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The Applied Probability Society is a subdivision of the Institute for Operations Research and the Management Sciences (INFORMS). The Society is concerned with the application of probability theory to systems that involve random phenomena, for example, manufacturing, communication network, computer network, service, and financial systems. The Society promotes the development and use of methods for the improvement of evaluation, control, and design of these systems. Such methods include (stochastic) dynamic programming, queueing theory, Markov decision process, discrete event dynamic systems, point processes, large deviations, reliability, and so on. Our members include practitioners, educators, and researchers with backgrounds in business, engineering, statistics, mathematics, economics, computer science, and other applied sciences.

The Applied Probability Society also publishes Stochastic Systems. This open-access journal seeks to publish high-quality papers that substantively contribute to the modeling, analysis, and control of stochastic systems. The contribution may lie in the formulation of new mathematical models, in the development of new mathematical methods, or in the innovative application of existing methods. A partial list of applications domains that are germane to this journal include: service operations; logistics, transportation, and communications networks (including the Internet); computer systems; finance and risk management; manufacturing operations and supply chains; and revenue management.

To keep up-to-date with all the latest developments in the Applied Probability Society community, INFORMS members should make sure to subscribe for real-time or digest correspondence through INFORMS Connect. For INFORMS members outside of the Society or for community members outside of INFORMS who would like to receive updates as well, register here to receive select Applied Probability Society communication directly.

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Jobs of Interest to APS

  • Houston, Texas, Rice University’s Department of Statistics is dedicated to the advancement of the scientific discipline of statistics, the innovative application of statistical science to meet modern scientific, engineering and societal challenges, the expert education of students in statistics and other disciplines, and leadership in the statistical sciences broadly construed, at the local, national and international levels. Pending budget approval, the Department of Statistics seeks applications for an open-rank tenure-track or tenured faculty position in the area of applied probability. Examples of relevant core areas of interest include, but are not limited to stochastic modeling, statistical inference for stochastic processes, stochastic control, stochastic differential equations, stochastic analysis, and the mathematics of insurance and risk management. Preference will be given to candidates whose research encompasses an intersection of applied probability and statistics and some of the core areas listed above. Areas of applications include, but are not limited to climate and environmental sciences, operations research, quantitative finance, biology, biomedical sciences, human health sciences, and other applied sciences. The successful candidate will be expected to teach undergraduate and graduate-level courses, conduct high-quality research and publish research findings.  A Ph.D. in Statistics or a related field is required by July 1, 2022 with proven excellence in research and teaching. Applicants at the Associate Professor level or higher are expected to be highly qualified and experienced, with an established research program, a history of successful funding, and some experience in academic administration. A demonstrated commitment to diversity, equity, and inclusion is required of all applicants, including any experience with recruiting from underrepresented groups into STEM fields, and a track record of other activities to promote diversity, equity, and inclusion. Applicants currently holding a tenure-track position are required to have a successful track record in supervising doctoral student dissertations, and preferably also in supervising undergraduate projects/capstone/theses. Applicants with experience interacting with industry partners in research and/or in instructional activities are encouraged to apply. Applicants with a strong international reputation and/or a track record of international activities are encouraged to apply. Application Instructions Applicants should submit the following materials: (1) cover letter, (2) curriculum vitae, (3) research statement, (4) statement of teaching philosophy, and (5) DEI statement describing their past and planned diversity, equity, and inclusion efforts. In addition, candidates will be asked to provide the names and contact information for at least three references. The priority deadline for applications is December 31, 2021; review of applications will commence December 15, 2021. The position is expected to be available July 1, 2022. The faculty line for this position is pending University approval. Assistant/Associate/Full Professor level
  • Blacksburg, Virginia, Assistant/Associate Professor, Stochastic Operations Research-Health Systems Faculty Position in the Grado Department of Industrial and Systems Engineering Virginia Tech   The Grado Department of Industrial and Systems Engineering (ISE) at Virginia Tech invites applications for a tenured/tenure-track faculty position at the rank of Assistant or Associate Professor, effective August 2022. We seek outstanding candidates for Stochastic Operations Research with a focus on Health Systems, with areas of interest including, but not limited to, health policy, healthcare analytics, healthcare operations, and/or medical decision making. We are seeking candidates motivated to contribute to a collegial, interdisciplinary community with a strong tradition of both fundamental and applied research, high-quality teaching, and mentoring.   The ISE Department has 34 tenured/tenure-track faculty, with an additional seven non-tenure-track instructional and research faculty. Four faculty are recent early investigator recipients, and numerous other faculty have received international or national recognition. Academic programs and research in the department encompass Human Factors and Ergonomics, Manufacturing, Management and Systems Engineering, and Operations Research. Candidates will thus have the opportunity to work with a broad range of departmental faculty, as well as with faculty in many other colleges, centers, and institutes at Virginia Tech. The department is home to approximately 620 undergraduate students, 80 master’s students, and 110 doctoral students. The undergraduate and graduate ISE programs are currently ranked third and eighth, respectively, by U.S. News & World Report.  Additional information is available at: www.ise.vt.edu .   Virginia Tech is a public land-grant university, committed to teaching and learning, research, and outreach to the Commonwealth of Virginia, the nation, and the world. Building on its motto of Ut Prosim (that I may serve), Virginia Tech is dedicated to Inclusive VT—serving in the spirit of community, diversity, and excellence. Virginia Tech actively seeks a broad spectrum of candidates to join our community in preparing leaders for the world. The College of Engineering undergraduate program ranks 13 th and graduate program ranks 31 st among all U.S. engineering schools (USN&WR). The mission of the College of Engineering is to educate and inspire our students to be critical thinkers, innovators and leaders. Our core values are inclusiveness, excellence, integrity, perseverance and stewardship. Virginia Tech’s main campus is located in Blacksburg, VA, in an area consistently ranked among the country’s best places to live. In addition, our facilities in the Washington, D.C., area offer unique proximity to government and industry partners and is also expanding rapidly, with Virginia Tech's exciting new Innovation Campus in Alexandria, VA, slated to open in 2024.   Candidates are expected to lead innovative scholarship and research, develop and sustain an externally-funded research program, teach and mentor undergraduate and graduate students, and serve the university and the profession. The position requires a Ph.D. in industrial and systems engineering, operations research, or a closely related field. Successful candidates will have a record of academic accomplishments and a demonstrated ability to work collaboratively, commitment to interdisciplinary research, and a willingness to expand disciplinary boundaries to address complex technical and societal challenges. The successful candidate will be required to have a criminal conviction check as well as documentation of COVID-19 vaccination or receive approval from the university for a vaccination exemption due to a medical condition or sincerely held religious belief.   Applicants must apply online at jobs.vt.edu (posting number 517936 ). Application materials include a cover letter, CV, three separate statements limited to three pages each (teaching interests and philosophy, research interests, and contributions to advancing diversity, equity, and inclusion), up to three relevant research publications, and contact information for at least three professional references. Review of applications will commence December 6, 2021 and continue until the position is filled. Questions regarding the positions should be directed to Dr. Kwok-Leung Tsui ( ktsui@vt.edu, 540-231-9088). For assistance submitting the application, please contact ise-search@vt.edu.   The department fully embraces Virginia Tech’s commitment to increase faculty, staff, and student diversity; to ensure a welcoming, affirming, safe, and accessible campus climate; to advance our research, teaching, and service mission through inclusive excellence; and to promote sustainable transformation through institutionalized structures. Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, national origin, political affiliation, race, religion, sexual orientation, or veteran status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law. If you are an individual with a disability and need an accommodation, please contact Ms. Heather Parrish (parrish1@vt.edu, 540-231-9079).
  • Rochester, Minnesota, The Research shield at Mayo Clinic is committed to creating a diverse environment and recognizes that diverse research teams make better decisions, are more equipped to solve complex problems and adapt to change, and produce better outcomes. That diversity is about opening up to unconventional ideas that create better outcomes, while recognizing colleagues' unique contributions. Become part of the legacy that embraces these differences and enables us to provide the best care to patients from all over the world. A Research Associate position is available in the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery’s Health Care Systems Engineering Program , focused on Operations Research and Management Science.  This appointment is under the mentorship of Dr. Yu Li Huang who specializes in health care process improvement and operation decision making using operations research, systems engineering, and data science principles to improve health outcomes and delivery. This position will work with Dr. Huang to develop solutions for practice process improvement. The objectives for this  position are:   Learning health systems dynamics to define what practices actually need                 Developing implementable solutions to ensure physical and system feasibility                 Training to present and communicate project findings to practice partners                 Disseminating findings via journal and conference publications. The successful candidate will have :   Technical skills including mathematical modeling and algorithm development                 Experience in statistical/machine learning modeling, simulation modeling, heuristic modeling, programming, data extraction and manipulation, and creating tool/interface/software                  Knowledge and experience with optimization methods (e.g. linear vs non-linear, stochastic vs deterministic, Markov decision process), statistical methods (e.g. inference, regression, Bayesian, principle component)                 Machine learning (e.g. neural network, support vector machine, natural language process)                 Demonstrated experience in project management, facilitation and recommendation of changes, communicating of findings and working with multidisciplinary staff                 Research skills including conducting literature reviews, grant writing, and preparing journal articles, conference proceedings, and conference presentations                 Must be able to self-learn new software and tools Project and research activities will span Mayo Clinic Midwest and Enterprise practice. The work includes developing optimization and simulation methods along with predictive analytics to redesign scheduling, practice workflows, and staffing to balance workload, maximize capacity utilization, and improve patient access across all practice departments.  The majority of the time this position will work remotely, but the candidate does need to live within driving distance of Mayo Clinic in Rochester, Minnesota for occasional on-campus work. Seeking a Ph.D. trained in Operations Research and Management Science, demonstrating strong evidence of combining theory and practice especially in healthcare, with a degree from Operations Research, Operations Management, Industrial Engineering, Health Policy, Statistics, or Computer Science. Mayo Clinic is located in the heart of downtown Rochester, Minnesota, a vibrant, friendly city that provides a highly livable environment for more than 34,000 Mayo staff and students. The city is consistently ranked among the best places to live in the United States because of its affordable cost of living, healthy lifestyle, excellent school systems and exceptionally high quality of life.
  • Nationwide, POSITION SUMMARY  The Research Associate (Simulation) applies the science of distribution to Fortna’s products and services by building simulation and mathematical models to support distribution center design, by developing novel algorithms for Fortna’s Warehouse Execution System (FortnaWES™), and by processing data to support solution design projects. The overarching goal is to build Fortna’s intellectual property (IP) as embodied in the FortnaDCdesign Suite™ and FortnaWES™. PRIMARY RESPONSIBILITIES: Develop simulation and mathematical models of distribution operations in order to answer design questions for projects and to give insight to other Fortna Associates. Develop models and algorithms for FortnaWES™ in consultation with the leadership of Solutions R&D and Product (Software) R&D. Serve as a Data Team member by running FortnaDCmodeler® on Operations Design projects and supporting teams with needed data analysis and modeling. Collaborate with other Associates to create thought leadership around Fortna IP to further Fortna’s brand in the marketplace. Assist with efforts to train Associates on the application of Fortna IP. REQUIRED QUALIFICATIONS: Research-based master’s degree in Industrial and Systems Engineering, Systems Engineering, or a related technical field Experience developing discrete-event simulation models of manufacturing or distribution operations using a commercial discrete-event simulation package. Experience with FlexSim is preferred, though not required. Experience using analytical models to represent production or logistics systems Strong interpersonal, organizational and time management skills and the desire to work in a team environment Excellent written and oral communication skills Excellent collaboration skills Excellent structured problem-solving skills Excellent project management skills Ability to travel domestically Ability to perform essential functions of the job   DESIRED QUALIFICATIONS: PhD in Industrial and Systems Engineering or Systems Engineering Experience developing mathematical models (optimization, stochastic, computational) for distribution center design and operations Excellent technical communication skills as evidenced by journal articles, white papers, conference papers/presentations, corporate presentations, etc. Experience with data analysis through SQL-based packages Experience with software tool development, especially Python. Experience educating and training others   WORKING CONDITIONS: When duties are performed in a typical office environment, extended periods of sitting at a desk and viewing a computer screen will be required. Also required is the ability to talk and hear, in person and by telephone; use of hands to handle, feel, or operate standard office equipment; and ability to reach with hands and arms. Associates are frequently required to walk and stand. The noise level in this work environment is usually quiet. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position. When travelling to Client sites, essential requirements of this position may require physical presence in various environments and locations. Physical stamina may be required for prolonged standing, bending, stooping, and/or working in cramped quarters. Exposure to potentially dangerous tools and equipment amidst a variety of building materials is probable, as is occasional exposure to moving mechanical parts. The noise level in the work environment can vary from being relatively quiet, to moderate, to excessive. Safety shoes or boots may be required in certain situations. Additional safety clothing including gloves, hardhats, and devices to protect eyes, mouth, or hearing, will be worn as necessary. This position description should not be considered all-inclusive.
  • Lake Buena Vista, Florida, Job Description We seek candidates for roles at multiple levels within the Decision Science team. The specific job level for an individual candidate will be determined based on their education, prior experience, and demonstrated technical and leadership proficiencies. The Decision Science team focuses on creating innovative mathematical models to inform business decision making through data. The team is interested in all fields related to data science, with emphasis on statistics, operations research, econometrics, and machine learning. The Decision Science team resides within the Disney Decision Science + Integration (DDSI) organization. DDSI provides internal consulting services for clients across The Walt Disney Company, including Disney Parks, Experiences and Products (covering Parks & Resorts, Signature Experiences, and Consumer Products, Games and Publishing) and Disney Media & Entertainment Distribution (covering content from Studios, General Entertainment and Sports). The DDSI team sits at the intersection of business strategy, advanced analytics, and technology integration to help our partners explore opportunities for analytics, shape business decisions, and drive value. Our work includes conceptualizing new solutions, solution design and development, implementation and integration with business processes, and ongoing business support.   Responsibilities Achieve business goals and objectives—Research and develop decision science models and act as a consultant to all business units within The Walt Disney Company Pursue innovation—Conduct research on analytical techniques and translate that research into usable and sustainable solutions for users Put your skills to the test—Perform data collection and data mining, and build state-of-the-art decision science algorithms, tools, and systems Drive value—Model and analyze revenue management and pricing related issues using various mathematical, statistical, and simulation techniques Tell the story—Present science results to business partners and clients Support a global and enterprise-wide mission—Identify and apply best practices in the field of advanced analytics for multiple businesses such as theme parks and resorts (e.g. Walt Disney World), Disney Cruise Line, media (e.g. ABC, ESPN), movies (e.g. Walt Disney Animation Studios), live shows (e.g. Disney on Broadway), and retail (e.g. shopDisney) Basic Qualifications Must exhibit competence in at least one of the following four analytical fields, inclusive of topics as noted: Statistics (two or more topics): Bayesian statistics Generalized linear models Mixture models Nonparametric regression Structural equation models Time series (state space models, ARIMA, etc.) Optimization (at least one topic): Decision analysis or multiple criteria decision making Mixed-integer optimization Nonlinear optimization Stochastic optimization Discrete-event simulation and stochastic models Econometrics (two or more topics): Generalized method of moments Instrumental variables Limited dependent variables Panel data Simultaneous equations models Machine learning (two or more topics): Boosting CART Clustering Graphical models Neural networks Random forests Reinforcement learning Support vector machines Must exhibit competence in at least one computing topic: General computing (Python, Java, C, C++, etc.) Statistical computing (R, SAS, etc.) Scientific computing (Julia, MATLAB, etc.) Roles beyond the entry level require the ability to work with little guidance on science methodologies and modeling approaches, based on sound business acumen, and to provide peer leadership to junior colleagues Higher roles require the ability to work concurrently across lines of business or functional areas, to direct the design and development of advanced analytical models, or to provide project leadership to ensure successful implementation of solutions across all project phases Preferred Qualifications Proven knowledge of revenue management context, including demand forecasting, resource allocation, and pricing Required Education Master’s or Ph.D. degree in Statistics, Operations Research, Industrial Engineering, Mathematics, Machine Learning, Econometrics, or related field

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