Upcoming Events

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Past Events

Thursday 01.07.16NSF workshop
NSF Workshop 2016
"Data Science, Learning, and Applications to Biomedical & Health Sciences (DSLA-BHS2016)" SPONSORED BY: Rutgers I-DSLA, the NSF Smart and Connected Health Program, the NSF North East Big Data Innovation Hub and in Partnership with the NYAS

Thursday, January 7, 2016 to Friday, January 8, 2016
Friday 05.15.15Spring 2015 Speaker Series
Connecting natural and artificial neural networks with functional brain imaging
Abstract: Functional neuroimaging and neural network modeling were both introduced to cognitive science in the 1980s, and have both produced influential research. Yet surprisingly, the programs have advanced with little mutual influence. I will describe two different approaches to more directly connecting cognitive neural network models with... more info

Friday, May 15, 2015 - 12:30pm to 1:30pm
Saturday 04.11.15Nano Composites
Novel Tools in Computational Chemistry Coding workshop (NTC3)
Computer hardware/software development, and computational chemistry codes originate in separate scientific communities, i.e., computer science/electrical engineering on one side, and chemistry/physics on the other side. Nowadays, the birth of novel hardware solutions, such as GPUs and coprocessors (e.g., Xeon Phi), demands a shift in the common... more info

Saturday, April 11, 2015 - 8:15am to 6:30pm
Thursday 01.15.15Brain Cell Signal
Decision Models of Medical Signal and Imaging Data to Improve Medical Diagnoses
The overarching goal of our research is to develop new data analytics techniques based on applied optimization and machine learning. The main driving application of our techniques is to assist physicians in recognizing abnormality patterns (and/or patterns of interest) in medical signal and imaging data. The main focus of our work is on feature... more info

Thursday, January 15, 2015 - 1:30pm to 2:30pm
Wednesday 01.14.15Global Data
Probabilistic modeling for data science and science: Nonparametric Bayes and beyond
High-dimensional datasets containing data of multiple data types have become commonplace across business, health, and science. In theory, these rich data sets support fine-grained inferences; however, the data analysis problem becomes more difficult as the data become more varied, noisy, and sparsely observed. Moreover, people with the skills to... more info

Wednesday, January 14, 2015 - 1:30pm to 2:30pm