Scientific Computing minor highlighted in Vanderbilt Engineering magazine
The new Scientific Computing program (Thomas Palmeri, co-Director) is highlighted in Vanderbilt Engineering magazine: Computing: It's Not Just for Computer Scientists and Engineers Anymore.
Mike Mack wins Jum Nunnally Dissertation Award!
Michael Mack is the 2011 winner of the Jum Nunnally Dissertation Award.
The Jum Nunnally Dissertation Award recognizes a recent outstanding doctoral dissertation in the Department of Psychology. The recipient receives a certificate and a $500 award. Mike's thesis was nothing short of excellent, and well deserving of this award.
Jum Nunnally came to Vanderbilt in 1960. In 1961, he became the second chair of the department. He served as chair from 1961-1964 and again from 1967-1970. Under Jum’s leadership, the department grew substantially in stature, including significant increases in both the number and quality of the faculty. A memorial fund to support student awards was established in 1982 by his friends and family. Proceeds from this fund were used to establish the Jum Nunnally Dissertation Award in 2010.
This makes the second CatLab PhD to win this prestigious award. Jenn Richler was co-winner of the award in 2010.
Journal of Neuroscience article featured in Research News@Vanderbilt
Our recent Journal of Neuroscience article entitled Neural basis of adaptive response time adjustment is featured in Research News@Vanderbilt:
http://news.vanderbilt.edu/2011/08/new-insight-into-impulse-control/
Pouget, P., Logan, G.D., Palmeri, T.J., Boucher, L., & Schall, J.D. (in press). Neural basis of adaptive response time adjustment. Journal of Neuroscience.
New Grant from NIH
Palmeri, Logan, and Schall have been awarded a new grant from the National Eye Institute, National Institutes of Health, entitled Stochastic Models of Visual Search.
Recent papers from the CatLab
Schall, J.D., Purcell, B.A., Heitz, R.P., Logan, G.D., & Palmeri, T.J. (2011). Neural mechanisms of saccade target selection: Gated accumulator model of visual-motor cascade. European Journal of Neuroscience.
Richler, J.J., Gauthier, I., & Palmeri, T.J. (2011). Automaticity of basic-level categorization accounts for labeling effects in visual recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition.
Richler, J.J., Mack, M.L., Palmeri, T.J., & Gauthier, I. (2011). Inverted faces are (eventually) processed holistically. Vision Research.
Stephen Denton to join the CatLab
Stephen Denton will be joining the CatLab as a postdoctoral fellow this fall. Stephen earned his PhD from Indiana University with John Kruschke in 2009. His thesis was entitled Exploring active learning in a Bayesian framework. For the past two years he has remained at Indiana as a postdoctoral fellow with Rich Shiffrin and Rob Nosofsky. Stephen will be joining the lab in September.
Congratulations Dr. Mack!
On Friday May 20, Mike Mack successfully defended his dissertation entitled "The Dynamics of Categorization: Rapid Categorization Unraveled". Mike is moving on to a postdoctoral fellowship at UT Austin with Brad Love and Allison Preston.
VSS 2011 CatLab Posters
Michael Mack – The Dynamics of Categorization: Rapid Categorization Unraveled
Jonathan Folstein – Category learning causes differentiation of psychological dimensions in dimensional but not polar morphspaces
David Ross – Face Adaptation: Comparing Norm- and Exemplar-Based Models
CatLab awarded Discovery Grant
Our lab has just been awarded a two-year Vanderbilt University Discovery Grant entitled "Online Web-based Experiments of Real-World Perceptual Expertise".
New undergraduate minor in Scientific Computing
Palmeri was part of a group that was a awarded an NSF grant (Revitalizing Computing Education Through Computational Science) to develop an undergraduate minor in Scientific Computing at Vanderbilt. The minor is now officially on the books: New Minor in Scientific Computing Launched.
It also has a web site: http://www.vanderbilt.edu/scientific_computing/
Students in the program in Scientific Computing are taught techniques for understanding complex physical, biological, and social systems. Students are introduced to computational methods for simulating and analyzing models of complex systems, to scientific visualization and data mining techniques needed to detect structure in massively large multidimensional data sets, to high performance computing techniques for simulating models on computing clusters with hundreds or thousands of parallel, independent processors and for analyzing terabytes or more of data that may be distributed across a massive cloud or grid storage environment.