Machine Learning, Optimization & Statistics

  • Professors Chris Jermaine, Tasos Kyrillidis, Risa Myers, Anshumali Shrivastava, and Devika Subramanian are leading research in Machine Learning and Data Science, including the mining, analysis, and management of data, the optimization of models, and the applications in healthcare, human learning, and risk assessment.  
  • Professors Anshumali Shrivastava, Santiago Segarra and Tasos Kyrillidis develop scalable machine learning algorithms and optimization methods for big data. A recent project proposed new methods to filter fake news.  
  • Professor Ron Goldman’s current research interests lie in the mathematical representation, manipulation, and analysis of shape using computers. He is particularly interested in algorithms for polynomial and piecewise polynomial curves and surfaces, and he has investigated both parametrically and implicitly represented geometry.  
  • Professor Joe Warren’s primary research interest is in computer graphics and computational geometry protocols, with applications in Graphics and geometric modeling, Bioinformatics, Computer Gaming, and Education.  
  • Professor Maarten de Hoop researches on theoretical and computational seismology, in which he employs inversion and deep learning to explore the center of the earth and help discover oil and gas reservoirs.  
  • Professors Illya Hicks, Joey Huchette, and Andrew Schaefer have won Rice’s COVID-19 research fund, where they will use optimization models to plan nursing schedules during times of uncertainty, when a hospital’s needs are highly variable.  
  • Professors Matthias Heinkenschloss, Illya Hicks, Richard Tapia, and Yin Zhang lead the Optimization Group of Rice. Optimization is an active area in the field of computational and applied mathematics, focusing on providing best possible solutions to systems described by mathematical models.  
  • Professors Behnaam Aazhang, Genevera Allen, Richard Baraniuk, Caleb Kemere, Yingyan Lin, Michael Orchard, Ankit Patel, Xaq Pitkow, Jacob Robinson, Ashutosh Sabharwal, Akane Sano, Santiago Segarra, Peter Varman, and Ashok Veeraraghavan are leading research in Data Science, using digital signal processing algorithms to collect and understand the structure in data, looking for compelling patterns, telling the story that’s buried in the data. They get to the questions at the heart of complex problems and devise creative approaches to making progress in a wide variety of application domains.  
  • Professor Dan Kowal has been awarded the Rice Covid-19 research funds. He is building a predictive model or the trajectory of COVID-19 cases in Houston by borrowing information from locations that are similar to Houston and further along the disease incidence curve. The model is expected to improve the accuracy of real-time predictions and to inform key policy decisions.  
  • Professors John Dobelman, Katherine Ensor, Philip Ernst, and Daniel Kowal lead the Center for Computational Finance and Economic Systems (CoFES), dedicated to the quantitative study of financial markets and their ultimate impact on society.  
  • Professors Lauren Stadler and Katherine Ensor work with the health department in Houston to develop a tool that utilizes machine learning to track COVID-19 infection dynamics in water treatment plants. This allows the city to identify increases in infection in a community before hospital or testing data.  
  • Professor Kirsten Siebach is among 13 scientists recently selected by NASA to conduct research and, as part of their duties, operate NASA’s Perseverance rover. Siebach and her colleagues are receiving funding to create algorithms and machine learning methods that will help identify samples to be returned to Earth.  
  • Professors Arko Barman and Su Chen are in Rice’s Data to Knowledge (D2K). The Rice D2K Lab provides students with engagement, enrichment, and experiential learning opportunities by connecting students with real-world data science challenges from companies, community organizations and researchers.
  • Professors Barbara Ostdiek’s research is in the Accounting field of the Jones Graduate School of Business, focusing on volatility and information flow, indicates that informational market linkages can be quite strong, that volatility is predictable, and that modeling cross-market linkages and volatility dynamics has economic value for market participants.