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.
I am actively recruiting to fill 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.
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.
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.
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.)
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.
A. Aghazadeh, A. Y. Lin, M. A. Sheikh, A. L. Chen, L. M. Atkins, C. L. Johnson, J. F. Petrosino, R. A. Drezek, R. G. Baraniuk, “Universal Microbial Diagnostics using Random DNA Probes,” Science Advances, vol. 2, 28 September 2016.
Abstract: Early identification of pathogens is essential for limiting development of therapy-resistant pathogens and mitigating infectious disease outbreaks. Most bacterial detection schemes use target-specific probes to differentiate pathogen species, creating time and cost inefficiencies in identifying newly discovered organisms. We present a novel universal microbial diagnostics (UMD) platform to screen for microbial organisms in an infectious sample, using a small number of random DNA probes that are agnostic to the target DNA sequences. Our platform leverages the theory of sparse signal recovery (compressive sensing) to identify the composition of amicrobial sample that potentially contains novel or mutant species. We validated the UMD platform in vitro using five random probes to recover 11 pathogenic bacteria. We further demonstrated in silico that UMD can be generalized to screen for common human pathogens in different taxonomy levels. UMD’s unorthodox sensing approach opens the door to more efficient and universal molecular diagnostics.