Welcomes New Faculty

Computer Science and Engineering

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Yanxi Liu received her B.S. degree in physics/electrical engineering in Beijing and her Ph.D. degree in computer science for group theory applications in robotics from the University of Massachusetts. Before joining the computer science

and engineering and electrical engineering departments at Penn State in fall 2006 as a tenured faculty member, Dr. Liu had been a faculty member of the Robotics Institute of Carnegie Mellon University, and affiliated with the machine learning department at CMU. She is also an adjunct associate professor in the radiology department of the University of Pittsburgh, and a guest professor of the computer science department, Huazhong University of Science and Technology in China.

Suzanne Shontz received her B.A. in mathematics and B.S. in chemistry (both summa cum laude) at the University of Northern Iowa in 1999, her M.S. in computer science and applied mathematics from Cornell University in 2002, and her Ph.D. in applied mathematics from Cornell University in January 2005. Prior to

joining the department of computer science and engineering at Penn State as an assistant professor in 2006, she was a postdoctoral associate and a Minnesota Supercomputing Institute Research Scholar for the department of computer science and engineering at the University of Minnesota. Her research interests are in the areas of scientific and parallel computing, mesh generation, optimization, and nonlinear solvers. Shontz has worked on applications in materials science, cardiology, and nonlinear elasticity.

Sofya Raskhodnikova completed all her university education at M.I.T. She received her Ph.D. in 2003 under the supervision of Michael Sipser. She was awarded a Lady Davis Fellowship at the Hebrew University of Jerusalem for 2003-2004, and then spent two years as a postdoctoral fellow at the Weizmann Institute of Science. Her areas of research are randomized algorithms and computational complexity. Her main interest is the design and analysis of sublinear-time algorithms for combinatorial problems. Sublinear algorithms produce approximate answers to a question after reading only a small portion of the input. Such algorithms are important for dealing with massive datasets. Her research focus is two-fold: her work (1) gives algorithms and lower bounds for specific problems and (2) identifies classes of problems solvable by sublinear algorithms and problems that are not amenable to sublinear solutions. The specific problems include visual properties of discretized images, edit distance between strings, compressibility of strings and monotonicity of functions. Adam Smith completed his B.Sc. at McGill University and his M.S. and Ph.D. at M.I.T. under the supervision of Madhu Sudan. His research focuses on cryptography and its connections with information theory, statistics, and coding theory. Most recently he has been working on protocols for handling noisy keys in cryptography, such as those based on biometrics, and on privacy-preserving methods for publishing aggregate statistical data.

New Faculty - 2006 New Faculty - 2007