OPENINGS

Faculty position

Faculty Position: Full-time teaching position (Lecturer) in the area of systems engineering

The Whiting School of Engineering at Johns Hopkins University seeks applicants for a full-time teaching position (Lecturer) in the area of systems engineering to be part of the teaching faculty in the department of Civil and Systems Engineering (CaSE). This position will serve a key role in the department’s new program in Systems Engineering and the growing Center for Systems Science and Engineering (CSSE). More experienced candidates may be considered for a Senior Lecturer position. (Read More)

post-doc position

Post-Doctoral Fellowship: Optimization Methods and Healthcare

Applications are invited for a full-time postdoctoral position at the Department of Civil Engineering and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University under the supervision of Associate Professor Lauren Gardner. The applicant will be expected to undertake and develop research on the topic of spatial epidemiology, with a focus on the development of models to infer outbreak risk factors, predict outbreaks, and optimize resource allocation for outbreak mitigation. Candidates should have expertise in one or more of the following areas: network modelling, optimization, machine learning, statistical modelling, and data visualization, with previous experience working on epidemiological applications. (Read More)
post-doc position

Post-Doctoral Fellowship: Network Modeling of Infectious Diseases

Applications are invited for a full-time postdoctoral position at the Department of Civil Engineering and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University under the supervision of Associate Professor Lauren Gardner. The applicant will be expected to undertake and develop research on the topic of spatial epidemiology, with a focus on the development of models to infer outbreak risk factors, predict outbreaks, and optimize resource allocation for outbreak mitigation. Candidates should have expertise in one or more of the following areas: network modelling, optimization, machine learning, statistical modelling, and data visualization, with previous experience working on epidemiological applications. (Read More)
post-doc position

Post-Doctoral Fellowship: Optimization Models


Applications are invited for a full-time postdoctoral position at the Department of Civil Engineering, the Center for Systems Science and Engineering (CSSE), and the Malone Center for Engineering in Healthcare at Johns Hopkins University under the supervision of Assistant Professor Kimia Ghobadi <kimia@jhu.edu>. This position focuses on developing inverse optimization models and solutions techniques. The models will be applied to real-world healthcare settings that include various feasible and infeasible past observations of the decisions with the goal of recovering hidden constraints and objectives. Candidates should have expertise in linear, integer or convex optimization models and solution techniques. The candidate should be enthusiastic and capable of theoretical research while striving to solve real-world problems of high impact. Prior experience with healthcare models is desired but not required. Candidates should send their CV, cover letter, and contact info of a letter writer to kimia@jhu.edu with the subject line “postdoc application 2022”. Applications received by January 31st, 2022 will receive a full review. 

InernSHIP position

Internship Position: COVID-19 Data Analysis

We are seeking an enthusiastic intern who is interested in COVID-19 data analysis. The intern will work on applications of mathematical techniques, data analytics, and data visualization in the operational impacts of COVID-19 in healthcare and society. Learn more here. 

InernSHIP position

Internship Position: Healthcare Operations

We are seeking an enthusiastic intern who is interested in COVID-19 data analysis. The intern will work on applications of mathematical techniques, data analytics, and data visualization in the operational impacts of COVID-19 in healthcare and society. The intern may interact with faculty, students, data stewards, and clinicians at the Whiting School of Engineering, the Department of Emergency Medicine at Johns Hopkins School of Medicine (CDEM https://cdem.jh.edu/), the Capacity Command Center at Johns Hopkins Health System, and the Center for Systems Science and Engineering. Learn more here. 

Assume that a decision-maker’s uncertain behavior is observed. We develop a an inverse optimization framework to impute an objective function that is robust against misspecifications of the behavior. In our model, instead of considering multiple data points, we consider an uncertainty set that encapsulates all possible realizations of the input data. We adopt this idea from robust optimization, which has been widely used for solving optimization problems with uncertain parameters. By bringing robust and inverse optimization together, we propose a robust inverse linear optimization model for uncertain input observations. We aim to find a cost vector for the underlying forward problem such that the associated error is minimized for the worst-case realization of the uncertainty in the observed solutions. That is, such a cost vector is robust in the sense that it protects against the worst misspecification of a decision-maker’s behavior. 

As an example, we consider a diet recommendation problem. Suppose we want to learn the diet patterns and preferences of a specific person and make personalized recommendations in the future. The person’s choice, even if restricted by nutritional and budgetary constraints, may be inconsistent and vary over time. Assuming the person’s behavior can be represented by an uncertainty set, it is important to find a cost vector that renders the worst-case behavior within the uncertainty set as close to optimal as possible. Note that the cost vector can have a general meaning and may be interpreted differently depending on the application (e.g., monetary cost, utility function, or preferences). Under such a cost vector, any non-worst-case diet will thus have a smaller deviation from optimality.  

We introduce a new approach that combines inverse optimization with conventional data analytics to recover the utility function of a human operator. In this approach, a set of final decisions of the operator is observed. For instance, the final treatment plans that a clinician chose for a patient or the dietary choices that a patient made to control their disease while also considering her own personal preferences. Based on these observations, we develop a new framework that uses inverse optimization to infer how the operator prioritized different trade-offs to arrive at her decision. 

We develop a new inverse optimization framework to infer the constraint parameters of a linear (forward) optimization based on multiple observations of the system. The goal is to find a feasible region for the forward problem such that all given observations become feasible and the preferred observations become optimal. We explore the theoretical properties of the model and develop computationally efficient equivalent models. We consider an array of functions to capture various desirable properties of the inferred feasible region. We apply our method to radiation therapy treatment planning—a complex optimization problem in itself—to understand the clinical guidelines that in practice are used by oncologists. These guidelines (constraints) will standardize the practice, increase planning efficiency and automation, and make high-quality personalized treatment plans for cancer patients possible.