-
Patrick Heney
I started programming on a TI‑99/4A when I was about 8 years old.I learned how to build computers during an internship with a technology company in Washington DC.I started my career in the...
https://nanohub.org/members/194443
-
Muhammad Bilal
Bilal’s research focuses on data-driven solutions for the environmental and health impact assessment of engineered nanomaterials (ENMs) using advanced machine learning/data mining and simulation...
https://nanohub.org/members/179709
-
Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?
14 Aug 2017 | Contributor(s):: Hanjing Xu, Pradeep Kumar Gurunathan
Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions...
-
Predicting Locations of Pollution Sources using Convolutional Neural Networks
07 Aug 2017 | Contributor(s):: Yiheng Chi, Nickolas D Winovich, Guang Lin
Pollution is a severe problem today, and the main challenge in water pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or...
-
S Kiran Kadam
https://nanohub.org/members/172030
-
IPython Notebooks for Machine Learning
Collections |
21 May 2017 |
Posted by Tanya Faltens
https://nanohub.org/groups/ncnure2017/collections/technical-resources
-
Dedy Farhamsa
https://nanohub.org/members/170299
-
Claire Battye
"Research is creating new knowledge."Neil Armstrong
https://nanohub.org/members/169180
-
Marius Stan
https://nanohub.org/members/156713
-
Model Selection Using Gaussian Mixture Models and Parallel Computing
20 Jul 2016 | | Contributor(s):: Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin
Model Selection Using Gaussian Mixture Models
-
Lukasz Burzawa
https://nanohub.org/members/147258
-
Juan Sebastian Martinez
I am a senior in Electronic Engineering and Systems and Computer Engineering at Universidad de los Andes in Bogotá. Throughout my learning, I have gained experience with different programming...
https://nanohub.org/members/145729
-
Gaussian process regression in 1D
26 Nov 2014 | | Contributor(s):: Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve
Use Gaussian processes to represent x-y data
-
Ilias Bilionis
Dr. Ilias Bilionis is an Assistant Professor at the School of Mechanical Engineering, Purdue University. His research is motivated by energy and material science applications and it focuses on the...
https://nanohub.org/members/107467
-
Rohit Kaushal Tripathy
https://nanohub.org/members/106614
-
German Felipe Giraldo
https://nanohub.org/members/85538
-
Ahmed-Amine Homman
https://nanohub.org/members/82074
-
anupam ghosh
M.Sc. Physics (2007), 1 year experience in neuroscience (2008-09), 1.5 yrs experience in synthesis and characterization of Nickel nano-wires (2010-11), 1 year experience in simulation of...
https://nanohub.org/members/81944
-
Mahika Dubey
University of Illinois Urbana Champaign Class of 2016 (Urbana, IL) B.S. Computer Engineering (Department of ECE) Minor in Statisics, iFoundry Innovation Certificate Program Monta Vista High School...
https://nanohub.org/members/80897
-
Random Forest Model Objects for Pulmonary Toxicity Risk Assessment
09 Apr 2013 | | Contributor(s):: Jeremy M Gernand
This download contains MATLAB treebagger or Random Forest (RF) model objects created via meta-analysis of nanoparticle rodent pulmonary toxicity experiments. The ReadMe.txt file contains object descriptions including output definitions, input parameter descriptions, and applicable limits.