ecology and Evolutionary Biology at Princeton University.
My research is focused on using spatial and social network analyisis to prevent infectious diseases, with a focus on tuberculosis and diarrheal disease, and to understand social and epidemiological systems characterized by complex spatiotemporal dynamics.