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Computer Science

D-Index
37
Citations
7404
World Ranking
10586
National Ranking
4434

Overview

Aditya V. Nori is affiliated with Microsoft in the United States and has contributed to research primarily in the fields of Computer Science and Medicine. Their scholarly work spans multiple subfields including Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Health Informatics, Statistics and Probability, and Computational Theory and Mathematics.

Nori's research covers a range of topics, with significant focus on:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Artificial Intelligence in Healthcare and Education
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Algorithms
  • Computability, Logic, AI Algorithms
  • Medical Imaging Techniques and Applications

The researcher has published extensively with major contributions appearing in the venue arXiv (Cornell University), where eight of their works have been featured. Recent notable papers include:

  • "Secure Medical Image Analysis with CrypTFlow," 2020, arXiv (Cornell University)
  • "Exploring the Boundaries of GPT-4 in Radiology," 2023, arXiv (Cornell University)
  • "Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs," 2021, arXiv (Cornell University)
  • "Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models," 2024, arXiv (Cornell University)
  • "Beyond Words: A Mathematical Framework for Interpreting Large Language Models," 2023, arXiv (Cornell University)

Collaborations form an important aspect of Nori's research profile. Frequent co-authors include Javier Alvarez-Valle, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, and Hoifung Poon, with multiple joint publications reflecting sustained research partnerships.

Best Publications

  • Artificial intelligence in healthcare: transforming the practice of medicine

    Junaid Bajwa;Usman Munir;Aditya Nori;Bryan Williams

  • Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Christian F. Baumgartner;Christian Ledig;Virginia F. J. Newcombe

  • Probabilistic programming

    Andrew D. Gordon;Thomas A. Henzinger;Aditya V. Nori;Sriram K. Rajamani

  • DeepMedic for Brain Tumor Segmentation

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Enzo Ferrante;Sarah Parisot;Christian Ledig

  • HOLMES: Effective statistical debugging via efficient path profiling

    Trishul M. Chilimbi;Ben Liblit;Krishna Mehra;Aditya V. Nori

  • SYNERGY: a new algorithm for property checking

    Bhargav S. Gulavani;Thomas A. Henzinger;Yamini Kannan;Aditya V. Nori

  • Measuring Neural Net Robustness with Constraints

    Osbert Bastani;Yani Ioannou;Leonidas Lampropoulos;Dimitrios Vytiniotis

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

    Patrice Godefroid;Aditya V. Nori;Sriram K. Rajamani;Sai Deep Tetali

  • Proofs from Tests

    N E Beckman;A V Nori;S K Rajamani;R J Simmons

  • Merlin: specification inference for explicit information flow problems

    Benjamin Livshits;Aditya V. Nori;Sriram K. Rajamani;Anindya Banerjee

  • Proofs from tests

    Nels E. Beckman;Aditya V. Nori;Sriram K. Rajamani;Robert J. Simmons

  • A data driven approach for algebraic loop invariants

    Rahul Sharma;Saurabh Gupta;Bharath Hariharan;Alex Aiken

  • Automating Software Testing Using Program Analysis

    P. Godefroid;P. de Halleux;A.V. Nori;S.K. Rajamani

  • Autofocus Layer for Semantic Segmentation

    Yao Qin;Konstantinos Kamnitsas;Siddharth Ancha;Jay Nanavati

  • Automatically refining abstract interpretations

    Bhargav S. Gulavani;Supratik Chakraborty;Aditya V. Nori;Sriram K. Rajamani

  • R2: an efficient MCMC sampler for probabilistic programs

    Aditya V. Nori;Chung-Kil Hur;Sriram K. Rajamani;Selva Samuel

  • Interpolants as classifiers

    Rahul Sharma;Aditya V. Nori;Alex Aiken

  • FairSquare: Probabilistic Verification of Program Fairness

    Aws Albarghouthi;Loris D'Antoni;Samuel Drews;Aditya Nori

  • The Yogi Project: Software Property Checking via Static Analysis and Testing

    Aditya V. Nori;Sriram K. Rajamani;Saideep Tetali;Aditya V. Thakur

  • A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions.

    Isabel Chien;Angel Enrique;Jorge Palacios;Tim Regan

  • Verification as Learning Geometric Concepts

    Rahul Sharma;Saurabh Gupta;Bharath Hariharan;Alex Aiken

Frequent Co-Authors

Sriram K. Rajamani
Sriram K. Rajamani Microsoft (United States)
Antonio Criminisi
Antonio Criminisi Microsoft (United States)
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Ben Glocker
Ben Glocker Imperial College London
Andrew D. Gordon
Andrew D. Gordon Microsoft (United States)
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Ozan Oktay
Ozan Oktay Imperial College London
Mayur Naik
Mayur Naik University of Pennsylvania
Thore Graepel
Thore Graepel University College London
Alex Aiken
Alex Aiken Stanford University

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