Post-doctoral positions at Sandia National Labs

 The Scalable Algorithms Department at Sandia National Labs is seekingcandidates in the areas of high performance numerical linear algebra including parallel preconditioners, parallel graph algorithms, block Krylov methods, mixed-precision computations, algorithms for multicore/manycore architectures, and object-oriented scientific software engineering.

Experience in one of more of the following areas is highly desired:

-- CUDA, OpenCL, Threaded Building Block, or other advanced parallel programming paradigms.
-- Parallel implicit and explicit mesh based PDE solution approaches.
-- Parallel particle methods such as Molecular Dynamics and Direct Simulation Monte Carlo.
-- Advanced object-oriented software engineering practices and processes.
-- High-performance computing on distributed, parallel and/or other specialized architectures.
-- Scientific programming using Trilinos and /or Zoltan.
-- Algebraic multigrid.

You can find the official announcement and apply at http://preview.tinyurl.com/7mfuktl, job #639457.

Best regards,
Jonathan Hu

Senior Operations Research Scientists (Stochastic Optimization); Principal Scientists (and Sabbaticals)

Inventory Planning and Control (IPC) group at Amazon is looking for experienced operations research scientists with expertise in stochastic optimization. These positions are in Seattle, WA. Please forward this to your current/past graduate students who might be interested.

We are also looking for Principal Operations Research Scientists (8-10 years of experience) and experienced faculty members (with experience in inventory optimization, supply chain management, simulation-optimization methods etc.) who might be interested in spending their sabbatical with us or who might be interested in full-time positions.

Thanks

Deepak Bhatia
Senior Manager, IPC

 

Job Description:

Inventory Planning and Control (IPC) is looking for experienced operations research scientists with expertise in stochastic optimization and its application in inventory optimization, supply chain management, vendor selection etc.

Candidate will be responsible for developing solutions to better manage/optimize worldwide inventory, while providing the best experience to our customers at the lowest possible price. This position will focus on identifying opportunities to improve existing business processes, analyzing these opportunities and developing new strategies (and better models) to further improve the existing inventory planning/optimization and vendor selection processes/algorithms. The successful candidate will be a person who enjoys and excels at diving into data to analyze root causes and implement long term solutions – changes in business policy or processes, updates to the models in our systems, creation of effective metrics and functionality.  The goal is to improve the Amazon customer experience through improved selection, product availability, and fulfillment reliability while enhancing profitability. This high visibility role requires partnering with retail, finance and operations teams to impact our bottom-line.

Inventory Planning and Control (IPC) owns Amazon’s global inventory planning systems: we decide what, when, where, and how much we should buy to meet Amazon’s business goals and to make our customers happy.   We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are on the cutting edge in automated large scale supply chain planning and optimization systems. IPC fosters new game-changing ideas, continuously improves, resulting in sophisticated, intelligent and self-learning models. IPC is unique in that we’re simultaneously developing the science of supply chain planning and solving some of the toughest computational challenges at Amazon.  Unlike many companies who buy existing off-the-shelf planning systems, IPC is responsible for studying, designing, and building systems to suit Amazon’s needs.  Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best research scientists and software developers in the business, shaping our roadmap to drive real impact on Amazon’s long-term profitability. If you’re interested in getting closer to the “action,” IPC is the place for you.

Basic Qualifications:

PhD degree in operations research, management science, statistics, engineering, mathematics, or computer science and 4-5 years of related work experience

    * Expertise in stochastic optimization
    * Ability to develop system prototypes
    * Experience with SQL and statistical tools (SAS, R, SPSS)
    * Experience utilizing problem solving and analytical skills

Preferred Qualifications:

    * Proficiency in one or more of programming languages JAVA, C++,
    * Expertise in one or more of the following: optimization, probability theory, queuing theory, game theory, simulation, decision analysis, stochastic models, system dynamics, forecasting and mathematical modeling.
    * A good understanding of analysis of algorithms and computational complexity is desirable.
    * Technical aptitude and familiarity with the design and use of complex logistics software systems
    * Familiarity with inventory planning, supply chain management, capacity management (forecasting, planning, optimization, and logistics) gained through work experience or through graduate level education
    * Project management experience desired for working on cross-functional projects
    * Excellent written and verbal communication skills

Amazon is an equal opportunity employer

Full Time Position in Mathematical Optimization at ExxonMobil Research and Engineering Company

ExxonMobil's Corporate Strategic Research laboratory has an immediate opening for a full-time staff position in mathematical optimization within our Complex Systems Science Section. The laboratory is located 50 miles from New York City in scenic western New Jersey.

We are looking for a creative and resourceful individual to join our research and development team to solve challenging problems in the area of large-scale mixed-integer programming. The successful candidate will join a dynamic group of scientists performing breakthrough research across all sectors of the corporation, developing new approaches to solve our most challenging optimization problems.

The applicant should have a Ph.D. degree in Applied Mathematics, Industrial Engineering, Chemical Engineering, Operations Research or other related scientific discipline, with theoretical and practical experience in mathematical programming modeling and algorithm development. Expertise in large-scale mixed-integer programming is required. Knowledge and background in methods for mixed integer nonlinear programming, global optimization or optimization under uncertainty are desirable. Candidates with strong computing skills are preferred. The applicant must have excellent communication skills and a demonstrable ease of interaction with researchers and business partners of varying backgrounds. The candidate should be willing and able to learn new skills and grow into new science areas.

ExxonMobil offers an excellent working environment and a competitive compensation and benefits package. Please submit your cover letter and resume to our website at www.exxonmobil.com/ex and apply to Mathematical Optimization.

ExxonMobil is an Equal Opportunity Employer

Ahmet B. Keha, Ph.D.

Corporate Strategic Research

ExxonMobil Research & Engineering

1545 Route 22 East

Annandale, NJ 08801

(908) 730-2803

ahmet.b.keha@exxonmobil.com

TITLE: Robust Optimization with Multiple Ranges and Chance Constraints

TITLE:                      Robust Optimization with Multiple Ranges and Chance Constraints

SPEAKER :                Ruken Duzgun, Ph.D. Candidate
                                    Department of Industrial and Systems Engineering

DATE :                        Tuesday, December 6, 2011 at 3:00 p.m.

LOCATION :             Room 453 Mohler, 200 W. Packer Avenue

ABSTRACT:  The first part of the dissertation focuses on the case when the uncertain parameters come from multiple ranges.  This arises when the uncertain parameters, such as cash flows, depend on underlying discrete random variables, such as the success level of a new project.  Using multiple ranges has the same tractability as the traditional robust optimization approach but leads to less conservative results, while providing better representation of underlying random factors.  The second part of the dissertation briefly introduces how robust optimization can be connected with chance constraints.  We show that the Bernstein approximation of robust binary optimization problems leads to robust counterparts of the same structure as the deterministic models, but with modified objective coefficients that depend on a single new parameter introduced in the approximation.
 
BIOGRAPHY:  Ruken Duzgun is currently a doctoral candidate in Industrial Engineering at Lehigh University.  She received her B.S. degree in Industrial Engineering from Middle East Technical University in Ankara, Turkey, in 2007 and her M.S. degree in Management Science from Lehigh University, in 2010.  Her research introduced new methods in robust optimization, with applications to investment planning and project selection.  She is a Rossin Doctoral Fellow since 2010, and a member of INFORMS and Women in OR/MS (WORMS) Society.

DISSERTATION DEFENSE ANNOUNCEMENT

DISSERTATION DEFENSE ANNOUNCEMENT
TITLE:                      A Continuous-Time Model for the Valuation of Mortgage-Backed Securities
SPEAKER:                Stephen M. Mansour, PhD Candidate
                                    Department of Industrial and Systems Engineering

DATE:                        Friday, December 9, 2011 from 3:30 – 5:30 pm 
LOCATION:             Room 451 Mohler, 200 W. Packer Avenue

ABSTRACT:  A mortgage model consists of three basic parts:  the amortization model which examines the mortgage cash flows, the interest rate model which affects the mortgage price, and the prepayment model which measures the rates of mortgage termination when a property is sold, refinanced or foreclosed.  A technique known as eigenfunction expansion has proven to be useful in pricing continuous-time mortgages.   

The first part of this presentation involves generalizing the existing interest rate Cox-Ingersoll-Ross model and including as an alternative the simpler Vasicek model and then comparing the results obtained by these methods.  We also refine the relationship between interest rates and prepayments to reflect empirical data more accurately, particularly in low-interest rate scenarios by expanding the existing single-threshold prepayment model to include a secondary prepayment threshold.   

The second problem expands the existing continuous prepayment model to include mortgage defaults.   We use the default model to examine the price sensitivity of mortgages to loss severity and foreclosure rates.  We also examine two practical applications of this model:  accounting for wider spreads between mortgage yields and treasury yields during periods of economic stress, and estimating the value of the mortgage guarantee that government agencies such as Ginnie Mae provide to investors of mortgage-backed securities.   

BIOGRAPHY:  Stephen M. Mansour is a PhD candidate in the Department of Industrial and Systems Engineering at Lehigh University.   He received a Bachelor’s Degree in Mathematics with Distinction in General Scholarship from the University of California at Berkeley in 1981 where he graduated Phi Beta Kappa.  He worked at IBM East Fishkill, New York from 1982 to 1994 as an APL programmer. While at IBM he received a Division Award for “Outstanding Team Leadership During the Development of the ALORS2 Data Base”. During his tenure at IBM, he also received a Master’s Degree in Operations Research and Applied Statistics from Union College in 1992.   He worked at Check-Free Corporation in Jersey City, New Jersey from 1994-1996 where he developed a billing system for portfolio managers, and at The Carlisle Group in Scranton, Pennsylvania from 1996-2008 where he developed an APL-based pricing and portfolio optimization system for use by mortgage companies.   He is currently teaching statistics at the University of Scranton and at Penn State Worthington Scranton.

Dan Scansaroli's Ph.D. defense 12-1-11

DISSERTATION DEFENSE ANNOUNCEMENT

TITLE:                    Stochastic Modeling with Temporally Dependent Gaussian Processes: Applications to Financial Engineering, Pricing and Risk Management

SPEAKER:            Daniel J. Scansaroli, PhD Candidate Department of Industrial and Systems Engineering

DATE:                   Thursday, December 1, 2011 from 3:00 – 5:00 pm

LOCATION:         Room 375, Mohler Laboratory, 200 W. Packer Avenue

ABSTRACT: This thesis studies two classes of the most often applied temporally dependent Gaussian processes. Computationally efficient and accurate techniques are
developed for modeling and parameter estimation with the goal of improving decision making, risk management, pricing and hedging in finance.

We first focus on the widely used fractional Brownian motion (fBm) processes. We explore the advantages and disadvantages of modeling with the processes and present
new consistent estimators of the Hurst index for a Weiner type fBm process. Simulation studies demonstrate that the new estimators are highly competitive to leading estimators
in accuracy, especially on small data sets, and much more time efficient. This makes the estimators ideal for fast paced financial markets, which is demonstrated on a variety of indices.
We conclude our study by demonstrating that the dependency structure of fBm may explain the term structure of volatility commonly observed in practice.

The second part of this presentation focuses on Gaussian Markov (GM) processes. GM processes allow for a wide range of properties including long or short-range dependence,
 non-stationarity, and heteroscedasticity. We prove that the quadratic variation leads to a new, computationally efficient, consistent estimator of a model’s diffusion parameter.
Consistency is proven on a finite time interval, making it well suited for real world applications. This contrasts with existing MLE methods that require an infinite time horizon for
consistency. The convergence rate and confidence interval bounds for the estimator are also obtained. We demonstrate how the quadratic variation changes Option Pricing Theory
and extend the Black-Scholes formula for general continuous sample path GM processes.

The accuracy of diffusion parameter estimation techniques is demonstrated by simulating an Ornstein-Uhlenbeck process and applying the quadratic variation and Maximum Likelihood
estimators. We use the Likelihood function to express all model parameter estimators in closed-form, eliminating the need for estimation through three dimensional numerical optimization methods.

The final part of the presentation addresses the pricing of American style derivatives through the discretization of any continuous path GM processes into a recombining n-period binomial tree. The
Central Limit Theorem for Stochastic Processes is used to prove that the tree converges to its continuous path GM process. We apply our method to create a tree for the Vasicek interest rate model
and price an American put option.

BIOGRAPHY: Daniel Jonathan Scansaroli is a Ph.D. candidate in the Department of Industrial and Systems Engineering at Lehigh University. Educated at Lehigh for both undergraduate and graduate
degrees, he received a Bachelor of Science in Mechanical Engineering in 2005. Awarded Lehigh University's Presidential Scholarship, he received the degree of Master of Science in
Applied Mathematics in May 2006 and a Master of Science in Management Science in January 2009. Currently, Dan is employed in asset management as a quantitative analyst for
Lehigh University's endowment office.

Coopr 3.1 Release

It is pleased to announce the release of Coopr 3.1 (3.1.5325). Coopr
is a collection of Python software packages that supports a diverse
set of optimization capabilities for formulating and analyzing
optimization models.

The following are highlights of this release:

- Solvers
   * Interfaces for !OpenOpt solvers
   * Many solver interface improvements
   * A solver checker to validate solver interfaces
   * Improved support for SOS constraints (cplex, gurobi)
   * PH supports nonlinear models
   * PH-specific solver servers

- Modeling
   * Comprehensive rework of blocks and connectors for modular modeling
   * New !VarList component
   * Added comprehensive support for set expressions

- Usability enhancements
   * New 'coopr' command has subcommands that consolidate Coopr scripting
   * Added support to connect to databases with ODBC
   * Made JSON the default results format

- Other
   * Efficiency improvements in model generation, memory, runtime, etc.
   * Preliminary support for black-box applications
   * Deprecated modeling syntax in Coopr 3.0 is no longer legal

See https://software.sandia.gov/trac/coopr/wiki/GettingStarted for
instructions for getting started with Coopr.  Installers are available
for MS Windows and Unix operating systems to simplify the installation
of Coopr packages along with the third-party Python packages that they
depend on.  These installers can also automatically install extension
packages from Coin Bazaar.

Enjoy!

Householder Fellowship at the Oak Ridge National Laboratory

Householder Fellowship

Purpose The Computer Science and Mathematics (CSM) Division at the Oak Ridge

National Laboratory (ORNL) invites outstanding candidates to apply for

the Alston S. Householder Fellowship in Scientific Computing.

Description

The Fellowship honors Dr. Alston S. Householder, founding director of the

Mathematics Division (now CSM Division) at ORNL and recognizes his

seminal research contributions to the fields of numerical analysis and

scientific computing. Funding for the Householder Fellowship comes from

the Computational Mathematics Project, which is supported by the Office

of Mathematical, Information, and Computational Sciences of the U.S.

Department of Energy (http://www.science.doe.gov/ascr).

Additional information about the math group at ORNL can be found at

http://www.csm.ornl.gov/comp_math/.

The purpose of the Householder Fellowship is to promote innovative

research in scientific computing on advanced computer architectures and

to facilitate technology transfer from the laboratory research

environment to industry and academia through advanced training of new

computational scientists. The applied mathematics research efforts

provide the fundamental mathematical methods and algorithms needed to

model complex physical, chemical, and biological systems. The computer

science research efforts enable scientists to efficiently implement

these models on the highest performance computers available and to

store, manage, analyze, and visualize the massive amounts of data that

result. Networking research provides the techniques to link the data

producers, e.g., supercomputers and large experimental facilities, with

the data consumers, i.e., scientists who need the data.

Qualifications Required The position requires a Ph.D. in computer science, mathematics, or

statistics. Candidates nearing completion of the Ph.D.but no more than

three years beyond completion can be considered. The successful

candidate will have a strong background and interest in multi-scale

methods for scientific computing. Principal research areas include:

- boundary element method

- dense matrix computations

- direct methods for sparse matrix computations

- iterative methods for linear systems

- algorithms for solving differential equations

- large eigenvalue computations

- computational geometry and mesh generation

* A security clearance is not required for this position

SELECTION: Finalists for the Fellowship will be invited to visit ORNL to present a seminar and visit the area. The selected Fellow must be available to begin the appointment during calendar year 2012.Appointments are for one year with the option to renew for a second year. Each Householder Fellowship is a staff-level appointment that provides access to state-of-the-art computational facilities(high-performance workstations and parallel architectures), and collaborative research opportunities in active research programs in

To Apply

Please visit http://jobs.ornl.gov to officially apply to this Fellowship. For additional questions please contact Kate Carter at carterka@ornl.gov or Ed D'Azevedo dazevedoef@ornl.gov.

Announcing the winner of the 2011 COIN-OR INFORMS Cup

The submission "OpenSolver: Open Source Optimisation for Excel using COIN-OR", by Andrew Mason and Iain Dunning, has been selected as the winner of the 2011 edition of the COIN-OR INFORMS Cup. OpenSolver is an "Open Source linear and integer optimizer for Microsoft Excel. OpenSolver is an Excel VBA add-in that extends Excel’s built-in Solver with a more powerful Linear Programming solver." (from http://opensolver.org)

All entrants and their supporters are welcome to join in the celebration and regale (rile) the prize winners.

Date: Sunday, November 13
Time: 8pm-10pm

Location: The Fox and Hound
330 North Tryon St.
Charlotte, NC 28202
(Directions: http://tinyurl.com/75zhm7k)

The celebration is sponsored by IBM.

Thanks to all those who submitted to the COIN-OR INFORMS Cup.

The COIN-OR INFORMS Cup committee:

Pietro Belotti
Matthew Galati
R. Kipp Martin
Stefan Vigerske

Position in Computational Stochastic Programming at Sandia National Laboratories

The Discrete Mathematical and Complex Systems Department of Sandia National Laboratories in Albuquerque, New Mexico, is soliciting CVs  for consideration regarding a position in computational stochastic programming, with specific application to day-ahead planning for the electrical grid. 

The position is anticipated to be open for 2 years, with the possibility of extension. Requirements for the position include:

• Significant practical experience in group coding projects, using one of: C++, C, or Python.
• A working understanding of mathematical programming, including core modeling tools and algorithms.
• Experience formulating, solving, and analyzing stochastic programs.
• A minimum of an MS (PhD preferred, but not required) in Computer Science, Operations Research, or a related field.

Additional skills desired include:

• Experience with electrical grid applications.
• Experience with high-performance computing, specifically large-scale distributed-memory clusters.

If you meet these requirements, please e-mail your CV to: jwatson@sandia.gov (Dr. Jean-Paul Watson) for potential consideration.

BNSF is hiring Sr. Operations Research Specialists (multiple positions)

The Operations Research group at BNSF is growing and we would like to invite fresh and experienced Operations Research professionals to be a part of our growth story. Below is a description of the position. You must apply online at www.bnsf.com/careers<http://www.bnsf.com/careers> (search posting reference SeniorResearchSpecialist1).

Please forward this job position if you know someone who is in the job market. If you have any questions, please forward them to Pooja.Dewan@BNSF.com<mailto:Pooja.Dewan@BNSF.com>.

Thanks
Homarjun Agrahari, PhD
Operations Research
BNSF Railway

------------------------------------
Senior Operations Research Specialist


BNSF Railway operates one of the nation's largest rail networks, with approximately 32,000 route miles operating through 28 states across the western United States. BNSF is headquartered in Fort Worth, Texas. For more than 160 years we have proudly served our customers by safely and efficiently delivering commodities such as coal, grain, steel and consumer products. The dedication, talent and creativity of our 38,000 employees have helped distinguish BNSF as an innovative and progressive leader within the transportation industry. To learn more about our company, our culture and our opportunities, please visit us online at www.bnsf.com/careers<http://www.bnsf.com/careers>.


APPLICATION DEADLINE: November 30, 2011 at midnight (CST)

POSITIONS AVAILABLE: multiple
LOCATION: Fort Worth, TX

SALARY BAND 29/30


REPORTS TO: General Director Decision Systems

START DATE: 4th Quarter 2011 (Subject to change)

DUTIES & RESPONSIBILITIES:

As a member of the BNSF Operations Research Group, you will be responsible for finding solutions to some of the many challenging problems facing the railroad.

Duties include:

*Interfacing with internal customers to understand the business and identify opportunities for improvement

*Interfacing with BNSF Technology Services personnel to understand existing data structures and IT processes

*Identifying solution techniques and implementing them independently, with external vendors, through academic alliances, or with BNSF Technology Services teams

*Working with end-users to validate and enhance the tools

*Identifying and initiating new projects part of technology initiatives

*Communicating status and findings to senior management and multiple teams

Extracting and cleaning large volumes of data to derive insights that can be used for process improvements.  Developing models to solve the business problems.

QUALIFICATIONS:

*A Masters or Ph.D. degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science or a related field.  Ph.D. is preferred.  Master's degree with experience will be considered.

*Strong programming skills in an object-oriented programming language, such as Java, C++, or C#

*Excellent written, verbal, and interpersonal communication skills

*Ability to identify underlying problems and appropriate techniques for solving them

*Ability to manipulate and extract information from very large, complex data sets

*Expertise in using commercial solver software, such as CPLEX, Gurobi, or Frontline solver

*Practical experience applying quantitative techniques to solve real-world problems

BACKGROUND INVESTIGATION ELEMENTS:
*Verification of last 7 years of criminal, driving and employment history to include military service.
*Social Security number verification
*Academic and Education verification

DRUG TEST: BNSF is committed to a safe and drug free work place. All new hires are required to undergo a hair drug test which detects the presence of illegal drugs for months prior to testing. We appreciate your cooperation in keeping BNSF safe and drug free.

The duties and responsibilities in this posting are representative categories to be used in deciding whether to apply for the position. These general categories do not necessarily constitute an exhaustive list of duties of the position.
We are proud to be an EEO/AA employer M/F/D/V. We maintain a drug-free workplace and perform pre-employment substance abuse testing.

Transportation Worker Identification Credential (TWIC): Federal authority requires BNSF employees, whose work requires unescorted access to secure areas of port facilities, to obtain a TWIC. A TWIC is a condition of employment for such positions and requires candidates to those positions to submit to a TSA security assessment (to include, but not limited to, providing: biographic information; identity documents; fingerprints; digital photograph). More information is available at www.tsa.gov/twic<http://www.tsa.gov/twic>.

2012 Wilkinson Fellowship in Scientific Computing at Argonne National Laboratory

WILKINSON FELLOWSHIP IN SCIENTIFIC COMPUTING
Mathematics and Computer Science Division
Argonne National Laboratory

The Mathematics and Computer Science (MCS) Division of Argonne National Laboratory invites outstanding candidates to apply for the J. H. Wilkinson Fellowship in Scientific Computing. The appointment is for one year and may be renewed for another year.

This fellowship was created in memory of Dr. James Hardy Wilkinson, F.R.S., who had a close association with the Mathematics and Computer Science Division as a consultant and guiding spirit for the EISPACK and LINPACK projects. The Wilkinson Fellowship is intended to encourage scientists actively engaged in state-of-the-art research in scientific computing. Candidates must have received a recent Ph.D. prior to the beginning of the appointment. The benefits of the appointment include a highly competitive salary, moving expenses, and a generous professional travel allowance. For additional details, including past recipients, see http://www.mcs.anl.gov/research/opportunities/wilkinsonfellow/

The appointment will be in the MCS Division, which has strong programs in scientific computing, software tools, and computational mathematics. Of special interest are algorithms and software for linear algebra, optimization, differential equations, computational differentiation, stochastic systems, and unstructured mesh computations; software tools for parallel computing; and numerical methods for computational science problems. For further information, see http://www.mcs.anl.gov/LANS/ .

Internationally recognized for innovative research in high-performance computing, the MCS Division supports an excellent computational environment that includes large Linux clusters, a distributed systems laboratory, and a virtual environments laboratory. Researchers also have access to a Blue Gene/P supercomputer. For more information, see www.mcs.anl.gov .

Argonne is located in the southwestern Chicago suburbs, offering the advantages of affordable housing, good schools, and easy access to the cultural attractions of the city.

Interested candidates should consult the website http://recruit.mcs.anl.gov/wilkinson for details on how to apply. The application must include a curriculum vitae; statement of research interests; a list of publications, abstracts, and significant presentations; and three letters of recommendation. Applications will be accepted starting August 31, 2011. Applications received before December 15, 2011, are assured maximum consideration. The closing date for applications is January 15, 2012. Application material will be reviewed by a selection committee and a candidate announced in March 2012.

Seminar : Optimal Newton-type Methods for Nonconvex Smooth Optimization

INDUSTRIAL AND SYSTEMS ENGINEERING (ISE) SEMINAR

Speaker:

Coralia Cartis

Assistant Professor

School of Mathematics

University of Edinburgh

Date, Time, Location:

Thursday, October 27, 2011

2:30pm - 3:30pm

Mohler Laboratory, Room 453

Title:

Optimal Newton-type Methods for Nonconvex Smooth Optimization

Abstract:

We show that the steepest-descent and Newton's methods for unconstrained nonconvex optimization under standard assumptions may both require a number of iterations and function evaluations arbitrarily close to the steepest-descent's global worst-case complexity bound. This shows that the latter upper bound is essentially tight for steepest descent and that Newton's method may be as slow as the steepest-descent method in the worst case. Then the cubic regularization of Newton's method (Griewank (1981), Nesterov & Polyak (2006)) is considered and extended to large-scale problems, while preserving the same order of its improved worst-case complexity (by comparison to that of steepest-descent); this improved worst-case bound is also shown to be essentially tight. We further show that the cubic regularization approach is, in fact, optimal from a worst-case complexity point of view amongst a class of second-order methods. The worst-case problem-evaluation complexity of constrained optimization will also be discussed, time permitting. This is joint work with Nick Gould (Rutherford Appleton Laboratory, UK) and Philippe Toint (Namur University, Belgium).

Biography:

Coralia Cartis has been a tenured assistant professor at Edinburgh University in Scotland, United Kingdom since 2007. Previously, she held postdoctoral appointments within the numerical analysis groups at Rutherford Appleton Laboratory and Oxford University. She pursued her PhD research in optimization under the supervision of Prof Mike Powell at Cambridge University (2005).

Coralia's research addresses the development, convergence and complexity analyses and implementation of algorithms for linear and nonlinear nonconvex smooth optimization problems, suitable for large-scale problems. She is also interested in the interconnections between dynamical systems and continuous optimization; and optimization aspects of compressed sensing and sparse approximation.

Diagnosing ill conditioning at CPLEX

http://www-01.ibm.com/support/docview.wss?uid=swg21399993

Seminar : Adaptive and Robust Radiation Therapy

Speaker:

Timothy C. Y. Chan

Assistant Professor

Mechanical & Industrial Engineering

University of Toronto

Date, Time, Location:

Monday, October 17, 2011

3:00pm - 4:00pm

Mohler Laboratory, Room 451

Title:

ARRT: Adaptive and Robust Radiation Therapy

Abstract:

The traditional approach to robust intensity-modulated radiation therapy treatment planning involves creating an appropriate uncertainty set to model the uncertain effect, solving a single treatment planning problem, and then delivering the same treatment over multiple weeks. In this talk, I will present an adaptive robust optimization approach to IMRT optimization, where information gathered in previous treatment sessions is used to update a model of uncertainty and guide treatment plan re-optimization for the next session. Such an approach allows for the estimate of the uncertain effect to improve as the treatment progresses. This approach involves solving a sequence of linear programs, and is therefore highly tractable. I will present computational results for a lung cancer case where the dominant uncertainty is in the patient’s breathing motion. Using this adaptive robust method, I demonstrate that it is possible to attain significant and simultaneous improvement in both tumor coverage and organ sparing over the non-adaptive approach. I also show that it is possible to closely approximate “prescient” solutions, and provide some theoretical insight as to why this occurs.

Biography:

Timothy C. Y. Chan is an Assistant Professor in the department of Mechanical and Industrial Engineering at the University of Toronto.  His primary research interests are in optimization under uncertainty and the application of optimization methods to radiation therapy, health care operations and sustainability.  He received his B.Sc. in Applied Mathematics from the University of British Columbia, and his Ph.D. in Operations Research from the Massachusetts Institute of Technology.  Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company, a global management consulting firm.  During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.

Seminar : Improved Inventory Targets in the Presence of Limited Historical Demand Data

Speaker:

Bahar Biller

Associate Professor of Operations Management

Tepper School of Business

Carnegie Mellon University

Date, Time, Location:

Friday, October 7, 2011

11:00am - 12:00pm

Mohler Laboratory, Room 453

Abstract:

Most of the literature on inventory management assumes that the demand distribution and the values of its parameters are known with certainty. We consider a repeated newsvendor setting where this is not the case and study the problem of setting inventory targets when there is a limited amount of historical demand data. Consequently, we achieve the following objectives:  (1) to quantify the inaccuracy in the inventory-target estimation as a function of the length of the historical demand data, the critical fractile, and the shape parameters of the demand distribution; and (2) to determine the inventory target that minimizes the expected cost and accounts for the uncertainty around the demand parameters estimated from limited historical data. We achieve these objectives by using the concept of expected total operating cost and representing the demand distribution with the highly flexible Johnson translation system. We further consider the demand data that may be auto-correlated or intermittent as well as the data sets that may contain sales rather than demand realizations. Our procedures require no restrictive assumptions about the first four moments of the demand random variables, and they can be easily implemented in practical settings with reduced expected total operating costs.

Biography:

Bahar Biller is an Associate Professor in the Tepper School of Business at Carnegie Mellon University. Her research focuses on three distinct yet related areas: (1) developing a comprehensive input modeling framework for stochastic system simulations with the ability to represent, fit, and generate multivariate time-series input processes with arbitrary marginal distributions and dependence structures; (2) accounting for the uncertainty of multivariate input-distribution parameters which are estimated from finite historical input data on simulation outputs; and (3) testing and proving the efficacy of the developed methods on a wide range of industrial applications in Operations Management and Finance. She received a National Science Foundation CAREER award in 2006 and the Presidential Early Career Award for Scientists and Engineers in 2007. She is a member of INFORMS and currently the Vice President and President-elect of the INFORMS Simulation Society.

Automated Configuration of MIP solvers

Here is an interesting link about automating configuration  of MIP solvers

http://www.cs.ubc.ca/labs/beta/Projects/MIP-Config/

Some tips on effective presentations

Below are some materials on effective presentations  during Teacher Development Program from Greg Reihman, Director of Faculty Development, Lehigh University

Know your material
The advice below assumes that your know something about the topic and have something to say. But don't focus on everything you know, focus instead on the key things you want your audience to learn

Determine 3-5 objectives: what do you want really your audience to learn?
Your audience will learn more than that, but if you can teach someone 3-5 new things you will have done a great job

Tell a story
People understand and connect with ideas that they can fit together into a story. A good story sets up an interesting problem, challenge or puzzle and works its way toward resolution. At a minimum, plan a beginning, a middle, and an end that makes sense together.

Be a body
Make eye contact, use gestures, be open with your arms, face the audience, smile. Practice this

Plan some form of "energy shift " every 15-20 minutes
Your audience should be doing something significantly different after 15 - 20 minutes. To Accomplish this, you might lecture for 15 minutes then offer a personal anecdote, a short video clip. Or it might mean giving your students something to do (5 minutes to write or discuss or stretch or take a quiz). Or it might mean presenting a case study, talking about it for 10 minutes with your audience, then presenting the next case. What matters most is that you do something to change how the room feels; to have your audience use a different part of their mind.

Make some part of your presentation personal , but not "about you"
It's great to include a story or an example that is about you or your experience; however, if you do, make sure you offer it in a way that helps you connect with your audience or helps them understand your point. If your story is meant to impress, it probably won't

Speak with an easy authority 
When you start speaking, your audience assumes you have something to teach them. If you mutter or apologize, if you are self-effacing or disorganized, you will give them reason to think that you do not. This does not mean being authoritarian (tyrannical, bossy, strict) but rather being authoritative (in control , knowledgeable, influential)

Aim to teach, not to impress
If you present your talk thinking, "I have to show them that I know what I'm talking about" then, at best, they will leave having learned "Wow, she knows what she's talking about" (at worst, they will find you arrogant, unapproachable, and ineffective). If you present your talk thinking, "I have to help them understand what I'm talking about" then, at best, they will leave having learned, "Wow, I get it" (or , at worst, they will learn find you helpful, kind, approachable and will come back hoping to learn more).

A SHORT LIST OF RESOURCES ON EFFECTIVE PRESENTATIONS

1. HOW TO SPEAK: LECTURE TIPS FROM MIT's Patrick WINSTON
    From Harvard's Bok Center for Teaching Excellence

2.Videos of good presenters
   www.ted.com

3. Preparation Tips
http://www.garrreynolds.com/Presentation/index.html

4. Delivery Tips
http://www.garrreynolds.com/Presentation/delivery.html

5. Stanford Center for Teaching and Learning "Speaking of Teaching " newsletter on "Oral Communication in the Academy"
www.stanford.edu/dept/CTL/Newsletter/oral_comm.pdf
6. "Giving a Job Talk in the Sciences" (Rick Reis, Chronicle of Higher Education)
http://chronicle.com/article/Giving-a-Job-Talk-in-hte-Sc/45375

7. A presentation does not have to include PowerPoint/Keynote, but if it does consider these
 http://blog.guykawasaki.com/2005/12/the_102030_rule.html

http://www.presentationzen.com/
 
http://www.garrreynolds.com/Presentation/slides.html

2012 Mixed Integer Programming workshop, Conference Announcement

Date: July 16-19, 2012
Location: University of California, Davis

We are pleased to announce that the 2012 workshop in Mixed Integer
Programming (MIP 2012) will be held July 16-19, 2012 at the University
of California, Davis. The 2012 Mixed Integer Programming workshop will
be the ninth in a series of annual workshops held in North America
designed to bring the integer programming community together to discuss
very recent developments in the field. The workshop series consists of a
single track of invited talks and also features a poster session as an
additional opportunity to share and discuss recent research.
 Registration details and a call for participation in the poster session
will be announced later.

Confirmed speakers:

• Gennadiy Averkov, Otto-von-Guericke-Universität Magdeburg
• Sam Burer, The University of Iowa
• Philipp Christophel, SAS
• Jesús A. De Loera, University of California, Davis
• Alberto Del Pia, ETH Zurich
• Ricardo Fukasawa, University of Waterloo
• Vineet Goyal, Columbia University
• Yongpei Guan, University of Florida
• Volker Kaibel, Otto-von-Guericke-Universität Magdeburg
• Kiavash Kianfar, Texas A&M University
• Mustafa Kılınç, University of Pittsburgh
• Fatma Kılınç-Karzan, Carnegie Mellon University
• David Morton, The University of Texas at Austin
• Ted Ralphs, Lehigh University
• Edward Rothberg, Gurobi Optimization
• Siqian Shen, University of Michigan
• Dan Steffy, ZIB and Oakland University
• Alejandro Toriello, University of Southern California
• Christian Wagner, ETH Zurich

Sincerely,

Claudia D'Ambrosio, CNRS - École Polytechnique
Matthias Köppe, UC Davis
Jim Luedtke, University of Wisconsin-Madison
François Margot, Carnegie Mellon University
Juan Pablo Vielma, University of Pittsburgh

(MIP 2012 Organizing Committee, mip2012@math.ucdavis.edu)

Teacher Development Series Fall 2011

Session 1 - Presentations & Communications
When: Thursday September 29th, 2:30-4:00
Where: Sinclair Laboratory Auditorium
Topics: An opportunity to learn more about-and practice-effective preseentation skills.
Who: Greg Reihman (Faculty Development/CAS-Philosophy).

Session 2 – Advice from Current Graduate Students on Effective Teaching
When: Thursday October 13th, 2:30-4:00
Where: Sinclair Laboratory Auditorium

Session 3 -Engaging Students
When: Thursday October 27th, 2:30-4:00
Where: Sinclair Auditorium Laboratory
Topics: How do effective faculty engage and inspire students? How do they ensure content mastery and promote higher-order thinking?
Who: Greg Reihman (Faculty Development/CAS-Philosophy).

Session 4 - Academic Support Services for Students
When: Thursday November 10th 2:30-4:00
Where: Sinclair Laboratory Auditorium
Topics: A chance to learn about the many factors affectiing student learning (including learning disabilities, cultural differenes, students at risk) and find out what other support servie are available to your students.

Session 5 - Symposium on Teaching & Learning
When: Thursday November 17th TBA
Where: Linderman Library
Topics: This symposium will highlight various innovative ways Lehigh faculty are using instructional technology and media in their teahing. Poster presentations by various faculty and staff, plus keynote presentation.

Junior Research Fellowship Scheme at Imperial College London

Centre for Transport Studies
Imperial College London

Junior Research Fellowships


Imperial College London has created a significant number of new Junior
Research Fellowships, to enable outstanding young researchers to establish
academic careers. The Fellowships are for 3 years and are intended to to
enable the successful applicant to focus full time on developing an
independent research identity but with encouragement and support from a
senior academic mentor. Applicants will be expected normally to have
between two and four years post-doctoral experience at the time of
application.

Proposals will be judged primarily on the basis of their scientific merit,
track record and potential for development of an independent research
programme. Applicants will need to identify an Imperial College academic
staff member to act as their sponsor and to provide relevant facilities
and mentorship for the tenure of the Fellowship.

This is a tremendous opportunity for a talented young researcher to
establish an academic career at Imperial.

The Centre for Transport Studies is keen to encourage suitable applicants
with interests in any of our areas of research activity, which include
 *      Travel demand modelling
 *      Transport network operations
 *      Transport and the environment
 *      Intelligent transport systems
 *      Transport economics, policy and regulation
 *      Transport risk, safety and security
 *      Railway operations and management
 *      Engineering geomatics
 *      Air transport and air traffic management
 *      Freight transport and logistics
 *      Port and maritime operations

We are also happy to hear from potential applicants with complimentary
disciplinary or research interests that could contribute to our
multi-disciplinary research activities.

Further information about the Junior Fellowship Scheme can be found at
www.imperial.ac.uk/jrf and further information about the Centre for
Transport Studies can be found at www.imperial.ac.uk/cts.

The closing date for applications is 29 October 2010.

Potential applicants wishing to discuss this opportunity informally are
welcome to contact me.