Kevin Scheibe

Dept: Supply Chain and Information Systems

Kevin Scheibe is an Associate Professor of Management Information Systems. He has been at Iowa State University in the Supply Chain & Information Systems department since 2003. His research interests include supply chain risk, supply chain information systems, spatial decision support systems, and business analytics. His current research projects include a simulation based decision support system to optimize the normal vehicle replacement model for the Iowa Department of Transportation, predicting supply chain vulnerabilities, optimizing supply chain network robustness, and knowledge sharing to mitigate supply chain disruption frequency and severity.

Keywords: decision support systems, simulation, optimization, heuristics, genetic algorithms

Email: kscheibe at, Phone: 4-0545

Adina Howe

Dept: Agricultural and Biosystems Engineering

Our research program focuses on identifying specific drivers and mechanisms of productive performance.  We apply statistical and analytical methods to study the health, stability, and productivity of microbial players in environmental systems.  More specifically, our research goals are:

  • Identifying microbial drivers of global change
  • Detection of biological indicators of environmental health
  • Unification of heterogeneous datasets
  • Investigation of interactions within complex microbial communities

Keywords:  Microbiology; community dynamics; environment; biology

Email: adina at, Phone: 4-0176

Anuj Sharma

Dept: Civil, Construction and Environmental Engineering

Keywords: Transportation decision making, big data analytics, connected and automated vehicles, urban analytics, smart communities, deep learning

Dr. Sharma is an associate professor in the Civil Construction and Environmental Engineering Department at Iowa State University. He also holds a joint appointment as a Research Scientist with the Institute of Transportation.  In these positions, he teaches transportation engineering courses to undergraduate and graduate civil engineering students, conducts research in the transportation operations area, and participates in numerous professional organizations.

His research uses big data driven discoveries to help make better short term (usually automated control) and long term (policy) decisions. Dr. Sharma is currently leading the REACTOR (REaltime AnalytiCs of TranspORtation data) laboratory. The High Performance Cluster assembled for the lab is able to ingest multiple streams of real-time data from multiple sources. After successful installation of the REACTOR-HPC, efforts are focused on ingestion, real-time analytics, batch processing, visualization/front end development, and archiving of data streams. Some of the tools developed under this effort can be found at These tools are being used for managing traffic workzones, traffic incident management, transportation infrastructure investment. REACTOR-HPC and a memorandum of understanding with Iowa DOT, has placed Iowa State University among one of the very few facilities in the US transportation arena using big data analytics in the field of transportation. Currently, efforts are being made to increase the ingestion of more data streams, improve visualization and visual querying functionality of archived data, develop batch processing for automatic reporting of performance measures, and conduct real-time analytics on data coming from multiple/heterogeneous sources.

Email: anujs at, Phone: 4-2140

Drew Zhang

Dept: Supply Chain and Information Systems

Keywords: Business analytics, artificial intelligence, machine learning, natural language processing, search engines

I am a computer scientist working in the technical domain of information systems. The aspiration of my research is to conquer the “information overload” problem, and the key technical challenge is to extract knowledge from unstructured data sources (particularly text). More specifically, the crux of my research is at the intersection of information retrieval and search engines (IRSE), text mining or natural language processing (TM/NLP), and applied machine learning (AML). Ultimately, these three themes converge under my long-term career goal: to build intelligent information systems (in particular business intelligence systems) that can naturally interact with users through intuitive interfaces (e.g., human-like languages or gestures) and can adapt to different situations (e.g., user interest/profile, location, interaction history, etc.) through (machine) learning.

Email: zhuzhang at, Phone: 4-7433

Ivy Yuan

Dept: Supply Chain and Information Systems

Keywords: Non-rational, subconscious processes, neurophysiology, social media, electronic commerce

My research interest is the impact of non-rational cognition on individuals’ communication, behavior, and decision making in computer mediated environments. My research has focused on how non-rational cognition has influenced these behaviors. The methodologies I have applied in my current research include lab experiment (behavioral and psychophysiological experiment), survey, and secondary data analysis. For instance, my research papers, Trading on Twitter: The Financial Information Content of Emotion in Social Media and The Happiness Premium: The Impact of Emotion on Individuals’ Willingness to Pay in Online Auctions, focused on the impact of emotion on individual behavior using Twitter data.  Another research paper, Understanding the impact of anthropomorphism on consumer’s willingness to pay in the context of online auctions, investigates the impact of anthropomorphism on non-rational cognition and its impact on online shopping behavior. Notably, while all my papers are related to Information System discipline, their theoretical foundations are deeply rooted in psychology, sociology, and neurophysiology research.

Email: lyuan at, Phone: 4-3659

Sree Nilakanta

Dept: Supply Chain and Information Systems

Keywords: Analytics, dimensionality reduction, metadata, innovation diffusion, health technology

My current research focus is issues related to big data analytics. Specifically, the areas of descriptive, diagnostic, and predictive analytics are where I have directed my attention. This includes data visualization, data quality and metadata analysis, and dimensionality reduction. In addition to data analytics, I work in the area of innovation diffusion, studying how firms and individuals adopt innovations. My focus on innovation has been on health technology innovations.

Email: nilakant at, Phone: 4-8113

Yoshinori Suzuki

Dept: Supply Chain and Information Systems

Keywords: Transportation, trucking industry, vehicle routing problem

I view myself as a transportation specialist, as I have focused on conducting research only in the transportation industry, especially in the trucking industry, throughout my career.  Two of my recent research areas (problems addressed) are described below.

The first is the vehicle-refueling problem.  After fuel prices increased dramatically since 2008, many researchers have started to consider this problem, which seeks to find the optimal (cost minimizing) set of refueling points (gas stations or truck stops) for a given vehicle.  My first work showed that the fixed-route vehicle-refueling problem (FRVRP), the most basic form of the vehicle-refueling problem, can be formulated as a mixed-integer linear program meaning that the optimal solution can be obtained by using the simplex algorithm (before this paper was published all studies treated the FRVRP as a complex problem, for which a special solution technique must be developed).  Most of my other work has considered extensions of the basic problem form, in which both the vehicle-refueling problem and the vehicle-routing problem are solved jointly, and proposed solution techniques for these problems (exact and heuristic algorithms).  My most recent work in this area proposed a cutting-plane technique for various versions of the vehicle-refueling problem. I showed that by using this technique the solution (CPU) time can be reduced dramatically, especially for large instances.

The second is the pollution routing problem (PRP).  This is a variant of the classic vehicle-routing problem (VRP), which seeks to minimize the fuel consumption of a fleet of vehicles.  My initial work was among the first ones to propose a mathematical formulation of the PRP.  This paper also showed, by conducting simulation experiments, that the optimal routes under this new formulation (that minimize fuel consumption) are notably different from those under the classic VRP formulation (that minimize travel distance or time).  In the second paper I proposed a simple, yet effective, heuristic that is designed specifically for small motor carriers (who typically do not have technical personnel) to solve the PRP.  Given that the majority of motor carriers are small in many countries (e.g., 87% of U.S. motor carriers operate 6 or fewer trucks), developing a simple PRP technique that can be used conveniently by small carriers is quite meaningful from the standpoint of reducing trucks’ total carbon footprints.  In the third paper I developed a hybrid metaheuristic method that generates high-quality PRP solutions.  Computational testing with standard test instances showed that this method compares very favorably with the benchmark methods available in the literature, obtaining best-known results in many instances.

Email: ysuzuki at, Phone: 4-5577

Chinmay Hegde

Dept: Electrical and Computer Engineering

Keywords: Algorithms, big data analytics, machine learning, imaging, optimization.

My research involves developing new methods for massive data acquisition and analysis at the algorithmic level. Specifically, I am interested in design and analysis of algorithms for acquiring, storing, and extracting actionable information from massive datasets.

My algorithmic work spans optimization, statistics, and machine learning. From a foundational standpoint, I am interested in understanding the fundamental limits of algorithmic techniques in terms of both their statistical and computational performance.

My primary application areas are in imaging and computer vision, particularly in the domains of biomedical imaging, computational photography, and tomographic imaging. Additional applications of my work (via ongoing collaborations with domain experts) include designing large-scale recommender systems; prognostics and health management of electrical systems; efficient phenotyping for agronomy; traffic incident management; and materials informatics.

Anyone who is interested in operations research/machine learning/optimization/business analytics would be a good fit with my research.

Email: chinmay at, Phone: 4-6291

In-Ho Cho

Dept: Civil, Construction and Environmental Engineering

Current Collaborators: Statistics faculty Jae-Kwang Kim and Raymond Wong for the below research topics:

  1. First topic focuses on advanced statistical learning and prediction of complex real-world responses, notably tackling a large number of variables and their interactions. The key enabling factor is the harmonious combination of advanced statistical theory and parallel computing technology.

We demonstrated promising results from recent applications to problems of FAA (Federal Aviation Administration) and Earthquake Engineering fields. Prediction accuracy and computational efficiency have proven promising. I believe this research topic will be helpful for prediction of Economics-related problems as long as there is formidable complexity in the data of a large number of variables. [Keyword: multivariate prediction, multivariable learning, data-driven learning, complex database, parallel computing for statistical prediction]

  1. Second topic focuses on curing big data by multivariate imputation theory. This research seeks to “cure” missing data of an existing database by using a rigorous statistical theory in conjunction with advanced parallel computing technology. We believe this will substantially benefit general databases of Economics-related research since the cured database will facilitate and improve fundamental data-driven research of any discipline of Economics or financial research groups. [Keyword: multivariable imputation, missing data problem, fractional hot deck imputation, parallel computing for imputation]

Email: icho at, Phone: 4-3241

Lizhi Wang

Dept: Industrial and Manufacturing Systems Engineering

Keywords: Optimization, energy, transportation, manufacturing, quantitative genetics

Dr. Wang’s research interest is optimization and its applications. On the methodology side, I design new algorithms for complex, large scale optimization problems for decision making under uncertainty. On the application side, I have worked on power systems, transportation network resiliency assessment and enhancement, supply chain management for manufacturing facilities, and plant/animal breeding and quantitative genetics.

Email: lzwang at, Phone: 4-1757

Adarsh Krishnamurthy

Dept: Mechanical Engineering

Keywords: Computer-Aided Design (CAD), Cardiovascular Biomechanics, Computational Mechanics, GPU and Parallel Computing, Ultrasonic Non-Destructive Evaluation

Dr. Krishnamurthy has been working on developing new tools that can accelerate modeling and simulations. The overarching goal of his research is the advancement of the state of the art in design and translational medicine with the help of computational modeling and interactive analysis tools for computer-aided design and cardiac biomechanics. This includes developing new methods for multiscale simulations and parallel algorithms that take advantage of modern hardware, such as Graphics Processing Units (GPUs), to improve the performance of simulations. Part of his research focuses on computational modeling of heart failure, where identifying patients who will best respond to a particular therapeutic intervention is difficult. Computational models, developed from patient-specific clinical data, can help refine the diagnosis and personalize heart failure intervention therapies. His research has recently been used to ascertain a possible mechanism for improvement due to cardiac resynchronization therapy, which uses implantable pacemakers to synchronize ventricular function, for the first time. This preliminary investigation suggests the possibility of extracting important diagnostic information from clinical measurements using computational models.



Email: adarsh at, Phone: 4-5568

Guiping Hu

Dept: Industrial and Manufacturing Systems Engineering

Keywords: Keywords: operations research, supply chain design, data-driven decision making, energy production, sustainability.

Dr. Hu’s research interests lie in operations research and mathematical decision support system design. Her recent projects have focused on supply chain design and operational planning, data-driven real time decision making under uncertainty, renewable energy production system analysis, and sustainable production systems.

Email: gphu at, Phone: 4-8638

Mingyi Hong

Dept: Industrial and Manufacturing Systems Engineering

Keywords: Optimization, large-scale computation, resource allocation, text analysis, deep learning.

We live in an era of data explosion. The rapid advances in sensor, communication and storage technologies has made data acquisition more ubiquitous than at any time in the past. It is estimated that the digital information to be created in 2020 will be 44 times greater than it was in 2009, reaching a total of 44 ZB (1ZB=109 TB). We are inevitably facing an information dilemma, in which the excessive amount of data available cannot be fully processed to yield useful and actionable information.

My research aims at resolving our modern-day information dilemma from the standpoint of mathematical modeling and optimization. It revolves around a central question: How can data represented in the forms such as signals, images, and time series, be effectively modeled and processed with unprecedented scale. Theoretically, I have been advocating for approaching big data problems using non-convex models and algorithms. Practically, I have worked closely with national labs and leading industry companies to apply my modeling and optimization expertise to large-scale data acquisition, delivery and analytics applications, such as designing next generation 5G network system, text and document analysis, applications of deep learning, etc.

Email: mingyi at, Phone: 4-2943

Toyin Clottey

Dept: Supply Chain and Information Systems

Keywords: Closed-loop supply chains, Reverse logistics, Supply forecasting, Empirical research methods, Value of information sharing

Research Interests

My research thus far has focused on two areas: 1) supply management for reusable products and 2)survey research methods for supply chain management. Both of these areas are critical insupply chain management research. The first area has high relevancy to sustainability practices, while the second area has implications for how supply chain management research can effectively contribute to practice. Specific details about my research in each area are provided below.

Supply management for reusable products

There is a growing focus on sustainability in corporate objectives. A sustainable operation is an important driver for increased profitability and effective management. The focus of my research in this area is on the management of the supply of reusable products (i.e., cores) for sustainable operations. Specifically, my research has focused on the acquisition of cores from third parties (e.g., core brokers and salvage operators) in a remanufacturing environment. Remanufacturing operations often have multiple sources of cores. The primary and least expensive source of cores is product returns from the customer due to end-of-use. Secondary sources of cores are third -party suppliers of cores. Planning core acquisition activities is difficult because of the uncertainty in the quantity and quality of cores from the primary and secondary sources. Two major research issues which have captivated my interest in this area are: (1) Forecasting methods used for planning acquisition quantities (2) Sourcing relationships for implementing an acquisition plan. I have applied statistical (e.g., Bayesian modelling) and optimization approaches to addressing these two issues. I have also conducted a survey of north American automotive parts remanufacturers to delve deeper into the practical issues faced by such companies in utilizing third-party vendors extensively for their supply of cores.

Outside of the above two research issues, I have been investigating another factor that is often critical to the success of a sourcing relationship- information sharing. In particular, how the sharing of delivery progress information by suppliers of reuse material can be effectively used to decrease supply chain costs and reduce the risk of production delays.

Survey research methods for supply chain management

My other research interest is in the area of survey research methods. In particular, I have been investigating ways to improve non-response bias assessment in supply chain management research. An analysis of the statistical power of tests for non-response bias, in leading journals in operations management and logistics, indicated that some of these tests suffered from very low statistical power. By considering the concepts of individual and complete statistical power I was able to provide tailored recommendations for how to conduct tests for non-response bias in logistics and operations management research. I incorporated my analyses and findings, into two papers which were published in Decision Sciences and the International Journal for Physical Distribution and Logistics Management (IJPDLM). I am currently investigating appropriate ways of using survey sampling weights in supply chain management research involving dyads.

Email: tclottey at, Phone: 4-8198

Haozhe Chen

Dept: Supply Chain and Information Systems

Keywords: Empirical behavioral research, reverse logistics, returns management, relationship management, supply chain

I am interested in many aspects of supply chain management from a managerial perspective such as supply chain integration and collaboration, buyer-seller relationships, third-party logistics (3PL) relationships, etc. However, one area that is especially attractive to me is reverse logistics, which includes topics such as returns management, sustainability, remanufacturing, and secondary market. I have completed and are working on serval research projects in this big area with multiple articles having been published. My major research methods include empirical survey study, qualitative research methods (such as interviews and case studies), and behavioral experiments. I am actively involved with several industry organizations: I serve on the Board of Advisors of Reverse Logistics and Sustainability Council, and I am an active member of Reverse Logistics Association. I was also recently appointed to Council of Supply Chain Management Professionals’ Academic Strategy Committee. Right now, I want to explore reverse logistics research topics on company-consumer interactions.

Email: hzchen at, Phone: 4-7216

Johanna Amaya

Dept: Supply Chain and Information Systems

Keywords: Disaster response logistics, response operations and policy recommendations, mathematical programming, impacts of freight polices, survey design and analysis of panel data

Ms. Amaya is an Assistant Professor in the Department of Supply Chain and Information Systems in the College of Business. She received her B.Sc. in Industrial Engineering in Colombia. She got a M.Sc. in Industrial and Systems Engineering, from University of Florida and completed her PhD. in Transportation Engineering at RPI. Her research interests are in the areas of disaster response logistics and urban freight transportation. She has several publications in such areas and has been part of diverse research projects and committees.  She has experience in writing proposals and reports for NSF, NCFRP, UTCs, USAID, and the World Bank among others.

Areas of Research

  • Disaster response logistics (DRL): DRL response operations and policy recommendations
    1. The research focuses on understanding the complexity of disaster situations and the requirements of the supply chain to respond. I have conducted research and field work following recent disasters such as Hurricane Sandy and the earthquakes that devastated Nepal in 2015 and Ecuador in 2016. Data sources include: Interviews, GIS maps, News, Relief Agencies data about resources distributed, etc.
    2. Modeling: Mathematical programming to model relief distribution after disasters with assumptions and constraints identified from field work. Based on results and different scenarios, policies are proposed to relief agencies.
  • Urban freight transportation systems, my work has concentrated on evaluating sustainable initiatives to mitigate the negative impacts of the freight activity.
    1. Analysis of potential impacts of freight policies such as: parking regulations, delivery bans or restrictions, demand management and receivers’ behavior, among others
    2. Modeling: Survey design and analysis of panel data to identify key aspects generating certain behaviors. Use of discrete choice models and regression analysis.

Email: amayaj at, Phone: 4-8296