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D-Index & Metrics

Computer Science

D-Index
32
Citations
5118
World Ranking
13064
National Ranking
5260

Overview

Jon Feldman is a researcher affiliated with Google in the United States. Their work focuses predominantly on the fields of Medicine and Physics and Astronomy, with a strong emphasis on Pulmonary and Respiratory Medicine, Radiation, Radiology, Nuclear Medicine and Imaging, Otorhinolaryngology, and Genetics. The main topics addressed in their research involve Advanced Radiotherapy Techniques, Radiation Therapy and Dosimetry, Brain Metastases and Treatment, Head and Neck Cancer Studies, Radiomics and Machine Learning in Medical Imaging, Glioma Diagnosis and Treatment, and Medical Imaging Techniques and Applications.

Feldman has contributed to several scientific publications. Recent papers include:

  • Real world clinical experience using daily intelligence-assisted online adaptive radiotherapy for head and neck cancer (2024), published in Radiation Oncology
  • Short report: Plasma based biomarkers detect radiation induced brain injury in cancer patients treated for brain metastasis: A pilot study (2023), published in PLoS ONE
  • Commissioning of a novel gantry-less proton therapy system (2024), published in Frontiers in Oncology
  • Stereotactic implantation of diffusing alpha-emitters radiation therapy sources in the swine brain: a potential new focal therapy for brain tumors (2025), published in Journal of Neuro-Oncology
  • Surrogate Modelling of Active Flow Control (2023), published in AIAA SCITECH 2023 Forum

Their frequent co-authors include:

  • Aron Popovtzer
  • Philip Blumenfeld
  • Yair Hillman
  • Alexander Pryanichnikov
  • Marc Wygoda

Feldman's research has appeared in multiple publication venues, with repeated contributions to the International Journal of Particle Therapy, Radiotherapy and Oncology, Radiation Oncology, PLoS ONE, and Frontiers in Oncology.

Best Publications

  • Using linear programming to Decode Binary linear codes

    J. Feldman;M.J. Wainwright;D.R. Karger

  • Growth codes: maximizing sensor network data persistence

    Abhinav Kamra;Vishal Misra;Jon Feldman;Dan Rubenstein

  • Online Stochastic Matching: Beating 1-1/e

    Jon Feldman;Aranyak Mehta;Vahab Mirrokni;S. Muthukrishnan

  • Online stochastic packing applied to display ad allocation

    Jon Feldman;Monika Henzinger;Nitish Korula;Vahab S. Mirrokni

  • Decoding error-correcting codes via linear programming

    Jon Feldman;David R. Karger

  • Yield Optimization of Display Advertising with Ad Exchange

    Santiago R. Balseiro;Jon Feldman;Vahab Mirrokni;Shan Muthukrishnan

  • Online Ad Assignment with Free Disposal

    Jon Feldman;Nitish Korula;Vahab Mirrokni;S. Muthukrishnan

  • On the Capacity of Secure Network Coding

    Jon Feldman;Tal Malkin;Rocco A. Servedio;Cliff Stein

  • On distributing symmetric streaming computations

    Jon Feldman;S. Muthukrishnan;Anastasios Sidiropoulos;Cliff Stein

  • Budget optimization in search-based advertising auctions

    Jon Feldman;S Muthukrishnan;Martin Pal;Cliff Stein

  • Sponsored Search Auctions with Markovian Users

    Gagan Aggarwal;Jon Feldman;S. Muthukrishnan;Martin Pál

  • LP Decoding Corrects a Constant Fraction of Errors

    J. Feldman;T. Malkin;R.A. Servedio;C. Stein

  • Learning Mixtures of Product Distributions over Discrete Domains

    Jon Feldman;Ryan O'Donnell;Rocco A. Servedio

  • A New Linear Programming Approach to Decoding Linear Block Codes

    Kai Yang;Xiaodong Wang;J. Feldman

  • Learning mixtures of product distributions over discrete domains

    J. Feldman;R. O'Donnell;R.A. Servedio

  • The Directed Steiner Network Problem Is Tractable for a Constant Number of Terminals

    Jon Feldman;Matthias Ruhl

  • PAC learning axis-aligned mixtures of gaussians with no separation assumption

    Jon Feldman;Rocco A. Servedio;Ryan O’Donnell

  • Bidding to the top: VCG and equilibria of position-based auctions

    Gagan Aggarwal;Jon Feldman;S. Muthukrishnan

  • An online mechanism for ad slot reservations with cancellations

    Florin Constantin;Jon Feldman;S. Muthukrishnan;Martin Pál

  • Decoding turbo-like codes via linear programming

    J. Feldman;D.R. Karger

Frequent Co-Authors

s muthukrishnan
s muthukrishnan Rutgers, The State University of New Jersey
Vahab Mirrokni
Vahab Mirrokni Google (United States)
Rocco A. Servedio
Rocco A. Servedio Columbia University
Ryan O'Donnell
Ryan O'Donnell Carnegie Mellon University
Monika Henzinger
Monika Henzinger Institute of Science and Technology Austria
Guy Even
Guy Even Tel Aviv University
Dan Rubenstein
Dan Rubenstein Columbia University

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