Forrest Iandola

10.7k total citations · 2 hit papers
19 papers, 1.4k citations indexed

About

Forrest Iandola is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Forrest Iandola has authored 19 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 5 papers in Signal Processing. Recurrent topics in Forrest Iandola's work include Advanced Neural Network Applications (6 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Music and Audio Processing (5 papers). Forrest Iandola is often cited by papers focused on Advanced Neural Network Applications (6 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Music and Audio Processing (5 papers). Forrest Iandola collaborates with scholars based in United States, Switzerland and Israel. Forrest Iandola's co-authors include Trevor Darrell, John Platt, C. Lawrence Zitnick, Rupesh K. Srivastava, Xiaodong He, Piotr Dollár, Geoffrey Zweig, Li Deng, Margaret Mitchell and Saurabh Gupta and has published in prestigious journals such as Infoscience (Ecole Polytechnique Fédérale de Lausanne), arXiv (Cornell University) and OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

In The Last Decade

Forrest Iandola

17 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

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.

From captions to visual concepts and back

2015 774 citations Hao Fang, Saurabh Gupta et al. profile →
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

2024 74 citations Yunyang Xiong, Lemeng Wu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Forrest Iandola United States	9	1.2k	542	73	50	45	19	1.4k
Bo Xiong United States	15	980 0.8×	412 0.8×	47 0.6×	55 1.1×	38 0.8×	23	1.3k
Congyan Lang China	16	901 0.8×	392 0.7×	74 1.0×	105 2.1×	33 0.7×	107	1.2k
Hefeng Wu China	18	998 0.9×	605 1.1×	60 0.8×	84 1.7×	32 0.7×	54	1.3k
Chih‐Yao Ma United States	11	734 0.6×	413 0.8×	72 1.0×	58 1.2×	39 0.9×	18	898
Hongyang Li China	14	806 0.7×	374 0.7×	126 1.7×	90 1.8×	37 0.8×	25	1.0k
Marco Pedersoli Canada	17	814 0.7×	379 0.7×	116 1.6×	79 1.6×	21 0.5×	61	1.1k
Bingpeng Ma China	25	2.0k 1.7×	360 0.7×	44 0.6×	55 1.1×	40 0.9×	76	2.2k
Rohit Girdhar United States	13	794 0.7×	388 0.7×	87 1.2×	29 0.6×	21 0.5×	19	1.2k
Keze Wang China	16	874 0.7×	543 1.0×	42 0.6×	110 2.2×	26 0.6×	38	1.3k

Countries citing papers authored by Forrest Iandola

Since Specialization

Citations

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

Fields of papers citing papers by Forrest Iandola

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Forrest Iandola

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

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

1.

Iandola, Forrest, et al.. (2025). SqueezeMe: Mobile-Ready Distillation of Gaussian Full-Body Avatars. 1–11. 1 indexed citations

2.

Xiang, Xiaoyu, et al.. (2025). Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds. 4872–4881. 1 indexed citations

3.

Xu, Dejia, Forrest Iandola, Rakesh Ranjan, et al.. (2024). Taming Mode Collapse in Score Distillation for Text-to-3D Generation. 9037–9047. 3 indexed citations

4.

Chang, Ernie, Forrest Iandola, Zechun Liu, et al.. (2024). On the Open Prompt Challenge in Conditional Audio Generation. 5315–5319.

5.

Xiong, Yunyang, Lemeng Wu, Xiaoyu Xiang, et al.. (2024). EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything. 16111–16121. 74 indexed citations breakdown →

6.

Chang, Ernie, et al.. (2024). Stack-and-Delay: A New Codebook Pattern for Music Generation. 34. 796–800. 1 indexed citations

7.

Chang, Ernie, et al.. (2024). In-Context Prompt Editing for Conditional Audio Generation. 5320–5324. 1 indexed citations

8.

Li, Yang, Liangzhen Lai, Yuan Shangguan, et al.. (2024). Folding Attention: Memory and Power Optimization for On-Device Transformer-Based Streaming Speech Recognition. 11901–11905. 1 indexed citations

9.

Iandola, Forrest & Kurt Keutzer. (2017). Small neural nets are beautiful. 1–10. 26 indexed citations

10.

Jin, Peter, et al.. (2016). How to scale distributed deep learning?. arXiv (Cornell University). 1. 32 indexed citations

11.

Iandola, Forrest, et al.. (2016). STOMP: Statistical Techniques for Optimizing and Modeling Performance of Blocked Sparse Matrix Vector Multiplication. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 93–100. 2 indexed citations

12.

Girshick, Ross, Forrest Iandola, Trevor Darrell, & Jitendra Malik. (2015). Deformable part models are convolutional neural networks. 437–446. 253 indexed citations

13.

Iandola, Forrest, Matthew W. Moskewicz, & Kurt Keutzer. (2015). libHOG: Energy-Efficient Histogram of Oriented Gradient Computation. 1248–1254. 1 indexed citations

14.

Ashraf, Khalid, Benjamin Elizalde, Forrest Iandola, et al.. (2015). Audio-Based Multimedia Event Detection with DNNs and Sparse Sampling. 611–614. 9 indexed citations

15.

Fang, Hao, Saurabh Gupta, Forrest Iandola, et al.. (2015). From captions to visual concepts and back. 1473–1482. 774 indexed citations breakdown →

16.

Iandola, Forrest, David Sheffield, Michael J. Anderson, Phitchaya Mangpo Phothilimthana, & Kurt Keutzer. (2013). Communication-minimizing 2D convolution in GPU registers. 2116–2120. 15 indexed citations

17.

Zhang, Ning, Ryan Farrell, Forrest Iandola, & Trevor Darrell. (2013). Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction. 729–736. 137 indexed citations

18.

Maasoumy, Mehdi, Pierluigi Nuzzo, Forrest Iandola, et al.. (2013). Optimal load management system for Aircraft Electric Power distribution. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2939–2945. 30 indexed citations

19.

Iandola, Forrest, et al.. (2011). Real-time capacity of networked data fusion. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign). 1–8.

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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