MLSS

From RISE
Revision as of 15:33, 22 June 2012 by Ravi (Talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

Contents

Objective

Machine Learning (ML) (Parameter Estimation, Data Mining, Data Analytics, Pattern Recognition, Text Mining, Statistical Natural Language Processing, and their ilk) is currently a very actively explored area of research both in academia and in the industry. People are looking at application of techniques derived from statistical learning approaches to various domains, as well as exploring new theoretical advances in the learning paradigms themselves. The ML seminar series will facilitate interaction among students, faculty members and ML experts. It will feature a mixture of presentations from our students, Faculty, and invited speakers from academia and industry.

The seminar series started on Dec. 7, 2009 with an inaugural talk by Sunita Sarawagi.

Venue

All talks will be held at BSB 361, Department of CSE, IIT Madras unless otherwise noted. Snacks will be served!

Past Talks

Organizing Committee

Current/Recent Talk

Ranking Genes, Ranking Documents, Ranking Drug Candidates: A Unified Machine Learning Approach

Dr. Shivani Agarwal
Indian Institute of Sciences, Bangalore. March 25, 2011 @ 04:00 pm
BSB 361, Dept. of CSE, IIT Madras



Abstract

Prioritizing information is a ubiquitous need in our daily lives. With the growth in digital data sources, computational methods for prioritizing information are an increasing need in the 21st century.

In this talk, I will describe the application of ranking methods in machine learning -- an emerging technology and currently an active area of research -- to prioritizing information in three different fields: computational biology, information retrieval, and drug discovery. I will give a brief glimpse of some of the recent theoretical and algorithmic developments related to ranking methods in machine learning, and then show how these methods give state-of-the-art performance in information retrieval, outperform existing methods in drug discovery, and have led to the identification of new genes related to leukemia and colon cancer in biology.

Bio

Shivani Agarwal is currently an Assistant Professor in the Department of Computer Science & Automation at the Indian Institute of Science. Prior to this she was a postdoctoral lecturer and associate in the Computer Science and Artificial Intelligence Laboratory at MIT. She obtained her PhD in Computer Science at the University of Illinois, Urbana-Champaign, where she received the Liu Award for her research; an MA in Computer Science at Trinity College, University of Cambridge, where she was a Nehru Scholar; and a BSc with Honors in Mathematics at St Stephen's College, University of Delhi. Her research interests include machine learning and learning theory, in particular the study of ranking and other new learning problems, as well as applications of machine learning methods, particularly in the life sciences. More broadly, she is excited by research at the intersection of computer science, mathematics, and statistics, and its applications in scientific discovery.

Related Seminars Elsewhere!

Personal tools
Namespaces
Variants
Actions
Navigation
Toolbox