Post-Docs & Students
Post-Doc Research Associates
Dayna Lee Martinez, PhD
Title: Post-Doctoral Research Associate (HSyE)
Email: d.martinez@neu.edu
Joined HSyE: January 2013
Hometown: San Juan, Puerto Rico
Education: B.S, Industrial Engineering, University of Puerto Rico, Mayagüez Campus (2006)
M.E, Industrial Engineering, University of South Florida, Tampa, FL (2008)
Ph.D., Industrial Engineering, University of South Florida, Tampa, FL (2012)
Dissertation: Non-pharmaceutical interventions for pandemic influenza mitigation
Description of Work: Dayna is a post-doc research associate at the Healthcare Systems Engineering Institute. Her post-doc position includes applied work, research, and mentoring. She is currently working in two projects with MGH as part of CMS. One focuses on reducing CLABSIs on ICUs and the other one looks at improving the Neurology department’s pre-consultation process. Dayna is currently mentoring several Co-op students and is also involved with CHOT. Her research plans include exploring how her dissertation work regarding the spread of pandemic influenza could be applied to understanding the spread of improvement ideas across healthcare networks.
Research Interests: Simulation, stochastic processes, applied statistics, optimization, healthcare delivery systems, disease modeling
Hobbies: dancing, jewelry making, reading, running, events (concerts, shows, sports)
Graduate Students
Nick Andrianas
Email: andrianas.n@husky.neu.edu
Projects: IAP Network Mapping, CMS - Anesthesia in Endoscopy
Joined HSyE: October 2012
Hometown: Miller Place, NY
Education: B.S., Business Administration, Northeastern University (2012)
Description of Work: Nick received his B.S. in Business Administration from Northeastern University in May 2012 with a minor in Industrial Engineering and is now pursing his M.S. in Industrial Engineering. He is currently working on appointment scheduling problems with regards to predictive appointment time allocation, provider resource utilization, patient waits, and appointment access as part of the CMS center. This includes modeling both static and dynamic scheduling systems and the application of traditional techniques, such as bin-packing and defragmentation, to patient scheduling processes. Nick is also interested in quality control, patient safety, and optimization in healthcare.
Research Interests: Appointment scheduling, simulation, quality control, logistics, queuing
Hobbies: Guitar, snowboarding, reading, music, and DIY recording
Corey Balint
Email: coreybalint@gmail.com
Projects: CHOT
Joined HSyE: Undergraduate: June 2010 / Graduate: September 2011
Hometown: North Brunswick, New Jersey
Education: BS, Industrial Engineering, Northeastern University (2011)
Dissertation/Thesis: Accelerating Healthcare Improvement with Industrial Design of Experiments
Description of Work: As an undergraduate student at NU, Corey worked on a few various projects at the university. His first move into research was a study on 'The Use of Peers as Quality Managers in Engineering Class Instruction' with Professor Beverly Jaeger. He later worked with Professor Jaeger again on an independent study for the Digital Simulations course. He worked on this project for over a semester helping generate content for the course as well as teaching a course, all with the 'Quality Managers' in mind. Corey has worked in the center for a few years as a co-op student at the VA medical center, and then a work study student editing and reviewing papers. This year, he worked on simulating a new outpatient facility for Massachusetts Eye and Ear Infirmary. He has currently started his research into experimental design and its potential applications in the healthcare field. Corey has a strong interest in curriculum building and undergraduate projects. As a graduate student, Corey's work has focused on the implementation of experimental design in healthcare. Still in its initial stages, the project's aim is to use this method that is time tested in manufacturing and apply it in various healthcare systems.
Research Interests: Simulation, Strategy, Experimental Design, Education, Curriculum building, Undergraduate development
Hobbies: Sports, Sports Management, Pop culture, Travel, Cooking
Brendan Bettinger
Email: b.bettinger@neu.edu
Projects: Systems Engineering Approaches in Readmissions (CHOT)
Joined HSyE: September 2010
Hometown: Bothell, WA
Education: BS, Mathematical Economics, Wake Forest University (2010)
Description of Work: The focus of Brendan's research is to develop cooperative competition models within a game theoretic framework to address the unique dynamics of healthcare systems. Guided by the IHI Triple Aim, these models are designed to reduce waste and inefficient allocation of resources (controlling the cost of care); prevent failures in care coordination (enhancing the quality of care); and achieve an efficient equilibrium among regional healthcare partners and/or competitors (improving the health of the population). Brendan is also the lead student on the CHOT project that employs system engineering approaches to analyze the problem of hospital readmissions. In collaboration with industry partners, current tasks in this project include developing simulation models to evaluate workflow redesign and a statistical characterization of the readmission rate and time until readmission for a patient population.
Research Interests: cooperative competition, game theory, optimization, simulation, applied statistics
Hobbies: movies, music, lists of three
Sam Davis
Email: s.davis@neu.edu
Projects: BMC Appointment Access and Patient Flow, MGH Interventional Radiology Scheduling, Hallmark Health Urgent Care Clinic Staffing
Joined HSyE: January 2013
Hometown: Ithaca, NY
Education: B.S. and MEng, Operations Research and Information Engineering, Cornell University (2007, 2012)
Description of Work: Sam is currently working on improving patient flow and appointment access at Boston Medical Center, Massachusetts General Hospital, and Hallmark Health. The value of his work is measured along the Institute for Healthcare Improvement Triple Aim. Prior to joining HSyE, Sam worked in management consulting, health benefits consulting, and health insurance pricing and fraud detection.
Research Interests: Simulation, optimization, patient flow, appointment access, scheduling
Hobbies: Taekwondo, skiing, backpacking, guitar
Serkan Erbis
Email: erbis.s@neu.edu
Projects: Modeling Approaches for Nanomanufacturing Production and Healthcare Capacity Scale-up Planning
Joined HSyE: January 2010
Hometown: Istanbul, Turkey
Education: B.S, Metallurgical and Material Engineering, Istanbul Technical University (2007)
M.S, Industrial Engineering, Northeastern University (2009)
Dissertation: In progress
Description of Work: The focus of Serkan's research is to develop mathematical models for nanomanufacturing production scale-up planning problem. As nanotechnology moves from development to commercialization, interest has grown in understanding the full scale nanomanufacturing and its production costs. Meanwhile, the research and commercialization efforts of this young technology continue at full strength and intensity. Since, this young technology has been growing rapidly and the companies, thus, want to produce more products; the manufacturers will need a production scaling-up planning. Serkan is currently working on various models such as deterministic, MC, and stochastic programming models for nanomanufacturing production scale-up planning which is sort of a capacity planning problem for start-up companies. Serkan is also working on statistical analysis of series of experiments being conducted under the project: "A Multi-tiered high Throughput Screening Approach for Evaluating the Toxicity of Engineered NMs". Statistical analysis will include regression and correlation analysis, analysis of variance and descriptive statistics. He applies his work in nano manufacturing, healthcare, and service systems.
Research Interests: Monte Carlo Simulation, Stochastic Programming with recourse, Optimization, Statistics
Hobbies: Soccer, Outdoor Running, Traveling, Sailing, Comics
Selen Isci
Email: selen.isci@gmail.com
Projects: Inventory Management Improvement Opportunities within the VA Boston Healthcare System, Data Inaccuracy in Inventory Records (NE-VERC)
Joined HSyE: September 2011
Hometown: Izmir, Turkey
Education: BS, Industrial Engineering, Bahcesehir University (2011)
Thesis: Approaches to Optimize Inventory Control given Inaccurate Data in Healthcare Systems (In progress)
Description of Work: Selen worked on prioritization of sustainable supply chain measurement indicators which are calculated by using Fuzzy Analytic Hierarchy Process (AHP) method as an undergraduate student. Her current research is focused on determining the effects of inaccuracy in inventory records in healthcare and improving the decision making of ordering process by simulation optimization and applying inventory models. In healthcare systems, inventory management plays a crucial role since decision makers try to avoid stock outs and maintain availability of medical supplies as much as possible. However due to the errors in the records, the data of the actual inventory level can be different than the actual physical quantity which may result in additional cost or shortages. Thus, Selen aims to minimize the effects of inaccuracy in healthcare inventory records by using inventory management tools in order to eliminate the shortages and waste of resources.
Research Interests: Fuzzy Analytic Hierarchy Process, Monte Carlo Simulation, Inventory Theory, Supply chain management
Hobbies: Traveling, reading historical novels, swimming
Zeynep Karakus
Email: karakus.z@husky.neu.edu
Projects: Specialty Care
Joined HSyE: September 2010
Hometown: Ankara, Turkey
Education: BS, Statistics, Middle East Technical University (2010)
Thesis: Determining the best control chart setting to monitor healthcare services
Description of Work: Zeynep is working on statistical process control (SPC) methods. SPC is an efficient tool to monitor and investigate healthcare processes for higher quality and lower cost. She has been applying statistical methods and control charts to analyze and monitor healthcare data. She is working in specialty care project for Veterans Affairs (VA) which aims to investigate the performance of specialty care services of VA system over time via SPC methods. The focus of her master thesis is the performances of the several control charts in some circumstances. The first part of this work involves the relative performance (average run lengths) of p, g and EWMA control charts across a range of different scenarios, baseline adverse event rates, and chart design parameters, including the result that optimal change detection performance for g charts. On the other hand, in various fields such as engineering and medicine, the measurements of a statistical experiment are classified into intervals rather than obtaining the exact values. The second part of the study includes various estimation methods and control charts to observe the performances of the charts based on their ARLs.
Research Interests: Statistical quality control, quality control in healthcare, mathematical and applied statistics
Hobbies: Oil painting, photographing, rollerblading
Rachel Miller
Email: rachjmiller@gmail.com
Projects: Care-team scheduling optimization (CMS)
Joined HSyE: September 2012
Hometown: Meriden, CT
Education: B.S., Industrial Engineering, Northeastern University (2012)
Description of Work: The focus of Rachel's research is to tackle complex scheduling problems, especially in the primary care setting. This venture addresses the unique dynamics of a physician's many competing priorities across the spectrum of healthcare. Guided by the IHI Triple Aim, her work seeks to improve the use of care-teams; care-teams are theorized to improve continuity of care by optimizing the portion of visits in which patients are seen by a physician whom they know, and who knows them. Work in this area will enhance preventative care services in primary care, serving as a catalyst for savings in efficiency and cost in the future of the US healthcare system for patients and providers alike. Rachel is also currently a candidate for fellowship in the Gordon Engineering Leadership program at NU. The Gordon program promotes broad engineering competence and personal growth in the areas of public speaking, professionalism, project management, and leadership.
Research Interests: optimization, simulation, integer / linear programming
Hobbies: music, skiing, travel, kickboxing
Hande Musdal
Email: hande.musdal@gmail.com
Projects: NE-VERC
Joined HSyE: May 2008
Hometown: Istanbul, Turkey
Education: B.S, Industrial Engineering, Yildiz Technical University (2005)
M.S, Industrial Engineering, Yildiz Technical University (2007)
Dissertation: Systems Engineering Models for the "Silent Injuries" of Modern Military Conflicts (In progress)
Description of Work: Hande's research is focusing on applying mathematical modeling and optimization to improve the screening, detection, and treatment processes for a new class of "silent injuries" in the military. Modern military conflicts are producing dramatic increases in this type of injuries, in part due to the changing manners by which war is waged and to better protective equipment. Foremost among these are traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), depression, sleep disorders, and various mental health issues. An estimated 19.6% of all U.S. service men and women, for example, suffer from some degree of TBI, called one of the signature injuries of the Iraq and Afghanistan wars. PTSD, on the other hand, always has been a part of military life, and is an increasingly important problem among U.S service members, with estimates of 15% or more of Operations Enduring Freedom and Iraqi Freedom (OEF/OIF) veterans with PTSD. Since associated problems with these disorders often are cognitive, emotional, and/or behavioral, many cases go undetected or untreated indefinitely, linked with significant psychological disorders, long-term disabilities, and economic burden. Hande has been developing several operations research models to optimize the overall design, effectiveness, and capacity for detecting and treating these injuries. Proposed modeling approaches include probability networks, fuzzy logic, logistic regression, neural networks, Markov, Monte Carlo simulation, and deterministic and stochastic programming. Up-to-date, Hande developed several predictive diagnostic models to determine the probability that an individual has TBI and/or categorize him into the most likely severity state. Preliminary results indicate that these models can help reduce the number of undetected cases and optimize the effectiveness for detecting TBI. She also worked on developing deterministic and stochastic programming models for the location and allocation of the VA's sleep apnea and PTSD services across the New England region, with the results indicating significant opportunity to simultaneously reduce total cost and travel distances, and increase within-network access. Currently, she is focusing on probabilistic modeling approaches to determine the overall system cost, sensitivity (true positives), specificity (true negatives), and total amount of resources required under the current and several proposed process configurations for PTSD screening in the VA.
Research Interests: Operations research problems in healthcare, mathematical optimization, Monte Carlo simulation, multi-criteria decision making
Hobbies: Photography, movies, reading, running
Serpil Mutlu
Email: serpil.mutlu@gmail.com
Projects: NE-VERC, CHOT
Joined HSyE: September 2009
Hometown: Izmir, Turkey
Education: B.S, Industrial Engineering, Middle East Technical University (2009)
Dissertation: Healthcare scheduling models (In progress)
Description of Work: Serpil is working on several novel scheduling problems in healthcare environment. The purpose of her research is to identify areas where complexities of healthcare environment causes scheduling operation to be critical and open to improvement; and designing improved systems using industrial engineering approaches to enhance existing practices. Her current projects focus on three identified problems; care team co-scheduling, co-availability problem, and downstream linked scheduling. Several mathematical programming techniques are being applied for modeling these problems while heuristics and simulation algorithms are being utilized for solving purposes. Related application projects aim to advance care coordination with a focus on continuity of care to achieve safety, timeliness, efficiency, and patient centeredness.
Research Interests: Implementation of OR techniques in healthcare, system dynamics, constraint programming
Hobbies: Reading fantasty fiction books, watching anime, cooking
Sara Nourazari
Email: sara@coe.neu.edu
Projects: CHOT
Joined HSyE: September 2011
Hometown: Urmia, Iran
Education: BS, Electrical Engineering, K.N.Toosi University (2007)
MS, Electrical Engineering, University of Oklahoma (2009)
Dissertation/Thesis: Adaptive Control of Healthcare Systems (In Progress)
Description of Work: Sara completed her undergraduate studies (B.S.) and Master's degree (M.S.) in Electrical Engineering with focusing on Systems and Control. Over the course of her graduate studies, her research area was mainly centered on mathematical modeling and non-linear optimization. She has worked on different research projects including: 1. Developing advanced methods for inversion of data collected by nanosensors to estimate unknown properties of complex environments, funded by AEC (Advanced Energy Consortium) 2. Dynamic physics based modeling and optimization, supported by the Dynamic Structures Sensing and Control (DySSC) Center at University of Oklahoma 3. Rule extraction from Support Vector Machines and its applications to medical diagnosis. Currently as a Ph.D student, her research focuses on adaptive controlling of healthcare queues. In this research she explores the use of control theory to optimally adapt service capacities in order to achieve specified mean and threshold performance levels, including optimal minimal cost controllers under a variety of assumptions. Sara has also served as the trainer of Professional Ethics Training, President of OU INFORMS chapter and a member of graduate students senate at the University of Oklahoma.
Research Interest: Mathematical Modeling and Simulation, Queueing Theory, Non-Linear Optimization
Hobbies: Dancing, horseback riding, traveling, writing
Luke Romeo
Email: lromeo16@gmail.com
Projects: CHOT
Hometown: Commack, New York
Education: BS, Industrial and Systems Engineering, Binghamton University (2011) Minor, Global Studies
Dissertation: Resource Allocation in Hospital Readmissions (in progress)
Description of Work: Since joining Northeastern for graduate work in 2011, Luke has been focused on the use of industrial engineering techniques in preventing hospital readmissions. Preventable readmissions cost Medicare an estimated $17 billion annually. Luke is creating simulation and optimization models that can aid hospitals in developing and refining intervention policies aimed at minimizing unnecessary readmissions. These models are able to give hospital specific recommendations based upon patient population and resource limitations. The models also account for financial impacts to hospitals as part of the new changes for Medicare readmission payments.
Research Interests: Simulation, Operations Research, Healthcare Policy, Hospital Readmissions
Hobbies: Traveling, Reading, Surfing, Jiu-Jitsu
Onur Uzunlar
Email: uzunlar.o@husky.neu.edu
Projects: NE-VERC
Joined HSyE: September 2011
Hometown: Manisa, Turkey
Education: B.S, Industrial Engineering, Gazi University (2009)
M.S, Industrial Engineering, Bilkent University (2011)
Thesis: (In Progress)
Description of Work: Onur Uzunlar received his B.S. and M.S. degrees from Turkey. He has been awarded Tubitak (National Science Foundation of Turkey) Scholarship throughout his M.S. study. His master's thesis is titled as "Joint Routing, Gateway Selection, Scheduling, and Power Management Optimization in Wireless Mesh Networks". Currently, he is working on operations research problems in healthcare. He is applying mathematical models and optimization to determine the best locations and capacities of resources for TeleHealth which has emerged as a promising solution for the needs of today's demanding health system. Many hospitals in the U.S. are trying to implement the TeleHealth to reduce cost and increase access. This implementation process arouses lots of strategic and operational planning problems including the locations and service levels of the resources and also the assignment of patients to these resources. He is using Recourse Programming to consider the uncertainty of the future demand.
Research Interests: Optimization, Mathematical Programming, Simulation
Hobbies: Reading fantastic fiction, video games, cinema
Nooshin Valibeig
Email: nooshin.valibeig@gmail.com
Projects: CHOT
Joined HSyE: July 2011
Hometown: Isfahan, Iran
Education: BS, Industrial Engineering, Isfahan University of Technology (2003)
MBA, Sharif University of Technology (2006)
Dissertation/Thesis: Best Practices of Industrial and Systems Engineering Applications in Healthcare
Description of Work: The focus of Nooshin's research is on finding the best practices of industrial and systems engineering applications in healthcare and providing this best practices for healthcare providers to increase the care quality and decrease the costs. First topic for these studies is the application of real time location systems in in healthcare, by doing this project, not only best practices would be assessed and documented but also the general process of best practice studies would be developed. Nooshin also conducting some simulation models in operation room scheduling methods, urgent operations facility allocation and emergency department observation unit.
Research Interests: Simulation, strategic planning, system dynamics, applied statistics , process analysis
Hobbies: Movies, Cooking, sport
Alumni
Claire Bond
Email: bond.cl@neu.edu
Projects: NE-VERC
Joined HSyE: September 2009
Hometown: Lakewood, OH
Education: B.S, Industrial Engineering, Clemson University (2009)
Thesis Focus: Healthcare queuing networks (in progress)
Description of Work: Claire's research focuses on investigating applications of industrial and system engineering methods to reusable medical equipment (RME) processes. The equipment that undergoes the RME process is very expensive and is used in departments throughout the entire hospital. If the system is not run efficiently, the tools may not be ready when needed for a hospital procedure, resulting in severe economic consequences and low patient satisfaction. Claire introduces possible applications of statistical quality control (SQC), failure modes and effects analysis (FMEA), forcing functions, redundant design, process layout, and inventory methods to be implemented for improvement of the RME process.
Another focus of Claire's research is on exploring applications of open and closed queuing networks to healthcare processes. Currently, she is investigating the RME queuing network, which is a closed network system comprised of several service facilities, multiple servers, and multiple job classes. The research being conducted on this system aims to address the optimal number of servers for each individual process, the efficiency of the servers, the optimal number of tools in the system, and priorities for different job classes, all while optimizing cost. By applying queuing theory to healthcare processes a system can develop a number of benefits, including decreased system delays, less idle time for staff and machines, higher patient and staff satisfaction, fewer missed appointments, and lower operating costs. There are a number of other queuing networks, both open and closed, that this research can be applied to in healthcare. Investigating these processes with queuing theory can provide optimal methods by which to run the system.
Research Interests: Queuing theory, inventory theory, scheduling
Hobbies: Reading, running and other outdoor activities, cooking and baking, traveling
Mehmet Erkan Ceyhan,Ph.D.
Title: Research Associate (NE-VERC)
Email: erkanceyhan@gmail.com
Research/project: Drug Safety Risk-Benefit Models
Joined HSyE: May 2007
Hometown: Ankara, Turkey
Education: B.S, Industrial Engineering, Baskent University (2002)
M.S, Engineering Management, Northeastern University (2005)
Ph.D., Industrial Engineering, Northeastern University (2010)
Dissertation: Proportional Estimates and Longitudinal Targets in Data Envelopment Analysis (DEA)
Description of Work: Mehmet is a post-doc research associate and NE-VERC project manager. He is working on logistical models. His PhD research focus is on two modeling issues that arise in the application of Data Envelopment Analysis (DEA) - estimated proportions and longitudinal targets. Periodically DEA is conducted on data that include estimated proportions, such as defect, satisfaction, or adverse event rates estimated from sample data. These estimates can produce statistically biased and variable estimates of DEA results, even as sample sizes become fairly large. Several approaches are proposed, including Monte Carlo (MC), chance constraint, bootstrapping, and optimistic/pessimistic methods. Results of each above method are compared to those if the true proportions were known, with emphasis on the MC approach. While all methods perform fairly well, the MC approach tends to produce slightly better results and be fairly easy to implement. A second practical problem is that the typical interpretation of DEA targets as performance goals that will move an inefficient decision making unit to the frontier only will occur if all other units maintain their same input and output levels, an implicit assumption that is rarely the case in practice. Three alternate approaches for interpreting and setting future performance targets therefore are developed, including forward and backward looking analysis, forecasting, and MC. All methods were demonstrated using 18 data sets, including from World Health Organization, VA medical facilities, and Commonwealth Fund. This research also developed two Excel add-ins that solve standard DEA models and those developed in this research.
Research Interests: Monte Carlo simulation, efficiency and productivity analysis, simulation, Bayesian Inference, applied statistics, optimization
Hobbies: Skiing, swimming, travelling, movies, Yoga, video games
Senay Demirkan Delice
Email: senaydemirkan@gmail.com
Projects: NE-VERC, CHOT, Hospital readmission models, Purdue RCHE
Joined HSyE: October 2009
Hometown: Eskisehir, Turkey
Education: B.S, Textile Engineering, Istanbul Technical University (2002)
M.S, Textile Engineering, Istanbul Technical University (2006)
Thesis: Operations research models of hospital readmissions
Description of Work: The focus of Senay's research is on exploring several possible systems engineering approaches to measuring, modeling, and improving unplanned hospital patient readmissions. Possible approaches include statistical methods to predict readmissions, quality control methods to detect or verify process changes, simulation and system dynamic models to test proposed improvements, and Markov decision process and feedback control models to optimize timing of various interventions. Senay is currently working on analyzing readmission data from different hospitals and different departments, developing models reflecting the readmission dynamics and predicting the effects and timing of different types of interventions to prevent readmissions. Senay also serves as operations manager of the QPRL lab.
Research Interests: Monte Carlo simulation, statistical process control, Markov Chain Models, Markov Decision Processes, Feedback Control Models
Hobbies: Dancing, reading historical books, traveling, movies, biking
Oguz Erdogan
Email: oguz.erdogan@yahoo.fr
Projects: Patient No-show Optimization
Joined HSyE: September 2010
Hometown: Istanbul, Turkey
Education: B.S., Industrial Engineering, Galatasaray University (2008)
M.S., Industrial Engineering, University of New Haven (2010)
Dissertation: In progress
Description of Work: At University of New Haven, he worked on a project that optimizes the solar module layout on rooftops. He constructed a linear model combined with a heuristic. His model explores the available locations for the solar modules regarding the possible obstacles on the roof area, and then places the modules without overlapping.
Research Interests: Implementation of IE techniques in healthcare systems, Operations Research, Decision Making
Hobbies: Movies, reading fantastic books, traveling, swimming
Zeynep Damla Ok
Email: zeynepdok@gmail.com
Projects: Center for High-Rate Nanomanufacturing
Joined HSyE: May 2006
Hometown: Antalya, Turkey
Education: B.S, Manufacturing Systems & Industrial Engineering, Sabanci University (2004)
PhD, Industrial Engineering, Northeastern University (2010)
Dissertation: Risk Management Models for Nanomanufacturing
Where are they now: Manager, Special Projects - Personal Market Claims at Liberty Mutual
Description of Work: The focus of Zeynep's Ph.D. research was to develop mathematical models and decision support tools to help nanotechnology researchers, businesses, and policy makers make informed development, workplace, commercialization, and regulatory decisions. Despite the many touted benefits of nanotechnology, significant concerns exist regarding possible environmental, health, and safety (EHS) risks of engineered nanomaterials, and in turn, there is significant uncertainty about appropriate workplace safeguards, commercialization regulations, full-scale production economics, and future market conditions. Given these uncertainties, Zeynep's research explored and illustrated the utility of several systems engineering approaches - Monte Carlo, goal programming, stochastic programming, and desirability functions - in managing safety, production, and cost trade-offs among nanotechnology development decisions. The mathematical modeling study is then extended and Zeynep developed decision support tools based on 1) Monte Carlo risk models to assess production costs and occupational health exposure trade-offs of single-walled carbon nanotube manufacturing and 2) desirability function models to optimize nanomanufacturing production scale-up decisions.
Research Interests: Risk management, uncertainty analysis, decision making under uncertainty, trade-off analysis, multi-criteria decision making
Hobbies: Marathon running, portrait photography, Mediterranean cooking
Rashmi Shenoy
Email: rashmishenoy1@gmail.com
Projects: Monitoring, Control, and Adjustment of Non-Homogeneous Healthcare and Patient Data
Joined HSyE: September 2006
Hometown: Dubai, United Arab Emirates
Education: B.S, Industrial Engineering, Purdue University (2005)
M.S, Operations Research, Northeastern University (2008)
Thesis: Misuse and performance of individuals charts in statistical process control for single parameter distributions of unknown stability
Where are they now: Mumbai, Maharashtra
Description of Work: Rashmi's research focuses on the incorrect use of "individual" XmR control charts on non-normal data, primarily time between (exponential), number between (geometric), Poisson, and binomial data. The significant consequences of these mis-uses on detection performance was investigated extensively and compared to the correct methods via Monte Carlo simulation. In each case, control limits were calculated using four different methods: 3-sigma limits, probability limits, moving range limits, and moving range limits applied to normalized data. Each of these methods were analyzed for different amounts of startup data and out-of-control data: post limit (in-control (IC) data used to calculate limits), percent (different percentages of OOC data used to calculate the limits), and alternating (different amounts of alternating IC and OOC data used to calculate limits). Although often recommended by non-expert tutorials in all cases this chart is shown to perform worse than the correct method and should not be used.
Research Interests: Quality Control, Monte Carlo Simulation
Seda Sinangil
Email: sinangilseda@gmail.com
Projects: NE-VERC
Joined HSyE: May 2010
Hometown: Istanbul, Turkey
Education: B.S, Industrial Engineering, Sabanci University (2009)
Thesis: Deterministic and stochastic healthcare service co-location models (In progress)
Description of Work: Seda has been investigating methods aiming to reduce healthcare costs and provide better service quality. Location of health facilities is one of the major topics that she has been searching to find opportunities that will lead to higher efficiency. She aims to develop a location-allocation model that includes the uncertainty in future demand, and provides efficient solutions since U.S. Census Bureau predictions indicate that hospital demand is expected to grow tremendously with the aging of baby boomers, and reach plateau at 2030. Another topic that she has been working on is inventory management. Expenditures in U.S. exceeded $2.3 trillion in 2008, and inventory investment is one of the major factors driving the growth in spending. She has been searching on inventory management of critical and non-critical items (items that are extremely expensive and have short life cycle) in health care. She aims to adapt supply chain strategies such as resource sharing, risk pooling in order to cut waste spending in healthcare. Seda also serves as coordinator of our healthcare systems engineering research colloquia seminar series.
Research Interests: Optimization problems in transportation, logistics, computational Optimization
Hobbies: Skiing, meditation, tennis, movies
Aysun Sunnetci, Ph.D.
Email: aysun.sunnetci@gmail.com
Projects: Modeling issues in Data Envelopment Analysis
Joined HSyE: September 2005
Hometown: Istanbul, Turkey
Education: B.S, Industrial Engineering, Koc University (2005)
Ph.D., Industrial Engineering, Northeastern University (2008)
Dissertation: Handling proportional data and weight constraints in data envelopment analysis (DEA)
Where are they now: Senior Research Scientist at FM Global, Norwood, MA
Description of Work: Aysun's research addressed two problems that frequently arise when handling proportional data and weight constraints in Data Envelopment Analysis (DEA), motivated by several healthcare studies. The primary contributions of this research are methods for (1) handling proportional and bounded data, (2) rationally constraining the input-output weights, and (3) measuring efficiency robustness over ranges of possible weight constraints. The first problem is motivated by the fact that in some DEA applications the usual assumption is violated that all data must only be nonnegative, namely for proportional data bound between 0 and 1 (e.g., mortality, adverse event, defect, or market penetration rates). Solving conventional constant-returns-to-scale (CRS) DEA models in such cases can produce output targets exceeding their upper bounds (e.g., 130% survival). Values bound on a fixed interval (e.g., satisfaction scores between 1 and 5) present a similar problem. Given the common use of CRS models, Aysun's research proposes and investigates an odds-ratio transformation that forces all targets between their bounds. The second problem is motivated by periodic "irrational" weights, such as placing less (or no) weight on mortality than on patient satisfaction. Since the two most common approaches in the literature (rank ordering or setting lower bounds for individual weights) have scale, solution feasibility, and arbitrariness limitations, we propose and compare a method that constrains each weight by a percent of the total (POT) of all weights. To remove the subjectivity of these percentages and as a measure of efficiency robustness, Aysun develops iterative search, numeric, and Monte Carlo algorithms (the last implemented in an Excel-based program) that determine POT regions within which each DMU is on the frontier and compute an overall "hyper efficiency" score. She demonstrates all her methods on several analyses of VA medical facilities, the U.S. News and World Report (USNWR) "best" departments, and national healthcare systems using data from the World Health Organization (WHO).
Research Interests: Efficiency and productivity analysis, risk and uncertainty analysis, multivariate predictive modeling, design and analysis of experiments
Hobbies: Traveling, cooking
Aysun Taseli
Title: Research Associate (CHOT/NE-VERC)
Email: ataseli@gmail.com
Projects: Monitoring, Control, and Adjustment of Non-Homogeneous Healthcare and Patient Data
Drug Safety Risk-Benefit Models
Joined HSyE: September 2005
Hometown: Ankara, Turkey
Education: BS, Statistics, Middle East Technical University (1998)
MS, Industrial Engineering, Middle East Technical University (2004)
PhD, Industrial Engineering, Northeastern University (2011)
Dissertation: Exact and approximate risk adjusted sequential probability ratio tests
Description of Work: Aysun's research is addressing statistical monitoring, cluster detection, and approximation problems involving a new mixed-risk probability distribution that arises as a convolution of non-identical binomial distributions. Sequential probability ratio tests (SPRT) and resetting SPRT charts have been derived for cases for which the outcome of each Bernoulli event or only the total count is known. Accuracy and detection performance is shown to significantly differ than if homogeneity were assumed.
A second area of focus is on detection of geographical clusters under natural heterogeneity via a new risk-adjusted scan statistic we have developed. Since both the SPRT and scan methods are computationally exhaustive, a third research area investigates the efficient approximation methods based on a normalized cumulant based orthogonal polynomial (Gram-Charlier) expansion and saddle point approximations. Important applications in healthcare include patient mortality, care bundles, hospital readmissions, drug abuse across different patient types. Aysun also serves as our resident statistical expert and mentor.
Research Interests: statistical quality control and improvement, quality control in healthcare, risk-adjusted surveillance, mathematical and applied statistics, Monte Carlo simulation, design of experiments
Hobbies: independent and international cinema, documentaries, reading, meditation, fitness
Natassia Taylor
Email: Natassia.taylor@gmail.com
Projects: Improved Inventory Management: Schedule Based Inventory
Joined HSyE: September 2011
Hometown: Rogers, Arkansas
Education: BS, Industrial Engineering, University of Arkansas (2011)
Dissertation/Thesis: Schedule Based Inventory Management
Description of Work: Natassia's research focuses on investigating the use of industrial engineering tools to reduce healthcare costs through improved inventory management. Inventory and inventory management costs are second only to staffing in healthcare environments. Thus, substantial potential exists for reducing the overall costs of healthcare through improving inventory systems. Natassia's research is currently focused primarily on improving Operating Room Inventory logistics. In an operating room setting, approximately seventy to eighty percent of procedures are scheduled in advance. Given a schedule of procedures, some inventory may be ordered based on the schedule, thereby lowering inventory levels and reducing the frequency in which items expire, are subject to shrinkage, or are misplaced. However, some level of inventory must also be held to account for unscheduled, emergency or add-on procedures. Natassia's research looks to create a hybrid, schedule-based inventory management system that holds an optimal level of inventory for unscheduled procedures while also developing best practices for ordering inventory based on the operating room schedule. Ultimately, the findings of this study will determine the potential for decreasing inventory costs while improving employee satisfaction and delivery of patient care.
Research Interests: Application of IE techniques in healthcare settings, Supply Chain Logistics, Simulation
Hobbies: Traveling, Reading, Cooking, Movies
Aysegul Topcu, Ph.D.
Email: topcu@alumni.neu.edu
Projects: Reconfigurable facility models
Joined HSyE: September 2006
Hometown: Mersin, Turkey
Education: B.S, Industrial Engineering, University of Wisconsin-Madison (2002)
M.S, Industrial Engineering, University of Wisconsin-Madison (2004)
Ph.D., Industrial Engineering, Northeastern University (2009)
Dissertation: A heuristic approach based on golden section simulation-optimization for reconfigurable remanufacturing inventory space planning
Description of Work: Aysegul's research was on decision-making and modeling issues that arise in facility and warehouse designs in a remanufacturing context. Key components that decision-support systems need to address in such settings include uncertainty in yield rates and demand, reconfigurable and flexible designs, interdependencies between returned products, and type of inventory control system. In order to address these issues, Aysegul first developed a mixed-integer, multi-period and multi-component stochastic programming recourse (SPR) optimization model which identifies optimal schedules of inventory storage space for a given time period. Due to the explosion of possible scenarios and the number of variables and constraints when the SPR model is extended, it is recommended that heuristic methodologies be used to overcome the resulting problems of solving large combinatorial optimization models. Later, in order to better emulate a generalized remanufacturing facility with random receiving patterns, component yields, and refurbished demand over multiple time periods, Aysegul developed a Monte Carlo simulation model. Inventory storage space capacity is reconfigured as space needs change at a specified cost following a set of reconfiguration logic rules. Finally, she implemented a heuristic approach based on a multi-dimensional golden section search algorithm to identify the optimal storage capacities and reconfiguration decisions that minimize long-term expected total costs. The computation time with the heuristic approach is successfully reduced by 97% from 49.2 hours to 83.9 minutes with a higher number of inventory capacities. In several cases, total cost with this approach tends to be only 0.67% higher, which is sufficient in practical applications. Using the heuristic approach, Aysegul calculated the savings from reconfiguration under different yield rate and cost scenarios. The results demonstrate that reconfiguration becomes very important and can save a company substantial sums when the difference in yield rates among part types is high. In addition, she used experimental design analysis and response surface models to examine the impact of each inventory storage capacity on the total cost, and to develop useful heuristics for practitioners.
Research Interests: Monte Carlo simulation, heuristic optimization, stochastic programming recourse modeling, reverse logistics, facilities planning, environmentally conscious manufacturing
Hobbies: Tennis, running, traveling, painting, cinema
