Monday, February 10, 2014 - 4:00pm to 4:50pm
KEC 1001

Speaker Information

Janardhan Rao (Jana) Doppa
PhD. Student
School of EECS
Oregon State University


We are witnessing the rise of the “Big Data” paradigm, in which massive amounts of data (e.g., text, images, videos, speech) -- much of it collected as a side-effect of ordinary human activity -- can be analyzed to make sense of the data, and to make useful predictions. To fully realize the promise of Big Data, we need automated systems that can transform structured inputs to structured outputs (e.g., parsing a sentence, resolving coreferences of entity and event mentions in a piece of text, interpreting a visual scene, translating from one language to another). Problems such as these are often referred to as structured prediction problems in the machine learning community. These prediction problems pose severe learning challenges due to the huge number of possible outputs (e.g., many possible parse trees for a sentence). In this talk, I will introduce a new framework to solve these structured prediction problems called HC-Search. The problem of structured prediction is formulated as an explicit search process in the combinatorial space of outputs. The search seeks to optimize the cost function C using a heuristic H to guide the search. Both the cost function and the heuristic are learned from supervised data to minimize a given task loss function. I show that my HC-Search framework achieves state-of-the-art results in a wide range of structured prediction problems that arise in natural language processing and computer vision, exceeding the previous best results by significant margins. I will close with some on-going work on applications of this framework and challenging open problems.

Speaker Bio

Janardhan Rao (Jana) Doppa is a final year PhD student with the Artificial Intelligence group at Oregon State University. He received his M.Tech degree in computer science from Indian Institute of Technology (IIT), Kanpur, India. His general research interests are in Artificial Intelligence (AI) and Machine learning. His dissertation explores how to integrate two fundamental branches of AI, namely learning and search to solve structured prediction problems arising in natural language processing (NLP) and computer vision (CV). He received an Outstanding Paper Award at the AAAI 2013 conference for his structured prediction work, and an Outstanding Graduate Research Assistant Award (2013) from the College of Engineering, Oregon State University.