PHDs and PHD Candidate Opportunities at Accretive Health (NYSE: AH) Chicago, IL

Accretive Health is looking for creatively brilliant professionals to leverage computational science and statistics in the pursuit of vastly better health outcomes.

You will develop and apply your knowledge of statistical analysis, machine learning techniques and operations research to extremely large multidimensional datasets.

We are looking for excellent researchers who have a burning desire to apply their capabilities in the pursuit of tangible results.

Responsibilities

·         Generate health insights through exploratory analysis of large multi-dimensional datasets (over 1 billion observations)

·         Apply machine learning and data mining techniques to health outcome and revenue cycle sample sets

·         Develop algorithms to discover implicit sequences, affinities and networks

·         Develop predictive health outcomes and revenue cycle models

·         Evaluate the effectiveness of competing algorithms

·         Work with engineering and data management teams to implement models in production-ready tools

Requirements

·         Expert in computational science fundamentals:  algorithms, data-structures, and their various tradeoffs

·         Comfortable pulling and integrating ideas from diverse academic disciplines (e.g. economics, statistics, sociology, operations research, and computer science)

·         Demonstrated ability to clearly articulate ideas and points of view

·         Ability to effectively translate algorithms and models into high-quality, high-performance software tools

·         Ph.D.  and PHD candidates in Computer Science, with a machine learning focus is optimal or  a Statistics and Operations Research background

·         Experience handling extremely large datasets

·         Experience with relational databases (SQL Server a plus)

·         Demonstrated ability to create economic impact

·         A degree from Stanford, Berkeley, CalTech, Carnegie Mellon, MIT, ITT and other top ranked schools are not mandatory but are preferred

About Accretive Health

Accretive Health is a leading provider of services to healthcare providers. Our business purpose is to help U.S. hospitals, physicians and other healthcare providers more efficiently manage their revenue cycle operations and population-based health management initiatives. Our distinctive operating model that includes people, processes and sophisticated integrated technology, which we refer to as our solutions, helps our customers realize sustainable improvements in their operating margins and improve the satisfaction of their patients, physicians and staff. Our customers typically are multi-hospital systems, including faith-based or community healthcare systems, academic medical centers and independent ambulatory clinics, and their affiliated physician practice groups. Our revenue cycle solution spans our customers' entire revenue cycle, unlike competing services that we believe address only a portion of the revenue cycle or focus solely on cost reductions. Our revenue cycle management customers have historically achieved significant improvements in cash collections measured against the contractual amount due for healthcare services, which we refer to as net revenue yield. Our population health management infrastructure spans the entire healthcare delivery continuum and enables providers to manage the health of their patient populations delivering higher quality care while reducing aggregate cost of care. For more information, please visit www.accretivehealth.com.

Postdoc positions available in the area of Uncertainty Quantification at ICES, UT Austin.

A number of postdoc positions involving

(1) uncertainty quantification and stochastic algorithms,
(2) numerical optimization (constrained, integer programming),
(3) numerical analysis (FEM, Newton's method, preconditioners),
(4) software engineering (C++, FORTRAN, object oriented design,
version control, Linux),
(5) high performance computing (MPI), and
(6) multiphysics modeling (various physical models, e.g. for fluids,
chemical reactions, turbulence),

are available at The Institute for Computational Engineering and
Sciences (ICES), at The University of Texas at Austin. ICES
(www.ices.utexas.edu) provides a worldwide unique environment for
truly interdisciplinary research.

There is opportunity for theoretical work, but the emphasis is on
practical research, using (and improving) mathematical algorithms,
parallel computing and complex software libraries to tackle very
challenging scientific and engineering problems, many of them in
collaboration with US National Laboratories and other academic
institutes, under the sponsor of various US agencies, such as DOE, NSF
and AFOSR. US Citizenship is not required.

We are looking for candidates who have a strong mathematical
background (any combination of (1), (2), and (3), but preferably (1)),
demonstrated expertise on (4), and at least familiarity with (5). Some
background on (6) is desirable, but not mandatory. We are also looking
for candidates who are self-driven, have clear thinking, have good
writing skills, communicate themselves well, know how to breakdown
complex problems into smaller ones, and enjoy interacting with
multidisciplinary teams of mathematicians, engineers and computer
scientists.

Some positions are available immediately, while others will become
available throughout the year. Please email your application,
including a CV, to either

Dr. Ernesto Esteves Prudencio ( prudenci@ices.utexas.edu ), or
Professor Omar Ghattas ( omar@ices.utexas.edu ), or
Professor Robert Moser ( rmoser@ices.utexas.edu ),

Risk and Decision Analyst Position

Job Description:

The successful candidate for this position will apply a wide range of risk analysis, operations research, and applied mathematics techniques to provide decision insight to clients in diverse fields such as energy, environment and homeland security.  The analyst will work with senior scientists and engineers to develop and apply innovative solutions to problems of national importance.  The candidate will help to define problem scope and develop technical approaches, build and run models, interpret outputs, and prepare written reports and presentations to document and communicate results.  Responsibilities include:

• Developing analysis tools and techniques with senior scientists
• Implementing technical solutions with minimal oversight
• Applying analysis skills and domain knowledge to understand and interpret model results
• Providing clear, concise, and reliable results to project managers and customers
• Delivering timely and accurate products and effectively managing assigned tasks

Qualifications:

Possess broad range of skills and familiarity with several of the following areas: probability, statistics, decision analysis, risk analysis, modeling and simulation, and mathematical modeling.  Preference for proficiency with several of the following analysis tools: Excel, Access, VBA, SAS, Matlab, Pertmaster, @Risk, and Crystal Ball.  Preference for BS plus 2-3 year’s experience or MS plus 2-3 year’s experience.

Minimum Qualifications:

BS in a technical field (preferably Engineering, Operations Research, Statistics) and 2-3 years experience, MS in a technical field and 0-2 years experience, or PhD in a technical field and 0-1 years experience. 

Equal Employment Opportunity

Pacific Northwest National Laboratory (PNNL) is an Affirmative Action / Equal Opportunity Employer and supports diversity in the workplace.  All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information.  All staff at the Pacific Northwest National Laboratory must be able to demonstrate the legal right to work in the United States.

For more information about this position and or Pacific Northwest National Laboratory, please go to: http://jobs.pnnl.gov and search under Job ID 301191.

Engineering and Scientific Subroutine Library (ESSL) and Parallel ESSL

http://www-03.ibm.com/systems/software/essl/

 

ESSL and Parallel ESSL are collections of state-of-the-art mathematical subroutines specifically designed to improve the performance of engineering and scientific applications on the IBM POWER™ processor-based servers and blades. ESSL and Parallel ESSL are commonly used in the aerospace, automotive, electronics, petroleum, utilities and scientific research industries for applications such as:

• Structural Analysis
• Time Series Analysis
• Computational Chemistry
• Computational Techniques
• Fluid Dynamics Analysis
• Mathematical Analysis

• Seismic Analysis
• Dynamic Systems Simulation
• Reservoir Modeling
• Nuclear Engineering
• Quantitative Analysis
• Electronic Circuit Design

ESSL and Parallel ESSL support 32-bit and 64-bit Fortran, C and C++ serial, SMP and SPMD applications running under AIX® and Linux®.

ESSL

ESSL

Post Doc Position in Optimisation in Melbourne, Australia

There is a three-year post-doctoral research position at Monash University 
in modelling and optimisation, focusing on designing systems for supply chain optimisation.
The position is advertised at 
http://jobs.monash.edu.au/jobDetails.asp?sJobIDs=500232
Closing Date: March 1st

The researcher will work with colleagues in our Centre for Optimisation in Travel, 
Transport and Logistics, and the Optimisation group at NICTA.


Professor Mark Wallace
http://www.infotech.monash.edu.au/~wallace

Faculty of Information Technology

Monash University,
Melbourne, Australia

Two-years Post-Doc position in structured MINLP

The Operations Research Group of the Department of Computer Science of the University of Pisa will welcome applications for a fully-funded, two-year Post-Doc position. The field of interest will be the development of innovative approaches to structured Mixed-Integer (Linear and) NonLinear Programs and their application to real-world problems, usually with a strong graph-network structure, in areas such as telecommunications (e.g. routing and design problems in power- and latency-constrained networks) and transportation/logistics (e.g. scheduling and routing problems in home care applications). The ideal candidate should combine previous expertise, or at least keen interest, in the above-mentioned topics (MILP and MINLP techniques, convex optimization) with a taste for experimental work; experience with MINLP modeling/solution tools and with best practice in optimization software development will be a definite plus.

The position will be formally announced at

http://www.unipi.it/ateneo/bandi/assegni/2012/info/index.htm

in the next few days. The selection process will (necessarily, by law) comprise a meeting for discussion with the candidates; however, it will be possible (and strongly encouraged) that the latter is accomplished by any form of remote communication means (skype, ...). The candidates are therefore encouraged to contact

Antonio Frangioni ( frangio@di.unipi.it)
Maria Grazia Scutella' (scut@di.unipi.it)

after submission to arrange for these technical details; requests for clarifications about the position and/or help in the submission process are also welcome. The selection process is expected to be finished by the end of April, with the position starting shortly thereafer; some (but not much) wiggle room could be possible with the dates if strictly required. The salary for the position will be 23000 Euro per annum gross (about 18000 net), and the position will have the legal status according to the applicable Italian laws and regulations.

Ph.D. Career Development Workshop by INFORMS Chapter

ISE DEPARTMENT PH.D. CAREER DEVELOPMENT WORKSHOP ANNOUNCEMENT

Title:

Recent Progress in SAS Optimization Tools and Solutions

Speaker: 

Dr. Yan Xu,   SAS Institute Inc.

Time and location:

2:30-3:30pm, Feb. 23, 375 Mohler Lab

Abstract: 

SAS has significantly increased its investment in the area of Optimization over the past several years.  It is an area of growing importance with the current economic climate     being a major driving force behind it.  SAS provides a suite of optimization tools which includes a powerful algebraic modeling language that transparently represents model formulations, and a set of optimization solvers for linear, mixed-integer, quadratic and nonlinear programs. Based on these tools and leveraging the power of SAS in other areas, a number of solutions have been successfully developed for tackling industrial problems like optimizing retail pricing, increasing marketing effectiveness, reducing inventory cost, etc. In this talk, we first present the latest techniques that we used to improve SAS solvers. Then, we show how to model and solve real world optimization problem by using SAS optimization tools. Finally, we discuss several challenges that we are facing in further improving optimization products.

Biography:

Yan Xu is an Analytical Solutions manager in the Operations Research department at SAS Institute Inc., Cary, North Carolina, where he leads the numerical optimization team to develop linear, mixed-integer, nonlinear, and local search optimization solvers. Dr. Xu received his undergraduate and master degree from Fudan University in China and his Ph.D. from Lehigh University in the area of parallel tree searching algorithms. Dr. Xu’s current work focuses on the design and implementation of the algorithms for solving general linear and mixed-integer optimization problems.  Dr. Xu is a full member of COIN-OR. He has published papers in journals such as INFORMS Journal of Computing, and won several awards in the area of computational optimization.

ISE seminar2-13-12

INDUSTRIAL AND SYSTEMS ENGINEERING DEPARTMENT
SPRING 2012 SEMINAR SERIES

TITLE: Risk Neutral and Risk Averse Approaches to Multistage Stochastic Programming 
SPEAKER: Dr. Alexander Shapiro, School of Industrial and Systems Engineering

Georgia Institute of Technology

DATE / TIME:           Monday, February 13, 2012

3:00 pm – 4:00 pm

LOCATION:               Room 453 Mohler Lab, 200 W. Packer Avenue

ABSTRACT: In many practical situations one has to make decisions sequentially based on data available at the time of the decision and facing uncertainty of the future.  This leads to optimization problems which can be formulated in a framework of multistage stochastic programming.  In this talk we consider risk neutral and risk averse approaches to multistage stochastic programming.  We discuss conceptual and computational issues involved in formulation and solving such problems.  As an example we give numerical results based on the Stochastic Dual Dynamic Programming method applied to planning of the Brazilian interconnected power system.

BIOGRAPHY: Alexander Shapiro is a Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology.  He has published more than 120 research articles in peer review journals and is a coauthor of several books (see below).  His research is widely cited and he was listed as an ISI Highly Cited Researcher in 2004 (ISI = Institute for Scientific Information) http://isihighlycited.com/, links to his research ID:
http://www2.isye.gatech.edu/_ashapiro/research.html

Dr. Shapiro is on the editorial board of several professional journals, such as Mathematics of Operations Research, Mathematical Programming, ESAIM: Control, Optimization and Calculus of Variations, Computational Management Science.  For 2009 - 2011 he was an area editor (Optimization) of Operations Research.  He gave numerous invited keynote and plenary talks, including invited section talk (section Control Theory & Optimization) at the International Congress of Mathematicians 2010, Hyderabad, India http://www.icm2010.in/scientific-program/invited-speakers

Published Books

1.  Rubinstein, R.Y. and Shapiro, A., Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, John Wiley and Sons, New York, 1993.
2.  Bonnans, J. F. and Shapiro, A., Perturbation Analysis of Optimization Problems, Springer, New York, 2000.
3.  Handbook on Stochastic Programming, edited by: A. Ruszczynski and A. Shapiro, North-Holland Publishing Company, Amsterdam, 2003.
4.  Shapiro, A., Dentcheva, D. and Ruszczynski, A., Lectures on Stochastic Programming: Modeling and Theory, SIAM, Philadelphia, 2009.

ISE seminar 2-3-12

INDUSTRIAL AND SYSTEMS ENGINEERING DEPARTMENT
SPRING 2012 SEMINAR SERIES
 
TITLE:                     Multi-Objective Multi-Expert Optimization using Weight Robustness and
                                  Stochastic Dominance: Concepts, Properties, and Applications

SPEAKER:              Dr. Sanjay Mehrotra, Industrial Engineering and Management Sciences
                                   McCormick School of Engineering, Northwestern University

DATE / TIME:        Friday, February 3, 2012
                                  2:30 pm – 3:30 pm

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

ABSTRACT: Multivariate multi-objective decision problems arise in a large number of situations in areas such as healthcare, security, energy, logistics, sustainability, finance, and manufacturing. The decisions involve input from multiple experts weighing in on the decision objectives. The parameters of the functions modeling objectives and constraints are uncertain, and decisions are often made in reference to a random benchmark that is to be exceeded.
 
This presentation will cover mathematical optimization techniques for formulating and solving such problems based on my current research.  In particular, the presentation will (i) introduce a newly developed concept of Robust Pareto optimality and a multi-criteria robust optimization with weights (McRow) modeling framework; (ii) discuss the properties of the class of McRow models involving both deterministic and stochastic weight sets; (iii) illustrate the usefulness of McRow models using examples from resource allocation for diabetes management, disaster planning, and agriculture economics; (iv) time permitting, next we will introduce concepts to model problems with multiple random benchmarks using stochastic dominance (McSwd); (iv) present analytical properties of the McSwd models; (v) discuss the usefulness of McSwd models in stochastic comparison, and also the use of combined McRow-McSwd framework for budgeting in homeland security..
 
BIOGRAPHY:  Professor Mehrotra has made significant research contributions to the field of mathematical optimization.  He is also an active researcher within the area of healthcare engineering.  He is widely known for his predictor-corrector method, is currently the chair-elect of the INFORMS Optimization Society.  He is a department editor for Optimization for the Institute of Industrial Engineers society journal IIE Transactions, and the journal Asia Pacific Journal of Operations Research.  He has served as a vice-president of chapter/fora and a member of Institute for Operations Research and Management Sciences Board of Directors.  His optimization research is published in journals such as Mathematical Programming, SIAM Journal on Optimization, Operations Research, Mathematics of
Operations Research, Optimization Methods and Software, IIE-Transactions, INFORMS Journal on Computing, Journal of Global Optimization, SIAM Journal on Computing, SIAM Journal on Numerical Analysis, Analyst. His healthcare engineering research is published in journals such as Bioinformatics, BMC BioInformatics, Healthcare Management Science, and Annals of Surgery.   His current research
 is supported by NSF, ONR, DOE, and NIH.

NSF CAREER AWARD: Stochastic Optimization for Water Resources Management

 http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1151226


The research objective of this Faculty Early Career Development (CAREER) project is to investigate stochastic optimization models and methodologies that will assist in managing and adapting water resources and infrastructures in an effort to ensure a sustainable future. The project will focus on two problems. The first is a large-scale multi-period water allocation problem under uncertainty (Lower Colorado River Basin) and the second is a regional-scale water reuse infrastructure design and operation problem. The proposed research will address these water resources management problems in a comprehensive manner by: (1) formulating mathematical optimization models under known and ambiguous uncertainties; (2) investigating decomposition-based solution methods that exploit the special structures of these models; and (3) advancing theory and methodology to evaluate the resulting solutions and other multi-period adaptation and mitigation policies.

The project will have a significant impact on the well-being of the 25-30 million people who reside in the southwestern United States. The models and methods developed can be applied wherever water is scarce. The project also addresses two of the grand challenges identified by the National Academy of Engineering: to help restore and improve urban infrastructure and to provide access to clean water. While the project focuses on water resources management, if successful, it will result in modeling, algorithmic and computational advances in stochastic optimization. These advances may help solve other important optimization problems. The curricular materials and tutorials developed will serve to educate the next generation of engineers on the methods and real-world applications of stochastic optimization. The project will also promote and increase the visibility of women students through a "Women in Industrial and Systems Engineering Research (WISER)" program.