Post-Doctoral Position at IBM Research: (in Mathematical Programming)

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Post-Doctoral  Position at IBM Research: (in Mathematical Programming) ===================================================================
The Mathematical Programming  group at IBM Research invites applications for a post-doctoral  position. The initial contract is for one year but it can be renewed for a second year subject to mutual agreement. The expected starting date is the second half of 2013. 
The position is primarily a research position and the ideal candidate is expected to have a strong publication  record in mathematical optimization. Depending on the department activities at the time, there might also be opportunities to get exposed to applied optimization work jointly with other department members.
Our group offers a unique environment that combines basic research with applied optimization projects for our internal and external customers. Current members of the group include: Amirali Ahmadi, Francisco  Barahona, Sanjeeb Dash, Joao Goncalves, Oktay Gunluk, Aida Khajavirad and Leo Liberti. 
IBM T.J. Watson Research Center (http://www.watson.ibm.com/general_info_ykt.shtml) is located
30 miles north of New York City in Yorktown Heights, NY. 30 miles north of New York City in Yorktown Heights, NY. 
Interested candidates are invited to send their CVs with names of references to gunluk@us.ibm.comby February 28, 2013.
IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, orveteran status.
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Oktay Gunluk, Math. Sciences , IBM Research ----  gunluk@us.ibm.com, http://www.research.ibm.com/people/o/oktay/   

CAREER: Reduced-order Methods for Big-Data Challenges in Nonlinear and Stochastic Optimization

A recent NSF Career Award was given to Dr.Lan in University of Florida. Details of the award are below

The objective of this Faculty Early Career Development (CAREER) Program project is to develop a set of new reduced-order algorithms to tackle the big-data challenges in optimization. The last several years have seen an unprecedented growth in the amount of available data. While nonlinear, especially convex programming (CP) models are important to extract useful knowledge from raw data, high problem dimensionality, large data volumes and inherent uncertainty present significant challenges to the design of optimization algorithms. This research aims to attack these challenges by investigating: (i) novel first-order methods for deterministic CP that converge faster, require little structural information and do not rely on line search, based on level methods; (ii) stochastic first-order methods that handle data uncertainty in an optimal manner, based on stochastic approximation; (iii) novel randomization schemes for solving certain challenging deterministic CP problems beyond the capability of first-order methods; and (iv) stochastic first- and zeroth-order methods for general, not necessarily convex, stochastic programs. The research focuses on two fundamental issues across these topics: (i) the study of complexity which provides guarantees on algorithmic performance; and (ii) the exploitation of structures that leads to the design of algorithms with stronger complexity and superior practical performance.

If successful, a set of new algorithmic schemes will advance the state-of-the-art in nonlinear and stochastic optimization, bringing many practically relevant data analysis problems within the range of tractability. Example applications include algorithms for faster and more accurate medical image reconstruction and classification, which will be beneficial to healthcare. In addition, in seismology, effective stochastic programming methods will help to build predictive models by measuring thousands of earthquakes detected at seismic stations. The project will also support the PI's educational goals to improve students' learning in operations research, broaden the representation of underrepresented groups in the PhD program, and contribute to open research infrastructure through the development of optimization solvers.