Rice and Baylor College of Medicine Assistant Professor Xaq Pitkow has received an NSF CAREER award for his project "Distributed Nonlinear Neural Computation." The project will develop new quantitative theories that explain brain function as distributed nonlinear neural computations that implement principles of statistical reasoning. To test these theories, he is collaborating with experimentalists to design and interpret neuroscience experiments that quantify both what information about naturalistic tasks is encoded in neural populations and what aspects of that information are actually decoded.
DSP faculty member 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.
Rice Assistant Professor Anshumali Shrivastava has received an NSF CAREER award for his project "Hashing and Sketching Algorithms for Resource-Frugal Machine Learning." The project will develop new probabilistic hashing techniques to advance state-of-the-art machine learning algorithms. Apart from being exponentially cheaper, the new algorithms will also be massively parallelizable. The project capitalizes on several recent ideas, including asymmetric hashing, hash-based kernels, densified hashing schemes, sub-linear adaptive sampling, and adaptive sketching, to push learning algorithms to the extreme-scale.
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.
Thanks for making the Rice Machine Learning Workshop on January 24, 2017 a great success! Over 400 attendees participated in a range of sessions on not just new machine learning theory and algorithms but their applications in the energy, medical, financial, and legal industries.
Rice University faculty speakers: Genevera Allen on sparse learning, Richard Baraniuk on advanced data analytics, Paul Hand on machine vision, Ankit Patel on deep learning, and Anshumali Shrivastava on large-scale learning.
Industry speakers: Thomas Halsey (ExxonMobil) on machine learning in the energy industry, Satyam Priyadarshy (Halliburton) on machine learning in the upstream oil and gas industry, Craig Rusin (Medical Informatics Corp.) on patient informatics; Hardeep Singh (VA Hospital Houston) on reducing medical misdiagnosis through machine learning, and Alan Lockett (CS Disco) on machine learning in the legal domain.
Keynote speaker: Alfred Spector (Two Sigma)
See you next year at ML@RICE 2018!
How thin can a camera be? Very, say Rice University researchers who have developed patented prototypes of their technological breakthrough.
FlatCam, invented by the labs of Rice DSP faculty members Richard Baraniuk and Ashok Veeraraghavan, is little more than a thin sensor chip with a mask that replaces the lenses in a traditional camera.
Making it practical are the sophisticated computer algorithms that process what the sensor detects and converts the sensor measurements into images and videos. More info.