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.
How much training is needed in multiple-antenna wireless links?
20031.6k citationsBabak Hassibi et al.IEEE Transactions on Information Theoryprofile →
On the capacity of MIMO broadcast channels with partial side information
2005921 citationsBabak Hassibi et al.IEEE Transactions on Information Theoryprofile →
High-rate codes that are linear in space and time
2002904 citationsBabak Hassibi et al.IEEE Transactions on Information Theoryprofile →
This map shows the geographic impact of Babak Hassibi'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 Babak Hassibi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Babak Hassibi more than expected).
This network shows the impact of papers produced by Babak Hassibi. 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 Babak Hassibi. The network helps show where Babak Hassibi may publish in the future.
Co-authorship network of co-authors of Babak Hassibi
This figure shows the co-authorship network connecting the top 25 collaborators of Babak Hassibi.
A scholar is included among the top collaborators of Babak Hassibi 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 Babak Hassibi. Babak Hassibi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Abbasi, Ehsan, et al.. (2019). Universality in Learning from Linear Measurements. CaltechAUTHORS (California Institute of Technology). 32. 12372–12382.2 indexed citations
9.
Azizan, Navid & Babak Hassibi. (2018). Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization. CaltechAUTHORS (California Institute of Technology).6 indexed citations
Douik, Ahmed & Babak Hassibi. (2018). Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering. CaltechAUTHORS (California Institute of Technology). 1299–1308.6 indexed citations
12.
Khisti, Ashish, et al.. (2017). Sequential Coding of Gauss-Markov Sources over Packet-Erasure Channels with Feedback.. arXiv (Cornell University).1 indexed citations
13.
Hassibi, Babak, et al.. (2016). Crowdsourced Clustering: Querying Edges vs Triangles. Neural Information Processing Systems. 29. 1316–1324.20 indexed citations
14.
Thrampoulidis, Christos, Samet Oymak, & Babak Hassibi. (2015). Regularized Linear Regression: A Precise Analysis of the Estimation Error. CaltechAUTHORS (California Institute of Technology). 1683–1709.59 indexed citations
15.
Oymak, Samet & Babak Hassibi. (2013). Asymptotically Exact Denoising in Relation to Compressed Sensing. arXiv (Cornell University).4 indexed citations
16.
Mao, Wei, et al.. (2012). On the Ingleton-Violating Finite Groups and Group Network Codes. arXiv (Cornell University).2 indexed citations
Hassibi, Babak & T. Kailath. (1995). H" ADAPTIVE FILTERING. CaltechAUTHORS (California Institute of Technology). 949–952.22 indexed citations
19.
Hassibi, Babak & T. Kailath. (1994). H∞ Optimal Training Algorithms and their Relation to Backpropagation. CaltechAUTHORS (California Institute of Technology). 7. 191–198.6 indexed citations
20.
Hassibi, Babak, Ali H. Sayed, & T. Kailath. (1993). Hoo Optimality Criteria for LMS and Backpropagation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 6. 351–358.9 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.