The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Moises Goldszmidt sits on this spectrum.
This scientist: 102 publications — 9th percentile
9% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Moises Goldszmidt sits on this spectrum.
This scientist: 38 D-Index — 30th percentile
30% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Moises Goldszmidt is affiliated with Apple in the United States, contributing to research primarily within the field of Computer Science. Their work predominantly addresses topics related to Artificial Intelligence, with a specialized focus on subfields such as Machine Learning and Algorithms, Machine Learning and Data Classification, as well as Gaussian Processes and Bayesian Inference.
The recent scholarly output from Goldszmidt includes a publication titled Active Learning with Expected Error Reduction, released in 2022 through the arXiv repository hosted by Cornell University. This paper has accumulated a modest number of citations, indicating engagement from the research community.
Collaboration appears as a notable aspect of Goldszmidt's research activity. Frequent coauthors include:
Goldszmidt's publication venues are currently concentrated in preprint archives, with arXiv (Cornell University) being the principal outlet for disseminating their latest contributions. This choice may reflect an emphasis on accessibility and early-stage research communication.
The main topics of research covered by Goldszmidt's works are identified as:
This profile demonstrates a consistent engagement with methodologies and theoretical frameworks that intersect at the core of artificial intelligence and computational learning theories. The combination of fields and topics highlights an integrative approach to advancing machine learning capabilities and probabilistic modeling.
Nir Friedman;Dan Geiger;Moises Goldszmidt
Craig Boutilier;Nir Friedman;Moises Goldszmidt;Daphne Koller
Nir Friedman;Moises Goldszmidt
Ira Cohen;Moises Goldszmidt;Terence Kelly;Julie Symons
Craig Boutilier;Richard Dearden;Moisés Goldszmidt
Craig Boutilier;Richard Dearden;Moises Goldszmidt
Moisés Goldszmidt;Judea Pearl
Ira Cohen;Steve Zhang;Moises Goldszmidt;Julie Symons
Nir Friedman;Moises Goldszmidt
Nir Friedman;Moisés Goldszmidt
Peter Bodik;Moises Goldszmidt;Armando Fox;Dawn B. Woodard
Nir Friedman;Moises Goldszmidt;Abraham Wyner
M. Goldszmidt;P. Morris;J. Pearl
Moisés Goldszmidt;Judea Pearl
Yinglian Xie;Fang Yu;Kannan Achan;Eliot Gillum
Craig Boutilier;Moisés Goldszmidt;Bikash Sabata
S. Zhang;I. Cohen;M. Goldszmidt;J. Symons
Dawn Woodard;Galina Nogin;Paul Koch;David Racz
Nir Friedman;Moises Goldszmidt
Moises Goldszmidt
Craig Boutilier;Moisés Goldszmidt
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