Grace Hopper Celebration of Women in Computing in Portland, Oregon 11/9-12
Internships with the Federal Government | 2:30 PM in Career Services, Science Library G-50
Building Cascading Style Sheets (CSS) With Dreamweaver | 9:30 AM in the Interactive Media Center B15, uptown campus
ISSA Meeting | 3:15 PM in Draper Student Lounge, downtown campus
ASIS&T Meeting | 3:15 PM in Draper Student Lounge, downtown campus
From Software Engineering to Systems Biology: The adventures of a computer scientist from industry to academia | 4:15 PM in LI-98c, uptown campus
Abstract Computer scientists are in a unique position to be able to have a positive impact on any discipline where they choose to apply their skills. There is an increasing demand for individuals who are skilled to work across disciplines. A simple willingness to learn a new language and ask questions can lead to an exciting, rewarding and varied career. In this wide-ranging talk we will explore a number of different application domains before focusing on the numerous opportunities for computer science to be applied to biological sub-domains. Key vocabulary and challenges will be introduced in the context of real projects. A number of the example projects demonstrate the leverage of machine learning and evolutionary computing techniques in highly complex environments.Speaker Biography Dr. Kiehl is a Computing Innovation Fellow working in the NanoBioScience Constellation at the College of Nanoscale Science and Engineering. Previously Dr. Kiehl spent time as a postdoctoral fellowat Albany Medical College in their Center for Immunology and Microbial Disease. Dr. Kiehl completed his Ph.D. in Multidisciplinary Scienceat Rensselaer Polytechnic Institute and holds B.S. and M.S. degrees in Computer Science from Rensselaer. Prior to his return to university for doctoral studies, Dr. Kiehl worked in the Computational Intelligence Lab at GE Global Research in Niskayuna for 10 years. Dr. Kiehl is currently pursuing projects in computational neuroscience, evolutionary design of bio-active nano-scale systems and pattern/rule deduction in biological datasets.