Announcement of the 2007 Pierre Devijver Award Winner
On the request of the IAPR the Techinical Committee on Statistical Pattern Recognition Techniques (TC1) established a special award to commemorate Pierre Devijver, one of the founders of statistical pattern recognition, who left us all in 1996. It was decided to set up a Pierre Devijver Award Lecture, to be presented at regular TC1 workshops. The presenter is selected from outstanding scientists who have contributed significantly to the field fo statistical pattern recognition.
We are very happy to announce that the Pierre Devijver Award winner for 2007 is:
Dr Tin Kam Ho
She will present the Pierre Devijver Lecture during the next S+SSPR Workshop in Orlando, Florida, December 4-6, 2008.
Tin Kam Ho Short Biography
Tin Kam Ho received the Ph.D. in computer science from the State University of New York at Buffalo, Buffalo, NY, in 1992. She is a Member of Technical Staff in the Computing Sciences Research Center, Bell Laboratories, Lucent Technologies, Murray Hill, NJ. She has received six U.S. patents for her work in pattern recognition and image analysis. Her interests are in pattern recognition, data mining, and computational modeling and simulation. Dr. Ho received the ICDAR Young Scientist Award for her contributions to document image analysis and recognition in 1999. She is a Fellow IEEE (2006), and a Fellow of the International Association for Pattern Recognition (IAPR). She is the Editor-in-Chief of the journal Pattern Recognition Letters, and has served on the editorial board of several other journals.
On her web-site Dr. Tin Kam Ho states: "The fundamental theme of my research is on observation and algorithmic modeling of complex systems and phenomena in the physical world. This theme develops into algorithms, tools, and applications of pattern recognition, data mining, performance monitoring, and computational modeling and simulation.
I seek to discover and represent knowledge embedded in large, high-dimensional data sets by algorithmic processes. More specifically, I pursue methods for natural partitioning of data, systematic search for correlations, characterizing complexity of dependences, generation and evaluation of feature transformations, dynamic adjustment of established models, and above all, classification. On classification, I explored methods for multiple classifider systems, random decision forests, and more recently, data complexity analysis.
To facilitate these, I also explore methods and tools for interactive data visualization and analysis. For my special interests in data coming from sensors and imaging devices, my methods emphasize joint explorations with the raw data and all levels of abstraction resulting from pre-processing, feature extraction, and decision making algorithms.
Besides pattern recognition, I pursue an interest in computational modeling and simulation of complex systems. I have built models of reading processes, sensor networks, and optical transport systems."
