Mert Pilancı

1.2k total citations
47 papers, 368 citations indexed

About

Mert Pilancı is a scholar working on Computational Mechanics, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mert Pilancı has authored 47 papers receiving a total of 368 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computational Mechanics, 25 papers in Artificial Intelligence and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mert Pilancı's work include Sparse and Compressive Sensing Techniques (25 papers), Stochastic Gradient Optimization Techniques (19 papers) and Statistical and numerical algorithms (4 papers). Mert Pilancı is often cited by papers focused on Sparse and Compressive Sensing Techniques (25 papers), Stochastic Gradient Optimization Techniques (19 papers) and Statistical and numerical algorithms (4 papers). Mert Pilancı collaborates with scholars based in United States, Türkiye and United Kingdom. Mert Pilancı's co-authors include Martin J. Wainwright, Yun Yang, Laurent El Ghaoui, Orhan Arıkan, Venkat Chandrasekaran, Mustafa Ç. Pı̆nar, Alfred O. Hero, Ali Cafer Gürbüz, Boris Murmann and Hessam Mahdavifar and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and Magnetic Resonance in Medicine.

In The Last Decade

Mert Pilancı

38 papers receiving 352 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mert Pilancı United States 9 188 186 71 59 49 47 368
Max Simchowitz United States 6 148 0.8× 140 0.8× 17 0.2× 69 1.2× 52 1.1× 18 326
Anastasios Zouzias Canada 9 147 0.8× 211 1.1× 26 0.4× 106 1.8× 25 0.5× 13 353
Shenglong Zhou China 13 141 0.8× 118 0.6× 20 0.3× 110 1.9× 70 1.4× 41 443
Hongwei Sun China 11 194 1.0× 146 0.8× 107 1.5× 89 1.5× 21 0.4× 33 434
Yuyuan Ouyang United States 5 243 1.3× 141 0.8× 20 0.3× 39 0.7× 134 2.7× 17 348
Simon S. Du United States 12 90 0.5× 340 1.8× 21 0.3× 109 1.8× 29 0.6× 40 507
Vugar E. Ismailov Azerbaijan 11 53 0.3× 225 1.2× 26 0.4× 52 0.9× 27 0.6× 35 390
Venkat Chandrasekaran United States 10 158 0.8× 90 0.5× 42 0.6× 65 1.1× 12 0.2× 26 369
Yunwen Lei China 11 111 0.6× 242 1.3× 19 0.3× 97 1.6× 10 0.2× 51 355
Ying Cui United States 11 143 0.8× 43 0.2× 48 0.7× 44 0.7× 112 2.3× 27 297

Countries citing papers authored by Mert Pilancı

Since Specialization
Citations

This map shows the geographic impact of Mert Pilancı'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 Mert Pilancı with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mert Pilancı more than expected).

Fields of papers citing papers by Mert Pilancı

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mert Pilancı. 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 Mert Pilancı. The network helps show where Mert Pilancı may publish in the future.

Co-authorship network of co-authors of Mert Pilancı

This figure shows the co-authorship network connecting the top 25 collaborators of Mert Pilancı. A scholar is included among the top collaborators of Mert Pilancı 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 Mert Pilancı. Mert Pilancı is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Candès, Emmanuel J., et al.. (2025). Overparameterized ReLU Neural Networks Learn the Simplest Model: Neural Isometry and Phase Transitions. IEEE Transactions on Information Theory. 71(3). 1926–1977.
2.
Ergen, Tolga & Mert Pilancı. (2025). The Convex Landscape of Neural Networks: Characterizing Global Optima and Stationary Points via Lasso Models. IEEE Transactions on Information Theory. 71(5). 3854–3870.
3.
Pilancı, Mert, et al.. (2024). Gradient Coding With Iterative Block Leverage Score Sampling. IEEE Transactions on Information Theory. 70(9). 6639–6664. 1 indexed citations
4.
Pilancı, Mert, et al.. (2024). Neural spectrahedra and semidefinite lifts: global convex optimization of degree-two polynomial activation neural networks in polynomial-time. Mathematical Programming. 213(1-2). 737–769. 1 indexed citations
5.
Pilancı, Mert, et al.. (2024). Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization. SIAM Journal on Mathematics of Data Science. 6(4). 978–999. 1 indexed citations
6.
Pilancı, Mert, et al.. (2023). Securely Aggregated Coded Matrix Inversion. IEEE Journal on Selected Areas in Information Theory. 4. 405–419. 3 indexed citations
7.
Iyer, Siddharth, Zhitao Li, Christopher M. Sandino, et al.. (2023). Coil-sketched unrolled networks for computationally-efficient deep MRI reconstruction. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 1 indexed citations
8.
Saha, Rajarshi, Mert Pilancı, & Andrea Goldsmith. (2022). Efficient Randomized Subspace Embeddings for Distributed Optimization Under a Communication Budget. IEEE Journal on Selected Areas in Information Theory. 3(2). 183–196.
9.
Pilancı, Mert, et al.. (2022). Secure Linear MDS Coded Matrix Inversion. 1–8. 4 indexed citations
10.
Mahdavifar, Hessam, et al.. (2022). Orthonormal Sketches for Secure Coded Regression. 2022 IEEE International Symposium on Information Theory (ISIT). 826–831. 8 indexed citations
11.
Zhan, Xianghao, Xiaoqing Guan, Zhan Wang, et al.. (2021). Boost AI Power: Data Augmentation Strategies With Unlabeled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination With Electronic Nose. IEEE Sensors Journal. 21(20). 22995–23005. 11 indexed citations
12.
13.
Liu, Sifan, et al.. (2020). Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform. Neural Information Processing Systems. 33. 9725–9735. 5 indexed citations
14.
Pilancı, Mert, et al.. (2019). Polar Coded Distributed Matrix Multiplication.. arXiv (Cornell University). 4 indexed citations
15.
Ergen, Tolga & Mert Pilancı. (2019). Convex Optimization for Shallow Neural Networks. 79–83. 3 indexed citations
16.
Pilancı, Mert & Martin J. Wainwright. (2016). Iterative hessian sketch: fast and accurate solution approximation for constrained least-squares. Journal of Machine Learning Research. 17(1). 1842–1879. 56 indexed citations
17.
Pilancı, Mert, Martin J. Wainwright, & Laurent El Ghaoui. (2015). Sparse learning via Boolean relaxations. Mathematical Programming. 151(1). 63–87. 29 indexed citations
18.
Pilancı, Mert, Laurent El Ghaoui, & Venkat Chandrasekaran. (2012). Recovery of Sparse Probability Measures via Convex Programming. CaltechAUTHORS (California Institute of Technology). 25. 2420–2428. 25 indexed citations
19.
Pilancı, Mert & Orhan Arıkan. (2011). Recovery of sparse perturbations in Least Squares problems. Bilkent University Institutional Repository (Bilkent University). 3912–3915. 1 indexed citations
20.
Pilancı, Mert, Orhan Arıkan, & Erdal Arıkan. (2010). Polar compressive sampling: A novel technique using Polar codes. Bilkent University Institutional Repository (Bilkent University). 73–76. 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.

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