Aniket Kesari
Associate Professor
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolAniket Kesari is an associate professor and scholar at the intersection of law, technology, and data science with nine years of experience spanning academia and policy. Currently on faculty at Fordham Law after a postdoctoral fellowship at NYU’s Information Law Institute and research roles at UC Berkeley, he focuses on technology law and policy, economics, and data-driven approaches for social good. He holds a PhD in Jurisprudence and Social Policy from UC Berkeley and a JD from Yale, blending rigorous legal training with quantitative methods. His background includes hands-on data science fellowships and a legislative technology policy internship at GitHub, giving him practical experience translating technical insights into policy. Based in Berkeley, he brings an uncommon combination of classroom scholarship, empirical research, and policy engagement to debates about data governance. Colleagues note his ability to bridge disciplinary silos and produce work that informs both regulators and technologists.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy (PhD), Jurisprudence and Social Policy (Law & Economics), Doctor of Philosophy (PhD), Jurisprudence and Social Policy (Law & Economics) at University of California, BerkeleyDoctor of Law (JD), Law, Doctor of Law (JD), Law at Yale Law SchoolBachelors, Political Science and History, GPA: 3.9/4.0, Bachelors, Political Science and History, GPA: 3.9/4.0 at Rutgers University
English
Github Skills (51)
causal10
causal-inference10
language-processing10
inference9
computational-social-science9
collaboration9
science9
natural-language-processing8
machine-learning8
data-wrangling8
r8
rstudio7
control-flow7
prediction7
data-visualization6
Programming languages (5)
Github contributions (5)
Akesari12/LS123_Data_Prediction_Law_Spring-2019
Jan 2019 - Apr 2019
Contributions:16 commits, 13 pushes, 1 branch in 2 months
dlab-berkeley/Computational-Social-Science-Training-Program
Jul 2020 - Jun 2021
This course is a rigorous, year-long introduction to computational social science. We cover topics spanning reproducibility and collaboration, machine learning, natural language processing, and causal inference. This course has a strong applied focus with emphasis placed on doing computational social science.
Contributions:1 release, 81 commits, 4 PRs in 11 months
language-processingcausalcollaborationrigorousnatural-language-learning
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