Rice University engineers are building a flat microscope, called FlatScope (TM), and developing software that can decode and trigger neurons on the surface of the brain.  The goal as part of a new government initiative is to provide an alternate path for sight and sound to be delivered directly to the brain.  The project is part of a $65 million effort announced this week by the federal Defense Advanced Research Projects Agency (DARPA) to develop a high-resolution neural interface. Among many long-term goals, the Neural Engineering System Design (NESD) program hopes to compensate for a person’s loss of vision or hearing by delivering digital information directly to parts of the brain that can process it.

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A. Mousavi, G. Dasarathy, R. G. Baraniuk, “DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks,” arXiv:1707.03386, July 2017.

We develop a novel computational sensing framework for sensing and recovering structured signals called DeepCodec.  When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals.  This is in contrast to conventional compressive sensing (CS) systems that use random linear measurements and convex optimization or iterative algorithms for signal recovery.  We compare our new framework with ℓ1-minimization from the phase transition point of view and demonstrate that it outperforms ℓ1-minimization in the regions of phase transition plot where ℓ1-minimization cannot recover the exact solution.  In addition, we experimentally demonstrate how learning measurements enhances recovery performance, speeds up training, and reduces the number of parameters to learn.

DeepCodec learns a transformation from signals x to measurement vectors y and an approximate inverse transformation from measurement vectors y to signals x using a deep convolutional network that consists of convolutional and sub-pixel convolution layers.

Recovery comparison of DeepCodec vs. LASSO (with optimal regularization parameter).

Rice University-based nonprofit OpenStax, which is already changing the economics of higher education by providing free textbooks to more than 1 million college students per year, today launched a low-cost, personalized learning system called OpenStax Tutor Beta that analyzes how students learn to offer them individualized homework and tutoring.  In development for three years, the system will be available this fall for three courses: college physics, biology and sociology.  While students study using OpenStax Tutor, it  learns how they learn — what they struggle with, what helps them most — and it uses that information to offer just-in-time remediation and enrichment.  The system provides personalized assessment and spaced practice, helping students focus their studying efforts on their weak areas and remember what they learned earlier in the course.

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I am actively recruiting to fill two open postdoc positions for research in machine learning theory and methods, computational sensing/imaging, and sparse signal processing.  The Rice DSP and Machine Learning groups offer an energizing environment for research; 25 recent postdocs and PhD students have been placed in top faculty positions. Apply here!

Rice University professor and engineer Richard Baraniuk has been elected to the American Academy of Arts and Sciences. He is one of 228 new members announced today by the academy, which honors some of the world’s most accomplished scholars, scientists, writers, artists and civic, business and philanthropic leaders. The academy is one of the country’s oldest learned societies and independent policy research centers. It convenes leaders from the academic, business and government sectors to respond to the challenges facing – and opportunities available to – the nation and the world.

Rice University press release

Richard Baraniuk is one of 13 Vannevar Bush Faculty Fellows announced today by the U.S. Defense Department.  The fellows program provides extensive, long-term financial support for distinguished university scientists and engineers to pursue “blue sky” basic research that could produce revolutionary new technologies.  The program was launched in 2008 as the National Security Science and Engineering Faculty Fellowship (NSSEFF) program and renamed this year in honor of Vannevar Bush, the famed American engineer and inventor who headed U.S. scientific research during World War II and later helped found the National Science Foundation.  Baraniuk, Rice’s Victor E. Cameron Professor of Electrical and Computer Engineering, is a leading expert on compressive sensing, a branch of signal processing that enables engineers to glean useful information from far fewer data samples than would typically be required.

DOD press release
Rice University press release

Richard Baraniuk, Rice University’s Victor E. Cameron Professor of Electrical and Computer Engineering, has been elected a Fellow of the National Academy of Inventors.  He is one of 175 academic inventors named this year, and is now among 757 fellows representing 229 research universities and governmental and nonprofit research institutes.  NAI Fellows have demonstrated a prolific spirit of innovation to create or facilitate inventions that have made a tangible impact on quality of life, economic development and the welfare of society.  Combined, they are named inventors on more than 26,000 U.S. patents.

Press release

Siemens and GE are rolling out compressive sensing (CS) based mangetic resonance imaging (MRI) scanners featuring radically improved scan time.

Read more about Siemens’ FDA clearance here and the technical details of their approach here. Read more about the technical details of the GE approach here.

The National Educational Technology Plan for Higher Ed has been published by the US Department of Education.  The document is an outgrowth of the 2016 National Education Technology Plan (NETP), which presents a shared vision and call to action for transformational learning enabled by technology at all levels of our education system.  The NETP recommends actions that would enable everywhere, all-the-time learning and ensure greater equity and accessibility to learning opportunities over the course of a learner’s lifetime.  (Richard Baraniuk served on the Technical Working Group that reviewed drafts of the guide and provided feedback, writing, and examples from their experiences.)

Finally, a deep convolutional network that doesn’t require much “hand-holding” in the form of training examples.

Using the deep rendering mixture model, the network largely teaches itself how to distinguish handwritten digits using the MNIST dataset of 10,000 hand written digits.  Results presented this month at the Neural Information Processing Systems (NIPS) conference in Barcelona, Spain, demonstrate state-of-the-art training results using just 10 correct examples of each handwritten digit plus several thousand additional unlabelled examples.  The trained algorithm was more accurate at correctly distinguishing handwritten digits than almost all previous algorithms, even those trained with thousands of correct examples of each digit.

NIPS paper
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