Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
CHOMP: Covariant Hamiltonian optimization for motion planning
2013470 citationsAnca D. Dragan, Siddhartha S Srinivasa et al.profile →
Legibility and predictability of robot motion
2013332 citationsAnca D. Dragan, Siddhartha S Srinivasa et al.profile →
Engagement, user satisfaction, and the amplification of divisive content on social media
202514 citationsSmitha Milli, Anca D. Dragan et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Anca D. Dragan
Since
Specialization
Citations
This map shows the geographic impact of Anca D. Dragan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anca D. Dragan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anca D. Dragan more than expected).
This network shows the impact of papers produced by Anca D. Dragan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anca D. Dragan. The network helps show where Anca D. Dragan may publish in the future.
Co-authorship network of co-authors of Anca D. Dragan
This figure shows the co-authorship network connecting the top 25 collaborators of Anca D. Dragan.
A scholar is included among the top collaborators of Anca D. Dragan based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Anca D. Dragan. Anca D. Dragan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Milli, Smitha, et al.. (2020). Reward-rational (implicit) choice: A unifying formalism for reward learning. Neural Information Processing Systems. 33. 4415–4426.4 indexed citations
8.
Abbeel, Pieter, et al.. (2019). On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference.. International Conference on Machine Learning. 5670–5679.1 indexed citations
9.
Dragan, Anca D., et al.. (2019). SQIL: Imitation Learning via Regularized Behavioral Cloning.. arXiv (Cornell University).13 indexed citations
10.
Xu, Kelvin, et al.. (2018). Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning.2 indexed citations
Bajcsy, Andrea, Dylan P. Losey, Marcia K. O’Malley, & Anca D. Dragan. (2017). Learning Robot Objectives from Physical Human Interaction. Rice University's digital scholarship archive (Rice University). 78. 217–226.38 indexed citations
13.
Hadfield-Menell, Dylan, Stuart Russell, Pieter Abbeel, & Anca D. Dragan. (2016). Cooperative Inverse Reinforcement Learning. arXiv (Cornell University). 29. 3909–3917.56 indexed citations
Klusek, Z., et al.. (2010). Preliminary investigations on implementation of technology of broadband signals for marine biology and sediments recognition. Hydroacoustics. 13. 143–152.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.