Aditya Nori

3.3k total citations · 1 hit paper
20 papers, 1.1k citations indexed

About

Aditya Nori is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Networks and Communications. According to data from OpenAlex, Aditya Nori has authored 20 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Computer Networks and Communications. Recurrent topics in Aditya Nori's work include Radiomics and Machine Learning in Medical Imaging (4 papers), Coding theory and cryptography (4 papers) and Error Correcting Code Techniques (4 papers). Aditya Nori is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (4 papers), Coding theory and cryptography (4 papers) and Error Correcting Code Techniques (4 papers). Aditya Nori collaborates with scholars based in United Kingdom, United States and India. Aditya Nori's co-authors include Junaid Bajwa, Bryan Williams, Usman Munir, Javier Alvarez-Valle, Ozan Oktay, Anton Schwaighofer, Anja Thieme, David Carter, Sriram K. Rajamani and Daniel C. Castro and has published in prestigious journals such as Nature Communications, PLoS ONE and Physics in Medicine and Biology.

In The Last Decade

Aditya Nori

17 papers receiving 1.0k citations

Hit Papers

align trajectories sort by overperformance

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.

Artificial intelligence in healthcare: transforming the practice of medicine

2021 668 citations Junaid Bajwa, Usman Munir et al. Future Healthcare Journal profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Aditya Nori	427	307	271	100	99	20	1.1k
Stephen Gilbert	355 0.8×	344 1.1×	225 0.8×	97 1.0×	195 2.0×	101	1.5k
Kyle Lam	473 1.1×	245 0.8×	276 1.0×	55 0.6×	126 1.3×	32	1.2k
Sumaya N. Almohareb	617 1.4×	271 0.9×	294 1.1×	173 1.7×	110 1.1×	15	1.3k
Atheer Aldairem	617 1.4×	271 0.9×	294 1.1×	173 1.7×	110 1.1×	6	1.2k
Tariq Alqahtani	686 1.6×	286 0.9×	368 1.4×	176 1.8×	120 1.2×	30	1.6k
Nada Alsuhebany	618 1.4×	283 0.9×	297 1.1×	173 1.7×	111 1.1×	25	1.4k
Haoran Zhang	269 0.6×	234 0.8×	274 1.0×	35 0.3×	98 1.0×	68	1.1k
Shuroug A. Alowais	685 1.6×	284 0.9×	367 1.4×	177 1.8×	118 1.2×	23	1.5k
Irene Y. Chen	480 1.1×	251 0.8×	384 1.4×	111 1.1×	56 0.6×	30	1.0k
Mohammed Alrashed	686 1.6×	287 0.9×	368 1.4×	176 1.8×	119 1.2×	21	1.5k

Countries citing papers authored by Aditya Nori

Since Specialization

Citations

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

Fields of papers citing papers by Aditya Nori

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aditya Nori

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

All Works

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20 of 20 papers shown

Sharma, Harshita, Sarah Killcoyne, Daniel C. Castro, et al.. (2024). Enabling large-scale screening of Barrett’s esophagus using weakly supervised deep learning in histopathology. Nature Communications. 15(1). 2026–2026. 11 indexed citations

Liu, Fangyu, Qianchu Liu, Shruthi Bannur, et al.. (2023). Compositional Zero-Shot Domain Transfer with Text-to-Text Models. Transactions of the Association for Computational Linguistics. 11. 1097–1113. 2 indexed citations

Liu, Qianchu, Stephanie L. Hyland, Shruthi Bannur, et al.. (2023). Exploring the Boundaries of GPT-4 in Radiology. 14414–14445. 14 indexed citations

Chien, Isabel, Tim Regan, Ángel Enrique, et al.. (2023). Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety. PLoS ONE. 18(11). e0272685–e0272685. 4 indexed citations

Bannur, Shruthi, Stephanie L. Hyland, Qianchu Liu, et al.. (2023). Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing. 15016–15027. 62 indexed citations

Castro, Daniel C., Ryutaro Tanno, Anton Schwaighofer, et al.. (2022). Active label cleaning for improved dataset quality under resource constraints. Nature Communications. 13(1). 1161–1161. 55 indexed citations

Bajwa, Junaid, Usman Munir, Aditya Nori, & Bryan Williams. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal. 8(2). e188–e194. 668 indexed citations breakdown →

Oktay, Ozan, Anton Schwaighofer, David Carter, et al.. (2020). Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA Network Open. 3(11). e2027426–e2027426. 46 indexed citations

Chien, Isabel, Ángel Enrique, Jorge Palacios, et al.. (2020). A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions. JAMA Network Open. 3(7). e2010791–e2010791. 70 indexed citations

10.

Millstein, Todd, et al.. (2019). Overfitting in Synthesis: Theory and Practice. 1 indexed citations

11.

Phillips, Mark H., R. Jena, Aditya Nori, et al.. (2018). Autosegmentation of prostate anatomy for radiation treatment planning using deep decision forests of radiomic features. Physics in Medicine and Biology. 63(23). 235002–235002. 22 indexed citations

12.

Nori, Aditya, et al.. (2016). Scaling Relational Inference Using Proofs and Refutations. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 4 indexed citations

13.

Zhang, Xin, et al.. (2015). Solving Weighted Constraints with Applications to Program Analysis. SMARTech Repository (Georgia Institute of Technology). 1 indexed citations

14.

Nori, Aditya, et al.. (2014). R2: An Efficient MCMC Sampler for Probabilistic Programs. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1). 41 indexed citations

15.

Netrapalli, Praneeth, et al.. (2013). One-Bit Compressed Sensing: Provable Support and Vector Recovery. International Conference on Machine Learning. 154–162. 29 indexed citations

16.

Nori, Aditya. (2005). Business process management systems. 804–804. 24 indexed citations

17.

Nori, Aditya, et al.. (2004). Decoding codes on graphs. Resonance. 9(2). 39–49.

18.

Nori, Aditya & Priti Shankar. (2003). Tail-biting Trellises for Linear Codes and their Duals.

19.

Nori, Aditya, et al.. (2003). Decoding codes on graphs. Resonance. 8(9). 49–59. 1 indexed citations

20.

Nori, Aditya & Priti Shankar. (2003). A bcjr-like labeling algorithm for tail-biting trellises. 383–383.

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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