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Jonathan Baker, DAC Ph.D. student

Jonathan Baker earned a master’s degree in computational and applied math at Rice University in Houston, Texas, in 2015.

When Mark Embree, one of his professors at Rice, returned to his alma mater in Blacksburg to lead the Computational Modeling and Data Analytics program in the College of Science Academy of Integrated Science, Baker did not hesitate to follow him.

“Once I decided that I wanted to pursue a Ph.D. in math,” he said. “I knew the only professor I wanted to continue down that path with was Mark Embree.”

So Baker applied to Virginia Tech as a Ph.D. student in the Department of Mathematics. Advised by Embree, a professor of mathematics and DAC faculty member, Baker is studying how best to track the changes in vibration patterns over time, an extension of his existing research on spectral theory in linear dynamics and control.

“Monitoring vibrations is important for detecting changes and damage in buildings, bridges, and other structures,” said Baker, who is also a National Science Foundation research trainee in the UrbComp program administered through DAC.

Baker’s research is taking place in the College of Engineering’s flagship Goodwin Hall. There, roughly 240 accelerometers attached to 136 sensor mounts throughout the building’s ceilings detect information on where people are within the structure, measure normal structural settling and wind loads, and track building movement resulting from earthquakes similar to the event that struck Virginia in 2011. A sensor array mounted outside the building measures external vibrations, such as wind, the bustle of traffic on nearby Prices Fork Road, the thunderous boom of tens of thousands of Hokie fans celebrating a touchdown at Lane Stadium, and possible seismic activity.

In February 2016, Baker authored Strong Convexity Does Not Imply Radial Unboundedness in The American Mathematical Monthly. He has also contributed to the American Math Society’s grad student blog.

Baker earned his undergraduate degree in math at Brigham Young University.

Left to Right (top), Mark Embree, Tanushree Mitra; (bottom) Srijan Sengupta, Jia-Bin Huang

The Discovery Analytics Center welcomes four new faculty this fall who will help lead Virginia Tech’s big data research and education efforts on campus.

“Data analytics is inherently interdisciplinary and our new faculty bring expertise that will bolster our strengths in matrix computations, statistical methodology for network data, computer vision, and information credibility as we strive to find data solutions to modern problems,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Discovery Analytics Center.

The center has become a well-recognized force among the analytics community within the commonwealth and beyond and fosters multi-stakeholder collaborations with fellow universities, leading industry affiliates, government agencies, and nonprofit organizations. Officially housed within the Computer Science Department, faculty and graduate students represent computer science, statistics, electrical and computer engineering, and math.

The four new faculty are: Mark Embree, professor of mathematics and associate director of the Virginia Tech Smart Infrastructure Laboratory; Jia-Bin Huang, assistant professor of electrical and computer engineering; Tanushree (Tanu) Mitra, assistant professor of computer science; and Srijan Sengupta, assistant professor of statistics.

A Virginia Tech alumnus, Mark Embree received a bachelor’s degree in computer science and mathematics in 1996. He earned a doctor of philosophy degree in numerical analysis from Oxford University, where he was a Rhodes Scholar, and taught at Rice University from 2001 to 2013. In 2014, he returned to Virginia Tech in Blacksburg to lead the Computational Modeling and Data Analytics program in the College of Science Academy of Integrated Science.

Embree’s research interests include numerical analysis, especially matrix computations; data analytics for smart buildings; dynamics and perturbation theory for non-self-adjoint operators; and spectral theory for Schrödinger operators.

He has authored numerous papers and technical reports and is coauthor of “Spectra and Pseudospectra: The Behavior of Nonnormal Matrices and Operators,” published by Princeton University Press.

Jia-Bin Huang, joined Virginia Tech in 2016. He earned a bachelor’s degree in electronics engineering from National Chiao-Tung University in Taiwan and a Ph.D. in electrical and computer engineering at the University of Illinois at Urbana-Champaign.

In 2014, Huang received the best paper award at the Association for Computing Machinery Symposium on Eye Tracking Research and Applications. In 2012, he received the best student paper award at the International Association for Pattern Recognition conference for his work on computational modeling of visual saliency.

His research interests include computer vision; computer graphics; and machine learning with a focus on visual analysis and synthesis with physically grounded constraints.

Tanushree (Tanu) Mitra joined Virginia Tech after earning a Ph.D. in computer science from the Georgia Institute of Technology in August 2017, where the GVU Center named her a Foley Scholar, the highest award for student excellence in research contributions to computing.

She was an IBM Ph.D. Fellowship Recipient in 2016 and selected to attend the Consortium for the Science of Socio-Technical Systems, a National Science Foundation-funded workshop for promising junior investigators.

Mitra earned a master’s degree in computer science from Texas A&M University and a bachelor’s degree in computer engineering from Sikkim Manipal Institute of Technology in India. Her internships included IBM Research and Microsoft Research.

Mitra’s research combines computational techniques and social science principles to study complex social processes underlying human behavior in large-scale online social systems. Specific topics of focus include social computing; computational social science; social media content analysis; data mining; credibility perceptions; misinformation and deception; online communities; and quantitative and qualitative data analysis.

Srijan Sengupta joined Virginia Tech in 2016 as assistant professor of statistics after earning a Ph.D. in statistics from the University of Illinois at Urbana-Champaign. For his dissertation, “Statistical analysis of networks with community structure and bootstrap methods for big data,” Sengupta was awarded the university’s Norton Prize for Outstanding Ph.D. Thesis.

Sengupta received both a bachelor’s and master’s degree in statistics, both with first class distinction, from the Indian Statistical Institute.

His research interests are primarily in statistical methodology for network data; bootstrap and related resampling methods; big data; and computational statistics. Sengupta is also interested in statistical applications in wide-ranging problems in science and industry.