Amirali Aghazadeh

869 citations

18 papers · 479 indexed · 1 hit paper · h-index 8

Co-authors: Richard G. Baraniuk Debarshi Sen Satish Nagarajaiah Anand G. Dabak Ali Mousavi David H. Brookes Jennifer Listgarten Advait Balaji
Topics: Advanced Image and Video Retrieval Techniques (3 papers)Sparse and Compressive Sensing Techniques (2 papers)Non-Destructive Testing Techniques (2 papers)
Cited by: Structural Biology Aging Business and International Management
Journals: Proceedings of the National Academy of Sciences Nature Communications Nature Biotechnology
Partner nations: United States United Kingdom

In The Last Decade

Amirali Aghazadeh

17 papers receiving 466 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.

Current progress and open challenges for applying deep learning across the biosciences

2022 171 citations Nicolae Sapoval, Amirali Aghazadeh et al. Nature Communications profile →

Peers

Countries citing papers authored by Amirali Aghazadeh

Since Specialization

Citations

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

Fields of papers citing papers by Amirali Aghazadeh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amirali Aghazadeh

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

All Works

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18 of 18 papers shown

#	Work	Indexed citations
1	Discriminating abiotic and biotic organics in meteorite and terrestrial samples using machine learning on mass spectrometry data 2025 ·PNAS Nexus ·(unknown),(unknown),(unknown),José C. Aponte,Amirali Aghazadeh	0
2	Challenges and Opportunities in Using Amino Acids to Decode Carbonaceous Chondrite and Asteroid Parent Body Processes 2025 ·Astrobiology ·José C. Aponte,Hannah L. McLain,(unknown),Amirali Aghazadeh,Jamie E. Elsila,D. P. Glavin,Jason P. Dworkin	1
3	Efficiently Computing Sparse Fourier Transforms of q-ary Functions 2023 ·(unknown),(unknown),Amirali Aghazadeh,Kannan Ramchandran	2
4	Current progress and open challenges for applying deep learning across the biosciencesbreakdown → 2022 ·Nature Communications ·Nicolae Sapoval,Amirali Aghazadeh,Michael Nute,Dinler A. Antunes,Advait Balaji,Richard G. Baraniuk,CJ Barberan,Ruth Dannenfelser,Chen Dun,Mohammadamin Edrisi,R. A. Leo Elworth,Bryce Kille,Anastasios Kyrillidis,Luay Nakhleh,C. Wolfe,Zhi Yan,Vicky Yao,Todd J. Treangen	171
5	Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis 2021 ·Proceedings of the National Academy of Sciences ·(unknown),Cory Hargus,Assaf Ben‐Moshe,Amirali Aghazadeh,Hyun Dong Ha,Kranthi K. Mandadapu,A. Paul Alivisatos	42
6	On the sparsity of fitness functions and implications for learning 2021 ·Proceedings of the National Academy of Sciences ·David H. Brookes,Amirali Aghazadeh,Jennifer Listgarten	25
7	Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions 2021 ·Nature Communications ·Amirali Aghazadeh,Hunter Nisonoff,(unknown),David H. Brookes,Yijie Huang,O. Ozan Koyluoglu,Jennifer Listgarten,Kannan Ramchandran	23
8	CRISPRL and: Interpretable large-scale inference of DNA repair landscape based on a spectral approach 2020 ·Bioinformatics ·Amirali Aghazadeh,(unknown),Kannan Ramchandran	3
9	Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells 2019 ·Nature Biotechnology ·Ryan T. Leenay,Amirali Aghazadeh,Joseph Hiatt,David Tse,Theodore L. Roth,Ryan Apathy,Eric Shifrut,Judd F. Hultquist,Nevan J. Krogan,Zhenqin Wu,(unknown),Hera Canaj,Manuel D. Leonetti,Alexander Marson,Andrew P. May,James Zou	82
10	Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes 2019 ·Mechanical Systems and Signal Processing ·Debarshi Sen,Amirali Aghazadeh,Ali Mousavi,Satish Nagarajaiah,Richard G. Baraniuk,Anand G. Dabak	69
11	Insense: Incoherent Sensor Selection for Sparse Signals 2018 ·Edinburgh Research Explorer ·Amirali Aghazadeh,Mohammad Golbabaee,Andrew Lan,Richard G. Baraniuk	1
12	Mission: Ultra large-scale feature selection using count-sketches 2018 ·Amirali Aghazadeh,(unknown),(unknown),Gautam Dasarathy,(unknown),Richard G. Baraniuk	3
13	Insense: Incoherent sensor selection for sparse signals 2018 ·Signal Processing ·Amirali Aghazadeh,Mohammad Golbabaee,Andrew Lan,Richard G. Baraniuk	7
14	Sparsity-based approaches for damage detection in plates 2018 ·Mechanical Systems and Signal Processing ·Debarshi Sen,Amirali Aghazadeh,(unknown),Satish Nagarajaiah,Richard G. Baraniuk	34
15	RHash: Robust Hashing via L_infinity-norm Distortion 2017 ·Amirali Aghazadeh,Andrew Lan,Anshumali Shrivastava,Richard G. Baraniuk	2
16	RHash: Robust Hashing via ℓ∞-norm distortion 2017 ·Amirali Aghazadeh,Andrew Lan,Anshumali Shrivastava,Richard G. Baraniuk	2
17	Universal microbial diagnostics using random DNA probes 2016 ·Science Advances ·Amirali Aghazadeh,Adam Yuh Lin,Mona A. Sheikh,Allen L. Chen,(unknown),Coreen Johnson,Joseph F. Petrosino,Rebekah A. Drezek,Richard G. Baraniuk	11
18	Adaptive step size selection for optimization via the ski rental problem 2013 ·Amirali Aghazadeh,(unknown),(unknown),Tom Goldstein,(unknown),(unknown),Richard G. Baraniuk	1

About Amirali Aghazadeh

Amirali Aghazadeh is a scholar working on Signal Processing, Biophysics and Computer Vision and Pattern Recognition, having authored 18 papers that have together received 479 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (3 papers), Sparse and Compressive Sensing Techniques (2 papers) and Non-Destructive Testing Techniques (2 papers). The work is most often cited by research in Structural Biology (16 citations), Aging (14 citations) and Business and International Management (12 citations). Amirali Aghazadeh has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Richard G. Baraniuk, Debarshi Sen, Satish Nagarajaiah, Anand G. Dabak, Ali Mousavi, David H. Brookes, Jennifer Listgarten, Advait Balaji, Nicolae Sapoval and R. A. Leo Elworth. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Nature Biotechnology.

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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