Jiadong Wang

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

2013-01-16T18:20:00.004-08:00

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.

Open position - Optimization expert with Jeppesen, Sweden

2012-12-28T11:00:00.000-08:00

Optimization Expert to Jeppesen Gothenburg

Jeppesen’s crew and fleet solutions are mainly developed in Gothenburg, but with significant support from our offices in Montreal, Singapore, and New York. Jeppesen has more than 3,500 employees worldwide and is headquartered in Denver, Colorado, USA. Founded on the production of aeronautical charts, the company celebrated 75 years of guiding pilots safely to their destinations in 2009.

Jeppesen is a subsidiary of Boeing, the world’s leading aerospace company. Boeing is headquartered in Chicago, U.S.A., employing more than 160 000 people in 70 countries.
The successful candidate is goal-oriented and enjoys problem-solving, but still has an eye on the big picture. He or she must work well in an agile team and be very communicative and open. An interest in optimization and algorithms is a given. A strong software development background, especially in Object-Oriented programming is required. The new team member will be working with highly talented and experienced developers, having regular interaction with the other development teams, installation projects and clients.
The successful candidate is goal-oriented and enjoys problem-solving, but still has an eye on the big picture. He or she must work well in an agile team and be very communicative and open. An interest in optimization and algorithms is a given. A strong software development background, especially in Object-Oriented programming is required. The new team member will be working with highly talented and experienced developers, having regular interaction with the other development teams, installation projects and clients.
• PhD in Optimization/OR (or MSc + minimum 3 years of experience from developing similar applications)
• Strong C/C++ skills
Meriting Experience
• Airline or Railway applications
• Simulation packages
• Statistical analysis
• Data mining
• Python
Jeppesen Technology Services
_________________________

Jeppesen is looking to further strengthen the research and development team in airline crew and fleet optimization. We are solving some of the hardest combinatorial optimization problems you can imagine – in one of the most exciting and dynamic industries you can find. Delivering maximum value to our clients requires creativity, innovation and hard work. We believe there is no alternative to being number One – and that everyone can make a difference!

At Jeppesen in Gothenburg, over 300 employees from over 30 countries develop, implement and support cutting-edge resource optimization systems for airlines and railways. Clients include world-leading companies like Lufthansa, SAS, British Airways, Singapore Airlines, Delta Airlines, Virgin Atlantic Airways, Deutsche Bahn, Green Cargo and Qantas. Jeppesen has pioneered several new methods and algorithms for solving large, complex optimization problems, and has received significant recognition both in industry and academia, the latest by being awarded the INFORMS Prize in 2010.

As an Optimization Expert you will work with mathematical modeling, algorithmic development and performance tuning as well as taking part in research, design, implementation, testing and maintenance.

Requirements

Last day of application will be December 31, 2012.

Applications are only accepted through the Company website:

http://careers.jeppesen.com/JobDescription.asp?JobNumber=692783&lang=en

Tomas Gustafsson

Optimization & Research Strategist Crew & Fleet

SAS Summer Fellowship in Operations Research

2012-12-06T20:25:00.005-08:00

The Operations Research Development department at SAS is pleased to announce the annual SAS Summer Fellowship in Operations Research Program for the summer of 2013. Open to doctoral candidates in operations research, industrial engineering, computer science, applied mathematics and related graduate departments in the United States, the fellowship offers the opportunity to assist in the support of research and development in areas of operations research. The Operations Research fellow will contribute to activities such as literature review and research, implementation of numerical algorithms and software application development and/or testing and documentation. Specific areas of interest are mathematical optimization, constraint programming and large scale network algorithms. The program thus provides the graduate student with an excellent opportunity to explore software development as a career choice.

Eligible candidates must have completed at least two years of graduate work by the end of the spring semester of 2013, and must have demonstrated programming experience in low level languages like C, C++, Java, and knowledge of standard operating systems.

The program provides a salary and stipend for a twelve-week internship at SAS headquarters in Cary, North Carolina, during the summer of 2013. Interested candidates should apply online at the SAS careers site. To apply, go tohttp://www.sas.com/jobs/USjobs/# and select Fellowship Opportunities. The OR Fellowship is requisition # 12003018.

The deadline for submitting your application and attaching a letter of recommendation from two faculty members is February 1, 2013. The selected candidate will be notified by March 1, 2013.

SAS is located in the Research Triangle area, which encompasses three leading universities – Duke, the University of North Carolina, and North Carolina State University – and is also the home of the National Institute of Statistical Sciences and the Statistical and Applied Mathematical Sciences Institute. Ample intellectual and leisure pursuits exist to make this an exciting opportunity for the chosen student.

Positions at the University of Colorado Denver

2012-11-27T21:20:00.002-08:00

The Department of Mathematical and Statistical Sciences at the
University of Colorado Denver invites applications for a tenure-track
assistant professor position in Numerical Methods and Scientific
Computing and a tenure-track assistant professor position in
Statistics, to start August 2013.

The Department has 16 tenured or tenure-track faculty members whose
research and teaching cover a wide range of areas, including
computational biology, computational and and engineering mathematics,
probability, statistics, graph theory, combinatorics, operations
research, and optimization. The Department has approximately 70 M.S.
and Ph.D. students, and 100 undergraduate mathematics majors. Many
departmental faculty are involved with the Center for Computational
Mathematics, and the University owns a major TOP50 supercomputing
facility with large reserved allocations available.

The regular teaching load is 4 undergraduate or graduate courses per
year, which may be reduced for an initial period. The candidate will
be expected to develop a strong independent research program with
external funding and to teach a variety of courses at both the
graduate and undergraduate levels. A completed doctoral degree, or
expected completion by the start of the appointment, is required.

The candidate should have an excellent record in research commensurate
with the career stage, and strong commitment to quality teaching at
both undergraduate and graduate levels. Further preference will be
given to candidates with demonstrated potential for attracting
external research funding and with research interests compatible with
present faculty research areas which include numerical solutions of
partial differential equations, numerical linear algebra, parallel
computing, mathematical modeling involving systems of PDEs or
stochastic PDEs, data assimilation, quantification of uncertainty,
extreme value theory, high-dimensional data, spatial statistics,
statistical genetic and genomics, Bayesian statistics, and
environmental and ecological applications. In addition, an interest in
interacting with faculty in other research areas in the department
and/or University would be desirable.

Salary is commensurate with skills and experience. The University of
Colorado offers a full benefits package. Information on University
benefits programs, including eligibility, is located at
http://www.cu.edu/pbs/

Applications are accepted electronically at www.jobsatcu.comunder job
posting 819515 (numerical
http://www.jobsatcu.com/applicants/Central?quickFind=70811) and 819855
(statistics http://www.jobsatcu.com/applicants/Central?quickFind=71151).
Review of applications will begin January 7, 2013 and will continue
until the position is filled. The Department web page at
http://math.ucdenver.edu/ can supply further information.

The University of Colorado Denver is dedicated to ensuring a safe and
secure environment for our faculty, staff, students and visitors. To
assist in achieving that goal, we conduct background investigations
for all prospective employees.

The University of Colorado strongly supports the principle of
diversity. We encourage applications from women, ethnic minorities,
persons with disabilities and all veterans.

Presolve and statuses CPX_STAT_INForUNBD or IloCplex::InfOrUnbd.

2012-11-27T21:17:00.000-08:00

When using mip solver , it is often to encounter cases where a specific optimization model is either infeasible or unbounded (CPX_STAT_INForUNBD in CPLEX). Here is a nice post on this topic which provides insight on this flag message.

http://www-01.ibm.com/support/docview.wss?uid=swg21399996

Faculty Opening at Department of Computational and Applied Mathematics (CAAM) Rice University

2012-11-20T06:10:00.002-08:00

Department of Computational and Applied Mathematics (CAAM)
Rice University

The Department of Computational and Applied Mathematics
(http://www.caam.rice.edu/) invites applications for a tenure track
professorship beginning July 1, 2013. Candidates at the assistant
professor level are preferred, although outstanding candidates at all
levels will be considered. The Department offers an outstanding
research environment and hosts research programs in optimization,
numerical linear algebra, control and inverse problems, partial
differential equations, and scientific computing. Interdisciplinary
work is a fundamental aspect of the Department's program.

Preference will be given to candidates who are engaged in
high-performance computing and whose research complements existing
strengths of the Department. Candidates for this position should have
a PhD and have demonstrated potential for excellence in both research
and teaching.

Applications consisting of a letter of application, current curriculum
vitae containing a list of publications, a description of research,
and a statement on teaching should be submitted via the website
www.MathJobs.Org. In addition, candidates should arrange for at least
three letters of recommendations, which may be submitted on the same
website.

Alternatively, applications and letters of recommendations may be sent
by mail to:

Faculty Search Committee
Department of Computational and Applied Mathematics (CAAM)
Rice University
6100 Main Street, MS-134
Houston, TX 77005-1827

PLEASE DO NOT DUPLICATE YOUR APPLICATION!

To receive full consideration the complete application must be
received by January 11, 2013, but the committee will continue to
accept applications until the position is filled.

Rice University is an affirmative action/equal opportunity employer.

_____________________________________________________
Matthias Heinkenschloss
Professor and Department Chair

Department of Computational and Applied Mathematics - MS 134
Rice University
Houston, Texas 77005, USA

Phone 713-348-5176
Fax 713-348-5318
http://www.caam.rice.edu/~heinken

2 Two Year Postdoctoral Positions in Cambridge, UK

2012-09-18T08:12:00.002-07:00

Cambridge Systems Associates Limited (CSA), a small high end financial services analytics, consulting and software company (see www.cambridge-systems.com), has two 2 year postdoctoral positions, funded by the EU and starting as soon as possible.

The full time R&D work will involve development of CSA's iALM financial planning system and its patented STOCHASTICS stochastic optimization analyst software in high performance computing applications. Publications from the work will be expected.

Lloyds Banking Group and Citigroup are the financial partners and Numerical Applications Group (NAG), Maxeler and several leading European Universities are other partners on the EU High Performance Computing in Finance (HPC Finance) project. Please see the project website: www.hpcfinance.eu for a full list of partners, where CSA's detailed job descriptions may also be found (dates on these latter are irrelevant).

We are seeking recent PhDs (not more than 4 years from doctoral completion) in mathematics, statistics, OR, computer science or quantitative finance. Candidates must not be UK citizens, nor have spent more than one year in the UK to date.

The successful applicants will be CSA employees and salaries are generous, with moving allowances available. The active and exciting environment for these positions is Cambridge University. The company's elegant offices are in the centre of the University and the city.

Interested candidates should apply to Professor M.A.H. Dempster (mahd2@cam.ac.uk) at their earliest convenience.

An Effective and Time-efficient Approach to Solving Linear Discrete Optimization Problems using Discretized Network Flow

2012-09-13T22:20:00.001-07:00

A recent NSF grant is award to Shantanu Dutt. Here is the abstract below from the NSF website

Discrete optimization finds widespread use in almost all areas of human endeavor ranging from science to technology to business, and encompassing diverse applications such as chip design, power system design, robotics, bioinformatics, transportation, financial computing and industrial engineering. However, currently there is no general discrete optimization solver that can solve hard problems near-optimally in tractable runtimes (fast to moderate runtimes). To rectify this, this project will explore developing novel and efficient techniques for solving the class of 0/1 integer linear programming (ILP) problems that can be used to model a wide range of discrete optimization problems (DOPs). The approach used for this purpose is a solution technique termed discretized network flow (DNF), in which classical network flow (that solves a class of continuous linear programming problems), is constrained by special discrete requirements on the flow to yield valid solutions to 0/1 ILPs.

A successful completion of this project will yield algorithms and a software tool for solving large 0/1 ILP problems near-optimally and much faster than current techniques. This will represent a significant advance in the state-of-the-art of such solvers, and can be used to solve large DOPs more accurately and faster in many application areas ranging from genomics to chip design to robotics. This can help in answering fundamental issues in these application areas that have not been attempted so far, and also lead to better products and services in these areas. For example, in the area of chip design, the use of our solver can lead to much lower power and higher quality chips (e.g., with good performance and reliability) than are possible with current CAD tools, and thereby result in better and greener electronic products in several consumer and commercial application areas.

Optimization without Round-off Errors

2012-09-13T22:12:00.004-07:00

Recently, a NSF grant is awarded to Erick Moreno-Centeno on Optimization without Round-off Errors. It will be thrilling in the community since round-off erros have been a serious concern to solve large scale optimization problems, especially those with integer variables.

This Early-concept Grant for Exploratory Research (EAGER) provides funding to further develop an algorithm to solve systems of linear equations with a specific advantage over Gaussian Elimination in that the algorithm does not have round-off errors. The algorithm will be tailored and adapted to be used within existing optimization algorithms. The key property of the algorithm being further developed is that it maintains the integrality of the numbers throughout its execution. In particular, although the algorithm does contain divisions (which is crucial for the polynomial-time complexity of the

algorithm) all the divisions have a remainder of zero. A detailed complexity analysis of the algorithm will be performed; specifically, both its running time and the number of bits required to represent the numbers throughout the algorithm's execution will be investigated. A modification of the algorithm to perform LU factorizations will also be studied. Finally, the algorithm will be adapted to enable its use for the basis updates performed in state-of-the-art implementations of the revised simplex method.

If successful, the outcomes of this research will lead to a deeper understanding of computational linear algebra free of round-off errors and the associated applications within optimization algorithms. In the linear programming (LP) context, this research will enable us to solve large-scale LPs exactly; this, in turn, will significantly improve the accuracy and speed of integer programming solvers. In addition, since solving systems of linear equations is a critical subroutine of many numerical algorithms, this research will impact many application areas where linear systems frequently arise, including economics, physics, chemistry, and engineering.

postdoctoral position in Carnegie Mellon University

2012-09-11T20:55:00.000-07:00

A postdoctoral position is available in my group in the area of computational global optimization. I am looking for a person interested to work on the development and implementation of polyhedral cutting plane techniques for nonlinear and mixed-integer nonlinear problems in the context of the BARON project. For current group activities, see http://archimedes.cheme.cmu.edu Those interested in this position, please email me your CV/papers and let me know if you are available to meet during the INFORMS meeting in Phoenix.

Best wishes, Nick Sahinidis

Postdoc openings at Sandia National Labs in Livermore, CA

2012-07-29T16:23:00.002-07:00

Postdoctoral Appointee --- Large-Scale Graph Analysis

Sandia National Labs, Livermore, CA

JOB DESCRIPTION: We are seeking a postdoctoral researcher in the area

of large-scale graph analysis of time-evolving attributed networks,

with a focus on feature discovery and scalable models. The successful

applicant will be expected to conduct innovative research, to develop

open-source Hadoop MapReduce software for analyzing large-scale data,

to present his or her findings at leading conferences and workshops,

and publish his or her results in leading journals. This postdoctoral

position is for motivated and enthusiastic individuals with a

background in computer science, mathematics, statistics, or related

areas.

QUALIFICATIONS: *Required* (1) U.S. Citizenship; (2) advanced degree

(Ph.D. or equivalent) in computer science, mathematics, statistics, or

a related area, with a strong record of academic performance; (3)

ability to work in a collaborative research environment; (4) evidence

of relevant research expertise in the form of technical publications,

presentations, software, and/or knowledge of applications; (5)

software development competence in Java, C++, or a related language;

(6) proficiency in solving problems, prioritizing work, and making

decisions; (7) excellent written and oral communication skills.

*Desired* (1) Expertise in one or more of the following areas:

MapReduce algorithms, graph analysis and modeling, graph mining, cyber

security, social network analysis, numerical linear algebra and

optimization, statistics, discrete mathematics; (2) software

engineering proficiency, including an understanding of object-oriented

programming techniques and best practices, Hadoop MapReduce and/or

high-performance computing experience; (3) experience with standard

research tools such as LaTeX, MATLAB, PowerPoint; (4) a background in

solving practical problems in science and engineering that involve

encounters with real-world data; and (5) evidence of professional

service to the community, such as engagement in student service

activities or seminar/workshop organization.

HOW TO APPLY: Visit http://tinyurl.com/6q2keec and click "Apply Now".

Postdoctoral Appointee --- Informatics

Sandia National Labs, Livermore, CA

JOB DESCRIPTION: We have multiple openings for postdoctoral

researchers in computer science, computer engineering, mathematics,

and statistics. We are particularly looking for experts in informatics

and data sciences, numerical mathematics (especially linear algebra),

network and graph algorithms, and cyber defense. The successful

applicant will be expected to conduct innovative research, to develop

open-source software, to present his or her findings at leading

conferences and workshops, and to publish his or her results in

leading journals. This postdoctoral position is for motivated and

enthusiastic individuals with a background in computer science,

mathematics, statistics, or related areas.

QUALIFICATIONS: *Required* (1) Advanced degree (Ph.D. or equivalent)

in computer science, mathematics, statistics, or a related area, with

a strong record of academic performance; (2) ability to work in a

collaborative research environment; (3) evidence of relevant research

expertise in the form of technical publications, presentations,

software, and/or knowledge of applications; (4) software development

competence in Java, C++, or a related language; (5) proficiency in

solving problems, prioritizing work, and making decisions; (6)

excellent written and oral communication skills. *Desired:* (1)

Expertise in one or more of the following areas: machine learning,

data mining, graph analysis, linear algebra, applied statistics,

optimization, computational combinatorics, discrete mathematics, cyber

defense; (2) software engineering proficiency, including an

understanding of object-oriented programming techniques and best

practices, Hadoop MapReduce and/or high- performance computing

experience; (3) experience with standard research tools such as LaTeX,

MATLAB, PowerPoint; (4) a background in solving practical problems in

science and engineering that involve encounters with real-world data;

and (5) evidence of professional service to the community, such as

engagement in student service activities or seminar/workshop

organization.

HOW TO APPLY: Visit http://tinyurl.com/7xdwke8 and click "Apply Now"

Positions in the Computational Science Center at IBM Research

2012-07-29T16:21:00.002-07:00

IBM currently has the following three positions available in our Computational Science Center at IBM's T. J. Watson Research Center in Yorktown Heights, NY. All involve working with applications for IBM's current and future leading edge HPC systems and with development of future systems. We would appreciate it if you could pass on this information to potentially qualified candidates.

Research Staff Member
The Deep Computing Systems Group at IBM's T. J. Watson Research Center is seeking a Research Staff Member to work on development of future systems for compute intensive and data intensive applications. The Deep Computing Systems Group is heavily engaged in research to develop esascale class systems for research, industrial and commercial use. The group has lead the development of IBM's Blue Gene range of systems. It carries out a range of activities include design, development and implementation of processors, networks and storage systems, and development of systems and applications software for leading capability systems. The group works collaboratively with some of the leading high performance computing organizations around the world. The ideal candidate will have deep knowledge and a proven research track record in high performance computing with experience in analytics and in data intensive applications. A PhD in Computer Science, Computational Science, Applied Mathematics or related fields is preferred. (Job id RES-0501718)

Postdoctoral Researcher
The Computational Science Center at IBM T. J. Watson Research Center is seeking a Postdoctoral Fellow to carry out research on the design, optimization and evaluation of analytics algorithms and related data-intensive applications on leading-edge and next-generation HPC systems. The position requires a deep understanding of the strengths and weaknesses of existing analytics algorithms as well as extensive knowledge of the strengths and weakness of various system architectural design points. Advanced analytics codes will be characterized and methodologies to estimate performance on various architectures will be developed and applied. The design of novel algorithms is expected. A Ph.D. in a relevant field of computer science, engineering, or applied mathematics with experience in computational science is required along with knowledge of HPC architectures and their influence on performance. (Job id RES-0505182)

Postdoctoral Researcher
The HPC Applications and Tools group in the Computational Science Center at IBM's T. J. Watson Research Center is seeking a Postdoctoral Fellow to carry out research on the performance of large-scale high performance computing (HPC) application codes on leading-edge present and future HPC machine architectures. Advanced simulation codes in many domains of science and engineering will be characterized and methodologies to estimate performance on various architectures will be developed and applied. A Ph.D. in a relevant field of science, engineering, or applied mathematics with experience in computational science is required along with knowledge of HPC architectures and their influence on performance. (Job id RES-0501444)

Applicants may apply for these positions using the following IBM employment link. You may use the job ids listed above to search for these positions.
http://www.ibm.com/employment/us/

Dr. Kirk E. Jordan
Emerging Solutions Executive & Assoc. Program Director
Computational Science Center, IBM T.J. Watson Research
Member, IBM Academy of Technology
1 Rogers St, Cambridge, MA, 02142
kjordan@us.ibm.com -- office: (617) 693-4581 or T/L 693-4581

Personal Page: https://researcher.ibm.com/researcher/view.php?person=us-kjordan
IBM Research: http://www.research.ibm.com/

Research position at NeurOK Software

2012-07-29T16:19:00.002-07:00

NeurOK Software is looking for a specialist in machine learning and data mining to join our existing team. Our team has been working with advanced machine learning technologies for more than 15 years and we are growing. Our clients have been thrilled with our work and our employees are rewarded with an intellectually stimulating environment that challenges you to go beyond the obvious and discover insights that lead to excellence.

If you meet the requirements below and believe you are prepared to work with some of the brightest minds then we welcome your application.

Requirements:

1. Applicant should have a strong background in computer science and data mining with in-depth knowledge of such areas as pattern recognition and anomaly detection in large-scale, streaming data.

2. Deep knowledge of machine learning techniques including:

a. Artificial neural networks,

b. Wavelet, Fourier, Windows Fourier analysis,

c. Latin hypercube, decision trees,

d. Statistical methods in data analysis.

3. Master degree or PhD (preferable) in mathematics/physics.

4. Works well in a team - no soloists here.

5. A passion for machine learning and mathematics.

Optional:

1. Fluency in Russian is beneficial

Compensation:

Salary is competitively priced, including benefits. More information available upon application.

NeurOK Software is an equal opportunity employer.

Full Time Position as Research Engineer – Mathematical Optimization

2012-07-29T16:18:00.001-07:00

ExxonMobil Upstream Research Company is seeking qualified and experienced candidates to conduct research and software development in the area of mathematical programming. The qualified candidate will also serve as an internal consultant to ExxonMobil Upstream affiliates and will be based in Houston with some domestic and international travel.

Primary Job Functions:

Contribute to research and development effort in optimization and analytics for the petroleum and natural gas industry
Work within cross-disciplinary teams to perform breakthrough research, develop software, create new approaches and deploy technology for the upstream sector of the corporation
Plan, steward, and technically oversee internal and joint industry research programs
Provide training to ExxonMobil affiliates
Effectively work with contractors, vendors, and operators

Job Requirements:

Ph.D. degree in Operations Research, Industrial Engineering, Applied Mathematics, Mechanical Engineering, Chemical Engineering or a related discipline
Theoretical and practical experience in mathematical programming modeling and algorithm development for discrete optimization

Research emphasis on solving large-scale mixed integer programming problems in planning, scheduling or routing problemswould be preferred
Expertise in closely related areas such as stochastic programming, robust optimization constraint programming, approximate dynamic programming, mixed integer nonlinear programming is desirable, but not required

Strong background in programming, prior work or research-related experience in software development
Advanced proficiency with C++ or other object-oriented programming language
Strong applied math, engineering and applied research skills

Preferred Knowledge/Skills/Abilities:

Proficiency in current commercial and academic mathematical programming and optimization modeling languages and solvers
Experience in algorithm design and implementation that utilizes high performance computing
Demonstrated excellent communication, interpersonal, and personal leadership skills

ExxonMobil is an Equal Opportunity Employer

************************************************************************************************************************************

To learn more about ExxonMobil and apply, visit Web Site at www.exxonmobil.com/careers/usa

Under ‘Experienced or Professional Recruiting’ search for ‘15131BR’

Or Click on

https://sjobs.brassring.com/1033/asp/tg/cim_jobdetail.asp?jobId=1274335&PartnerId=6961&SiteId=5328&type=search&JobReqLang=1&recordstart=1

Postdoc position at SUTD

2012-06-18T05:36:00.000-07:00

A 2-year postdoc position is available at the Singapore University of
Technology and Design.

This position is part of a research project aimed at integrating
optimization with design practice in sustainable architecture. The
specific research area is the optimization of expensive black-box
functions, in particular optimizing the outcome of (highly nonlinear)
simulators that have continuous and discrete input parameters. This is
a challenging applied research project with important applications. It
involves collaboration with renown architects on real-world buildings,
with the potential of having an impact on design practice.

We are looking for motivated applicants with a strong theoretical and
computational background. The ideal candidate should be skilled in at
least one of: global optimization, mixed-integer nonlinear
programming, and related areas.

The position comes with an attractive remuneration package,
competitive on an international level, as well as travel support. The
start date is flexible, ideally between August 2012 and January 2013.
Interested applicants are encouraged to send a CV with a brief summary
of past research and names of references - all in PDF format - to:
Giacomo Nannicini, nannicini@sutd.edu.sg. All questions can be
directed to the same address.

Scientist/Researcher - Mathematical Optimization, Analytics, and Statistics

2012-06-13T13:28:00.000-07:00

SK Innovation’s System and Optimization Laboratory has immediate openings for full-time positions in mathematical and stochastic optimization, computing, analytics, statistics, and logistics and supply chain optimization. Located in Seoul, in the heart of Asia, the opportunity presents the discernable individual the opportunity to work in a dynamic environment, located in an exciting part of the world fast emerging as a leader in technology and innovation.

The successful candidate will join a growing multidisciplinary group of world class scientists performing breakthrough research and providing innovative solutions to support better decision making across all sectors of the corporation including (but not limited to) the oil and gas industry, battery and green energy, chemicals and lubricants, and I/E materials. The lab offers a unique environment that combines basic research with applied projects reflecting the need to work closely with end users of the technology while also conducting research in multidisciplinary groups, publish in leading journals, and present in scientific forums.

The applicant should have a Ph.D. degree in Applied Mathematics, Industrial Engineering, Chemical Engineering, Operations Research, Statistics, Computing, or other related scientific discipline, with both theoretical and practical experience in their respective areas. Applicants with expertise in large-scale mixed-integer programming, global optimization and non-linear programming, statistics, data mining and forecasting, or stochastic optimization and simulation are especially encouraged.

SK Innovation offers an excellent working environment and a competitive compensation and benefits package, and is committed to the ongoing development of their staff. Interested candidates are invited to send their cover letter and CV to Faramroze Engineer at f.engineer@sk.com.

Founded in 1962 as Korea’s first oil refiner, SK Innovation, formerly SK Energy, is one of the world’s leading energy and petrochemical companies. SK Innovation is a Fortune Global 100 company and posted US$60.7 billion in sales in 2011. Additional information about SK Innovation can be found at http://eng.skinnovation.com.

Postdoc position in optimization, U of Edinburgh, UK

2012-06-06T19:45:00.002-07:00

Applications are invited for an 18-month fixed-term Postdoctoral Research Assistant position in Optimization (ref: 3015812) in the School of Mathematics at the University of Edinburgh, United Kingdom.

Applicants must hold a Ph.D. in Operations Research, Computer Science, Mathematics, Statistics, Machine Learning or a related field by the starting date of the appointment, which is September 1, 2012 (+- 1 month, based on mutual agreement). The successful applicant will join the Edinburgh Research Group in Optimization (http://www.maths.ed.ac.uk/ERGO/) and will work on gradient algorithms for large-scale optimization. Strong publication record and good computational skills are essential; background in gradient methods, large-scale optimization and HPC desirable.

More information and online application forms:
https://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.detail&vacancy_ref=3015812&CFID=93551117&CFTOKEN=70587149

Postdoc Position on Stochastic Optimization at Argonne National Laboratory

2012-05-26T03:37:00.001-07:00

The Mathematics and Computer Science Division at Argonne National Laboratory is searching for a postdoctoral appointee to develop *scalable algorithms for stochastic and robust optimization with probabilistic constraints and supporting convergence theory*. The candidate will also perform scalability tests in parallel computing clusters. The problems of interest arise from the design and real-time coordination of next-generation national infrastructure systems such as electrical and natural gas transmission networks where uncertainties originate from rare events and spatio-temporal weather and demand fields.

Candidates must possess a comprehensive background in numerical optimization and statistics as well as solid C/C++ programming skills.

More information on the position can be found in:

http://web.anl.gov/jobsearch/detail.jsp?userreqid=319368+MCS&lsBrowse=POSTDOC

The interested candidate can also contact me directly.

Best regards,

Victor M. Zavala
Mathematics and Computer Science Division
Argonne National Laboratory
9700 S Cass Ave, Bldg 240, Argonne, IL 60439
E-mail: vzavala@mcs.anl.gov
Website: http://www.mcs.anl.gov/~vzavala/

Postdoc Position in Optimization at Northwestern IEMS

2012-05-10T18:12:00.002-07:00

We invite applications for a new postdoctoral research position in the area of optimization. Background in integer programming and ability to write and work with C++ programming language is a requirement. Knowledge of stochastic optimization and continuous optimization methodology will add strength to a qualified candidate’s application. The position may start immediately and may be extended to two more years.

For further information contact/email: mehrotra@iems.northwetern.edu

====

Sanjay Mehrotra, Ph.D.

Professor, Industrial Engineering and Management Sciences, McCormick School of Engineering

Professor, Institute for Health Studies, Feinberg School of Medicine

Northwestern University, IL 60208

Phone: 847 491 3155 : Fax: 847 491 8005 : Email: mehrotra@iems.northwestern.edu

http://healthcare-engineering.northwestern.edu/

http://users.iems.northwestern.edu/~mehrotra/

PhD positions in Optimization/Renewable Energy

2012-05-07T08:24:00.002-07:00

PhD positions in Optimization/Renewable Energy

At the Faculty of Mathematics and Natural Sciences at the University of Bergen, Norway, there are two open 4-year positions as PhD students in renewable energy. Possible research topics include industrial optimization, and applications from candidates with a recent master degree in optimization are therefore very welcome. The application deadline is 3 June 2012.

For more details about the positions and how to apply, please visit
http://www.jobbnorge.no/job.aspx?jobid=83156
and look for English text under the flag button.

Inquiries can be addressed by email to Professor Dag Haugland, Department of Informatics, Email: dag@ii.uib.no

EXCEPTIONAL, ENTREPRENEURIAL POST DOCTORAL FELLOW POSITION IN OPTIMIZATION AND MARKETS

2012-05-04T10:12:00.002-07:00

EXCEPTIONAL, ENTREPRENEURIAL POST DOCTORAL FELLOW POSITION IN OPTIMIZATION AND MARKETS

The US administration is appropriately interested in facilitating the transfer of university-generated technologies out into the world. As part of this effort, the National Science Foundation has a new highly-selective program for facilitating this. Prof. Sandholm recently received a grant from that program to commercialize some of the technology and know-how being developed in his Electronic Marketplaces laboratory at Carnegie Mellon University. The NSF grant covers a Post Doctoral Fellow, and the matching funds cover the operations of a new startup company. The topic is in the intersection of electronic marketplaces and integer programming. The first two products will be in advertising markets and electricity markets, respectively.

The postdoc positions in this NSF program are exceptional in that they are for cultivating next-generation entrepreneurs. The position is for 18-24 months at Carnegie Mellon University. After that, conditional on progress, the postdoc has the opportunity to transition into a position of significance at the startup company. This is a truly exceptional opportunity for someone who wants to change the world with technology.

The position is open immediately.

Requirements:

- Strong knowledge of integer programming (and ideally also stochastic/robust optimization).
- Strong skills and desire to build significant productized optimization software that is fast in practice.
- PhD in operations research, computer science, or equivalent field.
- Command of C++.
- Desire and ability to work hard in a fast-moving environment.
- Desire to change the world with technology.

Desired additional qualities (these are not required):

- Experience in developing commercial software.
- Experience in Software-as-a-Service/cloud.
- Command of Ruby on Rails and Java.
- Good written and oral communication skills.
- Ability to work independently and as part of a team.

The position offers the following:

- Opportunity to learn from, and work with, the world's leading experts in integer programming and market design.
- Opportunity to work on highly novel approaches to integer programming and tree search.
- Opportunity to learn from a serial entrepreneur and a mentor network how to become a successful entrepreneur.
- Opportunity to work on exciting new real-world problems.
- Opportunity to have your work fielded and change the world.
- Opportunity to publish.
- Competitive US postdoc salary.
- Opportunity to join an exciting new startup at a senior executive rank after the postdoc.

Candidates should email a letter of application, CV, and a list of references to:
Tuomas Sandholm
Professor
Computer Science Department
Carnegie Mellon University
sandholm@cs.cmu.edu
http://www.cs.cmu.edu/~sandholm/

Seminar on "Exponential Gaps in Mathematical Programming"

2012-05-01T19:34:00.004-07:00

Lehigh University

Industrial and Systems Engineering Department

INFORMS Lehigh Student Chapter Distinguished Speaker Seminar

Title: Exponential Gaps in Mathematical Programming
Date: Friday, May 4, 2012
Time: 2:30 pm – 3:30 pm
Place: Room 451, Mohler Lab

Speaker: Dr. Michael J. Todd, Cornell University

Abstract: We will discuss various exponential gaps that arise in the complexity of algorithms in optimization, and attempts that have been made to try to reduce or explain them.

Bio: Michael J. Todd received the B.A. degree in Mathematics from Cambridge University, UK, in 1968 and the Ph.D. degrees in Administrative Sciences from Yale University in 1972. Currently, he is a Leon C. Welch Professor of School of Operations Research and Information Engineering at Cornell University.

Professor Todd’s research interests are in algorithms for linear and convex programming, particularly semidefinite programming. He is also interested in developing and analyzing interior-point methods; previous research interests include homotopy methods, probabilistic analysis of pivoting methods, and extensions of complementary pivoting ideas to oriented matroids. He has more than 130 journals, as well as 4 books.

Professor Todd was the recipient of John von Neumann Theory Prize by INFOMRS (2003), the recipient of the George B. Dantzig Prize by SIAM (1988), and the recipient of Guggenheim Fellowship (1980-1981). He was elected to the INFOMRS fellow in 2004, and SIAM fellow in 2008. He was the co-editor, associate editor, or editor-in-chief in several journals, such as Mathematical Programming (1980-present), Mathematics of Operation Research (1978-2000), Operation Research (1982-1986), and SIAM Journal on Optimization (1997-2008).

[ISE-PhD-Students] [ISE-Seminars] András Prékopa, Friday, April 13, 2:30-3:30pm

2012-04-09T20:42:00.003-07:00

INDUSTRIAL AND SYSTEMS ENGINEERING (ISE) SEMINAR

Speaker:

András Prékopa

Professor of Operations Research, Statistics, and Mathematics

Rutgers University

Date, Time, Location:

Friday, April 13, 2012

2:30pm - 3:30pm

Mohler Laboratory, Room 451

Title:

Single Commodity Stochastic Network Design Under Probabilistic Constraint

Abstract:

Single commodity networks are considered, where demands at nodes are random. The problem is to find minimum cost optimal capacities at the nodes and arcs subject to the constraint that all demands should be met on a prescribed probability level (reliability constraint) and some constraints on the capacities should be satisfied. The probabilistic constraint is formulated in terms of the Gale-Hoffman feasibility inequalities but their number is reduced by elimination technique. The concept of a p-efficient point is used in a smart way to convert and then relax the problem into a large scale LP. Two solution techniques will be presented. In one of them all p-efficient points are known while in the other one they are simultaneously generated with the LP algorithm, The joint distribution of the relatively small number of the demands is used to obtain the p-efficient points for all non-eliminated stochastic inequalities and the solution of a knapsack problem is used to generate new p-efficient points. The network design method can be applied to interconnected power systems, flood control and water supply networks, design of shelter and road capacities in evacuation, parking lot capacities, financial networks, etc. Numerical examples will be presented.

Biography:

András Prékopa was born in Nyíregyháza, Hungary. He received his BA and MA degrees in mathematics and physics from the University of Debrecen in 1949 and, 1952, respectively. In 1952 he became a PhD student (aspirant) at the Institute for Applied Mathematics of the Hungarian Academy of Sciences (HAS) and defended his thesis, entitled “On Stochastic Set Functions”, in 1956. Between 1956-68 he was first assistant, later associate professor at the Department of Probability Theory of the L. Eötvös University. In 1968 he became full professor at the Department of Mathematics of the Technical University of Budapest, where he remained until 1983. In that year he returned to the Eötvös Univesity, and became the founder, professor and first chairman of the Department of Operations Research (OR). He retired from there in 2000. Since 1985 he has been distinguished professor of OR, statistics and mathematics at Rutgers University. Currently he is the graduate director of the PhD program in OR. Prékopa’s part-time appointments were also very important in his scientific career in Hungary. In 1958 he founded the first research department in OR at the Math. Inst. of the HAS and in 1977 the Department of Applied Math. at the Computing and Automation Inst. of the HAS. Prékopa is the father of the Hungarian OR in many ways: developed research school, education curricula, organized international and local conferences, formed academic committee, founded scientific periodical, etc. He published more than a dozen books about 350 papers and supervised 52 PhD students, many of them are internationally known academics and industrial leaders, all-over the world. In 1979 he was elected a corresponding member and in 1985 full member of the HAS. He was also elected a foreign member of the National Academy of Engineering of Mexico, fellow of the Econometric Society, member of the International Statistical Institute, honorary president of the J. Bolyai Math. Society and the Hungarian OR Society, among others. He is the recipient of the Széchenyi Prize (1996) and Middle Cross of the Republic of Hungary (2005). The highest recognition he was awarded for his achievements is the Gold Medal of the European OR Societies, a major international distinction that Prékopa received in 2003. He is married to Kinga Széchenyi, educator, sculptor and writer, they have two children and two grandchildren.

All full-time ISE PhD students are required to attend.

INFORMS Lehigh Student Chapter Distinguished Speaker Seminar

2012-03-27T13:56:00.002-07:00

Title: The Simplex and Policy-Iteration Methods are Strongly Polynomial for the Markov Decision Problem with a Constant Discount Factor

Speaker: Professor Yinyu Ye (Stanford University)

Date: Friday, March 30

Time: 2:30 pm – 3:30 pm

Place: Room 451 Mohler Lab, 200 W. Packer Avenue

Abstract: We prove that the classic policy-iteration method (Howard 1960), including the Simplex method (Dantzig 1947) with the most-negative-reduced-cost pivoting rule,
is a strongly polynomial-time algorithm for solving the Markov decision problem (MDP) with a constant discount factor. Furthermore, the computational complexity of the
policy-iteration method (including the Simplex method) is superior to that of the only known strongly polynomial-time interior-point algorithm for solving this problem.
The result is surprising since the Simplex method with the same pivoting rule was shown to be exponential for solving a general linear programming (LP) problem,
the Simplex (or simple policy-iteration) method with the smallest-index pivoting rule was shown to be exponential for solving an MDP regardless of discount rates,
and the policy-iteration method was recently shown to be exponential for solving a undiscounted MDP. We also extend the result to solving MDPs with sub-stochastic
and transient state transition probability matrices.

Bio: Yinyu Ye received the B.S. degree in System Engineering from the Huazhong University of Science and Technology, Wuhan, China, and the M.S. and Ph.D. degrees in
Management Science & Engineering from Stanford University, Stanford. Currently, he is a full Professor of Management Science and Engineering and Institute of
Computational and Mathematical Engineering and the Director of the MS&E Industrial Affiliates Program, Stanford University. His current research interests include
Continuous and Discrete Optimization, Mathematical Programming, Algorithm Design and Analysis, Computational Game/Market Equilibrium, Metric Distance
Geometry, Graph Realization, Dynamic Resource Allocation, and Stochastic and Robust Decision Making, etc.

He was the recipient of John von Neumann Theory Prize by INFORMS (2009), and recipient of the 2009 IBM Faculty Award. He was elected vice chair of the SIAM
Activity Group on Optimization (SIAG/OPT) in 2008. He was elected to theINFORMS fellow in 2006, and awarded the Farkas prize by INFORMS Optimization Society
in the same year. He was the area editor of Math of Operation Research (2009-now), Operation Research (2006-2009), and the Co-Chief Editor of Pacific J. of Optimization (2005-2009).

ALL FULL-TIME ISE PHD STUDENTS ARE REQUIRED TO ATTEND AND

ALL ISE GRADUATE STUDENTS ARE ENCOURAGED TO ATTEND

NSF Award on Polynomial Optimization: Solution Methods and Applications

2012-03-20T18:24:00.004-07:00

Recently, a NSF grant was award to Shuzhong Zhang on polynomial optimization.

The research objective of this project is to develop numerical tools for solving polynomial and tensor optimization models arising from engineering applications such as design of radar waveforms in signal processing, information extraction in magnetic resonance imaging, and gene expression data analysis in bioinformatics. An integrated approach for polynomial and tensor optimization models will be studied and tested in this project. The key components of the integrated approach include relaxation techniques, approximations to optimal solutions, randomized sampling techniques utilizing known structure of the model, and disciplined local improvements of feasible solutions. Numerical tools to be developed under the framework of the integrated approach will be investigated experimentally and validated using the data obtained from practical engineering applications including signal processing and bioinformatics. Performance of the algorithms developed from the project will be measured and compared against currently existing benchmarks.

If successful, the results of this research will enhance the effectiveness of methods for solving nonlinear optimization models where polynomial functions are found in the objective as well as in the constraints. In particular, such algorithms will lead to substantial practical improvements in radar waveform design, image processing, and data co-clustering analysis. As polynomial functions are known to be pervasive in the context of engineering design and decision making, success of the project will lead to broad societal benefits. Moreover, the proposed research addresses fundamental challenges in optimization theory, because most current research has not gone beyond polynomial models of degree 2; thus, results extending the theory beyond these models would constitute a major advance on the theory side, as well.