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Bryon Aragam // University of Chicago

Personal homepage of Bryon Aragam, Assistant Professor at the University of Chicago.

Categories: Business
Topics: artificial intelligence, bayesian networks, computation, graphical models, high dimensional, machine learning, math, nonparametric, optimization, statistics

Semrush Rank: 29,915,712
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SciMath C/C++ Scientific Math Library
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persistentrealities.com persistentrealities.com           
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optivec.com optivec.com           
OptiVec: Library of Vector and Matrix Functions for PC Compilers
Comprehensive vector and matrix library - in total more than 3500 hand-optimized functions written in Assembler for superior speed and accuracy.



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cerebralab.com cerebralab.com           
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Programming is translating between levels of rigour



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PerformanceStar LLC
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refactorthis.net refactorthis.net           
refactorthis.net | A blog dedicated to Microsoft technologies, .NET development articles, and tutorials
A blog dedicated to Microsoft technologies, .NET development articles, and tutorials


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bayesserver.com bayesserver.com           
Bayes Server
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BDB-Decision Platform
BDB AI is Introduced with a vision for bringing benefits of AutoML to everyone. AI Integration makes products smarter and more beneficial. By implementing Tensorflow with Keras APIs, makes BDB AI more convenient, easier to create & train Deep Learning Models which can be carried out and used to serve many efficient Predictions, and refine future results assuring that BDB AI is moving along with latest Deep Learning Technology. BDB AI also aims to provide NLP based advanced Analytics for human language.



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azevedolab.net azevedolab.net           
azevedolab.net
We are a research group working on the development of computer models to simulate natural systems. Our primary goal is the research focused on computational systems biology. We have been working on the creation of novel computational models for unraveling the molecular mechanisms underlying enzyme inhibition and protein-ligand interactions. These computational models can be used to predict the binding affinity of a potential inhibitor for an enzyme; such knowledge has the potential to speed up drug discovery and decrease the cost of new drugs. Furthermore, the availability of computational models to predict binding affinity based on the atomic coordinates of protein-ligand complexes adds flexibility to the process of drug discovery, since it allows us to computationally test different scenarios where a potential new drug may interact with a protein target.


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wdss.io wdss.io           
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hallwaymathlete.com hallwaymathlete.com           
Hallway Mathlete
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randalolson.com randalolson.com           
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newlisp.org newlisp.org           
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researchmethods.org researchmethods.org           
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