Akshay Vashist

832 total citations · 1 hit paper
14 papers, 549 citations indexed

About

Akshay Vashist is a scholar working on Artificial Intelligence, Computer Networks and Communications and Molecular Biology. According to data from OpenAlex, Akshay Vashist has authored 14 papers receiving a total of 549 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 5 papers in Computer Networks and Communications and 2 papers in Molecular Biology. Recurrent topics in Akshay Vashist's work include Anomaly Detection Techniques and Applications (4 papers), Machine Learning and Data Classification (3 papers) and Network Security and Intrusion Detection (3 papers). Akshay Vashist is often cited by papers focused on Anomaly Detection Techniques and Applications (4 papers), Machine Learning and Data Classification (3 papers) and Network Security and Intrusion Detection (3 papers). Akshay Vashist collaborates with scholars based in United States and Japan. Akshay Vashist's co-authors include Vladimir Vapnik, Rauf Izmailov, Dmitry Pechyony, Ilya Muchnik, Casimir A. Kulikowski, Ritu Chadha, Casimir A. Kulikowski, Roberto Pagliari, Yitzchak M. Gottlieb and Konstantine Arkoudas and has published in prestigious journals such as Neural Networks, Investigative Radiology and IEEE/ACM Transactions on Computational Biology and Bioinformatics.

In The Last Decade

Akshay Vashist

13 papers receiving 534 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.

A new learning paradigm: Learning using privileged information

2009 436 citations Vladimir Vapnik, Akshay Vashist Neural Networks profile →

Peers

Countries citing papers authored by Akshay Vashist

Since Specialization

Citations

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

Fields of papers citing papers by Akshay Vashist

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akshay Vashist

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

All Works

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14 of 14 papers shown

#	Work	Indexed citations
1	Increasing Efficiency and Cost-Effectiveness by Automating the Authoring of the Development Safety Update Report 2021 ·Pharmaceutical Medicine ·(unknown), (unknown), Akshay Vashist, (unknown), Mirza I. Rahman	0
2	Insider attack detection using weak indicators over network flow data 2015 ·Roberto Pagliari, (unknown), Yitzchak M. Gottlieb, Ritu Chadha, Akshay Vashist, (unknown)	6
3	Multidimensional splines with infinite number of knots as SVM kernels 2013 ·Rauf Izmailov, Vladimir Vapnik, Akshay Vashist	15
4	Managing high volume data for network attack detection using real-time flow filtering 2013 ·China Communications ·(unknown), Yitzchak M. Gottlieb, (unknown), Akshay Vashist, (unknown), Ayumu Kubota, (unknown), Akira Yamada	2
5	Detecting communication anomalies in tactical networks via graph learning 2012 ·Akshay Vashist, Ritu Chadha, (unknown), (unknown)	4
6	A model-theoretic approach to data anonymity and inference control 2012 ·Konstantine Arkoudas, Akshay Vashist	1
7	Leveraging social network for predicting demand and estimating available resources for communication network management 2011 ·Akshay Vashist, Siun-Chuon Mau, (unknown), Ritu Chadha, (unknown)	3
8	SMO-Style Algorithms for Learning Using Privileged Information. 2010 ·Investigative Radiology ·Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vladimir Vapnik	37
9	Predicting End-to-end Network Load 2010 ·Akshay Vashist, Siun-Chuon Mau, (unknown), Ritu Chadha, (unknown)	2
10	A new learning paradigm: Learning using privileged information breakdown → 2009 ·Neural Networks ·Vladimir Vapnik, Akshay Vashist	436
11	Learning using hidden information (Learning with teacher) 2009 ·Vladimir Vapnik, Akshay Vashist, (unknown)	29
12	Ortholog Clustering on a Multipartite Graph 2007 ·IEEE/ACM Transactions on Computational Biology and Bioinformatics ·Akshay Vashist, Casimir A. Kulikowski, Ilya Muchnik	11
13	Combinatorial and statistical methods for part selection for object recognition 2007 ·International Journal of Computer Mathematics ·(unknown), Akshay Vashist, Ahmed Elgammal, Ilya Muchnik, Casimir A. Kulikowski	2
14	Automatic screening for groups of orthologous genes in comparative genomics using multiple-component clustering 2004 ·Akshay Vashist, Casimir A. Kulikowski, Ilya Muchnik	1

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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