DSP alum Marco Duarte (PhD, 2009) has been promoted to the position of Associate Professor with Tenure at the University of Massachusetts at Amherst effective September 2017. Marco is an expert in sparse signal processing, sensor networks, and pattern recognition. He has received the IEEE Signal Processing Society Overview Paper Award, an NSF/IPAM Mathematical Sciences Research Institutes Postdoctoral Fellowship, and the SPARS Best Student Paper Award. Congratulations!
TI Visiting Professor Alum Ron DeVore Elected to National Academy of Sciences
The National Academy of Sciences announced today the election of 84 new members and 21 foreign associates in recognition of their distinguished and continuing achievements in original research. Ron DeVore, the Walter E. Koss Professor in the Department of Mathematics at Texas A&M University, is a pioneer of approximation theory and sparse representations. His work at Rice as the TI Visiting Professor in 2005-2006 focused on compressive sensing.
Open Postdoc Positions @ Rice
Prof. Richard Baraniuk of the Rice DSP/Machine Learning group has two open postdoc positions for research in machine learning theory and methods, computational sensing/imaging, and sparse signal processing. The group offers an energizing environment for research; 25 recent postdocs and PhD students have been placed in top faculty positions. Apply here!
KDD Education Workshop 2017 – Call for Papers
The KDD 2017 Workshop on Advancing Education with Data will be held in conjunction with the Knowledge Discovery and Data Mining conference on the morning of 14 August 2017 in beautiful Halifax, Nova Scotia, Canada. The workshop will bring together data scientists and educators together to stimulate research in the interdisciplinary field of data science for education. At this year’s workshop, we are highlighting the following areas of interest: (1) Lifelong learning, (2) Assessments, (3) Learning Analytics and Personalization, and (4) Infrastructure.
KDD is a premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.
Key dates:
- Paper submission deadline: May 26, 2017
- Author notification: June 16, 2017
- Final version of accepted submissions: June 30, 2017
- Final workshop schedule: June 30, 2017
- Workshop: August 14, 2017
More details can be found on the workshop website.
DSP PhD Alum Eva Dyer Accepts Faculty Position at Georgia Tech
Rice DSP PhD Eva Dyer (PhD, 2014) has accepted an assistant professor position at Georgia Tech in the Department of Biomedical Engineering. She has spent the past two years as a postdoc and research scientist at the Rehabilitation Institute of Chicago at Northwestern University. Eva joins DSP PhD alums Jim McClellan, Doug Williams, Justin Romberg, Chris Rozell, and Mark Davenport and ECE PhD alum Rob Butera.
DSP Faculty Member Richard Baraniuk Elected to American Academy of Arts and Sciences
Rice University DSP faculty member 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.
Universal Microbial Diagnostics Receives Hershel M. Rich Invention Award
DSP PhD student AmirAli Aghazadeh, DSP PhD alum Mona Sheikh, Bioengineering professor Rebekah Drezek, Bioengineering alums Allen Chen and Adam Yuh Lin, and DSP professor Richard Baraniuk have been awarded the 2017 Hershel M. Rich Invention Award for their development of Universal Microbial Diagnostics (UMD). UMD is a radically new way to detect and classify bacteria and other microbials that is fast (minutes rather than days) and efficient. (Double congratulations to Dr. Aghazadeh, who defended his PhD thesis today!)
DSP Faculty Member Xaq Pitkow Receives NSF CAREER Award
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 Receives Vannevar Bush Fellowship
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.
DSP Faculty Member Anshumali Shrivastava Receives NSF CAREER Award
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.