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Hurwitz Memorial Lecture Series

Hurwitz Lectures 2014 at ETH Zurich

Prof. Mathukumumali Vidyasagar



HURWITZ LECTURE 1: Machine Learning Methods in the Computational Biology of Cancer

Abstract:

Cancer biology offers an excellent test bed for developing and testing new algorithms in machine learning, because of the diversity of the disease and the resulting need to develop personalized therapies. In this talk I will discuss some new algorithms that have been or are being applied to cancer data in a clinical translational setting. In the first, a sparse classification algorithm named "lone star" is used to identify a small number (15 or so) of features for predicting patient response and risk of metastasis. In the second, a sparse regression algorithm called "modified elastic net (MEN)" is used to predict the time to tumor recurrence, and the IC50 values (efficacy levels) of several natural product compounds. The next step is to use inferred gene regulatory networks to guide the selection of biologically meaningful features. The overall message is that cutting-edge research in algorithms has the potential of making an immediate impact on clinical practice in cancer.

Type of Seminar:

Public Seminar
Speaker:

Prof. Mathukumalli Vidyasagar
University of Texas, Dallas
Date/Time:

2014-05-15  / 14:15-15:15
Location:

HG E 5
Contact Person:

Prof. John Lygeros
File Download:

Request a copy of this publication.

HURWITZ LECTURE 2: Recent Advances in Compressed Sensing

Abstract:

Compressed sensing refers to the exact or approximate recovery of very high-dimensional but sparse vectors from a small number of possibly measuremnts. A recent concept is that of "nearly ideal behavior," whereby the estimation error of an algorithm is bounded by a universal constant times the error achievable by an "oracle" that knows the locations of the nonzero components of the vector to be determined. In this talk I will review the basic and well-known results and some recent results, and present some new results that provide a framework for constructing algorithms with near-ideal behavior.

Type of Seminar:

Public Seminar
Speaker:

Prof. Mathukumalli Vidyasagar
University of Texas, Dallas
Date/Time:

2014-05-16  / 14:15-15:15
Location:

HG E 5
Contact Person:

Prof. John Lygeros
File Download:

Request a copy of this publication.
Biographical Sketch:
Mathukumalli Vidyasagar received the B.S., M.S. and Ph.D. degrees in electrical engineering from the University of Wisconsin in Madison, in 1965, 1967 and 1969 respectively. Between 1969 and 1989, he was a Professor of Electrical Engineering at Marquette University, Milwaukee (1969-70), Concordia University, Montreal (1970-80), and the University of Waterloo, Waterloo, Canada (1980-89). Between 1989 and 2000, he was Director of the Centre for Artificial Intelligence and Robotics (CAIR) in Bangalore. In 2000 he moved to the Indian private sector as an Executive Vice President of India's largest software company, Tata Consultancy Services. In the city of Hyderabad, he created the Advanced Technology Center, an industrial R&D laboratory of around 80 engineers. In 2009 he retired from TCS and joined the Erik Jonsson School of Engineering & Computer Science at the University of Texas at Dallas, as a Cecil & Ida Green Chair in Systems Biology Science. In March 2010 he was named as the Founding Head of the newly created Bioengineering Department. His current research interests are in the application of stochastic processes and stochastic modeling to problems in computational biology, and control systems.

Vidyasagar has received a number of awards in recognition of his research contributions, including Fellowship in The Royal Society, the IEEE Control Systems (Field) Award, the Rufus Oldenburger Medal of ASME, and others. He is the author of eleven books and nearly 140 papers in peer-reviewed journals.



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