Thorsten Joachims
Location: (Ithaca, NY)
Personal Research Web Page: http://www.joachims.org
Keywords: Machine Learning, Information Retrieval, Support Vector Machines and Large-Margin Methods, Structured Output Prediction, Online Learning, Implicit Feedback
Posted on: Thursday, May 20th, 2010
Broad Research Area: AI / Machine Learning / Robotics / Vision
Research Interests:
Here are some of the high-level research questions I am interested in:
Structured Output Prediction: In conventional classification and regression, the prediction is a single number. Many application problems, however, require the prediction of complex multi-part objects like trees (e.g. natural language parsing), alignments (e.g. protein threading), rankings (e.g. search engines), and paths (e.g. navigation assistant). How can one tractably model and learn to make such complex predictions?
Humans in the Loop: Much of the data used for machine learning is gathered by observing human behavior (e.g. search engine logs, purchase data, fraud detection). However, it is known that this data is biased (e.g. users can click only on results that were presented). How can one learn despite these biases? Or how can the learning algorithm gather unbiased data by not being a passive observer, but by actively interacting with the human?
Understanding Archives: We are capturing and archiving more and more data (e.g. email, blogs, photos). While search engines give good microscopic access to individual data item, much work is needed to get a more macroscopic view of the content of an archive. How can machine learning help understand and summarize content, trends, dependencies, and idea flows in such archives?
Contact Information:
Email: tj@cs.cornell.edu
