Rensselaer AI and Reasoning Laboratory
Research and development in the RAIR Lab ranges across a number of applied projects, as well as across many of the fundamental questions AI raises (e.g., Are we machines ourselves? If so, what sort of machines?). Everything is to a high degree unified by the fact that the formalisms, tools, techniques, systems, etc. that underlie the lab's R&D are invariably based on reasoning.
Network Science and Technology Center
The Network Science and Technology (NEST) Center is focused on the fundamental research and engineering of natural and technological networks, ranging from social and cognitive networks to computer networks.
Rensselaer-IBM Artificial Intelligence Research Collaboration
The Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC), a member of the IBM AI Horizons, is dedicated to advancing the science of artificial intelligence and enabling the use of AI and machine learning in research investigations, innovations, and applications of joint interest to both Rensselaer and IBM.
Thomas R. Morgan
Dr. Morgan is a Senior Scientist at the Future of Computing Institute (FOCI) at Rensselaer Polytechnic Institute. He holds BS and MS degrees in Geology and Geophysics from Rensselaer and a PhD in Geophysics from the University of Houston.
He has 50 years of experience teaching, researching, designing and coding scientific/technical applications in remote sensing disciplines including: active source seismic and acoustics, medical ultrasonics, electromagnetic and sonic non-destructive testing, radio astronomy, LiDAR and optical imagery.
Health INCITE
The Rensselaer Health Informatics Challenges in Technology Education (INCITE) Pipeline recruits and prepares students at Rensselaer and worldwide to be data scientists in healthcare using early data analytics courses and experiential research projects centered on real-world health challenges.
With the advent of electronic healthcare records (EHR) and precision medicine, healthcare increasingly relies on health informatics (HI), the philosophy and tools of data science (DS) and their application in healthcare. Rensselaer Health INCITE is a innovative, replicable program that directly expands the health informatics workforce pipeline at the early undergraduate level for students at RPI and worldwide.
SCALES
This project addresses the challenge of agent driven smart contracts on the blockchain with semantics, advances in machine learning, and state of the art in multi agent systems research.
Block Coordinate Update Methods In Tensor Optimization
Multi-way (tensor) data arises in many applications such as seismic data interpolation, hyperspectral imaging, higher order web link analysis, face recognition, EEG and fMRI data analysis, and so on. To explore the intrinsic structure of the multi-way data, people treat the data in higher-order format instead of simply reshaping it into a vector, and formulate the problems to tensor optimization problems. In this talk, I will utilize the idea of "divide and conquer" and give different forms of block coordinate descent methods to solve these problems.
Reproducible Data Science in the Cloud
Despite the many amazing applications of statistics, machine learning, and visualization in industry, many attempts at doing "data science" are anything but scientific. Specifically, data science processes often lack reproducibility, a key tenet of science in general and a precursor to having true collaboration in a scientific (or engineering) community.