H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 31 Citations 4,891 77 World Ranking 7993 National Ranking 3730

Overview

What is he best known for?

The fields of study he is best known for:

  • Programming language
  • Artificial intelligence
  • Algorithm

Aditya V. Nori mainly investigates Theoretical computer science, Programming language, Data mining, Robustness and Artificial neural network. His work carried out in the field of Theoretical computer science brings together such families of science as Algorithm, Probabilistic logic, Mathematical proof and Operator. Aditya V. Nori has researched Probabilistic logic in several fields, including Estimation of distribution algorithm, Inference, Program specification and Scripting language.

The various areas that Aditya V. Nori examines in his Data mining study include Scalability, Overfitting, Linear programming, MNIST database and Debugging. His Robustness study improves the overall literature in Artificial intelligence. His Artificial neural network study incorporates themes from Domain, Scale-space segmentation and Transfer of learning.

His most cited work include:

  • Unsupervised domain adaptation in brain lesion segmentation with adversarial networks (272 citations)
  • Probabilistic programming (233 citations)
  • SYNERGY: a new algorithm for property checking (227 citations)

What are the main themes of his work throughout his whole career to date?

Aditya V. Nori mainly focuses on Theoretical computer science, Artificial intelligence, Probabilistic logic, Program analysis and Algorithm. His research in Theoretical computer science intersects with topics in Sampling, Programming language, Path and Speedup. His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.

His Probabilistic logic research incorporates elements of Probability distribution, Markov chain Monte Carlo, Correctness, Inference and Bayesian inference. His Program analysis study combines topics in areas such as Range, Property, Solver and Symbolic execution. His Algorithm research includes elements of Soundness, Mathematical proof and Alternation.

He most often published in these fields:

  • Theoretical computer science (30.97%)
  • Artificial intelligence (28.32%)
  • Probabilistic logic (20.35%)

What were the highlights of his more recent work (between 2017-2020)?

  • Artificial intelligence (28.32%)
  • Artificial neural network (11.50%)
  • Machine learning (16.81%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Artificial neural network, Machine learning, Inference and Implementation are his primary areas of study. The concepts of his Artificial intelligence study are interwoven with issues in Differential privacy and Causal model. His Artificial neural network research is multidisciplinary, incorporating perspectives in Probabilistic logic, Markov chain, Robustness and Pattern recognition.

His Probabilistic logic study combines topics from a wide range of disciplines, such as Abstract interpretation, Probability distribution, Correctness and Importance sampling. His work deals with themes such as Computation, Task and Sample size determination, which intersect with Inference. The study incorporates disciplines such as Theoretical computer science, Inductive synthesis and Overfitting in addition to Implementation.

Between 2017 and 2020, his most popular works were:

  • Autofocus Layer for Semantic Segmentation (40 citations)
  • Adaptive Neural Trees (37 citations)
  • Semi-Supervised Learning via Compact Latent Space Clustering (32 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Programming language
  • Algorithm

His main research concerns Artificial intelligence, Artificial neural network, Computation, Pattern recognition and Backpropagation. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Abstract interpretation and Machine learning, Markov chain. Aditya V. Nori interconnects Probability distribution, Probabilistic logic, Correctness and Importance sampling in the investigation of issues within Abstract interpretation.

His studies in Markov chain integrate themes in fields like Semi-supervised learning, Graph, Cluster analysis and Feature vector. His Computation research includes themes of Decision tree, Class, Feature learning and Task. His Backpropagation study frequently links to adjacent areas such as Inference.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Probabilistic programming

Andrew D. Gordon;Thomas A. Henzinger;Aditya V. Nori;Sriram K. Rajamani.
international conference on software engineering (2014)

362 Citations

HOLMES: Effective statistical debugging via efficient path profiling

Trishul M. Chilimbi;Ben Liblit;Krishna Mehra;Aditya V. Nori.
international conference on software engineering (2009)

317 Citations

SYNERGY: a new algorithm for property checking

Bhargav S. Gulavani;Thomas A. Henzinger;Yamini Kannan;Aditya V. Nori.
foundations of software engineering (2006)

294 Citations

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Konstantinos Kamnitsas;Konstantinos Kamnitsas;Christian F. Baumgartner;Christian Ledig;Virginia F. J. Newcombe.
international conference information processing (2017)

277 Citations

Compositional may-must program analysis: unleashing the power of alternation

Patrice Godefroid;Aditya V. Nori;Sriram K. Rajamani;Sai Deep Tetali.
symposium on principles of programming languages (2010)

244 Citations

Proofs from Tests

N E Beckman;A V Nori;S K Rajamani;R J Simmons.
IEEE Transactions on Software Engineering (2010)

213 Citations

DeepMedic for Brain Tumor Segmentation

Konstantinos Kamnitsas;Konstantinos Kamnitsas;Enzo Ferrante;Sarah Parisot;Christian Ledig.
international workshop on brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries (2016)

202 Citations

Merlin: specification inference for explicit information flow problems

Benjamin Livshits;Aditya V. Nori;Sriram K. Rajamani;Anindya Banerjee.
programming language design and implementation (2009)

174 Citations

Proofs from tests

Nels E. Beckman;Aditya V. Nori;Sriram K. Rajamani;Robert J. Simmons.
international symposium on software testing and analysis (2008)

165 Citations

Measuring Neural Net Robustness with Constraints

Osbert Bastani;Yani Ioannou;Leonidas Lampropoulos;Dimitrios Vytiniotis.
arXiv: Learning (2016)

149 Citations

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Best Scientists Citing Aditya V. Nori

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Joost-Pieter Katoen

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Hongseok Yang

Korea Advanced Institute of Science and Technology

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Daniel Rueckert

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Technical University of Munich

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Martin Vechev

Martin Vechev

ETH Zurich

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Xiangyu Zhang

Xiangyu Zhang

Purdue University West Lafayette

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Krishnendu Chatterjee

Krishnendu Chatterjee

Institute of Science and Technology Austria

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Jun Sun

Jun Sun

Singapore Management University

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Ben Glocker

Ben Glocker

Imperial College London

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Qi Dou

Qi Dou

Chinese University of Hong Kong

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Chao Wang

Chao Wang

Chinese Academy of Sciences

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Dirk Beyer

Dirk Beyer

Ludwig-Maximilians-Universität München

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Corina S. Pasareanu

Corina S. Pasareanu

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Matthew B. Dwyer

Matthew B. Dwyer

University of Virginia

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Thomas Reps

Thomas Reps

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Daniel Kroening

Daniel Kroening

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